
import warnings 
warnings.filterwarnings("ignore", category=DeprecationWarning)


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import seaborn as sns
from geopy.geocoders import Nominatim
import folium
from pysal.lib import weights
from pysal.explore import esda
from sklearn import cluster


%pwd

'/Users/cl/Documents/GEO448/Project'


# display all columns for every dataframe
pd.set_option("display.max_columns", 999)


# HIV-Dataset1: New HIV Cases by County
new_hiv_data = pd.read_excel('/Users/cl/Documents/GEO448/Project/AIDSVu_County_NewDX_2020.xlsx', header=3)
new_hiv_data


# Sort by New Diagnoses Cases and Verify Top 20 counties in the U.S. with highest number of new cases:
new_hiv_data.sort_values(by=['New Diagnoses Cases'], ascending=False).head(20)


# Extract data for California Only:
cond = new_hiv_data['State'] == 'California'
ca_new_hiv_data = new_hiv_data[cond]
ca_new_hiv_data


# Sort New Hiv Data in CA by New Diagnoses Cases:
ca_new_hiv_data.sort_values(by=['New Diagnoses Cases'], ascending=False)


# Get the Top 5 Counties with the highest number of new HIV cases:
ca_new_hiv_data.sort_values(by=['New Diagnoses Cases'], ascending=False).head(5)


# Check Variables and Data Types:
ca_new_hiv_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 187 to 244
Data columns (total 62 columns):
 #   Column                                                         Non-Null Count  Dtype  
---  ------                                                         --------------  -----  
 0   GEO ID                                                         58 non-null     int64  
 1   Year                                                           58 non-null     int64  
 2   State Abbreviation                                             58 non-null     object 
 3   State                                                          58 non-null     object 
 4   County Name                                                    58 non-null     object 
 5   New Diagnoses Rate                                             58 non-null     float64
 6   New Diagnoses Rate Stability                                   58 non-null     object 
 7   New Diagnoses Cases                                            58 non-null     int64  
 8   New Diagnoses Male Rate                                        58 non-null     float64
 9   New Diagnoses Male Rate Stability                              58 non-null     object 
 10  New Diagnoses Male Cases                                       58 non-null     int64  
 11  New Diagnoses Female Rate                                      58 non-null     float64
 12  New Diagnoses Female Rate Stability                            58 non-null     object 
 13  New Diagnoses Female Cases                                     58 non-null     int64  
 14  New Diagnoses Black Rate                                       58 non-null     float64
 15  New Diagnoses Black Rate Stability                             58 non-null     object 
 16  New Diagnoses Black Cases                                      58 non-null     int64  
 17  New Diagnoses White Rate                                       58 non-null     float64
 18  New Diagnoses White Rate Stability                             58 non-null     object 
 19  New Diagnoses White Cases                                      58 non-null     int64  
 20  New Diagnoses Hispanic Rate                                    58 non-null     float64
 21  New Diagnoses Hispanic Rate Stability                          58 non-null     object 
 22  New Diagnoses Hispanic Cases                                   58 non-null     int64  
 23  New Diagnoses Asian Rate                                       58 non-null     float64
 24  New Diagnoses Asian Rate Stability                             58 non-null     object 
 25  New Diagnoses Asian Cases                                      58 non-null     int64  
 26  New Diagnoses American Indian/Alaska Native Rate               58 non-null     float64
 27  New Diagnoses American Indian/Alaska Native Rate Stability     58 non-null     object 
 28  New Diagnoses American Indian/Alaska Native Cases              58 non-null     int64  
 29  New Diagnoses Multiracial Rate                                 58 non-null     float64
 30  New Diagnoses Multiracial Rate Stability                       58 non-null     object 
 31  New Diagnoses Multiracial Cases                                58 non-null     int64  
 32  New Diagnoses Native Hawaiian/Pacific Islander Rate            58 non-null     float64
 33  New Diagnoses Native Hawaiian/Pacific Islander Rate Stability  58 non-null     object 
 34  New Diagnoses Native Hawaiian/Pacific Islander Cases           58 non-null     int64  
 35  New Diagnoses Age 13-24 Rate                                   58 non-null     float64
 36  New Diagnoses Age 13-24 Rate Stability                         58 non-null     object 
 37  New Diagnoses Age 13-24 Cases                                  58 non-null     int64  
 38  New Diagnoses Age 25-34 Rate                                   58 non-null     float64
 39  New Diagnoses Age 25-34 Rate Stability                         58 non-null     object 
 40  New Diagnoses Age 25-34 Cases                                  58 non-null     int64  
 41  New Diagnoses Age 35-44 Rate                                   58 non-null     float64
 42  New Diagnoses Age 35-44 Rate Stability                         58 non-null     object 
 43  New Diagnoses Age 35-44 Cases                                  58 non-null     int64  
 44  New Diagnoses Age 45-54 Rate                                   58 non-null     float64
 45  New Diagnoses Age 45-54 Rate Stability                         58 non-null     object 
 46  New Diagnoses Age 45-54 Cases                                  58 non-null     int64  
 47  New Diagnoses Age 55+ Rate                                     58 non-null     float64
 48  New Diagnoses Age 55+ Rate Stability                           58 non-null     object 
 49  New Diagnoses Age 55+ Cases                                    58 non-null     int64  
 50  New Diagnoses Heterosexual Contact Percent                     58 non-null     float64
 51  New Diagnoses Heterosexual Contact Cases                       58 non-null     int64  
 52  New Diagnoses IDU Percent                                      58 non-null     float64
 53  New Diagnoses IDU Cases                                        58 non-null     int64  
 54  New Diagnoses Other Transmission Category Percent              58 non-null     float64
 55  New Diagnoses Other Transmission Category Cases                58 non-null     int64  
 56  New Diagnoses MSM Rate                                         58 non-null     float64
 57  New Diagnoses MSM Percent                                      58 non-null     float64
 58  New Diagnoses MSM Cases                                        58 non-null     int64  
 59  New Diagnoses MSM/IDU Percent                                  58 non-null     float64
 60  New Diagnoses MSM/IDU Cases                                    58 non-null     int64  
 61  2013 NCHS Urbanicity Code                                      58 non-null     int64  
dtypes: float64(21), int64(23), object(18)
memory usage: 28.5+ KB


#Extract Columns and Variables to Keep:
ca_new_hiv_df = ca_new_hiv_data.iloc[:,[0,1,3,4,5,7,14,16,17,19,20,22,23,25,26,28,29,31,32,34]]
ca_new_hiv_df


# Recheck Dataframe Info:
ca_new_hiv_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 187 to 244
Data columns (total 20 columns):
 #   Column                                                Non-Null Count  Dtype  
---  ------                                                --------------  -----  
 0   GEO ID                                                58 non-null     int64  
 1   Year                                                  58 non-null     int64  
 2   State                                                 58 non-null     object 
 3   County Name                                           58 non-null     object 
 4   New Diagnoses Rate                                    58 non-null     float64
 5   New Diagnoses Cases                                   58 non-null     int64  
 6   New Diagnoses Black Rate                              58 non-null     float64
 7   New Diagnoses Black Cases                             58 non-null     int64  
 8   New Diagnoses White Rate                              58 non-null     float64
 9   New Diagnoses White Cases                             58 non-null     int64  
 10  New Diagnoses Hispanic Rate                           58 non-null     float64
 11  New Diagnoses Hispanic Cases                          58 non-null     int64  
 12  New Diagnoses Asian Rate                              58 non-null     float64
 13  New Diagnoses Asian Cases                             58 non-null     int64  
 14  New Diagnoses American Indian/Alaska Native Rate      58 non-null     float64
 15  New Diagnoses American Indian/Alaska Native Cases     58 non-null     int64  
 16  New Diagnoses Multiracial Rate                        58 non-null     float64
 17  New Diagnoses Multiracial Cases                       58 non-null     int64  
 18  New Diagnoses Native Hawaiian/Pacific Islander Rate   58 non-null     float64
 19  New Diagnoses Native Hawaiian/Pacific Islander Cases  58 non-null     int64  
dtypes: float64(8), int64(10), object(2)
memory usage: 9.5+ KB


# HIV-Dataset2: Current (On-Going) HIV Cases by County
hiv_data = pd.read_excel('/Users/cl/Documents/GEO448/Project/AIDSVu_County_Prev_2020.xlsx', header=3)
hiv_data


# Sort by County Rate and Verify Top 20 counties in the U.S. with highest HIV population:
hiv_data.sort_values(by=['County Cases'], ascending=False).head(20)


# Extract data for California Only:
cond = hiv_data['State'] == 'California'
ca_hiv_data = hiv_data[cond]
ca_hiv_data


# Sort Current Hiv Data in CA by County Cases:
ca_hiv_data.sort_values(by=['County Cases'], ascending=False)


# Get the Top 10 Counties with the highest population of individuals living with HIV:
ca_hiv_data.sort_values(by=['County Cases'], ascending=False).head(10)


# Check Variables and Data Types:
ca_hiv_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 187 to 244
Data columns (total 63 columns):
 #   Column                                       Non-Null Count  Dtype  
---  ------                                       --------------  -----  
 0   GEO ID                                       58 non-null     int64  
 1   State Abbreviation                           58 non-null     object 
 2   State                                        58 non-null     object 
 3   County Name                                  58 non-null     object 
 4   Year                                         58 non-null     int64  
 5   County Rate                                  58 non-null     int64  
 6   County Rate Stability                        58 non-null     object 
 7   County Cases                                 58 non-null     int64  
 8   Male Rate                                    58 non-null     int64  
 9   Male Rate Stability                          58 non-null     object 
 10  Male Cases                                   58 non-null     int64  
 11  Female Rate                                  58 non-null     int64  
 12  Female Rate Stability                        58 non-null     object 
 13  Female Cases                                 58 non-null     int64  
 14  Black Rate                                   58 non-null     int64  
 15  Black Rate Stability                         58 non-null     object 
 16  Black Cases                                  58 non-null     int64  
 17  White Rate                                   58 non-null     int64  
 18  White Rate Stability                         58 non-null     object 
 19  White Cases                                  58 non-null     int64  
 20  Hispanic Rate                                58 non-null     int64  
 21  Hispanic Rate Stability                      58 non-null     object 
 22  Hispanic Cases                               58 non-null     int64  
 23  Age 13-24 Rate                               58 non-null     int64  
 24  Age 13-24 Rate Stability                     58 non-null     object 
 25  Age 13-24 Cases                              58 non-null     int64  
 26  Age 25-34 Rate                               58 non-null     int64  
 27  Age 25-34 Rate Stability                     58 non-null     object 
 28  Age 25-34 Cases                              58 non-null     int64  
 29  Age 35-44 Rate                               58 non-null     int64  
 30  Age 35-44 Rate Stability                     58 non-null     object 
 31  Age 35-44 Cases                              58 non-null     int64  
 32  Age 45-54 Rate                               58 non-null     int64  
 33  Age 45-54 Rate Stability                     58 non-null     object 
 34  Age 45-54 Cases                              58 non-null     int64  
 35  Age 55+ Rate                                 58 non-null     int64  
 36  Age 55+ Rate Stability                       58 non-null     object 
 37  Age 55+ Cases                                58 non-null     int64  
 38  MSM Rate                                     58 non-null     float64
 39  MSM Percent                                  58 non-null     float64
 40  Male and IDU Percent                         58 non-null     float64
 41  MSM/IDU Percent                              58 non-null     float64
 42  Male and Heterosexual Contact Percent        58 non-null     float64
 43  Female and IDU Percent                       58 non-null     float64
 44  Female and Heterosexual Contact Percent      58 non-null     float64
 45  MSM Cases                                    58 non-null     int64  
 46  Male and IDU Cases                           58 non-null     int64  
 47  MSM/IDU Cases                                58 non-null     int64  
 48  Male and Heterosexual Contact Cases          58 non-null     int64  
 49  Female and IDU Cases                         58 non-null     int64  
 50  Female and Heterosexual Contact Cases        58 non-null     int64  
 51  Heterosexual Contact Cases                   58 non-null     int64  
 52  Heterosexual Contact Percent                 58 non-null     float64
 53  IDU Cases                                    58 non-null     int64  
 54  IDU Percent                                  58 non-null     float64
 55  Other Transmission Route Cases               58 non-null     int64  
 56  Other Transmission Route Percent             58 non-null     float64
 57  Male and Other Transmission Route Cases      58 non-null     int64  
 58  Male and Other Transmission Route Percent    58 non-null     float64
 59  Female and Other Transmission Route Cases    58 non-null     int64  
 60  Female and Other Transmission Route Percent  58 non-null     float64
 61  Correctional Warning                         58 non-null     int64  
 62  2013 NCHS Urbanicity Code                    58 non-null     int64  
dtypes: float64(12), int64(37), object(14)
memory usage: 29.0+ KB


#Extract Columns and Variables to Keep:
ca_hiv_df = ca_hiv_data.iloc[:,[0,4,2,3,5,7,14,16,17,19,20,22]]
ca_hiv_df


# Recheck Dataframe Info:
ca_hiv_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 187 to 244
Data columns (total 12 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   GEO ID          58 non-null     int64 
 1   Year            58 non-null     int64 
 2   State           58 non-null     object
 3   County Name     58 non-null     object
 4   County Rate     58 non-null     int64 
 5   County Cases    58 non-null     int64 
 6   Black Rate      58 non-null     int64 
 7   Black Cases     58 non-null     int64 
 8   White Rate      58 non-null     int64 
 9   White Cases     58 non-null     int64 
 10  Hispanic Rate   58 non-null     int64 
 11  Hispanic Cases  58 non-null     int64 
dtypes: int64(10), object(2)
memory usage: 5.9+ KB


#Extract County Counts Only:
ca_hiv_df_prevalence = ca_hiv_df.iloc[:,[0,1,2,3,5]]
ca_hiv_df_prevalence


# HIV-Dataset3: PrEP Users by County
prep_data = pd.read_excel('/Users/cl/Documents/GEO448/Project/AIDSVu_County_PrEP_2020.xlsx', header=3)
prep_data


# Sort by County PrEP Users and Verify Top 20 counties in the U.S. with highest PrEP Users:
prep_data.sort_values(by=['County PrEP Users'], ascending=False).head(20)


# Extract data for California Only:
cond = prep_data['State'] == 'California'
ca_prep_data = prep_data[cond]
ca_prep_data


# Get the Top 10 Counties with the highest PrEP Users:
ca_prep_data.sort_values(by=['County PrEP Users'], ascending=False).head(10)


# Check Variables and Data Types:
ca_prep_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 187 to 244
Data columns (total 29 columns):
 #   Column                         Non-Null Count  Dtype 
---  ------                         --------------  ----- 
 0   GEO ID                         58 non-null     int64 
 1   County                         58 non-null     object
 2   State Abbreviation             58 non-null     object
 3   State                          58 non-null     object
 4   Year                           58 non-null     int64 
 5   County PrEP Users              58 non-null     int64 
 6   County PrEP Rate               58 non-null     int64 
 7   County PrEP Rate Stability     58 non-null     object
 8   Male PrEP Users                58 non-null     int64 
 9   Male PrEP Rate                 58 non-null     int64 
 10  Male PrEP Rate Stability       58 non-null     object
 11  Female PrEP Users              58 non-null     int64 
 12  Female PrEP Rate               58 non-null     int64 
 13  Female PrEP Rate Stability     58 non-null     object
 14  Age LE 24 PrEP Users           58 non-null     int64 
 15  Age LE 24 PrEP Rate            58 non-null     int64 
 16  Age LE 24 PrEP Rate Stability  58 non-null     object
 17  Age 25-34 PrEP Users           58 non-null     int64 
 18  Age 25-34 PrEP Rate            58 non-null     int64 
 19  Age 25-34 PrEP Rate Stability  58 non-null     object
 20  Age 35-44 PrEP Users           58 non-null     int64 
 21  Age 35-44 PrEP Rate            58 non-null     int64 
 22  Age 35-44 PrEP Rate Stability  58 non-null     object
 23  Age 45-54 PrEP Users           58 non-null     int64 
 24  Age 45-54 PrEP Rate            58 non-null     int64 
 25  Age 45-54 PrEP Rate Stability  58 non-null     object
 26  Age 55+ PrEP Users             58 non-null     int64 
 27  Age 55+ PrEP Rate              58 non-null     int64 
 28  Age 55+ PrEP Rate Stability    58 non-null     object
dtypes: int64(18), object(11)
memory usage: 13.6+ KB


#Extract Columns and Variables to Keep:
ca_prep_df = ca_prep_data.iloc[:,[0,4,3,1,5,6]]
ca_prep_df


# Recheck Dataframe Info:
ca_prep_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 187 to 244
Data columns (total 6 columns):
 #   Column             Non-Null Count  Dtype 
---  ------             --------------  ----- 
 0   GEO ID             58 non-null     int64 
 1   Year               58 non-null     int64 
 2   State              58 non-null     object
 3   County             58 non-null     object
 4   County PrEP Users  58 non-null     int64 
 5   County PrEP Rate   58 non-null     int64 
dtypes: int64(4), object(2)
memory usage: 3.2+ KB


# Merge ca_new_hiv_df and ca_hiv_df_prevalence
df2 = pd.merge(ca_new_hiv_df, ca_hiv_df_prevalence, how='inner')
df2


# Merge df2 to ca_prep_df
merged_df = pd.merge(df2, ca_prep_df, how='inner')
merged_df


merged_df.info() #check df info

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 0 to 57
Data columns (total 24 columns):
 #   Column                                                Non-Null Count  Dtype  
---  ------                                                --------------  -----  
 0   GEO ID                                                58 non-null     int64  
 1   Year                                                  58 non-null     int64  
 2   State                                                 58 non-null     object 
 3   County Name                                           58 non-null     object 
 4   New Diagnoses Rate                                    58 non-null     float64
 5   New Diagnoses Cases                                   58 non-null     int64  
 6   New Diagnoses Black Rate                              58 non-null     float64
 7   New Diagnoses Black Cases                             58 non-null     int64  
 8   New Diagnoses White Rate                              58 non-null     float64
 9   New Diagnoses White Cases                             58 non-null     int64  
 10  New Diagnoses Hispanic Rate                           58 non-null     float64
 11  New Diagnoses Hispanic Cases                          58 non-null     int64  
 12  New Diagnoses Asian Rate                              58 non-null     float64
 13  New Diagnoses Asian Cases                             58 non-null     int64  
 14  New Diagnoses American Indian/Alaska Native Rate      58 non-null     float64
 15  New Diagnoses American Indian/Alaska Native Cases     58 non-null     int64  
 16  New Diagnoses Multiracial Rate                        58 non-null     float64
 17  New Diagnoses Multiracial Cases                       58 non-null     int64  
 18  New Diagnoses Native Hawaiian/Pacific Islander Rate   58 non-null     float64
 19  New Diagnoses Native Hawaiian/Pacific Islander Cases  58 non-null     int64  
 20  County Cases                                          58 non-null     int64  
 21  County                                                58 non-null     object 
 22  County PrEP Users                                     58 non-null     int64  
 23  County PrEP Rate                                      58 non-null     int64  
dtypes: float64(8), int64(13), object(3)
memory usage: 11.3+ KB


#Rearrange and Extract Variables to Keep:
ca2020_hiv_df = merged_df.iloc[:,[0,1,2,21,20,23,22,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]]
ca2020_hiv_df


# Drop the Year & State columns:
ca2020_hiv_df2 = ca2020_hiv_df.drop(['Year', 'State'], axis=1)
ca2020_hiv_df2


ca_covid_data = pd.read_csv('/Users/cl/Documents/GEO448/Project/covid19cases_test.csv')
ca_covid_data.head(10)


# Check dataframe info
ca_covid_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61366 entries, 0 to 61365
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   date                        61305 non-null  object 
 1   area                        61366 non-null  object 
 2   area_type                   61366 non-null  object 
 3   population                  59354 non-null  float64
 4   cases                       60360 non-null  float64
 5   cumulative_cases            60360 non-null  float64
 6   deaths                      60360 non-null  float64
 7   cumulative_deaths           60360 non-null  float64
 8   total_tests                 61305 non-null  float64
 9   cumulative_total_tests      61366 non-null  int64  
 10  positive_tests              61305 non-null  float64
 11  cumulative_positive_tests   61366 non-null  int64  
 12  reported_cases              60360 non-null  float64
 13  cumulative_reported_cases   60360 non-null  float64
 14  reported_deaths             60360 non-null  float64
 15  cumulative_reported_deaths  60360 non-null  float64
 16  reported_tests              41175 non-null  float64
dtypes: float64(12), int64(2), object(3)
memory usage: 8.0+ MB


# Convert date to date type:
ca_covid_data['date']= pd.to_datetime(ca_covid_data['date'])


# Rename 'area' to 'County'
ca_covid_data = ca_covid_data.rename(columns={"area": "County"})


# Recheck dataframe info
ca_covid_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61366 entries, 0 to 61365
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype         
---  ------                      --------------  -----         
 0   date                        61305 non-null  datetime64[ns]
 1   County                      61366 non-null  object        
 2   area_type                   61366 non-null  object        
 3   population                  59354 non-null  float64       
 4   cases                       60360 non-null  float64       
 5   cumulative_cases            60360 non-null  float64       
 6   deaths                      60360 non-null  float64       
 7   cumulative_deaths           60360 non-null  float64       
 8   total_tests                 61305 non-null  float64       
 9   cumulative_total_tests      61366 non-null  int64         
 10  positive_tests              61305 non-null  float64       
 11  cumulative_positive_tests   61366 non-null  int64         
 12  reported_cases              60360 non-null  float64       
 13  cumulative_reported_cases   60360 non-null  float64       
 14  reported_deaths             60360 non-null  float64       
 15  cumulative_reported_deaths  60360 non-null  float64       
 16  reported_tests              41175 non-null  float64       
dtypes: datetime64[ns](1), float64(12), int64(2), object(2)
memory usage: 8.0+ MB


# Extract Columns and Variables to Keep:
ca_covid_df = ca_covid_data.iloc[:,[0,1,2,3,4,6,8,10,12,14,16]]
ca_covid_df.head(5)


# Extract Data with Dates from 01/01/2021 to 12/31/2021 Only:
start_date = '2021-01-01'
end_date = '2021-12-31'
mask = (ca_covid_df['date'] > start_date) & (ca_covid_df['date'] <= end_date)
ca_covid_df2 = ca_covid_df.loc[mask]


# Group data by monthly aggregation by sum for months:
caMonthly_covid_df = ca_covid_df2.groupby([pd.Grouper(freq='M',key='date'),'County', 'population']).sum()


# View CA Monhtly COVID Data:
caMonthly_covid_df


caMonthly_covid_df.info() #Check DF Info

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 708 entries, (Timestamp('2021-01-31 00:00:00', freq='M'), 'Alameda', 1685886.0) to (Timestamp('2021-12-31 00:00:00', freq='M'), 'Yuba', 79290.0)
Data columns (total 7 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   cases            708 non-null    float64
 1   deaths           708 non-null    float64
 2   total_tests      708 non-null    float64
 3   positive_tests   708 non-null    float64
 4   reported_cases   708 non-null    float64
 5   reported_deaths  708 non-null    float64
 6   reported_tests   708 non-null    float64
dtypes: float64(7)
memory usage: 42.0+ KB


# Group data by year aggregation:
caYear_covid_df = ca_covid_df2.groupby([pd.Grouper(freq='Y',key='date'),'County', 'population']).sum()


# View CA Yearly COVID Data:
caYear_covid_df


caYear_covid_df.info() #Check DF Info

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 59 entries, (Timestamp('2021-12-31 00:00:00', freq='A-DEC'), 'Alameda', 1685886.0) to (Timestamp('2021-12-31 00:00:00', freq='A-DEC'), 'Yuba', 79290.0)
Data columns (total 7 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   cases            59 non-null     float64
 1   deaths           59 non-null     float64
 2   total_tests      59 non-null     float64
 3   positive_tests   59 non-null     float64
 4   reported_cases   59 non-null     float64
 5   reported_deaths  59 non-null     float64
 6   reported_tests   59 non-null     float64
dtypes: float64(7)
memory usage: 4.5+ KB


CA2021_covid_df = caYear_covid_df.reset_index()
CA2021_covid_df


ca_vacc_data = pd.read_csv('/Users/cl/Documents/GEO448/Project/covid19vaccinesbycountybydemographic.csv')
ca_vacc_data.head(10)


# Check dataframe info
ca_vacc_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 911629 entries, 0 to 911628
Data columns (total 21 columns):
 #   Column                          Non-Null Count   Dtype  
---  ------                          --------------   -----  
 0   county                          911629 non-null  object 
 1   county_type                     911629 non-null  object 
 2   demographic_category            911629 non-null  object 
 3   demographic_value               911629 non-null  object 
 4   est_population                  778212 non-null  float64
 5   est_age_12plus_pop              778212 non-null  float64
 6   est_age_5plus_pop               778212 non-null  float64
 7   administered_date               911629 non-null  object 
 8   partially_vaccinated            897791 non-null  float64
 9   total_partially_vaccinated      897791 non-null  float64
 10  fully_vaccinated                897791 non-null  float64
 11  cumulative_fully_vaccinated     897791 non-null  float64
 12  at_least_one_dose               897791 non-null  float64
 13  cumulative_at_least_one_dose    897791 non-null  float64
 14  cumulative_unvax_total_pop      765188 non-null  float64
 15  cumulative_unvax_12plus_pop     765188 non-null  float64
 16  cumulative_unvax_5plus_pop      765188 non-null  float64
 17  suppress_data                   911629 non-null  bool   
 18  booster_recip_count             911629 non-null  int64  
 19  cumulative_booster_recip_count  911629 non-null  int64  
 20  booster_eligible_population     911629 non-null  int64  
dtypes: bool(1), float64(12), int64(3), object(5)
memory usage: 140.0+ MB


# Create a new variable date with date type using the administered date:
ca_vacc_data['date']= pd.to_datetime(ca_vacc_data['administered_date'])


# Extract Columns and Variables to Keep:
ca_vacc_df2 = ca_vacc_data.iloc[:,[0,7,8,10,18,21]]


# Extract Data with Dates from 01/01/2021 to 12/31/2021 Only:
start_date = '2021-01-01'
end_date = '2021-12-31'
mask = (ca_vacc_df2['date'] > start_date) & (ca_vacc_df2['date'] <= end_date)
ca_vacc_df3 = ca_vacc_df2.loc[mask]


# Recheck info for extracted data:
ca_vacc_df3.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 408030 entries, 290 to 911468
Data columns (total 6 columns):
 #   Column                Non-Null Count   Dtype         
---  ------                --------------   -----         
 0   county                408030 non-null  object        
 1   administered_date     408030 non-null  object        
 2   partially_vaccinated  401842 non-null  float64       
 3   fully_vaccinated      401842 non-null  float64       
 4   booster_recip_count   408030 non-null  int64         
 5   date                  408030 non-null  datetime64[ns]
dtypes: datetime64[ns](1), float64(2), int64(1), object(2)
memory usage: 21.8+ MB


# View the head for extracted data:
ca_vacc_df3.head(10)


# Group data by monthly aggregation by sum for months:
caMonthly_vacc_df = ca_vacc_df3.groupby([pd.Grouper(freq='M',key='date'),'county']).sum()
caMonthly_vacc_df


# Group data by monthly aggregation by sum for months:
ca2021_vacc_df = ca_vacc_df3.groupby([pd.Grouper(freq='Y',key='date'),'county']).sum()
ca2021_vacc_df


# Reindex and view the aggregated data:
ca2021_vacc_df2 = ca2021_vacc_df.reset_index()
ca2021_vacc_df2


# COVID Dataset: Remove the County, 'California' as this is an aggegation and not an actual county
cond = CA2021_covid_df['County'] != 'California'
CA2021_covid_df2 = CA2021_covid_df[cond]
CA2021_covid_df2.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 0 to 58
Data columns (total 10 columns):
 #   Column           Non-Null Count  Dtype         
---  ------           --------------  -----         
 0   date             58 non-null     datetime64[ns]
 1   County           58 non-null     object        
 2   population       58 non-null     float64       
 3   cases            58 non-null     float64       
 4   deaths           58 non-null     float64       
 5   total_tests      58 non-null     float64       
 6   positive_tests   58 non-null     float64       
 7   reported_cases   58 non-null     float64       
 8   reported_deaths  58 non-null     float64       
 9   reported_tests   58 non-null     float64       
dtypes: datetime64[ns](1), float64(8), object(1)
memory usage: 5.0+ KB


# Vaccine Dataset: Remove the County, 'Statewide' as this is an aggegation and not an actual county
cond = ca2021_vacc_df2['county'] != 'Statewide'
ca2021_vacc_df3 = ca2021_vacc_df2[cond]
ca2021_vacc_df3.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 0 to 58
Data columns (total 5 columns):
 #   Column                Non-Null Count  Dtype         
---  ------                --------------  -----         
 0   date                  58 non-null     datetime64[ns]
 1   county                58 non-null     object        
 2   partially_vaccinated  58 non-null     float64       
 3   fully_vaccinated      58 non-null     float64       
 4   booster_recip_count   58 non-null     int64         
dtypes: datetime64[ns](1), float64(2), int64(1), object(1)
memory usage: 2.7+ KB


# Rename columns in Vaccine Dataset: 
ca2021_vacc_df3 = ca2021_vacc_df3.rename(columns={'county': 'County', 'booster_recip_count': 'boosted'})


# Merge CA2021_covid_df2 and ca2021_vacc_df3:
merged_df = pd.merge(CA2021_covid_df2, ca2021_vacc_df3, how='inner')
merged_df


# Drop the date column:
ca2021_covidvacc_df = merged_df.drop(['date'], axis=1)
ca2021_covidvacc_df


# Calculate rates for cases, death, and tests 
ca2021_covidvacc_df['COVID Cases_Rate'] = (ca2021_covidvacc_df['cases']/ca2021_covidvacc_df['population'])*100
ca2021_covidvacc_df['COVID Deaths_Rate'] = (ca2021_covidvacc_df['deaths']/ca2021_covidvacc_df['population'])*100
ca2021_covidvacc_df['Total COVID Tests_Rate'] = (ca2021_covidvacc_df['total_tests']/ca2021_covidvacc_df['population'])*100
ca2021_covidvacc_df['Positive COVID Tests_Rate'] = (ca2021_covidvacc_df['positive_tests']/ca2021_covidvacc_df['population'])*100


# Calculate rates for vaccinations
ca2021_covidvacc_df['Partially Vaccinated_Rate'] = (ca2021_covidvacc_df['partially_vaccinated']/ca2021_covidvacc_df['population'])*100
ca2021_covidvacc_df['Fully Vaccinated_Rate'] = (ca2021_covidvacc_df['fully_vaccinated']/ca2021_covidvacc_df['population'])*100
ca2021_covidvacc_df['Boosted_Rate'] = (ca2021_covidvacc_df['boosted']/ca2021_covidvacc_df['population'])*100


# View DF with new features 
ca2021_covidvacc_df


# Check the head for HIV DF: 
ca2020_hiv_df2.head()


# Check the head for COVID DF: 
ca2021_covidvacc_df.head()


# Update the 'County' column in HIV DF so that it matches entries in COVID DF:
ca2020_hiv_df2['County'] = ca2020_hiv_df2['County'].str.replace('County', '')
ca2020_hiv_df2.head()


# Check Data Types and Data Quality for HIV DF:
ca2020_hiv_df2.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 0 to 57
Data columns (total 21 columns):
 #   Column                                                Non-Null Count  Dtype  
---  ------                                                --------------  -----  
 0   GEO ID                                                58 non-null     int64  
 1   County                                                58 non-null     object 
 2   County Cases                                          58 non-null     int64  
 3   County PrEP Rate                                      58 non-null     int64  
 4   County PrEP Users                                     58 non-null     int64  
 5   New Diagnoses Rate                                    58 non-null     float64
 6   New Diagnoses Cases                                   58 non-null     int64  
 7   New Diagnoses Black Rate                              58 non-null     float64
 8   New Diagnoses Black Cases                             58 non-null     int64  
 9   New Diagnoses White Rate                              58 non-null     float64
 10  New Diagnoses White Cases                             58 non-null     int64  
 11  New Diagnoses Hispanic Rate                           58 non-null     float64
 12  New Diagnoses Hispanic Cases                          58 non-null     int64  
 13  New Diagnoses Asian Rate                              58 non-null     float64
 14  New Diagnoses Asian Cases                             58 non-null     int64  
 15  New Diagnoses American Indian/Alaska Native Rate      58 non-null     float64
 16  New Diagnoses American Indian/Alaska Native Cases     58 non-null     int64  
 17  New Diagnoses Multiracial Rate                        58 non-null     float64
 18  New Diagnoses Multiracial Cases                       58 non-null     int64  
 19  New Diagnoses Native Hawaiian/Pacific Islander Rate   58 non-null     float64
 20  New Diagnoses Native Hawaiian/Pacific Islander Cases  58 non-null     int64  
dtypes: float64(8), int64(12), object(1)
memory usage: 10.0+ KB


# Check Data Types and Data Quality for COVID DF:
ca2021_covidvacc_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 58 entries, 0 to 57
Data columns (total 19 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   County                     58 non-null     object 
 1   population                 58 non-null     float64
 2   cases                      58 non-null     float64
 3   deaths                     58 non-null     float64
 4   total_tests                58 non-null     float64
 5   positive_tests             58 non-null     float64
 6   reported_cases             58 non-null     float64
 7   reported_deaths            58 non-null     float64
 8   reported_tests             58 non-null     float64
 9   partially_vaccinated       58 non-null     float64
 10  fully_vaccinated           58 non-null     float64
 11  boosted                    58 non-null     int64  
 12  COVID Cases_Rate           58 non-null     float64
 13  COVID Deaths_Rate          58 non-null     float64
 14  Total COVID Tests_Rate     58 non-null     float64
 15  Positive COVID Tests_Rate  58 non-null     float64
 16  Partially Vaccinated_Rate  58 non-null     float64
 17  Fully Vaccinated_Rate      58 non-null     float64
 18  Boosted_Rate               58 non-null     float64
dtypes: float64(17), int64(1), object(1)
memory usage: 9.1+ KB


# Merge the HIV and COVID DF (reset index and drop from each):
merged_df = pd.concat([ca2020_hiv_df2.reset_index(drop=True),ca2021_covidvacc_df.reset_index(drop=True)], axis=1)
merged_df ##covid df and hiv df combined


# Recheck Merged DF:
merged_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 58 entries, 0 to 57
Data columns (total 40 columns):
 #   Column                                                Non-Null Count  Dtype  
---  ------                                                --------------  -----  
 0   GEO ID                                                58 non-null     int64  
 1   County                                                58 non-null     object 
 2   County Cases                                          58 non-null     int64  
 3   County PrEP Rate                                      58 non-null     int64  
 4   County PrEP Users                                     58 non-null     int64  
 5   New Diagnoses Rate                                    58 non-null     float64
 6   New Diagnoses Cases                                   58 non-null     int64  
 7   New Diagnoses Black Rate                              58 non-null     float64
 8   New Diagnoses Black Cases                             58 non-null     int64  
 9   New Diagnoses White Rate                              58 non-null     float64
 10  New Diagnoses White Cases                             58 non-null     int64  
 11  New Diagnoses Hispanic Rate                           58 non-null     float64
 12  New Diagnoses Hispanic Cases                          58 non-null     int64  
 13  New Diagnoses Asian Rate                              58 non-null     float64
 14  New Diagnoses Asian Cases                             58 non-null     int64  
 15  New Diagnoses American Indian/Alaska Native Rate      58 non-null     float64
 16  New Diagnoses American Indian/Alaska Native Cases     58 non-null     int64  
 17  New Diagnoses Multiracial Rate                        58 non-null     float64
 18  New Diagnoses Multiracial Cases                       58 non-null     int64  
 19  New Diagnoses Native Hawaiian/Pacific Islander Rate   58 non-null     float64
 20  New Diagnoses Native Hawaiian/Pacific Islander Cases  58 non-null     int64  
 21  County                                                58 non-null     object 
 22  population                                            58 non-null     float64
 23  cases                                                 58 non-null     float64
 24  deaths                                                58 non-null     float64
 25  total_tests                                           58 non-null     float64
 26  positive_tests                                        58 non-null     float64
 27  reported_cases                                        58 non-null     float64
 28  reported_deaths                                       58 non-null     float64
 29  reported_tests                                        58 non-null     float64
 30  partially_vaccinated                                  58 non-null     float64
 31  fully_vaccinated                                      58 non-null     float64
 32  boosted                                               58 non-null     int64  
 33  COVID Cases_Rate                                      58 non-null     float64
 34  COVID Deaths_Rate                                     58 non-null     float64
 35  Total COVID Tests_Rate                                58 non-null     float64
 36  Positive COVID Tests_Rate                             58 non-null     float64
 37  Partially Vaccinated_Rate                             58 non-null     float64
 38  Fully Vaccinated_Rate                                 58 non-null     float64
 39  Boosted_Rate                                          58 non-null     float64
dtypes: float64(25), int64(13), object(2)
memory usage: 18.2+ KB


# Rename columns and extract columns in desired order:
ca_hivcovid_df = merged_df.iloc[:,[0,1,22,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,23,24,25,26,30,31,32,
                                  33, 34, 35, 36, 37, 38, 39]]
ca_hivcovid_df = ca_hivcovid_df.rename(columns={'County Cases': 'Overall HIV Cases', 'County PrEP Rate': 'PrEP Rate', 
                                                'County PrEP Users': 'PrEP Users', 'cases': 'COVID Cases', 
                                                'deaths': 'COVID Deaths', 'total_tests': 'Total COVID Tests',
                                                'positive_tests': 'Positive COVID Tests', 
                                                'partially_vaccinated': 'Partially Vaccinated', 
                                                'fully_vaccinated': 'Fully Vaccinated','boosted': 'Boosted'})


# Recheck head of DF:
ca_hivcovid_df.head()


ca_hivcovid_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 58 entries, 0 to 57
Data columns (total 36 columns):
 #   Column                                                Non-Null Count  Dtype  
---  ------                                                --------------  -----  
 0   GEO ID                                                58 non-null     int64  
 1   County                                                58 non-null     object 
 2   population                                            58 non-null     float64
 3   Overall HIV Cases                                     58 non-null     int64  
 4   PrEP Rate                                             58 non-null     int64  
 5   PrEP Users                                            58 non-null     int64  
 6   New Diagnoses Rate                                    58 non-null     float64
 7   New Diagnoses Cases                                   58 non-null     int64  
 8   New Diagnoses Black Rate                              58 non-null     float64
 9   New Diagnoses Black Cases                             58 non-null     int64  
 10  New Diagnoses White Rate                              58 non-null     float64
 11  New Diagnoses White Cases                             58 non-null     int64  
 12  New Diagnoses Hispanic Rate                           58 non-null     float64
 13  New Diagnoses Hispanic Cases                          58 non-null     int64  
 14  New Diagnoses Asian Rate                              58 non-null     float64
 15  New Diagnoses Asian Cases                             58 non-null     int64  
 16  New Diagnoses American Indian/Alaska Native Rate      58 non-null     float64
 17  New Diagnoses American Indian/Alaska Native Cases     58 non-null     int64  
 18  New Diagnoses Multiracial Rate                        58 non-null     float64
 19  New Diagnoses Multiracial Cases                       58 non-null     int64  
 20  New Diagnoses Native Hawaiian/Pacific Islander Rate   58 non-null     float64
 21  New Diagnoses Native Hawaiian/Pacific Islander Cases  58 non-null     int64  
 22  COVID Cases                                           58 non-null     float64
 23  COVID Deaths                                          58 non-null     float64
 24  Total COVID Tests                                     58 non-null     float64
 25  Positive COVID Tests                                  58 non-null     float64
 26  Partially Vaccinated                                  58 non-null     float64
 27  Fully Vaccinated                                      58 non-null     float64
 28  Boosted                                               58 non-null     int64  
 29  COVID Cases_Rate                                      58 non-null     float64
 30  COVID Deaths_Rate                                     58 non-null     float64
 31  Total COVID Tests_Rate                                58 non-null     float64
 32  Positive COVID Tests_Rate                             58 non-null     float64
 33  Partially Vaccinated_Rate                             58 non-null     float64
 34  Fully Vaccinated_Rate                                 58 non-null     float64
 35  Boosted_Rate                                          58 non-null     float64
dtypes: float64(22), int64(13), object(1)
memory usage: 16.4+ KB


# Copy DF without GEO ID and Population:
ca_hivcovid_df2 = ca_hivcovid_df.drop(['GEO ID', 'population'], axis = 1)


# Summary Statistics: 
ca_hivcovid_df2.describe()


# What is the total number of confirmed COVID cases in 2021? 
ca_hivcovid_df2['COVID Cases'].max()

896443.0


# What is the total number of confirmed HIV cases in 2021?
ca_hivcovid_df2['New Diagnoses Cases'].max()

1382


ca_hivcovid_df2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 58 entries, 0 to 57
Data columns (total 34 columns):
 #   Column                                                Non-Null Count  Dtype  
---  ------                                                --------------  -----  
 0   County                                                58 non-null     object 
 1   Overall HIV Cases                                     58 non-null     int64  
 2   PrEP Rate                                             58 non-null     int64  
 3   PrEP Users                                            58 non-null     int64  
 4   New Diagnoses Rate                                    58 non-null     float64
 5   New Diagnoses Cases                                   58 non-null     int64  
 6   New Diagnoses Black Rate                              58 non-null     float64
 7   New Diagnoses Black Cases                             58 non-null     int64  
 8   New Diagnoses White Rate                              58 non-null     float64
 9   New Diagnoses White Cases                             58 non-null     int64  
 10  New Diagnoses Hispanic Rate                           58 non-null     float64
 11  New Diagnoses Hispanic Cases                          58 non-null     int64  
 12  New Diagnoses Asian Rate                              58 non-null     float64
 13  New Diagnoses Asian Cases                             58 non-null     int64  
 14  New Diagnoses American Indian/Alaska Native Rate      58 non-null     float64
 15  New Diagnoses American Indian/Alaska Native Cases     58 non-null     int64  
 16  New Diagnoses Multiracial Rate                        58 non-null     float64
 17  New Diagnoses Multiracial Cases                       58 non-null     int64  
 18  New Diagnoses Native Hawaiian/Pacific Islander Rate   58 non-null     float64
 19  New Diagnoses Native Hawaiian/Pacific Islander Cases  58 non-null     int64  
 20  COVID Cases                                           58 non-null     float64
 21  COVID Deaths                                          58 non-null     float64
 22  Total COVID Tests                                     58 non-null     float64
 23  Positive COVID Tests                                  58 non-null     float64
 24  Partially Vaccinated                                  58 non-null     float64
 25  Fully Vaccinated                                      58 non-null     float64
 26  Boosted                                               58 non-null     int64  
 27  COVID Cases_Rate                                      58 non-null     float64
 28  COVID Deaths_Rate                                     58 non-null     float64
 29  Total COVID Tests_Rate                                58 non-null     float64
 30  Positive COVID Tests_Rate                             58 non-null     float64
 31  Partially Vaccinated_Rate                             58 non-null     float64
 32  Fully Vaccinated_Rate                                 58 non-null     float64
 33  Boosted_Rate                                          58 non-null     float64
dtypes: float64(21), int64(12), object(1)
memory usage: 15.5+ KB


# Check Correlation of Features through Correlation Matrix:
# Extract features with rates only to compare relationship between HIV and COVID 
corr_features = ca_hivcovid_df2.iloc[:,[2,4,6,8,10,12,14,16,18,27,28,29,30,31,32,33]]
plt.figure(figsize=(20, 16)) #set figure size
heatmap = sns.heatmap(corr_features.corr(), cmap='GnBu', annot=True) #use .corr() function for correlation
heatmap.set_title('Correlation Heatmap of Features by Rate', size=16) #set title
plt.show() #show plot


# Relationship of New Cases Diagnosis Rate & COVID Cases Rate with Joinplot:
sns.jointplot(ca_hivcovid_df2, y='New Diagnoses Rate', x='COVID Cases_Rate', color='mediumseagreen') # set color
plt.show() # show plot


# Relationship of PrEP Users Rate & Fully Vaccinated Rate with Joinplot:
sns.jointplot(ca_hivcovid_df2, y='PrEP Rate', x='COVID Cases_Rate', color='blue') # set color
plt.show() # show plot


# Check Relationship of New Diagnoses Rate & Fully Vaccinated with Joinplot:
sns.jointplot(ca_hivcovid_df2, y='New Diagnoses Rate', x='Fully Vaccinated_Rate', color='olive') # set color
plt.show() # show plot


# Relationship of New Diagnoses Hispanic Rate & Fully Vaccinated Rate with Joinplot:
sns.jointplot(ca_hivcovid_df2, y='New Diagnoses Hispanic Rate', x='Fully Vaccinated_Rate', color='coral') # set color
plt.show() # show plot


# CDC Social Vulnerability Index for 2020 for California by County:
CA_svi = gpd.read_file('/Users/cl/Documents/GEO448/Project/Shapefiles/SVI2020_CALIFORNIA_county.shp')


# Check the head of data: 
CA_svi.head(10)


# Check Shape of Data (ensure there are 58 counties)
CA_svi.shape

(58, 161)


# Check the Coordinate Reference System (CRS):
CA_svi.crs

<Geographic 2D CRS: EPSG:4269>
Name: NAD83
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: North America - onshore and offshore: Canada - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon. Puerto Rico. United States (USA) - Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming. US Virgin Islands. British Virgin Islands.
- bounds: (167.65, 14.92, -40.73, 86.45)
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich


# Change CRS to UTM Zone 10N - EPSG:26910
CA_svi2 = CA_svi.to_crs(26910)
CA_svi2.crs

<Derived Projected CRS: EPSG:26910>
Name: NAD83 / UTM zone 10N
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: North America - between 126°W and 120°W - onshore and offshore. Canada - British Columbia; Northwest Territories; Yukon. United States (USA) - California; Oregon; Washington.
- bounds: (-126.0, 30.54, -119.99, 81.8)
Coordinate Operation:
- name: UTM zone 10N
- method: Transverse Mercator
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich


#Map California Counties by Aggregating Tracts: 
ca_counties = CA_svi2.dissolve(by='STCNTY') #use STCNTY to map counties
ca_counties.plot(cmap='winter', figsize=(9,7)) #plot 
plt.show() #show plot


# Summary Statistics for All RPL_THEMES: 
CA_svi2[['RPL_THEME1', 'RPL_THEME2', 'RPL_THEME3', 'RPL_THEME4', 'RPL_THEMES']].describe()


# Show RPL_THEME1 distribution by county: 
ca_counties.plot(column='RPL_THEME1', figsize=(9,7), legend=True, cmap='gist_earth')
plt.title('RPL_THEME1')
plt.show()


# Show RPL_THEME2 distribution by county: 
ca_counties.plot(column='RPL_THEME2', figsize=(9,7), legend=True, cmap='gist_earth')
plt.title('RPL_THEME2')
plt.show()


# Show RPL_THEME3 distribution by county: 
ca_counties.plot(column='RPL_THEME3', figsize=(9,7), legend=True, cmap='gist_earth')
plt.title('RPL_THEME3')
plt.show()


# Show RPL_THEME4 distribution by county: 
ca_counties.plot(column='RPL_THEME4', figsize=(9,7), legend=True, cmap='gist_earth')
plt.title('RPL_THEME4')
plt.show()


# Show most vulernable for F_THEME1: 
ca_counties.loc[ca_counties['F_THEME1'] == 0.0, 'FS_THEME1'] = 'Not in the 90th Percentile'
ca_counties.loc[ca_counties['F_THEME1'] == 1.0, 'FS_THEME1'] = 'In the 90th Percentile'
ca_counties.plot(column='FS_THEME1', figsize=(9,7), legend=True, cmap='winter_r', missing_kwds={'color':'gainsboro'})
plt.title('Highest Vulnerability in Socioeconomic Status')
plt.show()


# Show most vulernable for F_THEME2: 
ca_counties.loc[ca_counties['F_THEME2'] == 0.0, 'FS_THEME2'] = 'Not in the 90th Percentile'
ca_counties.loc[ca_counties['F_THEME2'] == 1.0, 'FS_THEME2'] = 'In the 90th Percentile'
ca_counties.plot(column='FS_THEME2', figsize=(9,7), legend=True, cmap='cool_r', missing_kwds={'color':'gainsboro'})
plt.title('Highest Vulnerability in Household Charactertistics')
plt.show()


# Show most vulernable for F_THEME3: 
ca_counties.loc[ca_counties['F_THEME3'] == 0.0, 'FS_THEME3'] = 'Not in the 90th Percentile'
ca_counties.loc[ca_counties['F_THEME3'] == 1.0, 'FS_THEME3'] = 'In the 90th Percentile'
ca_counties.plot(column='FS_THEME3', figsize=(9,7), legend=True, cmap='winter_r', missing_kwds={'color':'gainsboro'})
plt.title('     Highest Vulnerability in Racial & Ethnic Minority Status')
plt.show()


# Show most vulernable for F_THEME4: 
ca_counties.loc[ca_counties['F_THEME4'] == 0.0, 'FS_THEME4'] = 'Not in the 90th Percentile'
ca_counties.loc[ca_counties['F_THEME4'] == 1.0, 'FS_THEME4'] = 'In the 90th Percentile'
ca_counties.plot(column='FS_THEME4', figsize=(9,7), legend=True, cmap='cool_r', missing_kwds={'color':'gainsboro'})
plt.title('  Highest Vulnerability in Housing Type/Transportation')
plt.show()


# Create a Geocoder from OSM Nominatim Server
geocoder = Nominatim(user_agent='myapp')


# Create a new column 'County/State' which includes 'CA' for each county:
ca_hivcovid_df2['County/State'] = ca_hivcovid_df2['County'].astype(str) + ',CA' #concats ',CA' to value
ca_hivcovid_df2.head()


# Apply the Geocoder to County/State and save output as new Location column:
ca_hivcovid_df2['Location'] = ca_hivcovid_df2['County/State'].apply(geocoder.geocode) #apply geocode to all
ca_hivcovid_df2['Location'] #view and check results

0     (Alameda County, CAL Fire Northern Region, Cal...
1     (Alpine County, California, United States, (38...
2     (Amador County, California, United States, (38...
3     (Butte County, California, United States, (39....
4     (Calaveras County, California, United States, ...
5     (Colusa County, California, United States, (39...
6     (Contra Costa County, California, United State...
7     (Del Norte County, CAL Fire Northern Region, C...
8     (El Dorado County, California, United States, ...
9     (Fresno, Fresno County, CAL Fire Southern Regi...
10    (Glenn County, California, United States, (39....
11    (Humboldt County, California, United States, (...
12    (Imperial County, California, United States, (...
13    (Inyo County, California, United States, (36.5...
14    (Kern County, CAL Fire Southern Region, Califo...
15    (Kings County, California, United States, (36....
16    (Lake County, California, United States, (39.0...
17    (Lassen County, California, United States, (40...
18    (Los Angeles, Los Angeles County, CAL Fire Sou...
19    (Madera County, California, United States, (37...
20    (Marin County, California, United States, (38....
21    (Mariposa County, California, United States, (...
22    (Mendocino County, CAL Fire Northern Region, C...
23    (Merced County, California, United States, (37...
24    (Modoc County, CAL Fire Northern Region, Calif...
25    (Mono County, California, United States, (37.9...
26    (Monterey County, CAL Fire Southern Region, Ca...
27    (Napa, Napa County, California, United States,...
28    (Nevada County, CAL Fire Northern Region, Cali...
29    (Orange County, California, United States, (33...
30    (Placer County, CAL Fire Northern Region, Cali...
31    (Plumas County, California, United States, (39...
32    (Riverside, Riverside County, California, Unit...
33    (Sacramento, Sacramento County, CAL Fire North...
34    (San Benito County, California, United States,...
35    (San Bernardino County, California, United Sta...
36    (San Diego, San Diego County, California, Unit...
37    (San Francisco, CAL Fire Northern Region, Cali...
38    (San Joaquin County, California, United States...
39    (San Luis Obispo County, California, United St...
40    (San Mateo County, California, United States, ...
41    (Santa Barbara, Santa Barbara County, CAL Fire...
42    (Santa Clara County, California, United States...
43    (Santa Cruz County, CAL Fire Northern Region, ...
44    (Shasta County, CAL Fire Northern Region, Cali...
45    (Sierra County, California, United States, (39...
46    (Siskiyou County, CAL Fire Northern Region, Ca...
47    (Solano County, California, United States, (38...
48    (Sonoma County, CAL Fire Northern Region, Cali...
49    (Stanislaus County, California, United States,...
50    (Sutter County, CAL Fire Northern Region, Cali...
51    (Tehama County, CAL Fire Northern Region, Cali...
52    (Trinity County, California, United States, (4...
53    (Tulare County, California, United States, (36...
54    (Tuolumne County, California, United States, (...
55    (Ventura County, California, United States, (3...
56    (Yolo County, California, United States, (38.7...
57    (Yuba County, California, United States, (39.2...
Name: Location, dtype: object


# Apply Lambda X to Location to extract Latitude and Longitude:
ca_hivcovid_df2['Lat'] = ca_hivcovid_df2['Location'].apply(lambda x: (x.latitude)) #apply, get latitude
ca_hivcovid_df2['Long'] = ca_hivcovid_df2['Location'].apply(lambda x: (x.longitude)) #apply, get longitude


# Check head of DF: 
ca_hivcovid_df2.head()


# Create a GeoSeries from X, Y coordinates (Longitude = X, Latitude = Y)
gs = gpd.points_from_xy(ca_hivcovid_df2.Long, ca_hivcovid_df2.Lat)


# Create a GeoDataFrame using ca_hivcovid_df2 and gs from above: 
ca_hivcovid_gs = gpd.GeoDataFrame(ca_hivcovid_df2, geometry=gs)
ca_hivcovid_gs.tail() #view tail


# Set CRS for ca_hivcovid_gs to GCS_NAD83 (horizontal datum for North America)
ca_hivcovid_gs.crs = '4269'
ca_hivcovid_gs.crs

<Geographic 2D CRS: EPSG:4269>
Name: NAD83
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: North America - onshore and offshore: Canada - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon. Puerto Rico. United States (USA) - Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming. US Virgin Islands. British Virgin Islands.
- bounds: (167.65, 14.92, -40.73, 86.45)
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich


# CDC Social Vulnerability Index for 2020 for California by County:
CA_svi = gpd.read_file('/Users/cl/Documents/GEO448/Project/Shapefiles/SVI2020_CALIFORNIA_county.shp')


# View tail of CA_svi:
CA_svi.tail()


# Check info on CA_svi GeoDataFrame:
CA_svi.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 58 entries, 0 to 57
Columns: 161 entries, ST to geometry
dtypes: float64(77), geometry(1), int64(76), object(7)
memory usage: 73.1+ KB


# Check CRS for CA_svi:
CA_svi.crs

<Geographic 2D CRS: EPSG:4269>
Name: NAD83
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: North America - onshore and offshore: Canada - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon. Puerto Rico. United States (USA) - Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming. US Virgin Islands. British Virgin Islands.
- bounds: (167.65, 14.92, -40.73, 86.45)
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich


# Check info on ca_hivcovid GeoDataFrame:
ca_hivcovid_gs.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 58 entries, 0 to 57
Data columns (total 39 columns):
 #   Column                                                Non-Null Count  Dtype   
---  ------                                                --------------  -----   
 0   County                                                58 non-null     object  
 1   Overall HIV Cases                                     58 non-null     int64   
 2   PrEP Rate                                             58 non-null     int64   
 3   PrEP Users                                            58 non-null     int64   
 4   New Diagnoses Rate                                    58 non-null     float64 
 5   New Diagnoses Cases                                   58 non-null     int64   
 6   New Diagnoses Black Rate                              58 non-null     float64 
 7   New Diagnoses Black Cases                             58 non-null     int64   
 8   New Diagnoses White Rate                              58 non-null     float64 
 9   New Diagnoses White Cases                             58 non-null     int64   
 10  New Diagnoses Hispanic Rate                           58 non-null     float64 
 11  New Diagnoses Hispanic Cases                          58 non-null     int64   
 12  New Diagnoses Asian Rate                              58 non-null     float64 
 13  New Diagnoses Asian Cases                             58 non-null     int64   
 14  New Diagnoses American Indian/Alaska Native Rate      58 non-null     float64 
 15  New Diagnoses American Indian/Alaska Native Cases     58 non-null     int64   
 16  New Diagnoses Multiracial Rate                        58 non-null     float64 
 17  New Diagnoses Multiracial Cases                       58 non-null     int64   
 18  New Diagnoses Native Hawaiian/Pacific Islander Rate   58 non-null     float64 
 19  New Diagnoses Native Hawaiian/Pacific Islander Cases  58 non-null     int64   
 20  COVID Cases                                           58 non-null     float64 
 21  COVID Deaths                                          58 non-null     float64 
 22  Total COVID Tests                                     58 non-null     float64 
 23  Positive COVID Tests                                  58 non-null     float64 
 24  Partially Vaccinated                                  58 non-null     float64 
 25  Fully Vaccinated                                      58 non-null     float64 
 26  Boosted                                               58 non-null     int64   
 27  COVID Cases_Rate                                      58 non-null     float64 
 28  COVID Deaths_Rate                                     58 non-null     float64 
 29  Total COVID Tests_Rate                                58 non-null     float64 
 30  Positive COVID Tests_Rate                             58 non-null     float64 
 31  Partially Vaccinated_Rate                             58 non-null     float64 
 32  Fully Vaccinated_Rate                                 58 non-null     float64 
 33  Boosted_Rate                                          58 non-null     float64 
 34  County/State                                          58 non-null     object  
 35  Location                                              58 non-null     object  
 36  Lat                                                   58 non-null     float64 
 37  Long                                                  58 non-null     float64 
 38  geometry                                              58 non-null     geometry
dtypes: float64(23), geometry(1), int64(12), object(3)
memory usage: 17.8+ KB


# Check CRS for ca_hivcovid:
ca_hivcovid_gs.crs

<Geographic 2D CRS: EPSG:4269>
Name: NAD83
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: North America - onshore and offshore: Canada - Alberta; British Columbia; Manitoba; New Brunswick; Newfoundland and Labrador; Northwest Territories; Nova Scotia; Nunavut; Ontario; Prince Edward Island; Quebec; Saskatchewan; Yukon. Puerto Rico. United States (USA) - Alabama; Alaska; Arizona; Arkansas; California; Colorado; Connecticut; Delaware; Florida; Georgia; Hawaii; Idaho; Illinois; Indiana; Iowa; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; North Dakota; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Utah; Vermont; Virginia; Washington; West Virginia; Wisconsin; Wyoming. US Virgin Islands. British Virgin Islands.
- bounds: (167.65, 14.92, -40.73, 86.45)
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich


# Plot and Overlay Both GeoDataFrames:
fig, ax = plt.subplots(figsize=(6,8))
CA_svi.plot(ax=ax, edgecolor='k', facecolor='None') # plot CA Counties
ca_hivcovid_gs.plot(ax=ax, marker='o', markersize=15, color='yellowgreen') # this plots marker for each county
plt.show() # show plot


# Perform Spatial Join of CA_svi and ca_hivcovid_gs
CA_svi_hivcovid = gpd.sjoin(CA_svi, ca_hivcovid_gs)


# Checked joined GeoDataFrame:
CA_svi_hivcovid.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
Int64Index: 58 entries, 0 to 57
Columns: 200 entries, ST to Long
dtypes: float64(100), geometry(1), int64(89), object(10)
memory usage: 91.1+ KB


# Check head of joined GeoDataFrame:
CA_svi_hivcovid.head()


# Show HIV Infection Rate by Counties: 
CA_svi_hivcovid.plot(column='New Diagnoses Rate', figsize=(9,7), legend=True, cmap='cool') #plot by Rate
plt.title('New HIV Diagnoses Rate') #set title
plt.show() #show plot


# Show HIV Infection Cases by Counties: 
CA_svi_hivcovid.plot(column='New Diagnoses Cases', figsize=(9,7), legend=True, cmap='cool') #plot by case count
plt.title('New HIV Diagnoses Cases') #set title
plt.show() #show plot


# Show PrEP User Rate by Counties: 
CA_svi_hivcovid.plot(column='PrEP Rate', figsize=(9,7), legend=True, cmap='spring_r') #plot by rate
plt.title('PrEP Rate') # set title
plt.show() # show plot


# Show New PrEP Users by Counties: 
CA_svi_hivcovid.plot(column='PrEP Users', figsize=(9,7), legend=True, cmap='spring_r') #plot by count
plt.title('New PrEP Users') #set title
plt.show() #show plot


# Show HIV Infection Rate by Race/Ethnic Group ---
# Create a list of all race/ethnic rate feature names:
byrace = ['New Diagnoses Black Rate', 'New Diagnoses White Rate', 'New Diagnoses Hispanic Rate', 
          'New Diagnoses Asian Rate', 'New Diagnoses American Indian/Alaska Native Rate', 
          'New Diagnoses Native Hawaiian/Pacific Islander Rate']

fig, axes = plt.subplots(3,2, figsize=(10,16)) # create a figure with 3 by 2 subplots
axes = axes.flatten() #flatten 2-D array to 1-D array

for i,col in enumerate(byrace): # each i axes, by col 
    CA_svi_hivcovid.plot(ax=axes[i], column=col, legend=True, cmap='cool') # map col to each axes
    axes[i].set_title(col,fontsize=10) # set title by col name
    axes[i].axis('off') # turn off axis display
plt.tight_layout()


# Show New HIV Cases by Race/Ethnic Group ---
# Create a list of all race/ethnic rate feature names:
byrace_cases = ['New Diagnoses Black Cases', 'New Diagnoses White Cases', 'New Diagnoses Hispanic Cases', 
          'New Diagnoses Asian Cases', 'New Diagnoses American Indian/Alaska Native Cases', 
          'New Diagnoses Native Hawaiian/Pacific Islander Cases']

fig, axes = plt.subplots(3,2, figsize=(10,16)) # create a figure with 3 by 2 subplots
axes = axes.flatten() #flatten 2-D array to 1-D array

for i,col in enumerate(byrace_cases): # each i axes, by col 
    CA_svi_hivcovid.plot(ax=axes[i], column=col, legend=True, cmap='spring_r') # map col to each axes
    axes[i].set_title(col,fontsize=10) # set title by col name
    axes[i].axis('off') # turn off axis display
plt.tight_layout()


# Show COVID Tests, Cases and Deaths  ---
# Create a list of all COVID Features:
covid = ['COVID Cases_Rate', 'COVID Deaths_Rate', 'Total COVID Tests_Rate', 'Positive COVID Tests_Rate']

fig, axes = plt.subplots(2,2, figsize=(10,16)) # create a figure with 3 by 2 subplots
axes = axes.flatten() #flatten 2-D array to 1-D array

for i,col in enumerate(covid): # each i axes, by col 
    CA_svi_hivcovid.plot(ax=axes[i], column=col, legend=True, cmap='spring_r') # map col to each axes
    axes[i].set_title(col,fontsize=10) # set title by col name
    axes[i].axis('off') # turn off axis display
plt.tight_layout()


# Show Vaccinations ---
# Create a list of all Vaccine Features:
vaccines = ['Partially Vaccinated_Rate', 'Fully Vaccinated_Rate', 'Boosted_Rate']

fig, axes = plt.subplots(3,1, figsize=(10,16)) # create a figure with 3 by 2 subplots
axes = axes.flatten() #flatten 2-D array to 1-D array

for i,col in enumerate(vaccines): # each i axes, by col 
    CA_svi_hivcovid.plot(ax=axes[i], column=col, legend=True, cmap='cool') # map col to each axes
    axes[i].set_title(col,fontsize=10) # set title by col name
    axes[i].axis('off') # turn off axis display
plt.tight_layout()


# Grab subset of the data with desired features
CA_hivcovid2 = CA_svi_hivcovid[['FIPS', 'COUNTY', 'New Diagnoses Rate', 'PrEP Rate', 'COVID Cases_Rate', 
                                'Total COVID Tests_Rate', 'Fully Vaccinated_Rate', 'EP_MINRTY', 
                                'EP_UNINSUR', 'geometry']]


CA_hivcovid2.head() #check the head of df


# Set index using existing 'FIPS' column
CA_hivcovid3 = CA_hivcovid2.set_index('FIPS', drop=False)
CA_hivcovid3.tail()


# Create spatial weight object using Queen's Method
w = weights.Queen.from_dataframe(CA_hivcovid3, idVariable='FIPS')


# Row Standardize Spatial Weights
w.transform = 'R'


# Calculate Local Moran's I 
lm = esda.Moran_Local(CA_hivcovid3['New Diagnoses Rate'], w)


# write z,p-value, quandrant type of Local Moran's I to GeoDataFrame
CA_hivcovid3['LMZ_NDR'] = lm.z # z-score of Local Moran's I
CA_hivcovid3['LMP_NDR'] = lm.p_sim # p-value of Local Moran's I
CA_hivcovid3['LMQ_NDR'] = lm.q # quadrant type of Local Moran's I
CA_hivcovid3.head(3) #view head


# Calculate Local Moran's I 
lm2 = esda.Moran_Local(CA_hivcovid3['PrEP Rate'], w)


# write z,p-value, quandrant type of Local Moran's I to GeoDataFrame
CA_hivcovid3['LMZ_PR'] = lm2.z # z-score of Local Moran's I
CA_hivcovid3['LMP_PR'] = lm2.p_sim # p-value of Local Moran's I
CA_hivcovid3['LMQ_PR'] = lm2.q # quadrant type of Local Moran's I
CA_hivcovid3.head(3) #view head


# Calculate Local Moran's I 
lm3 = esda.Moran_Local(CA_hivcovid3['COVID Cases_Rate'], w)


# write z,p-value, quandrant type of Local Moran's I to GeoDataFrame
CA_hivcovid3['LMZ_CCR'] = lm3.z # z-score of Local Moran's I
CA_hivcovid3['LMP_CCR'] = lm3.p_sim # p-value of Local Moran's I
CA_hivcovid3['LMQ_CCR'] = lm3.q # quadrant type of Local Moran's I
CA_hivcovid3.head(3) #view head


# Calculate Local Moran's I 
lm4 = esda.Moran_Local(CA_hivcovid3['Total COVID Tests_Rate'], w)


# write z,p-value, quandrant type of Local Moran's I to GeoDataFrame
CA_hivcovid3['LMZ_TCTR'] = lm4.z # z-score of Local Moran's I
CA_hivcovid3['LMP_TCTR'] = lm4.p_sim # p-value of Local Moran's I
CA_hivcovid3['LMQ_TCTR'] = lm4.q # quadrant type of Local Moran's I
CA_hivcovid3.head(3) #view head


# Calculate Local Moran's I 
lm5 = esda.Moran_Local(CA_hivcovid3['Fully Vaccinated_Rate'], w)


# write z,p-value, quandrant type of Local Moran's I to GeoDataFrame
CA_hivcovid3['LMZ_FVR'] = lm5.z # z-score of Local Moran's I
CA_hivcovid3['LMP_FVR'] = lm5.p_sim # p-value of Local Moran's I
CA_hivcovid3['LMQ_FVR'] = lm5.q # quadrant type of Local Moran's I
CA_hivcovid3.head(3) #view head


# Calculate Local Moran's I 
lm6 = esda.Moran_Local(CA_hivcovid3['EP_MINRTY'], w)


# write z,p-value, quandrant type of Local Moran's I to GeoDataFrame
CA_hivcovid3['LMZ_MIN'] = lm6.z # z-score of Local Moran's I
CA_hivcovid3['LMP_MIN'] = lm6.p_sim # p-value of Local Moran's I
CA_hivcovid3['LMQ_MIN'] = lm6.q # quadrant type of Local Moran's I
CA_hivcovid3.head(3) #view head


# Calculate Local Moran's I 
lm7 = esda.Moran_Local(CA_hivcovid3['EP_UNINSUR'], w)


# write z,p-value, quandrant type of Local Moran's I to GeoDataFrame
CA_hivcovid3['LMZ_UNI'] = lm7.z # z-score of Local Moran's I
CA_hivcovid3['LMP_UNI'] = lm7.p_sim # p-value of Local Moran's I
CA_hivcovid3['LMQ_UNI'] = lm7.q # quadrant type of Local Moran's I
CA_hivcovid3.head(3) #view head


# set color palette for LM Quadrant Type for HIV Features
lmq_h = {'HH':'darkmagenta','LL':'orchid','HL':'blue','LH':'paleturquoise'}

# set color palette for LM Quadrant Type for COVID Features
lmq_c = {'HH':'gold','LL':'yellow','HL':'olivedrab','LH':'darkgreen'}

# set color palette for LM Quadrant Type for SVI Features
lmq_s = {'HH':'deeppink','LL':'fuchsia','HL':'salmon','LH':'sandybrown'}


# Set conditions for each of Moran Plot's Quadrants
cond_hh = (CA_hivcovid3['LMQ_NDR'] == 1) & (CA_hivcovid3['LMP_NDR'] <= 0.05) #less than or equal to 0.05
cond_lh = (CA_hivcovid3['LMQ_NDR'] == 2) & (CA_hivcovid3['LMP_NDR'] <= 0.05) #less than or equal to 0.05
cond_ll = (CA_hivcovid3['LMQ_NDR'] == 3) & (CA_hivcovid3['LMP_NDR'] <= 0.05) #less than or equal to 0.05
cond_hl = (CA_hivcovid3['LMQ_NDR'] == 4) & (CA_hivcovid3['LMP_NDR'] <= 0.05) #less than or equal to 0.05

# 'LISA_Type' to 'HH' if quadrant type is 1 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hh,'NDR_LISA'] = 'HH'

# 'LISA_Type' to 'LH' if quadrant type is 2 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_lh,'NDR_LISA'] = 'LH'

# 'LISA_Type' to 'LL' if quadrant type is 3 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_ll,'NDR_LISA'] = 'LL'

# 'LISA_Type' to 'HL' if quadrant type is 4 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hl,'NDR_LISA'] = 'HL'

# Countplot showing the number of tracts that fall into the four quadrant types
sns.countplot(data=CA_hivcovid3, x='NDR_LISA', palette='winter') 
plt.show() #show plot


# Create Lisa Series and Drop NA Values:
NDR_lisa2 = CA_hivcovid3['NDR_LISA'].dropna()


# Define Style Function for NDA
def style_NDR(feature):
    # get LM Quadrant Type of GeoJson feature by FIPS code, and set it to lmq_type
    # set Null to lmq_type if there is no LM Quadrant Type in lisa_series 
    lmq_type = NDR_lisa2.get(feature['properties']['FIPS'],None) 
    # gray polygon if LMQ type is None (not statistically significant)
    if lmq_type is None:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':'silver', 'color':'white'}
    # use color palette for fillColor otherwise
    else:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':lmq_h[lmq_type], 'color':'white'}


# Set conditions for each of Moran Plot's Quadrants
cond_hh = (CA_hivcovid3['LMQ_PR'] == 1) & (CA_hivcovid3['LMP_PR'] <= 0.05) #less than or equal to 0.05
cond_lh = (CA_hivcovid3['LMQ_PR'] == 2) & (CA_hivcovid3['LMP_PR'] <= 0.05) #less than or equal to 0.05
cond_ll = (CA_hivcovid3['LMQ_PR'] == 3) & (CA_hivcovid3['LMP_PR'] <= 0.05) #less than or equal to 0.05
cond_hl = (CA_hivcovid3['LMQ_PR'] == 4) & (CA_hivcovid3['LMP_PR'] <= 0.05) #less than or equal to 0.05

# 'LISA_Type' to 'HH' if quadrant type is 1 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hh,'PR_LISA'] = 'HH'

# 'LISA_Type' to 'LH' if quadrant type is 2 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_lh,'PR_LISA'] = 'LH'

# 'LISA_Type' to 'LL' if quadrant type is 3 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_ll,'PR_LISA'] = 'LL'

# 'LISA_Type' to 'HL' if quadrant type is 4 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hl,'PR_LISA'] = 'HL'

# Countplot showing the number of tracts that fall into the four quadrant types
sns.countplot(data=CA_hivcovid3, x='PR_LISA', palette='cool') 
plt.show() #show plot


# Create Lisa Series and Drop NA Values:
PR_lisa2 = CA_hivcovid3['PR_LISA'].dropna()


# Define Style Function for PR
def style_PR(feature):
    # get LM Quadrant Type of GeoJson feature by FIPS code, and set it to lmq_type
    # set Null to lmq_type if there is no LM Quadrant Type in lisa_series 
    lmq_type = PR_lisa2.get(feature['properties']['FIPS'],None) 
    # gray polygon if LMQ type is None (not statistically significant)
    if lmq_type is None:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':'silver', 'color':'white'}
    # use color palette for fillColor otherwise
    else:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':lmq_h[lmq_type], 'color':'white'}


# Set conditions for each of Moran Plot's Quadrants
cond_hh = (CA_hivcovid3['LMQ_CCR'] == 1) & (CA_hivcovid3['LMP_CCR'] <= 0.05) #less than or equal to 0.05
cond_lh = (CA_hivcovid3['LMQ_CCR'] == 2) & (CA_hivcovid3['LMP_CCR'] <= 0.05) #less than or equal to 0.05
cond_ll = (CA_hivcovid3['LMQ_CCR'] == 3) & (CA_hivcovid3['LMP_CCR'] <= 0.05) #less than or equal to 0.05
cond_hl = (CA_hivcovid3['LMQ_CCR'] == 4) & (CA_hivcovid3['LMP_CCR'] <= 0.05) #less than or equal to 0.05

# 'LISA_Type' to 'HH' if quadrant type is 1 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hh,'CCR_LISA'] = 'HH'

# 'LISA_Type' to 'LH' if quadrant type is 2 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_lh,'CCR_LISA'] = 'LH'

# 'LISA_Type' to 'LL' if quadrant type is 3 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_ll,'CCR_LISA'] = 'LL'

# 'LISA_Type' to 'HL' if quadrant type is 4 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hl,'CCR_LISA'] = 'HL'

# Countplot showing the number of tracts that fall into the four quadrant types
sns.countplot(data=CA_hivcovid3, x='CCR_LISA', palette='winter') 
plt.show() #show plot


# Create Lisa Series and Drop NA Values:
CCR_lisa2 = CA_hivcovid3['CCR_LISA'].dropna()


# Define Style Function for PR
def style_CCR(feature):
    # get LM Quadrant Type of GeoJson feature by FIPS code, and set it to lmq_type
    # set Null to lmq_type if there is no LM Quadrant Type in lisa_series 
    lmq_type = CCR_lisa2.get(feature['properties']['FIPS'],None) 
    # gray polygon if LMQ type is None (not statistically significant)
    if lmq_type is None:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':'silver', 'color':'white'}
    # use color palette for fillColor otherwise
    else:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':lmq_c[lmq_type], 'color':'white'}


# Set conditions for each of Moran Plot's Quadrants
cond_hh = (CA_hivcovid3['LMQ_TCTR'] == 1) & (CA_hivcovid3['LMP_TCTR'] <= 0.05) #less than or equal to 0.05
cond_lh = (CA_hivcovid3['LMQ_TCTR'] == 2) & (CA_hivcovid3['LMP_TCTR'] <= 0.05) #less than or equal to 0.05
cond_ll = (CA_hivcovid3['LMQ_TCTR'] == 3) & (CA_hivcovid3['LMP_TCTR'] <= 0.05) #less than or equal to 0.05
cond_hl = (CA_hivcovid3['LMQ_TCTR'] == 4) & (CA_hivcovid3['LMP_TCTR'] <= 0.05) #less than or equal to 0.05

# 'LISA_Type' to 'HH' if quadrant type is 1 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hh,'TCTR_LISA'] = 'HH'

# 'LISA_Type' to 'LH' if quadrant type is 2 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_lh,'TCTR_LISA'] = 'LH'

# 'LISA_Type' to 'LL' if quadrant type is 3 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_ll,'TCTR_LISA'] = 'LL'

# 'LISA_Type' to 'HL' if quadrant type is 4 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hl,'TCTR_LISA'] = 'HL'

# Countplot showing the number of tracts that fall into the four quadrant types
sns.countplot(data=CA_hivcovid3, x='TCTR_LISA', palette='cool') 
plt.show() #show plot


# Create Lisa Series and Drop NA Values:
TCTR_lisa2 = CA_hivcovid3['TCTR_LISA'].dropna()


# Define Style Function for PR
def style_TCTR(feature):
    # get LM Quadrant Type of GeoJson feature by FIPS code, and set it to lmq_type
    # set Null to lmq_type if there is no LM Quadrant Type in lisa_series 
    lmq_type = TCTR_lisa2.get(feature['properties']['FIPS'],None) 
    # gray polygon if LMQ type is None (not statistically significant)
    if lmq_type is None:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':'silver', 'color':'white'}
    # use color palette for fillColor otherwise
    else:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':lmq_c[lmq_type], 'color':'white'}


# Set conditions for each of Moran Plot's Quadrants
cond_hh = (CA_hivcovid3['LMQ_FVR'] == 1) & (CA_hivcovid3['LMP_FVR'] <= 0.05) #less than or equal to 0.05
cond_lh = (CA_hivcovid3['LMQ_FVR'] == 2) & (CA_hivcovid3['LMP_FVR'] <= 0.05) #less than or equal to 0.05
cond_ll = (CA_hivcovid3['LMQ_FVR'] == 3) & (CA_hivcovid3['LMP_FVR'] <= 0.05) #less than or equal to 0.05
cond_hl = (CA_hivcovid3['LMQ_FVR'] == 4) & (CA_hivcovid3['LMP_FVR'] <= 0.05) #less than or equal to 0.05

# 'LISA_Type' to 'HH' if quadrant type is 1 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hh,'FVR_LISA'] = 'HH'

# 'LISA_Type' to 'LH' if quadrant type is 2 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_lh,'FVR_LISA'] = 'LH'

# 'LISA_Type' to 'LL' if quadrant type is 3 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_ll,'FVR_LISA'] = 'LL'

# 'LISA_Type' to 'HL' if quadrant type is 4 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hl,'FVR_LISA'] = 'HL'

# Countplot showing the number of tracts that fall into the four quadrant types
sns.countplot(data=CA_hivcovid3, x='FVR_LISA', palette='winter') 
plt.show() #show plot


# Create Lisa Series and Drop NA Values:
FVR_lisa2 = CA_hivcovid3['FVR_LISA'].dropna()


# Define Style Function for PR
def style_FVR(feature):
    # get LM Quadrant Type of GeoJson feature by FIPS code, and set it to lmq_type
    # set Null to lmq_type if there is no LM Quadrant Type in lisa_series 
    lmq_type = FVR_lisa2.get(feature['properties']['FIPS'],None) 
    # gray polygon if LMQ type is None (not statistically significant)
    if lmq_type is None:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':'silver', 'color':'white'}
    # use color palette for fillColor otherwise
    else:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':lmq_c[lmq_type], 'color':'white'}


# Set conditions for each of Moran Plot's Quadrants
cond_hh = (CA_hivcovid3['LMQ_MIN'] == 1) & (CA_hivcovid3['LMP_MIN'] <= 0.05) #less than or equal to 0.05
cond_lh = (CA_hivcovid3['LMQ_MIN'] == 2) & (CA_hivcovid3['LMP_MIN'] <= 0.05) #less than or equal to 0.05
cond_ll = (CA_hivcovid3['LMQ_MIN'] == 3) & (CA_hivcovid3['LMP_MIN'] <= 0.05) #less than or equal to 0.05
cond_hl = (CA_hivcovid3['LMQ_MIN'] == 4) & (CA_hivcovid3['LMP_MIN'] <= 0.05) #less than or equal to 0.05

# 'LISA_Type' to 'HH' if quadrant type is 1 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hh,'MIN_LISA'] = 'HH'

# 'LISA_Type' to 'LH' if quadrant type is 2 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_lh,'MIN_LISA'] = 'LH'

# 'LISA_Type' to 'LL' if quadrant type is 3 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_ll,'MIN_LISA'] = 'LL'

# 'LISA_Type' to 'HL' if quadrant type is 4 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hl,'MIN_LISA'] = 'HL'

# Countplot showing the number of tracts that fall into the four quadrant types
sns.countplot(data=CA_hivcovid3, x='MIN_LISA', palette='cool') 
plt.show() #show plot


# Create Lisa Series and Drop NA Values:
MIN_lisa2 = CA_hivcovid3['MIN_LISA'].dropna()


# Define Style Function for PR
def style_MIN(feature):
    # get LM Quadrant Type of GeoJson feature by FIPS code, and set it to lmq_type
    # set Null to lmq_type if there is no LM Quadrant Type in lisa_series 
    lmq_type = MIN_lisa2.get(feature['properties']['FIPS'],None) 
    # gray polygon if LMQ type is None (not statistically significant)
    if lmq_type is None:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':'silver', 'color':'white'}
    # use color palette for fillColor otherwise
    else:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':lmq_s[lmq_type], 'color':'white'}


# Set conditions for each of Moran Plot's Quadrants
cond_hh = (CA_hivcovid3['LMQ_UNI'] == 1) & (CA_hivcovid3['LMP_UNI'] <= 0.05) #less than or equal to 0.05
cond_lh = (CA_hivcovid3['LMQ_UNI'] == 2) & (CA_hivcovid3['LMP_UNI'] <= 0.05) #less than or equal to 0.05
cond_ll = (CA_hivcovid3['LMQ_UNI'] == 3) & (CA_hivcovid3['LMP_UNI'] <= 0.05) #less than or equal to 0.05
cond_hl = (CA_hivcovid3['LMQ_UNI'] == 4) & (CA_hivcovid3['LMP_UNI'] <= 0.05) #less than or equal to 0.05

# 'LISA_Type' to 'HH' if quadrant type is 1 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hh,'UNI_LISA'] = 'HH'

# 'LISA_Type' to 'LH' if quadrant type is 2 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_lh,'UNI_LISA'] = 'LH'

# 'LISA_Type' to 'LL' if quadrant type is 3 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_ll,'UNI_LISA'] = 'LL'

# 'LISA_Type' to 'HL' if quadrant type is 4 and p-value of local moran's I is less than or equal to 0.05
CA_hivcovid3.loc[cond_hl,'UNI_LISA'] = 'HL'

# Countplot showing the number of tracts that fall into the four quadrant types
sns.countplot(data=CA_hivcovid3, x='UNI_LISA', palette='winter') 
plt.show() #show plot


# Create Lisa Series and Drop NA Values:
UNI_lisa2 = CA_hivcovid3['UNI_LISA'].dropna()


# Define Style Function for PR
def style_UNI(feature):
    # get LM Quadrant Type of GeoJson feature by FIPS code, and set it to lmq_type
    # set Null to lmq_type if there is no LM Quadrant Type in lisa_series 
    lmq_type = UNI_lisa2.get(feature['properties']['FIPS'],None) 
    # gray polygon if LMQ type is None (not statistically significant)
    if lmq_type is None:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':'silver', 'color':'white'}
    # use color palette for fillColor otherwise
    else:
        return {'fillOpacity':0.5, 'weight':0.5, 'fillColor':lmq_s[lmq_type], 'color':'white'}


# Get XY Coordinates of Mean Center
mc_x = CA_hivcovid3.geometry.centroid.x.mean()
mc_y = CA_hivcovid3.geometry.centroid.y.mean()
print(mc_x, mc_y)

-120.72791323021316 37.845340064561746


# Create map centered around mean coordinates with zoom_start 6 
lisa_m = folium.Map(location=[mc_y, mc_x],zoom_start=6,tiles='cartodbpositron')

# Tool Tip for New Diagnoses Rate
tt_NDR = folium.GeoJsonTooltip(fields=['FIPS','COUNTY','New Diagnoses Rate','NDR_LISA'])

# Tool Tip for PrEP Rate
tt_PR = folium.GeoJsonTooltip(fields=['FIPS','COUNTY','PrEP Rate','PR_LISA'])

# Tool Tip for COVID Case Rate
tt_CCR = folium.GeoJsonTooltip(fields=['FIPS','COUNTY','COVID Cases_Rate','CCR_LISA'])

# Tool Tip for Total COVID Test Rate
tt_TCTR = folium.GeoJsonTooltip(fields=['FIPS','COUNTY','Total COVID Tests_Rate','TCTR_LISA'])

# Tool Tip for Fully Vaccinated Rate
tt_FVR = folium.GeoJsonTooltip(fields=['FIPS','COUNTY','Fully Vaccinated_Rate','FVR_LISA'])

# Tool Tip for EP_MINITRY Rate
tt_MIN = folium.GeoJsonTooltip(fields=['FIPS','COUNTY','EP_MINRTY','MIN_LISA'])

# Tool Tip for EP_UNINSUR Rate
tt_UNI = folium.GeoJsonTooltip(fields=['FIPS','COUNTY','EP_UNINSUR','UNI_LISA'])

# Set ToolTip objects to tooltip to be shown to map
folium.GeoJson(CA_hivcovid3, style_function=style_NDR, tooltip=tt_NDR, name='HIV Infection Rate').add_to(lisa_m) # NDR
folium.GeoJson(CA_hivcovid3, style_function=style_PR, tooltip=tt_PR, name='PrEP User Rate').add_to(lisa_m) # PR
folium.GeoJson(CA_hivcovid3, style_function=style_CCR, tooltip=tt_CCR, name='COVID Infection Rate').add_to(lisa_m) # CCR
folium.GeoJson(CA_hivcovid3, style_function=style_TCTR, tooltip=tt_TCTR, name='COVID Testing Rate').add_to(lisa_m) # TCTR
folium.GeoJson(CA_hivcovid3, style_function=style_FVR, tooltip=tt_FVR, name='Fully Vaccinated Rate').add_to(lisa_m) # FVR
folium.GeoJson(CA_hivcovid3, style_function=style_MIN, tooltip=tt_MIN, name='Percentage of Ethnic Minority').add_to(lisa_m) # MIN
folium.GeoJson(CA_hivcovid3, style_function=style_UNI, tooltip=tt_UNI, name='Percentage of Uninsured Health').add_to(lisa_m) # UNI

# Add LayerControl to turn on/off layers
folium.LayerControl().add_to(lisa_m)

lisa_m # zoom in to explore areas of your interest spatially


# Grab subset of the data with desired features
CA_hivcovid4 = CA_svi_hivcovid[['FIPS','New Diagnoses Black Rate', 'New Diagnoses White Rate', 
                                'New Diagnoses Hispanic Rate', 'New Diagnoses Asian Rate', 
                                'New Diagnoses American Indian/Alaska Native Rate', 
                                'New Diagnoses Native Hawaiian/Pacific Islander Rate', 
                                'COVID Cases_Rate', 'Total COVID Tests_Rate', 'Fully Vaccinated_Rate',
                                'RPL_THEME1', 'RPL_THEME3', 'geometry']]


# View Head of DF:
CA_hivcovid4.head(3)


# initialize random number to make it predictable
np.random.seed(123)


# Create list of features without FIPS and geometry:
clust_feats = CA_hivcovid4.columns[1:-1]


clust_feats #check feature list

Index(['New Diagnoses Black Rate', 'New Diagnoses White Rate',
       'New Diagnoses Hispanic Rate', 'New Diagnoses Asian Rate',
       'New Diagnoses American Indian/Alaska Native Rate',
       'New Diagnoses Native Hawaiian/Pacific Islander Rate',
       'COVID Cases_Rate', 'Total COVID Tests_Rate', 'Fully Vaccinated_Rate',
       'RPL_THEME1', 'RPL_THEME3'],
      dtype='object')


# Identify 7 Clusters ---
# Create cluster objects by fitting clusters into the 12 features:
cl_ag7 = cluster.AgglomerativeClustering(n_clusters=7).fit(CA_hivcovid4[clust_feats])

# Write cluster labels to the column 'Agglomerative Nonspatial'
CA_hivcovid4['Agglomerative Nonspatial-7_Clusters'] = cl_ag7.labels_

CA_hivcovid4.head() # View head


# Identify 6 Clusters ---
# Create cluster objects by fitting clusters into the 12 features:
cl_ag6 = cluster.AgglomerativeClustering(n_clusters=6).fit(CA_hivcovid4[clust_feats])

# Write cluster labels to the column 'Agglomerative Nonspatial'
CA_hivcovid4['Agglomerative Nonspatial-6_Clusters'] = cl_ag6.labels_

CA_hivcovid4.head() # View head


# Identify 7 Clusters with Spatial Contraints using Queen's Method for Spatial Weights ---
# Create a spatial weight matrix based on queen method
w2 = weights.Queen.from_dataframe(CA_hivcovid4)

# Create cluster objects by fitting clusters into the 12 features:
cl_sc7 = cluster.AgglomerativeClustering(n_clusters=7,connectivity=w.sparse).fit(CA_hivcovid4[clust_feats])

# Write cluster labels to the column "Agglomerative Spatial"
CA_hivcovid4['Agglomerative Spatial-7_Clusters'] = cl_sc7.labels_

CA_hivcovid4.head() # View head


# Identify 6 Clusters with Spatial Contraints using Queen's Method for Spatial Weights ---
# Create a spatial weight matrix based on queen method
w2 = weights.Queen.from_dataframe(CA_hivcovid4)

# Create cluster objects by fitting clusters into the 12 features:
cl_sc6 = cluster.AgglomerativeClustering(n_clusters=6,connectivity=w.sparse).fit(CA_hivcovid4[clust_feats])

# Write cluster labels to the column "Agglomerative Spatial"
CA_hivcovid4['Agglomerative Spatial-6_Clusters'] = cl_sc6.labels_

CA_hivcovid4.head() # View head


# Clustering Methods:
clust_list2 = ['Agglomerative Nonspatial-6_Clusters','Agglomerative Spatial-6_Clusters']

# Create figure and axes object with 1 row and 2 columns
# in which each column corresponds to each of clustering methods
fig,axes=plt.subplots(1,2,figsize=(10,16))

# Plot figure with 2 axes (subplots) showing clustering results
# Title of each axis is set to the name of clustering methods
for i, method in enumerate(clust_list2): # for each of a list of clustering methods
    # plot a categorical (unique value) map of gdf
    CA_hivcovid4.plot(column=method, legend=True, categorical=True, ax=axes[i], cmap='plasma_r')
    CA_svi.plot(edgecolor='k', facecolor='None', ax=axes[i]) # plot boundaries of CA Counties
    axes[i].set_title(method) # set title for each axis
    axes[i].axis('off')     # turn off an axis

plt.tight_layout() #make tight layout


# Clustering Methods:
clust_list = ['Agglomerative Nonspatial-7_Clusters','Agglomerative Spatial-7_Clusters']

# Create figure and axes object with 1 row and 2 columns
# in which each column corresponds to each of clustering methods
fig,axes=plt.subplots(1,2,figsize=(10,16))

# Plot figure with 2 axes (subplots) showing clustering results
# Title of each axis is set to the name of clustering methods
for i, method in enumerate(clust_list): # for each of a list of clustering methods
    # plot a categorical (unique value) map of gdf
    CA_hivcovid4.plot(column=method, legend=True, categorical=True, ax=axes[i], cmap='plasma')
    CA_svi.plot(edgecolor='k', facecolor='None', ax=axes[i]) # plot boundaries of CA Counties
    axes[i].set_title(method) # set title for each axis
    axes[i].axis('off')     # turn off an axis

plt.tight_layout() #make tight layout


# set a color palette of seaborn that matches up cluster labels
colors=[sns.color_palette('tab10')[i] for i in [0,1,3,5,6,-2,-1]]
sns.palplot(colors)
sns.set_palette(colors)


# Agglomerative spatially constrained cluster maps for features with boxplots 
# create a figure of 11 rows by 2 columns
fig,axes=plt.subplots(11,2,figsize=(10,80))

# for each of 11 explanatory variables
for i,col in enumerate(clust_feats):    
    # plot a cluster map in the first column in the ith row where i = 0,1,2,...,10    
    CA_hivcovid4.plot(column='Agglomerative Spatial-7_Clusters', legend=True, categorical=True, ax=axes[i,0])
    CA_svi.plot(edgecolor='k',facecolor='None',ax=axes[i,0])
    # plot a boxplot in the second column in the ith row
    sns.boxplot(x='Agglomerative Spatial-7_Clusters',y=col,data=CA_hivcovid4,ax=axes[i,1]) 
    # set a title to the second column in the ith row    
    axes[i,1].set_title(col, fontsize=12)
    # add a horizontal line at average of each explanatory variable
    axes[i,1].axhline(CA_hivcovid4[col].mean(),linestyle='--',alpha=0.5)

plt.tight_layout() #make tight layout

	GEO ID	Year	State Abbreviation	State	County Name	New Diagnoses Rate	New Diagnoses Rate Stability	New Diagnoses Cases	New Diagnoses Male Rate	New Diagnoses Male Rate Stability	New Diagnoses Male Cases	New Diagnoses Female Rate	New Diagnoses Female Rate Stability	New Diagnoses Female Cases	New Diagnoses Black Rate	New Diagnoses Black Rate Stability	New Diagnoses Black Cases	New Diagnoses White Rate	New Diagnoses White Rate Stability	New Diagnoses White Cases	New Diagnoses Hispanic Rate	New Diagnoses Hispanic Rate Stability	New Diagnoses Hispanic Cases	New Diagnoses Asian Rate	New Diagnoses Asian Rate Stability	New Diagnoses Asian Cases	New Diagnoses American Indian/Alaska Native Rate	New Diagnoses American Indian/Alaska Native Rate Stability	New Diagnoses American Indian/Alaska Native Cases	New Diagnoses Multiracial Rate	New Diagnoses Multiracial Rate Stability	New Diagnoses Multiracial Cases	New Diagnoses Native Hawaiian/Pacific Islander Rate	New Diagnoses Native Hawaiian/Pacific Islander Rate Stability	New Diagnoses Native Hawaiian/Pacific Islander Cases	New Diagnoses Age 13-24 Rate	New Diagnoses Age 13-24 Rate Stability	New Diagnoses Age 13-24 Cases	New Diagnoses Age 25-34 Rate	New Diagnoses Age 25-34 Rate Stability	New Diagnoses Age 25-34 Cases	New Diagnoses Age 35-44 Rate	New Diagnoses Age 35-44 Rate Stability	New Diagnoses Age 35-44 Cases	New Diagnoses Age 45-54 Rate	New Diagnoses Age 45-54 Rate Stability	New Diagnoses Age 45-54 Cases	New Diagnoses Age 55+ Rate	New Diagnoses Age 55+ Rate Stability	New Diagnoses Age 55+ Cases	New Diagnoses Heterosexual Contact Percent	New Diagnoses Heterosexual Contact Cases	New Diagnoses IDU Percent	New Diagnoses IDU Cases	New Diagnoses Other Transmission Category Percent	New Diagnoses Other Transmission Category Cases	New Diagnoses MSM Rate	New Diagnoses MSM Percent	New Diagnoses MSM Cases	New Diagnoses MSM/IDU Percent	New Diagnoses MSM/IDU Cases	2013 NCHS Urbanicity Code
205	6037	2020	CA	California	Los Angeles County	16.4	Y	1382	30.0	Y	1239	3.3	Y	143	42.8	Y	295	11.5	Y	265	18.4	Y	724	4.3	Y	56	24.2	N	4	23.0	Y	37	5.2	N	1	14.6	Y	220	34.8	Y	565	21.3	Y	291	15.2	Y	197	4.1	Y	109	9.8	136	5.1	70	0.1	2	0.6	90.2	1118	4.5	56	1
2624	48201	2020	TX	Texas	Harris County	24.0	Y	921	41.0	Y	777	7.4	Y	144	58.0	Y	425	9.2	Y	106	22.4	Y	360	5.2	Y	15	13.2	N	1	29.5	Y	13	50.4	N	1	29.9	Y	234	46.1	Y	347	23.9	Y	163	16.1	Y	94	8.0	Y	83	18.7	172	3.8	35	0.3	3	0.6	89.4	695	2.1	16	1
363	12086	2020	FL	Florida	Miami-Dade County	33.7	Y	781	59.6	Y	666	9.6	Y	115	55.4	Y	188	22.3	Y	66	31.9	Y	519	8.2	N	3	50.2	N	1	20.9	N	3	182.8	N	1	25.7	Y	96	66.5	Y	251	53.8	Y	198	31.4	Y	121	14.2	Y	115	23.7	185	1.7	13	0.0	0	0.7	86.8	578	0.8	5	1
611	17031	2020	IL	Illinois	Cook County	17.9	Y	774	31.4	Y	653	5.4	Y	121	45.1	Y	444	5.3	Y	100	17.4	Y	182	4.9	Y	17	58.2	N	3	50.2	Y	28	0.0	N	0	26.5	Y	198	34.7	Y	289	19.2	Y	134	12.3	Y	77	5.4	Y	76	16.5	128	5.2	40	1.3	10	0.4	88.4	577	2.9	19	1
2580	48113	2020	TX	Texas	Dallas County	30.6	Y	658	50.5	Y	531	11.6	Y	127	63.6	Y	314	16.2	Y	106	25.8	Y	212	5.4	N	8	0.0	N	0	66.6	Y	17	101.3	N	1	31.4	Y	137	56.2	Y	248	35.0	Y	126	30.6	Y	98	8.3	Y	49	21.6	142	5.3	35	0.2	1	0.5	87.0	462	3.4	18	1
104	4013	2020	AZ	Arizona	Maricopa County	12.5	Y	477	21.8	Y	411	3.4	Y	66	36.7	Y	78	8.0	Y	175	16.4	Y	182	5.2	N	9	29.1	Y	18	14.0	N	10	65.4	N	5	13.0	Y	95	29.1	Y	196	12.4	Y	74	10.8	Y	61	4.0	Y	51	12.6	60	7.8	37	0.2	1	0.4	83.2	342	9.2	38	1
447	13121	2020	GA	Georgia	Fulton County	52.0	Y	477	93.8	Y	412	13.6	Y	65	97.0	Y	380	15.1	Y	57	47.7	Y	29	0.0	N	0	77.7	N	1	59.8	N	9	589.5	N	1	58.5	Y	102	113.6	Y	214	43.9	Y	67	36.4	Y	53	16.0	Y	41	20.3	97	1.5	7	0.2	1	0.7	87.1	359	3.2	13	1
326	12011	2020	FL	Florida	Broward County	27.6	Y	460	46.2	Y	372	10.2	Y	88	48.3	Y	221	15.9	Y	95	24.8	Y	129	14.3	N	9	30.5	N	1	21.8	N	5	0.0	N	0	26.8	Y	71	49.5	Y	129	40.2	Y	106	29.9	Y	79	12.3	Y	75	39.8	183	4.8	22	0.0	0	0.4	66.1	246	2.2	8	2
1852	36047	2020	NY	New York	Kings County	21.0	Y	444	34.8	Y	342	9.1	Y	102	42.3	Y	270	6.8	Y	53	25.2	Y	98	2.6	N	7	0.0	N	0	46.7	Y	16	0.0	N	0	25.1	Y	85	36.1	Y	168	24.1	Y	86	16.9	Y	49	8.5	Y	56	24.1	107	7.9	35	0.5	2	0.6	85.1	291	2.6	9	1
368	12095	2020	FL	Florida	Orange County	31.1	Y	369	51.8	Y	299	11.5	Y	70	71.9	Y	167	12.6	Y	61	35.5	Y	135	4.4	N	3	0.0	N	0	10.6	N	2	72.7	N	1	34.3	Y	78	50.7	Y	119	39.6	Y	81	24.9	Y	45	13.6	Y	46	28.5	105	2.4	9	0.0	0	0.5	83.6	250	2.0	6	1
1749	32003	2020	NV	Nevada	Clark County	17.7	Y	343	30.7	Y	295	4.9	Y	48	51.2	Y	116	11.1	Y	94	18.8	Y	108	9.1	Y	19	21.9	N	2	1.6	N	1	21.3	N	3	16.6	Y	56	37.9	Y	131	21.8	Y	70	18.3	Y	55	4.9	Y	31	14.6	50	6.4	22	0.3	1	0.6	85.8	253	6.1	18	1
1831	36005	2020	NY	New York	Bronx County	29.4	Y	338	47.5	Y	253	13.8	Y	85	44.6	Y	152	16.5	Y	18	25.6	Y	163	4.1	N	2	0.0	N	0	29.9	N	3	0.0	N	0	25.7	Y	58	57.1	Y	127	37.7	Y	67	27.4	Y	46	11.2	Y	40	26.0	88	9.8	33	0.3	1	1.1	83.8	212	2.0	5	1
2295	42101	2020	PA	Pennsylvania	Philadelphia County	24.9	Y	331	40.6	Y	251	11.3	Y	80	42.0	Y	221	9.5	Y	46	29.7	Y	55	2.9	N	3	70.1	N	2	17.8	N	4	0.0	N	0	29.3	Y	69	42.9	Y	131	30.8	Y	63	20.9	Y	36	7.8	Y	32	22.4	74	13.0	43	0.3	1	0.7	80.9	203	4.0	10	1
1869	36081	2020	NY	New York	Queens County	16.7	Y	316	29.7	Y	271	4.6	Y	45	23.3	Y	80	8.1	Y	39	28.9	Y	148	5.6	Y	29	14.9	N	1	56.8	Y	19	0.0	N	0	18.0	Y	50	34.4	Y	121	21.3	Y	64	14.8	Y	43	5.6	Y	38	16.5	52	7.0	22	0.3	1	0.7	87.8	238	1.1	3	1
1859	36061	2020	NY	New York	New York County	20.6	Y	296	38.2	Y	258	5.0	Y	38	64.1	Y	113	8.2	Y	57	28.4	Y	101	5.4	N	10	0.0	N	0	56.2	Y	14	173.2	N	1	20.3	Y	40	36.8	Y	130	21.9	Y	51	16.7	Y	32	9.3	Y	43	13.9	41	8.1	24	0.3	1	0.3	86.4	223	2.7	7	1
223	6073	2020	CA	California	San Diego County	10.5	Y	296	18.1	Y	257	2.8	Y	39	32.7	Y	44	6.2	Y	81	17.3	Y	157	2.0	N	7	0.0	N	0	8.6	N	7	0.0	N	0	6.5	Y	34	21.2	Y	116	16.0	Y	73	12.2	Y	49	2.7	Y	24	12.2	36	8.1	24	0.3	1	0.4	88.3	227	3.5	9	1
2743	48439	2020	TX	Texas	Tarrant County	16.8	Y	292	29.1	Y	244	5.4	Y	48	45.6	Y	135	7.7	Y	63	16.9	Y	81	5.8	N	6	0.0	N	0	25.0	N	7	0.0	N	0	21.5	Y	77	32.4	Y	103	16.8	Y	49	14.3	Y	38	5.0	Y	25	20.5	60	4.5	13	0.3	1	0.5	85.7	209	4.1	10	1
2538	48029	2020	TX	Texas	Bexar County	17.0	Y	282	29.0	Y	236	5.4	Y	46	30.1	Y	38	10.0	Y	47	19.0	Y	187	3.8	N	2	0.0	N	0	37.9	N	8	0.0	N	0	19.3	Y	67	32.8	Y	106	20.9	Y	59	11.3	Y	27	4.9	Y	23	15.6	44	8.2	23	0.4	1	0.4	87.7	207	2.5	6	1
431	13089	2020	GA	Georgia	DeKalb County	42.4	Y	269	73.3	Y	216	15.6	Y	53	61.9	Y	211	13.3	Y	26	52.6	Y	24	9.9	N	4	104.8	N	1	30.6	N	3	0.0	N	0	52.5	Y	57	84.7	Y	109	45.2	Y	49	35.2	Y	34	10.4	Y	20	24.9	67	3.7	10	0.0	0	0.8	87.0	188	2.3	5	2
216	6059	2020	CA	California	Orange County	9.8	Y	264	17.7	Y	233	2.3	Y	31	30.2	Y	14	6.5	Y	72	16.9	Y	145	4.5	Y	27	0.0	N	0	8.0	N	5	13.3	N	1	9.8	Y	47	23.5	Y	108	12.2	Y	50	6.3	Y	27	3.5	Y	32	15.5	41	7.2	19	0.0	0	0.4	82.0	191	6.0	14	1

	GEO ID	Year	State Abbreviation	State	County Name	New Diagnoses Rate	New Diagnoses Rate Stability	New Diagnoses Cases	New Diagnoses Male Rate	New Diagnoses Male Rate Stability	New Diagnoses Male Cases	New Diagnoses Female Rate	New Diagnoses Female Rate Stability	New Diagnoses Female Cases	New Diagnoses Black Rate	New Diagnoses Black Rate Stability	New Diagnoses Black Cases	New Diagnoses White Rate	New Diagnoses White Rate Stability	New Diagnoses White Cases	New Diagnoses Hispanic Rate	New Diagnoses Hispanic Rate Stability	New Diagnoses Hispanic Cases	New Diagnoses Asian Rate	New Diagnoses Asian Rate Stability	New Diagnoses Asian Cases	New Diagnoses American Indian/Alaska Native Rate	New Diagnoses American Indian/Alaska Native Rate Stability	New Diagnoses American Indian/Alaska Native Cases	New Diagnoses Multiracial Rate	New Diagnoses Multiracial Rate Stability	New Diagnoses Multiracial Cases	New Diagnoses Native Hawaiian/Pacific Islander Rate	New Diagnoses Native Hawaiian/Pacific Islander Rate Stability	New Diagnoses Native Hawaiian/Pacific Islander Cases	New Diagnoses Age 13-24 Rate	New Diagnoses Age 13-24 Rate Stability	New Diagnoses Age 13-24 Cases	New Diagnoses Age 25-34 Rate	New Diagnoses Age 25-34 Rate Stability	New Diagnoses Age 25-34 Cases	New Diagnoses Age 35-44 Rate	New Diagnoses Age 35-44 Rate Stability	New Diagnoses Age 35-44 Cases	New Diagnoses Age 45-54 Rate	New Diagnoses Age 45-54 Rate Stability	New Diagnoses Age 45-54 Cases	New Diagnoses Age 55+ Rate	New Diagnoses Age 55+ Rate Stability	New Diagnoses Age 55+ Cases	New Diagnoses Heterosexual Contact Percent	New Diagnoses Heterosexual Contact Cases	New Diagnoses IDU Percent	New Diagnoses IDU Cases	New Diagnoses Other Transmission Category Percent	New Diagnoses Other Transmission Category Cases	New Diagnoses MSM Rate	New Diagnoses MSM Percent	New Diagnoses MSM Cases	New Diagnoses MSM/IDU Percent	New Diagnoses MSM/IDU Cases	2013 NCHS Urbanicity Code
187	6001	2020	CA	California	Alameda County	11.1	Y	157	19.9	Y	138	2.6	Y	19	34.4	Y	50	6.0	Y	27	18.2	Y	54	3.9	Y	18	0.0	N	0	10.0	N	5	26.2	N	3	10.7	Y	24	26.2	Y	72	10.1	Y	26	7.4	Y	16	4.3	Y	19	15.9	25	6.4	10	0.0	0	0.3	85.5	118	2.9	4	1
188	6003	2020	CA	California	Alpine County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
189	6005	2020	CA	California	Amador County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
190	6007	2020	CA	California	Butte County	5.5	N	10	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	6.1	N	8	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	0.2	-1.0	5	0.0	0	4
191	6009	2020	CA	California	Calaveras County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
192	6011	2020	CA	California	Colusa County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
193	6013	2020	CA	California	Contra Costa County	7.4	Y	72	13.1	Y	62	2.0	N	10	25.7	Y	22	4.2	Y	18	8.8	Y	21	4.4	N	8	0.0	N	0	5.9	N	2	21.3	N	1	5.3	N	9	22.7	Y	33	7.6	Y	12	7.0	N	11	2.0	N	7	15.3	11	1.4	1	0.0	0	0.1	91.9	57	3.2	2	2
194	6015	2020	CA	California	Del Norte County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	5
195	6017	2020	CA	California	El Dorado County	3.6	N	6	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	0.0	0	0.0	0	-1.0	-1.0	-1	0.0	0	2
196	6019	2020	CA	California	Fresno County	15.0	Y	120	25.5	Y	101	4.7	Y	19	32.2	Y	12	10.6	Y	26	18.3	Y	75	5.8	N	5	20.1	N	1	7.9	N	1	0.0	N	0	14.3	Y	25	26.0	Y	40	21.6	Y	28	11.0	Y	12	6.5	Y	15	11.7	14	10.0	12	0.0	0	0.9	90.1	91	3.0	3	3
197	6021	2020	CA	California	Glenn County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
198	6023	2020	CA	California	Humboldt County	4.3	N	5	8.7	N	5	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	0	0.0	0	0.0	0	0.2	100.0	5	0.0	0	5
199	6025	2020	CA	California	Imperial County	16.8	Y	24	25.9	Y	19	7.2	N	5	-1.0	-9	-1	-1.0	-9	-1	17.5	Y	21	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	15.8	N	5	56.0	Y	15	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-1	-1.0	-1	0.0	0	1.6	94.7	18	-1.0	-1	4
200	6027	2020	CA	California	Inyo County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
201	6029	2020	CA	California	Kern County	22.3	Y	160	38.8	Y	142	5.1	Y	18	50.0	Y	19	16.5	Y	41	25.3	Y	95	10.8	N	4	0.0	N	0	8.5	N	1	0.0	N	0	21.7	Y	35	44.7	Y	63	25.5	Y	30	19.4	Y	19	6.6	Y	13	7.5	12	13.1	21	0.0	0	1.5	85.2	121	4.9	7	3
202	6031	2020	CA	California	Kings County	6.5	N	8	11.6	N	8	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	0	-1.0	-1	0.0	0	0.7	75.0	6	-1.0	-1	4
203	6033	2020	CA	California	Lake County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	5
204	6035	2020	CA	California	Lassen County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	5
205	6037	2020	CA	California	Los Angeles County	16.4	Y	1382	30.0	Y	1239	3.3	Y	143	42.8	Y	295	11.5	Y	265	18.4	Y	724	4.3	Y	56	24.2	N	4	23.0	Y	37	5.2	N	1	14.6	Y	220	34.8	Y	565	21.3	Y	291	15.2	Y	197	4.1	Y	109	9.8	136	5.1	70	0.1	2	0.6	90.2	1118	4.5	56	1
206	6039	2020	CA	California	Madera County	6.3	N	8	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	7.1	N	5	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-1	0.0	0	0.0	0	0.7	-1.0	7	-1.0	-1	4
207	6041	2020	CA	California	Marin County	6.3	Y	14	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	3.1	N	5	15.3	N	5	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	22.3	N	5	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	0.1	-1.0	10	0.0	0	2
208	6043	2020	CA	California	Mariposa County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
209	6045	2020	CA	California	Mendocino County	6.8	N	5	13.9	N	5	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	0	0.0	0	0.0	0	0.5	100.0	5	0.0	0	5
210	6047	2020	CA	California	Merced County	14.0	Y	31	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	17.6	N	11	12.3	Y	16	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	N	0	16.5	N	9	34.1	Y	14	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	1.3	-1.0	25	0.0	0	3
211	6049	2020	CA	California	Modoc County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
212	6051	2020	CA	California	Mono County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
213	6053	2020	CA	California	Monterey County	3.1	N	11	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	4.1	N	8	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	0.2	-1.0	6	0.0	0	3
214	6055	2020	CA	California	Napa County	4.3	N	5	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-1	0.0	0	0.0	0	-1.0	-1.0	-1	-1.0	-1	4
215	6057	2020	CA	California	Nevada County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	5
216	6059	2020	CA	California	Orange County	9.8	Y	264	17.7	Y	233	2.3	Y	31	30.2	Y	14	6.5	Y	72	16.9	Y	145	4.5	Y	27	0.0	N	0	8.0	N	5	13.3	N	1	9.8	Y	47	23.5	Y	108	12.2	Y	50	6.3	Y	27	3.5	Y	32	15.5	41	7.2	19	0.0	0	0.4	82.0	191	6.0	14	1
217	6061	2020	CA	California	Placer County	5.6	Y	19	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	4.4	N	11	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	13.7	N	6	9.3	N	5	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	0.1	-1.0	11	-1.0	-1	2
218	6063	2020	CA	California	Plumas County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
219	6065	2020	CA	California	Riverside County	11.6	Y	239	21.1	Y	216	2.2	Y	23	24.1	Y	32	9.3	Y	68	12.4	Y	123	6.9	N	10	0.0	N	0	14.7	N	6	0.0	N	0	9.1	Y	38	28.1	Y	98	13.2	Y	43	10.3	Y	31	4.3	Y	29	8.4	20	3.8	9	0.4	1	0.4	95.8	207	1.4	3	1
220	6067	2020	CA	California	Sacramento County	11.8	Y	153	19.5	Y	123	4.5	Y	30	43.5	Y	55	7.8	Y	46	11.4	Y	33	5.0	N	11	15.0	N	1	11.2	N	6	6.5	N	1	10.0	Y	23	19.5	Y	48	16.3	Y	35	12.7	Y	24	5.5	Y	23	19.6	30	12.4	19	0.7	1	0.4	76.4	94	8.1	10	1
221	6069	2020	CA	California	San Benito County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	2
222	6071	2020	CA	California	San Bernardino County	14.7	Y	263	25.7	Y	227	4.0	Y	36	32.8	Y	48	8.7	Y	44	16.3	Y	154	7.7	N	11	0.0	N	0	15.5	N	5	18.2	N	1	15.3	Y	58	32.4	Y	110	16.3	Y	47	8.8	Y	23	4.9	Y	25	11.8	31	7.6	20	0.4	1	0.4	86.3	196	6.6	15	2
223	6073	2020	CA	California	San Diego County	10.5	Y	296	18.1	Y	257	2.8	Y	39	32.7	Y	44	6.2	Y	81	17.3	Y	157	2.0	N	7	0.0	N	0	8.6	N	7	0.0	N	0	6.5	Y	34	21.2	Y	116	16.0	Y	73	12.2	Y	49	2.7	Y	24	12.2	36	8.1	24	0.3	1	0.4	88.3	227	3.5	9	1
224	6075	2020	CA	California	San Francisco County	19.6	Y	153	33.8	Y	134	5.0	Y	19	69.1	Y	27	14.2	Y	45	54.5	Y	61	5.6	Y	16	130.3	N	2	4.3	N	1	35.1	N	1	19.4	Y	17	28.2	Y	56	24.5	Y	34	26.3	Y	29	7.0	Y	17	7.8	12	11.1	17	0.0	0	0.2	81.3	109	11.9	16	1
225	6077	2020	CA	California	San Joaquin County	13.3	Y	83	22.4	Y	69	4.5	Y	14	44.1	Y	20	11.1	Y	22	13.3	Y	33	7.6	N	8	0.0	N	0	0.0	N	0	0.0	N	0	14.4	Y	19	24.0	Y	26	18.5	Y	19	16.4	Y	15	2.1	N	4	16.9	14	9.6	8	0.0	0	1.0	84.1	58	4.3	3	3
226	6079	2020	CA	California	San Luis Obispo County	4.4	N	11	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	4.6	N	8	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	0.0	0	0.0	0	0.2	-1.0	7	-1.0	-1	3
227	6081	2020	CA	California	San Mateo County	6.9	Y	45	13.1	Y	42	0.9	N	3	19.7	N	3	5.4	Y	14	12.9	Y	19	3.9	N	8	0.0	N	0	5.2	N	1	0.0	N	0	9.3	N	9	10.5	Y	12	10.9	Y	12	5.9	N	6	2.6	N	6	13.3	6	0.0	0	0.0	0	0.1	85.7	36	7.1	3	2
228	6083	2020	CA	California	Santa Barbara County	6.2	Y	23	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	2.9	N	5	9.5	Y	15	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	8.3	N	5	17.7	N	9	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	0.3	-1.0	15	-1.0	-1	3
229	6085	2020	CA	California	Santa Clara County	6.5	Y	105	11.1	Y	91	1.8	Y	14	22.8	N	9	3.9	Y	20	16.1	Y	61	1.2	N	8	0.0	N	0	16.7	N	7	0.0	N	0	5.5	Y	15	13.4	Y	42	9.1	Y	25	5.9	Y	15	1.6	N	8	17.1	18	2.9	3	1.0	1	0.2	86.8	79	4.4	4	1
230	6087	2020	CA	California	Santa Cruz County	5.5	Y	13	5.2	N	6	5.9	N	7	0.0	N	0	5.7	N	8	6.9	N	5	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	15.4	N	5	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	38.5	5	-1.0	-1	0.0	0	-1.0	-1.0	-1	0.0	0	3
231	6089	2020	CA	California	Shasta County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	4
232	6091	2020	CA	California	Sierra County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
233	6093	2020	CA	California	Siskiyou County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
234	6095	2020	CA	California	Solano County	12.2	Y	46	17.2	Y	32	7.4	Y	14	24.6	Y	13	6.9	N	10	11.4	N	11	9.8	N	6	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	9.2	N	6	27.7	Y	18	27.2	Y	16	-1.0	-9	-1	-1.0	-9	-1	28.3	13	-1.0	-1	0.0	0	0.7	93.8	30	-1.0	-1	3
235	6097	2020	CA	California	Sonoma County	8.7	Y	37	13.6	Y	28	4.1	N	9	-1.0	-9	-1	6.9	Y	19	11.2	Y	12	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	17.8	N	11	17.5	N	11	11.7	N	7	-1.0	-9	-1	24.3	9	-1.0	-1	0.0	0	0.2	75.0	21	-1.0	-1	3
236	6099	2020	CA	California	Stanislaus County	6.1	Y	27	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	5.8	N	11	6.9	Y	14	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	9.6	N	9	10.0	N	8	-1.0	-9	-1	-1.0	-9	-1	3.7	N	5	-1.0	-1	-1.0	-1	0.0	0	0.3	-1.0	19	-1.0	-1	3
237	6101	2020	CA	California	Sutter County	7.6	N	6	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	0.0	0	0.0	0	-1.0	-1.0	-1	0.0	0	4
238	6103	2020	CA	California	Tehama County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	5
239	6105	2020	CA	California	Trinity County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
240	6107	2020	CA	California	Tulare County	7.9	Y	29	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	8.2	N	9	8.2	Y	19	-1.0	-9	-1	0.0	N	0	0.0	N	0	-1.0	-9	-1	6.8	N	6	16.4	N	11	9.9	N	6	11.8	N	6	0.0	N	0	-1.0	-1	-1.0	-1	0.0	0	0.6	-1.0	24	-1.0	-1	3
241	6109	2020	CA	California	Tuolumne County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	5
242	6111	2020	CA	California	Ventura County	7.7	Y	55	14.9	Y	52	0.8	N	3	7.5	N	1	3.0	N	10	14.9	Y	43	0.0	N	0	0.0	N	0	6.6	N	1	0.0	N	0	6.8	N	9	23.8	Y	27	9.5	N	10	3.7	N	4	2.0	N	5	7.3	4	1.8	1	0.0	0	0.8	96.2	50	0.0	0	3
243	6113	2020	CA	California	Yolo County	5.9	N	11	12.2	N	11	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	12.4	N	7	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	0	-1.0	-1	0.0	0	0.1	81.8	9	-1.0	-1	2
244	6115	2020	CA	California	Yuba County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	4

	GEO ID	Year	State Abbreviation	State	County Name	New Diagnoses Rate	New Diagnoses Rate Stability	New Diagnoses Cases	New Diagnoses Male Rate	New Diagnoses Male Rate Stability	New Diagnoses Male Cases	New Diagnoses Female Rate	New Diagnoses Female Rate Stability	New Diagnoses Female Cases	New Diagnoses Black Rate	New Diagnoses Black Rate Stability	New Diagnoses Black Cases	New Diagnoses White Rate	New Diagnoses White Rate Stability	New Diagnoses White Cases	New Diagnoses Hispanic Rate	New Diagnoses Hispanic Rate Stability	New Diagnoses Hispanic Cases	New Diagnoses Asian Rate	New Diagnoses Asian Rate Stability	New Diagnoses Asian Cases	New Diagnoses American Indian/Alaska Native Rate	New Diagnoses American Indian/Alaska Native Rate Stability	New Diagnoses American Indian/Alaska Native Cases	New Diagnoses Multiracial Rate	New Diagnoses Multiracial Rate Stability	New Diagnoses Multiracial Cases	New Diagnoses Native Hawaiian/Pacific Islander Rate	New Diagnoses Native Hawaiian/Pacific Islander Rate Stability	New Diagnoses Native Hawaiian/Pacific Islander Cases	New Diagnoses Age 13-24 Rate	New Diagnoses Age 13-24 Rate Stability	New Diagnoses Age 13-24 Cases	New Diagnoses Age 25-34 Rate	New Diagnoses Age 25-34 Rate Stability	New Diagnoses Age 25-34 Cases	New Diagnoses Age 35-44 Rate	New Diagnoses Age 35-44 Rate Stability	New Diagnoses Age 35-44 Cases	New Diagnoses Age 45-54 Rate	New Diagnoses Age 45-54 Rate Stability	New Diagnoses Age 45-54 Cases	New Diagnoses Age 55+ Rate	New Diagnoses Age 55+ Rate Stability	New Diagnoses Age 55+ Cases	New Diagnoses Heterosexual Contact Percent	New Diagnoses Heterosexual Contact Cases	New Diagnoses IDU Percent	New Diagnoses IDU Cases	New Diagnoses Other Transmission Category Percent	New Diagnoses Other Transmission Category Cases	New Diagnoses MSM Rate	New Diagnoses MSM Percent	New Diagnoses MSM Cases	New Diagnoses MSM/IDU Percent	New Diagnoses MSM/IDU Cases	2013 NCHS Urbanicity Code
205	6037	2020	CA	California	Los Angeles County	16.4	Y	1382	30.0	Y	1239	3.3	Y	143	42.8	Y	295	11.5	Y	265	18.4	Y	724	4.3	Y	56	24.2	N	4	23.0	Y	37	5.2	N	1	14.6	Y	220	34.8	Y	565	21.3	Y	291	15.2	Y	197	4.1	Y	109	9.8	136	5.1	70	0.1	2	0.6	90.2	1118	4.5	56	1
223	6073	2020	CA	California	San Diego County	10.5	Y	296	18.1	Y	257	2.8	Y	39	32.7	Y	44	6.2	Y	81	17.3	Y	157	2.0	N	7	0.0	N	0	8.6	N	7	0.0	N	0	6.5	Y	34	21.2	Y	116	16.0	Y	73	12.2	Y	49	2.7	Y	24	12.2	36	8.1	24	0.3	1	0.4	88.3	227	3.5	9	1
216	6059	2020	CA	California	Orange County	9.8	Y	264	17.7	Y	233	2.3	Y	31	30.2	Y	14	6.5	Y	72	16.9	Y	145	4.5	Y	27	0.0	N	0	8.0	N	5	13.3	N	1	9.8	Y	47	23.5	Y	108	12.2	Y	50	6.3	Y	27	3.5	Y	32	15.5	41	7.2	19	0.0	0	0.4	82.0	191	6.0	14	1
222	6071	2020	CA	California	San Bernardino County	14.7	Y	263	25.7	Y	227	4.0	Y	36	32.8	Y	48	8.7	Y	44	16.3	Y	154	7.7	N	11	0.0	N	0	15.5	N	5	18.2	N	1	15.3	Y	58	32.4	Y	110	16.3	Y	47	8.8	Y	23	4.9	Y	25	11.8	31	7.6	20	0.4	1	0.4	86.3	196	6.6	15	2
219	6065	2020	CA	California	Riverside County	11.6	Y	239	21.1	Y	216	2.2	Y	23	24.1	Y	32	9.3	Y	68	12.4	Y	123	6.9	N	10	0.0	N	0	14.7	N	6	0.0	N	0	9.1	Y	38	28.1	Y	98	13.2	Y	43	10.3	Y	31	4.3	Y	29	8.4	20	3.8	9	0.4	1	0.4	95.8	207	1.4	3	1
201	6029	2020	CA	California	Kern County	22.3	Y	160	38.8	Y	142	5.1	Y	18	50.0	Y	19	16.5	Y	41	25.3	Y	95	10.8	N	4	0.0	N	0	8.5	N	1	0.0	N	0	21.7	Y	35	44.7	Y	63	25.5	Y	30	19.4	Y	19	6.6	Y	13	7.5	12	13.1	21	0.0	0	1.5	85.2	121	4.9	7	3
187	6001	2020	CA	California	Alameda County	11.1	Y	157	19.9	Y	138	2.6	Y	19	34.4	Y	50	6.0	Y	27	18.2	Y	54	3.9	Y	18	0.0	N	0	10.0	N	5	26.2	N	3	10.7	Y	24	26.2	Y	72	10.1	Y	26	7.4	Y	16	4.3	Y	19	15.9	25	6.4	10	0.0	0	0.3	85.5	118	2.9	4	1
224	6075	2020	CA	California	San Francisco County	19.6	Y	153	33.8	Y	134	5.0	Y	19	69.1	Y	27	14.2	Y	45	54.5	Y	61	5.6	Y	16	130.3	N	2	4.3	N	1	35.1	N	1	19.4	Y	17	28.2	Y	56	24.5	Y	34	26.3	Y	29	7.0	Y	17	7.8	12	11.1	17	0.0	0	0.2	81.3	109	11.9	16	1
220	6067	2020	CA	California	Sacramento County	11.8	Y	153	19.5	Y	123	4.5	Y	30	43.5	Y	55	7.8	Y	46	11.4	Y	33	5.0	N	11	15.0	N	1	11.2	N	6	6.5	N	1	10.0	Y	23	19.5	Y	48	16.3	Y	35	12.7	Y	24	5.5	Y	23	19.6	30	12.4	19	0.7	1	0.4	76.4	94	8.1	10	1
196	6019	2020	CA	California	Fresno County	15.0	Y	120	25.5	Y	101	4.7	Y	19	32.2	Y	12	10.6	Y	26	18.3	Y	75	5.8	N	5	20.1	N	1	7.9	N	1	0.0	N	0	14.3	Y	25	26.0	Y	40	21.6	Y	28	11.0	Y	12	6.5	Y	15	11.7	14	10.0	12	0.0	0	0.9	90.1	91	3.0	3	3
229	6085	2020	CA	California	Santa Clara County	6.5	Y	105	11.1	Y	91	1.8	Y	14	22.8	N	9	3.9	Y	20	16.1	Y	61	1.2	N	8	0.0	N	0	16.7	N	7	0.0	N	0	5.5	Y	15	13.4	Y	42	9.1	Y	25	5.9	Y	15	1.6	N	8	17.1	18	2.9	3	1.0	1	0.2	86.8	79	4.4	4	1
225	6077	2020	CA	California	San Joaquin County	13.3	Y	83	22.4	Y	69	4.5	Y	14	44.1	Y	20	11.1	Y	22	13.3	Y	33	7.6	N	8	0.0	N	0	0.0	N	0	0.0	N	0	14.4	Y	19	24.0	Y	26	18.5	Y	19	16.4	Y	15	2.1	N	4	16.9	14	9.6	8	0.0	0	1.0	84.1	58	4.3	3	3
193	6013	2020	CA	California	Contra Costa County	7.4	Y	72	13.1	Y	62	2.0	N	10	25.7	Y	22	4.2	Y	18	8.8	Y	21	4.4	N	8	0.0	N	0	5.9	N	2	21.3	N	1	5.3	N	9	22.7	Y	33	7.6	Y	12	7.0	N	11	2.0	N	7	15.3	11	1.4	1	0.0	0	0.1	91.9	57	3.2	2	2
242	6111	2020	CA	California	Ventura County	7.7	Y	55	14.9	Y	52	0.8	N	3	7.5	N	1	3.0	N	10	14.9	Y	43	0.0	N	0	0.0	N	0	6.6	N	1	0.0	N	0	6.8	N	9	23.8	Y	27	9.5	N	10	3.7	N	4	2.0	N	5	7.3	4	1.8	1	0.0	0	0.8	96.2	50	0.0	0	3
234	6095	2020	CA	California	Solano County	12.2	Y	46	17.2	Y	32	7.4	Y	14	24.6	Y	13	6.9	N	10	11.4	N	11	9.8	N	6	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	9.2	N	6	27.7	Y	18	27.2	Y	16	-1.0	-9	-1	-1.0	-9	-1	28.3	13	-1.0	-1	0.0	0	0.7	93.8	30	-1.0	-1	3
227	6081	2020	CA	California	San Mateo County	6.9	Y	45	13.1	Y	42	0.9	N	3	19.7	N	3	5.4	Y	14	12.9	Y	19	3.9	N	8	0.0	N	0	5.2	N	1	0.0	N	0	9.3	N	9	10.5	Y	12	10.9	Y	12	5.9	N	6	2.6	N	6	13.3	6	0.0	0	0.0	0	0.1	85.7	36	7.1	3	2
235	6097	2020	CA	California	Sonoma County	8.7	Y	37	13.6	Y	28	4.1	N	9	-1.0	-9	-1	6.9	Y	19	11.2	Y	12	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	17.8	N	11	17.5	N	11	11.7	N	7	-1.0	-9	-1	24.3	9	-1.0	-1	0.0	0	0.2	75.0	21	-1.0	-1	3
210	6047	2020	CA	California	Merced County	14.0	Y	31	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	17.6	N	11	12.3	Y	16	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	N	0	16.5	N	9	34.1	Y	14	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	1.3	-1.0	25	0.0	0	3
240	6107	2020	CA	California	Tulare County	7.9	Y	29	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	8.2	N	9	8.2	Y	19	-1.0	-9	-1	0.0	N	0	0.0	N	0	-1.0	-9	-1	6.8	N	6	16.4	N	11	9.9	N	6	11.8	N	6	0.0	N	0	-1.0	-1	-1.0	-1	0.0	0	0.6	-1.0	24	-1.0	-1	3
236	6099	2020	CA	California	Stanislaus County	6.1	Y	27	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	5.8	N	11	6.9	Y	14	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	9.6	N	9	10.0	N	8	-1.0	-9	-1	-1.0	-9	-1	3.7	N	5	-1.0	-1	-1.0	-1	0.0	0	0.3	-1.0	19	-1.0	-1	3
199	6025	2020	CA	California	Imperial County	16.8	Y	24	25.9	Y	19	7.2	N	5	-1.0	-9	-1	-1.0	-9	-1	17.5	Y	21	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	15.8	N	5	56.0	Y	15	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-1	-1.0	-1	0.0	0	1.6	94.7	18	-1.0	-1	4
228	6083	2020	CA	California	Santa Barbara County	6.2	Y	23	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	2.9	N	5	9.5	Y	15	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	8.3	N	5	17.7	N	9	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	0.3	-1.0	15	-1.0	-1	3
217	6061	2020	CA	California	Placer County	5.6	Y	19	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	4.4	N	11	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	13.7	N	6	9.3	N	5	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	0.1	-1.0	11	-1.0	-1	2
207	6041	2020	CA	California	Marin County	6.3	Y	14	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	3.1	N	5	15.3	N	5	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	22.3	N	5	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	0.1	-1.0	10	0.0	0	2
230	6087	2020	CA	California	Santa Cruz County	5.5	Y	13	5.2	N	6	5.9	N	7	0.0	N	0	5.7	N	8	6.9	N	5	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	15.4	N	5	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	38.5	5	-1.0	-1	0.0	0	-1.0	-1.0	-1	0.0	0	3
226	6079	2020	CA	California	San Luis Obispo County	4.4	N	11	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	4.6	N	8	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	0.0	0	0.0	0	0.2	-1.0	7	-1.0	-1	3
213	6053	2020	CA	California	Monterey County	3.1	N	11	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	4.1	N	8	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	0.2	-1.0	6	0.0	0	3
243	6113	2020	CA	California	Yolo County	5.9	N	11	12.2	N	11	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	12.4	N	7	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	0	-1.0	-1	0.0	0	0.1	81.8	9	-1.0	-1	2
190	6007	2020	CA	California	Butte County	5.5	N	10	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	6.1	N	8	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	0.0	0	0.2	-1.0	5	0.0	0	4
206	6039	2020	CA	California	Madera County	6.3	N	8	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	7.1	N	5	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-1	0.0	0	0.0	0	0.7	-1.0	7	-1.0	-1	4
202	6031	2020	CA	California	Kings County	6.5	N	8	11.6	N	8	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	0	-1.0	-1	0.0	0	0.7	75.0	6	-1.0	-1	4
237	6101	2020	CA	California	Sutter County	7.6	N	6	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	0.0	0	0.0	0	-1.0	-1.0	-1	0.0	0	4
195	6017	2020	CA	California	El Dorado County	3.6	N	6	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	0.0	0	0.0	0	-1.0	-1.0	-1	0.0	0	2
214	6055	2020	CA	California	Napa County	4.3	N	5	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-1	0.0	0	0.0	0	-1.0	-1.0	-1	-1.0	-1	4
209	6045	2020	CA	California	Mendocino County	6.8	N	5	13.9	N	5	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	0	0.0	0	0.0	0	0.5	100.0	5	0.0	0	5
198	6023	2020	CA	California	Humboldt County	4.3	N	5	8.7	N	5	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	0	0.0	0	0.0	0	0.2	100.0	5	0.0	0	5
239	6105	2020	CA	California	Trinity County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
188	6003	2020	CA	California	Alpine County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
212	6051	2020	CA	California	Mono County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
211	6049	2020	CA	California	Modoc County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
241	6109	2020	CA	California	Tuolumne County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	5
192	6011	2020	CA	California	Colusa County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
204	6035	2020	CA	California	Lassen County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	5
197	6021	2020	CA	California	Glenn County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
232	6091	2020	CA	California	Sierra County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
194	6015	2020	CA	California	Del Norte County	0.0	N	0	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	5
238	6103	2020	CA	California	Tehama County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	5
191	6009	2020	CA	California	Calaveras County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
233	6093	2020	CA	California	Siskiyou County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
231	6089	2020	CA	California	Shasta County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	4
189	6005	2020	CA	California	Amador County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
221	6069	2020	CA	California	San Benito County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	2
218	6063	2020	CA	California	Plumas County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
215	6057	2020	CA	California	Nevada County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	5
208	6043	2020	CA	California	Mariposa County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
203	6033	2020	CA	California	Lake County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	5
200	6027	2020	CA	California	Inyo County	-1.0	-9	-1	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-9	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2.0	-2	-2.0	-2	6
244	6115	2020	CA	California	Yuba County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	4

	GEO ID	State Abbreviation	State	County Name	Year	County Rate	County Rate Stability	County Cases	Male Rate	Male Rate Stability	Male Cases	Female Rate	Female Rate Stability	Female Cases	Black Rate	Black Rate Stability	Black Cases	White Rate	White Rate Stability	White Cases	Hispanic Rate	Hispanic Rate Stability	Hispanic Cases	Age 13-24 Rate	Age 13-24 Rate Stability	Age 13-24 Cases	Age 25-34 Rate	Age 25-34 Rate Stability	Age 25-34 Cases	Age 35-44 Rate	Age 35-44 Rate Stability	Age 35-44 Cases	Age 45-54 Rate	Age 45-54 Rate Stability	Age 45-54 Cases	Age 55+ Rate	Age 55+ Rate Stability	Age 55+ Cases	MSM Rate	MSM Percent	Male and IDU Percent	MSM/IDU Percent	Male and Heterosexual Contact Percent	Female and IDU Percent	Female and Heterosexual Contact Percent	MSM Cases	Male and IDU Cases	MSM/IDU Cases	Male and Heterosexual Contact Cases	Female and IDU Cases	Female and Heterosexual Contact Cases	Heterosexual Contact Cases	Heterosexual Contact Percent	IDU Cases	IDU Percent	Other Transmission Route Cases	Other Transmission Route Percent	Male and Other Transmission Route Cases	Male and Other Transmission Route Percent	Female and Other Transmission Route Cases	Female and Other Transmission Route Percent	Correctional Warning	2013 NCHS Urbanicity Code
205	6037	CA	California	Los Angeles County	2020	595	Y	50243	1078	Y	44579	131	Y	5664	1380	Y	9502	551	Y	12752	599	Y	23584	67	Y	1010	472	Y	7668	754	Y	10295	985	Y	12787	697	Y	18483	19.6	87.6	3.1	6.6	2.3	19.3	76.9	39039	1397	2938	1011	1093	4353	5363	10.7	2490	5.0	413	0.8	194	0.4	218	3.8	0	1
1831	36005	NY	New York	Bronx County	2020	2408	Y	27676	3366	Y	17932	1580	Y	9744	3414	Y	11641	782	Y	852	2150	Y	13697	240	Y	541	1665	Y	3701	2505	Y	4455	3840	Y	6456	3520	Y	12523	52.1	55.5	21.1	8.5	12.5	28.6	67.0	9955	3790	1533	2241	2786	6533	8774	31.7	6576	23.8	838	3.0	414	2.3	425	4.4	0	1
2624	48201	TX	Texas	Harris County	2020	705	Y	27068	1087	Y	20584	333	Y	6484	1742	Y	12760	363	Y	4196	537	Y	8616	128	Y	1003	722	Y	5429	932	Y	6347	1100	Y	6437	756	Y	7852	13.1	78.2	5.1	5.6	10.4	14.4	83.1	16097	1060	1153	2136	936	5391	7527	27.8	1996	7.4	295	1.1	139	0.7	157	2.4	0	1
363	12086	FL	Florida	Miami-Dade County	2020	1147	Y	26559	1796	Y	20059	543	Y	6500	3102	Y	10533	848	Y	2515	803	Y	13045	139	Y	518	877	Y	3311	1256	Y	4621	1702	Y	6563	1425	Y	11546	17.9	74.5	3.9	3.5	17.3	8.6	88.6	14946	784	702	3477	560	5761	9238	34.8	1344	5.1	330	1.2	152	0.8	178	2.7	0	1
1859	36061	NY	New York	New York County	2020	1838	Y	26448	3296	Y	22247	550	Y	4201	4154	Y	7318	1139	Y	7908	2498	Y	8869	142	Y	280	788	Y	2784	1725	Y	4018	3110	Y	5953	2889	Y	13413	26.0	76.9	11.3	5.6	5.4	31.2	64.2	17101	2510	1256	1191	1309	2699	3889	14.7	3819	14.4	383	1.4	190	0.9	194	4.6	0	1
1852	36047	NY	New York	Kings County	2020	1244	Y	26236	1806	Y	17736	754	Y	8500	2242	Y	14295	350	Y	2706	1829	Y	7104	166	Y	564	789	Y	3671	1251	Y	4465	1999	Y	5795	1784	Y	11741	23.0	64.3	16.4	5.5	12.2	25.6	70.4	11396	2900	979	2159	2172	5987	8146	31.0	5072	19.3	644	2.5	303	1.7	341	4.0	0	1
611	17031	IL	Illinois	Cook County	2020	600	Y	25929	1014	Y	21090	216	Y	4839	1278	Y	12587	281	Y	5296	556	Y	5813	115	Y	862	581	Y	4834	735	Y	5144	975	Y	6102	634	Y	8987	10.4	80.3	6.2	6.6	6.0	21.6	74.9	16930	1315	1383	1266	1047	3623	4889	18.9	2363	9.1	365	1.4	196	0.9	169	3.5	0	1
326	12011	FL	Florida	Broward County	2020	1199	Y	19975	1851	Y	14918	588	Y	5057	2011	Y	9198	990	Y	5924	789	Y	4101	132	Y	351	858	Y	2237	1219	Y	3211	1892	Y	5007	1499	Y	9169	19.0	71.5	3.8	4.4	19.6	8.7	88.3	10671	564	655	2925	441	4463	7388	37.0	1004	5.0	257	1.3	104	0.7	153	3.0	1	2
2580	48113	TX	Texas	Dallas County	2020	884	Y	18983	1463	Y	15384	328	Y	3599	1641	Y	8101	687	Y	4486	623	Y	5113	139	Y	605	889	Y	3923	1222	Y	4394	1443	Y	4622	921	Y	5439	14.2	85.5	3.2	6.2	4.7	16.3	82.1	13158	496	950	722	585	2956	3678	19.4	1081	5.7	116	0.6	58	0.4	59	1.6	0	1
2295	42101	PA	Pennsylvania	Philadelphia County	2020	1283	Y	17019	1961	Y	12133	690	Y	4886	2059	Y	10847	532	Y	2577	1515	Y	2807	190	Y	448	875	Y	2675	1454	Y	2976	2328	Y	4009	1690	Y	6911	24.5	54.6	17.4	6.6	20.2	25.8	71.5	6630	2117	806	2452	1261	3495	5946	34.9	3378	19.8	258	1.5	128	1.1	130	2.7	0	1
1869	36081	NY	New York	Queens County	2020	847	Y	16064	1313	Y	11984	414	Y	4080	1459	Y	5016	408	Y	1972	1357	Y	6960	92	Y	255	593	Y	2084	926	Y	2786	1342	Y	3903	1041	Y	7036	23.4	70.3	13.4	4.3	10.7	22.6	73.4	8424	1604	516	1282	923	2993	4275	26.6	2527	15.7	323	2.0	159	1.3	164	4.0	0	1
447	13121	GA	Georgia	Fulton County	2020	1745	Y	16004	3092	Y	13578	508	Y	2426	2933	Y	11495	688	Y	2592	1665	Y	1012	257	Y	447	1819	Y	3428	2364	Y	3609	2560	Y	3731	1871	Y	4789	23.7	84.4	4.3	5.2	5.6	14.5	83.1	11454	587	712	761	352	2016	2777	17.4	939	5.9	122	0.8	64	0.5	58	2.4	0	1
223	6073	CA	California	San Diego County	2020	473	Y	13331	843	Y	11948	99	Y	1383	1179	Y	1585	413	Y	5427	595	Y	5413	40	Y	211	304	Y	1664	532	Y	2435	854	Y	3422	630	Y	5599	15.5	82.4	4.3	8.6	4.2	19.9	76.6	9841	519	1026	496	275	1059	1555	11.7	794	6.0	115	0.9	66	0.6	49	3.5	0	1
104	4013	AZ	Arizona	Maricopa County	2020	316	Y	12101	550	Y	10347	90	Y	1754	890	Y	1891	253	Y	5546	332	Y	3685	53	Y	385	309	Y	2087	395	Y	2359	533	Y	2999	338	Y	4271	11.0	81.5	5.2	8.6	3.7	22.6	73.7	8435	543	895	381	397	1292	1672	13.8	940	7.8	158	1.3	93	0.9	65	3.7	0	1
224	6075	CA	California	San Francisco County	2020	1515	Y	11803	2792	Y	11081	189	Y	722	3847	Y	1502	1912	Y	6046	2543	Y	2844	90	Y	79	500	Y	991	1381	Y	1916	2626	Y	2899	2427	Y	5918	18.0	79.4	3.4	15.4	1.6	45.4	52.2	8800	377	1709	177	328	377	554	4.7	705	6.0	34	0.3	18	0.2	16	2.2	0	1
1217	24510	MD	Maryland	Baltimore city	2020	1985	Y	9900	2848	Y	6544	1247	Y	3356	2658	Y	8137	532	Y	774	1641	Y	390	222	Y	188	1084	Y	1200	1905	Y	1469	3495	Y	2234	2960	Y	4809	18.1	49.3	26.6	8.3	14.9	37.5	59.5	3226	1743	543	975	1260	1998	2973	30.0	3003	30.3	155	1.6	57	0.9	99	2.9	0	1
219	6065	CA	California	Riverside County	2020	474	Y	9765	879	Y	8997	74	Y	768	635	Y	843	778	Y	5706	271	Y	2688	41	Y	170	268	Y	935	334	Y	1088	670	Y	2021	832	Y	5551	15.0	88.0	2.4	6.7	2.6	21.4	75.9	7920	214	603	233	164	583	816	8.4	378	3.9	48	0.5	27	0.3	22	2.9	0	1
1749	32003	NV	Nevada	Clark County	2020	484	Y	9385	822	Y	7901	152	Y	1484	1186	Y	2688	391	Y	3298	455	Y	2620	70	Y	237	534	Y	1845	641	Y	2056	746	Y	2243	473	Y	3004	16.0	83.8	4.4	7.3	4.1	18.5	78.1	6620	345	573	322	274	1159	1481	15.8	619	6.6	92	1.0	41	0.5	51	3.4	0	1
431	13089	GA	Georgia	DeKalb County	2020	1418	Y	8992	2440	Y	7186	532	Y	1806	1932	Y	6590	593	Y	1162	1546	Y	705	279	Y	303	1469	Y	1890	1835	Y	1990	2229	Y	2151	1385	Y	2658	25.8	84.7	3.5	4.4	6.8	11.3	86.3	6087	252	319	490	204	1559	2049	22.8	455	5.1	82	0.9	38	0.5	44	2.4	1	2
368	12095	FL	Florida	Orange County	2020	757	Y	8973	1186	Y	6840	350	Y	2133	1640	Y	3807	486	Y	2346	661	Y	2514	143	Y	326	654	Y	1536	891	Y	1822	1175	Y	2127	935	Y	3162	10.1	77.0	5.6	4.4	12.2	12.8	84.1	5267	382	300	837	273	1793	2630	29.3	654	7.3	122	1.4	54	0.8	68	3.2	0	1

	GEO ID	State Abbreviation	State	County Name	Year	County Rate	County Rate Stability	County Cases	Male Rate	Male Rate Stability	Male Cases	Female Rate	Female Rate Stability	Female Cases	Black Rate	Black Rate Stability	Black Cases	White Rate	White Rate Stability	White Cases	Hispanic Rate	Hispanic Rate Stability	Hispanic Cases	Age 13-24 Rate	Age 13-24 Rate Stability	Age 13-24 Cases	Age 25-34 Rate	Age 25-34 Rate Stability	Age 25-34 Cases	Age 35-44 Rate	Age 35-44 Rate Stability	Age 35-44 Cases	Age 45-54 Rate	Age 45-54 Rate Stability	Age 45-54 Cases	Age 55+ Rate	Age 55+ Rate Stability	Age 55+ Cases	MSM Rate	MSM Percent	Male and IDU Percent	MSM/IDU Percent	Male and Heterosexual Contact Percent	Female and IDU Percent	Female and Heterosexual Contact Percent	MSM Cases	Male and IDU Cases	MSM/IDU Cases	Male and Heterosexual Contact Cases	Female and IDU Cases	Female and Heterosexual Contact Cases	Heterosexual Contact Cases	Heterosexual Contact Percent	IDU Cases	IDU Percent	Other Transmission Route Cases	Other Transmission Route Percent	Male and Other Transmission Route Cases	Male and Other Transmission Route Percent	Female and Other Transmission Route Cases	Female and Other Transmission Route Percent	Correctional Warning	2013 NCHS Urbanicity Code
187	6001	CA	California	Alameda County	2020	425	Y	6030	727	Y	5048	135	Y	982	1542	Y	2239	380	Y	1707	457	Y	1355	54	Y	120	299	Y	823	400	Y	1028	669	Y	1452	584	Y	2607	11.8	79.9	4.8	8.1	6.6	22.4	74.2	4032	241	411	334	220	729	1063	17.6	461	7.6	63	1.0	31	0.6	33	3.4	0	1
188	6003	CA	California	Alpine County	2020	-1	-9	-1	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
189	6005	CA	California	Amador County	2020	514	Y	184	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
190	6007	CA	California	Butte County	2020	137	Y	249	226	Y	202	51	Y	47	792	Y	23	129	Y	169	149	Y	45	-1	-9	-1	90	Y	25	167	Y	41	314	Y	68	-1	-9	-1	4.4	65.3	-1.0	-1.0	-1.0	-1.0	-1.0	132	-1	-1	-1	-1	-1	45	18.1	40	16.1	-1	-1.0	-1	-1.0	-1	-1.0	0	4
191	6009	CA	California	Calaveras County	2020	122	Y	50	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
192	6011	CA	California	Colusa County	2020	103	Y	18	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
193	6013	CA	California	Contra Costa County	2020	278	Y	2709	474	Y	2237	94	Y	472	914	Y	783	223	Y	952	294	Y	701	25	Y	42	261	Y	379	308	Y	487	398	Y	623	341	Y	1178	4.5	83.2	5.1	6.8	4.3	21.2	75.6	1861	113	152	97	100	357	454	16.8	213	7.9	30	1.1	15	0.7	15	3.2	0	2
194	6015	CA	California	Del Norte County	2020	160	Y	38	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	1	5
195	6017	CA	California	El Dorado County	2020	125	Y	209	212	Y	175	40	Y	34	-1	-9	-1	127	Y	166	148	Y	30	20	N	5	62	Y	12	128	Y	29	208	Y	50	149	Y	113	2.9	72.0	-1.0	10.3	-1.0	-1.0	-1.0	126	-1	18	-1	-1	-1	41	19.6	18	8.6	6	2.9	-1	-1.0	-1	-1.0	0	2
196	6019	CA	California	Fresno County	2020	267	Y	2134	452	Y	1789	86	Y	345	825	Y	307	207	Y	509	289	Y	1185	56	Y	97	258	Y	396	363	Y	471	411	Y	447	312	Y	723	14.4	80.8	6.9	9.2	2.5	28.4	69.3	1446	124	164	45	98	239	284	13.3	221	10.4	19	0.9	10	0.6	9	2.6	0	3
197	6021	CA	California	Glenn County	2020	101	Y	23	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
198	6023	CA	California	Humboldt County	2020	185	Y	216	304	Y	175	69	Y	41	481	N	7	168	Y	148	275	Y	36	29	N	7	146	Y	26	215	Y	38	311	Y	45	234	Y	100	4.0	66.9	-1.0	17.1	-1.0	-1.0	-1.0	117	-1	30	-1	-1	-1	36	16.7	29	13.4	5	2.3	-1	-1.0	-1	-1.0	0	5
199	6025	CA	California	Imperial County	2020	259	Y	370	440	Y	323	68	Y	47	174	N	7	247	Y	38	267	Y	320	50	Y	16	254	Y	68	287	Y	64	525	Y	101	282	Y	121	22.1	78.0	-1.0	-1.0	-1.0	-1.0	-1.0	252	-1	-1	-1	-1	-1	64	17.3	32	8.6	-1	-1.0	-1	-1.0	-1	-1.0	1	4
200	6027	CA	California	Inyo County	2020	156	Y	24	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
201	6029	CA	California	Kern County	2020	269	Y	1928	445	Y	1630	85	Y	298	668	Y	254	214	Y	532	284	Y	1063	60	Y	96	291	Y	410	306	Y	359	457	Y	448	310	Y	615	14.3	68.7	13.3	10.8	6.7	30.5	66.1	1120	217	176	110	91	197	307	15.9	308	16.0	18	0.9	8	0.5	11	3.7	0	3
202	6031	CA	California	Kings County	2020	154	Y	189	230	Y	158	57	Y	31	273	Y	23	106	Y	43	171	Y	111	26	N	7	112	Y	29	195	Y	42	311	Y	52	190	Y	59	12.6	65.8	-1.0	-1.0	-1.0	-1.0	-1.0	104	-1	-1	-1	-1	-1	42	22.2	29	15.3	-1	-1.0	-1	-1.0	-1	-1.0	1	4
203	6033	CA	California	Lake County	2020	259	Y	141	432	Y	117	88	Y	24	821	N	8	250	Y	97	210	Y	23	-1	-9	-1	123	N	9	198	Y	14	432	Y	31	-1	-9	-1	11.3	68.4	-1.0	-1.0	-1.0	-1.0	-1.0	80	-1	-1	-1	-1	-1	25	17.7	17	12.1	-1	-1.0	-1	-1.0	-1	-1.0	0	5
204	6035	CA	California	Lassen County	2020	88	Y	23	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	1	5
205	6037	CA	California	Los Angeles County	2020	595	Y	50243	1078	Y	44579	131	Y	5664	1380	Y	9502	551	Y	12752	599	Y	23584	67	Y	1010	472	Y	7668	754	Y	10295	985	Y	12787	697	Y	18483	19.6	87.6	3.1	6.6	2.3	19.3	76.9	39039	1397	2938	1011	1093	4353	5363	10.7	2490	5.0	413	0.8	194	0.4	218	3.8	0	1
206	6039	CA	California	Madera County	2020	166	Y	211	250	Y	151	91	Y	60	740	Y	32	120	Y	55	160	Y	113	38	N	10	155	Y	34	199	Y	40	271	Y	48	196	Y	79	10.9	76.2	-1.0	-1.0	-1.0	-1.0	-1.0	115	-1	-1	-1	-1	-1	37	17.5	41	19.4	-1	-1.0	-1	-1.0	-1	-1.0	1	4
207	6041	CA	California	Marin County	2020	353	Y	789	643	Y	700	78	Y	89	1928	Y	115	253	Y	412	641	Y	210	36	Y	12	290	Y	65	383	Y	114	465	Y	180	421	Y	418	7.0	71.6	-1.0	10.7	-1.0	-1.0	-1.0	501	-1	75	-1	-1	-1	109	13.8	98	12.4	6	0.8	-1	-1.0	-1	-1.0	1	2
208	6043	CA	California	Mariposa County	2020	125	Y	19	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
209	6045	CA	California	Mendocino County	2020	237	Y	173	391	Y	141	86	Y	32	1351	N	8	252	Y	124	166	Y	28	-1	-9	-1	178	Y	17	162	Y	17	367	Y	36	-1	-9	-1	9.3	73.0	-1.0	-1.0	-1.0	-1.0	-1.0	103	-1	-1	-1	-1	-1	31	17.9	20	11.6	-1	-1.0	-1	-1.0	-1	-1.0	0	5
210	6047	CA	California	Merced County	2020	157	Y	347	262	Y	292	50	Y	55	406	Y	28	161	Y	101	148	Y	193	55	Y	30	185	Y	76	184	Y	64	234	Y	71	174	Y	106	12.3	78.8	-1.0	4.8	-1.0	-1.0	-1.0	230	-1	14	-1	-1	-1	60	17.3	37	10.7	5	1.4	-1	-1.0	-1	-1.0	1	3
211	6049	CA	California	Modoc County	2020	-1	-9	-1	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
212	6051	CA	California	Mono County	2020	63	N	8	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
213	6053	CA	California	Monterey County	2020	212	Y	745	354	Y	631	66	Y	114	799	Y	76	194	Y	220	206	Y	403	14	N	10	181	Y	110	209	Y	118	414	Y	207	272	Y	300	14.6	80.5	-1.0	6.7	-1.0	-1.0	-1.0	508	-1	42	-1	-1	-1	135	18.1	55	7.4	5	0.7	-1	-1.0	-1	-1.0	0	3
214	6055	CA	California	Napa County	2020	244	Y	287	429	Y	250	62	Y	37	1243	Y	31	243	Y	155	220	Y	83	45	N	9	180	Y	30	232	Y	40	377	Y	66	308	Y	142	16.2	77.2	-1.0	7.2	-1.0	-1.0	-1.0	193	-1	18	-1	-1	-1	42	14.6	27	9.4	7	2.4	-1	-1.0	-1	-1.0	0	4
215	6057	CA	California	Nevada County	2020	143	Y	126	252	Y	108	40	Y	18	-1	-9	-1	134	Y	101	218	Y	17	-1	-9	-1	103	N	10	165	Y	19	293	Y	32	-1	-9	-1	6.1	80.6	-1.0	-1.0	-1.0	-1.0	-1.0	87	-1	-1	-1	-1	-1	15	11.9	9	7.1	-1	-1.0	-1	-1.0	-1	-1.0	0	5
216	6059	CA	California	Orange County	2020	264	Y	7092	473	Y	6224	63	Y	868	815	Y	378	219	Y	2431	412	Y	3535	37	Y	176	225	Y	1033	365	Y	1491	426	Y	1827	282	Y	2565	11.6	84.8	5.0	6.2	3.4	17.9	79.1	5278	312	388	209	155	687	895	12.6	467	6.6	63	0.9	37	0.6	27	3.1	0	1
217	6061	CA	California	Placer County	2020	109	Y	371	181	Y	299	41	Y	72	313	Y	19	98	Y	243	179	Y	83	16	N	9	114	Y	50	119	Y	64	148	Y	77	126	Y	171	2.5	72.9	-1.0	10.7	-1.0	-1.0	-1.0	218	-1	32	-1	-1	-1	73	19.7	42	11.3	6	1.6	-1	-1.0	-1	-1.0	0	2
218	6063	CA	California	Plumas County	2020	126	Y	21	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
219	6065	CA	California	Riverside County	2020	474	Y	9765	879	Y	8997	74	Y	768	635	Y	843	778	Y	5706	271	Y	2688	41	Y	170	268	Y	935	334	Y	1088	670	Y	2021	832	Y	5551	15.0	88.0	2.4	6.7	2.6	21.4	75.9	7920	214	603	233	164	583	816	8.4	378	3.9	48	0.5	27	0.3	22	2.9	0	1
220	6067	CA	California	Sacramento County	2020	348	Y	4519	601	Y	3785	110	Y	734	875	Y	1107	336	Y	1976	359	Y	1034	48	Y	110	279	Y	686	361	Y	774	574	Y	1085	445	Y	1864	11.7	75.1	6.0	10.2	8.0	22.3	74.8	2841	227	387	304	164	549	853	18.9	391	8.7	48	1.1	27	0.7	21	2.9	0	1
221	6069	CA	California	San Benito County	2020	107	Y	56	178	Y	47	34	N	9	-1	-9	-1	84	Y	15	123	Y	38	0	N	0	69	N	6	134	Y	12	151	Y	12	159	Y	26	3.9	72.3	-1.0	12.8	-1.0	-1.0	-1.0	34	-1	6	-1	-1	-1	10	17.9	6	10.7	0	0.0	-1	-1.0	-1	-1.0	0	2
222	6071	CA	California	San Bernardino County	2020	272	Y	4845	456	Y	4028	91	Y	817	686	Y	1004	224	Y	1134	256	Y	2419	46	Y	176	279	Y	948	334	Y	962	439	Y	1145	313	Y	1614	6.0	78.1	7.8	8.0	5.4	22.3	73.9	3145	315	323	218	182	604	822	17.0	497	10.3	58	1.2	27	0.7	30	3.7	0	2
223	6073	CA	California	San Diego County	2020	473	Y	13331	843	Y	11948	99	Y	1383	1179	Y	1585	413	Y	5427	595	Y	5413	40	Y	211	304	Y	1664	532	Y	2435	854	Y	3422	630	Y	5599	15.5	82.4	4.3	8.6	4.2	19.9	76.6	9841	519	1026	496	275	1059	1555	11.7	794	6.0	115	0.9	66	0.6	49	3.5	0	1
224	6075	CA	California	San Francisco County	2020	1515	Y	11803	2792	Y	11081	189	Y	722	3847	Y	1502	1912	Y	6046	2543	Y	2844	90	Y	79	500	Y	991	1381	Y	1916	2626	Y	2899	2427	Y	5918	18.0	79.4	3.4	15.4	1.6	45.4	52.2	8800	377	1709	177	328	377	554	4.7	705	6.0	34	0.3	18	0.2	16	2.2	0	1
225	6077	CA	California	San Joaquin County	2020	230	Y	1432	370	Y	1142	92	Y	290	853	Y	387	194	Y	386	206	Y	511	39	Y	52	230	Y	249	259	Y	266	398	Y	364	266	Y	501	13.3	68.2	12.3	9.1	9.2	22.1	75.2	779	141	104	105	64	218	323	22.6	205	14.3	22	1.5	14	1.2	8	2.8	1	3
226	6079	CA	California	San Luis Obispo County	2020	156	Y	385	269	Y	336	40	Y	49	537	Y	25	134	Y	233	207	Y	108	14	N	8	132	Y	42	185	Y	60	306	Y	90	190	Y	185	7.1	72.0	-1.0	-1.0	-1.0	-1.0	-1.0	242	-1	-1	-1	-1	-1	61	15.8	33	8.6	-1	-1.0	-1	-1.0	-1	-1.0	1	3
227	6081	CA	California	San Mateo County	2020	257	Y	1674	457	Y	1465	63	Y	209	1007	Y	153	241	Y	618	414	Y	608	30	Y	29	190	Y	217	273	Y	299	384	Y	391	324	Y	738	3.9	82.0	5.3	7.0	4.6	14.4	79.9	1202	78	103	68	30	167	235	14.0	107	6.4	27	1.6	15	1.0	12	5.7	1	2
228	6083	CA	California	Santa Barbara County	2020	158	Y	591	271	Y	505	46	Y	86	459	Y	32	137	Y	240	185	Y	293	16	Y	15	105	Y	63	232	Y	118	304	Y	140	211	Y	255	8.0	78.6	-1.0	8.3	-1.0	-1.0	-1.0	397	-1	42	-1	-1	-1	95	16.1	52	8.8	5	0.8	-1	-1.0	-1	-1.0	1	3
229	6085	CA	California	Santa Clara County	2020	213	Y	3443	367	Y	3008	55	Y	435	883	Y	349	195	Y	991	407	Y	1539	25	Y	68	151	Y	471	275	Y	756	352	Y	898	250	Y	1250	7.5	82.4	4.1	7.8	5.3	14.5	80.9	2479	124	234	160	63	352	512	14.9	187	5.4	31	0.9	11	0.4	20	4.6	0	1
230	6087	CA	California	Santa Cruz County	2020	215	Y	503	368	Y	424	66	Y	79	626	Y	15	198	Y	277	252	Y	182	11	N	6	141	Y	46	256	Y	81	404	Y	129	288	Y	241	14.2	79.2	-1.0	8.5	-1.0	-1.0	-1.0	336	-1	36	-1	-1	-1	68	13.5	56	11.1	6	1.2	-1	-1.0	-1	-1.0	0	3
231	6089	CA	California	Shasta County	2020	135	Y	205	231	Y	170	45	Y	35	748	Y	12	127	Y	155	140	Y	21	21	N	5	100	Y	23	155	Y	33	184	Y	36	171	Y	108	5.1	60.0	-1.0	14.1	-1.0	-1.0	-1.0	102	-1	24	-1	-1	-1	35	17.1	36	17.6	8	3.9	-1	-1.0	-1	-1.0	0	4
232	6091	CA	California	Sierra County	2020	-1	-9	-1	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
233	6093	CA	California	Siskiyou County	2020	170	Y	63	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
234	6095	CA	California	Solano County	2020	342	Y	1287	586	Y	1093	102	Y	194	832	Y	439	283	Y	409	306	Y	295	42	Y	27	267	Y	174	342	Y	201	516	Y	276	453	Y	609	17.6	69.3	-1.0	10.2	-1.0	-1.0	-1.0	757	-1	112	-1	-1	-1	214	16.6	190	14.8	15	1.2	-1	-1.0	-1	-1.0	0	3
235	6097	CA	California	Sonoma County	2020	339	Y	1438	627	Y	1292	67	Y	146	1069	Y	75	363	Y	1004	251	Y	268	21	Y	14	174	Y	108	299	Y	188	440	Y	264	500	Y	864	11.5	81.7	-1.0	11.1	-1.0	-1.0	-1.0	1056	-1	143	-1	-1	-1	149	10.4	73	5.1	17	1.2	-1	-1.0	-1	-1.0	0	3
236	6099	CA	California	Stanislaus County	2020	185	Y	824	307	Y	674	67	Y	150	636	Y	79	162	Y	305	185	Y	374	27	Y	25	149	Y	119	225	Y	160	343	Y	217	222	Y	303	9.1	78.0	-1.0	7.9	-1.0	-1.0	-1.0	526	-1	53	-1	-1	-1	143	17.4	85	10.3	18	2.2	-1	-1.0	-1	-1.0	0	3
237	6101	CA	California	Sutter County	2020	151	Y	119	244	Y	95	60	Y	24	-1	-9	-1	155	Y	57	205	Y	48	-1	-9	-1	163	Y	22	157	Y	19	230	Y	25	-1	-9	-1	11.5	69.5	-1.0	-1.0	-1.0	-1.0	-1.0	66	-1	-1	-1	-1	-1	30	25.2	8	6.7	-1	-1.0	-1	-1.0	-1	-1.0	0	4
238	6103	CA	California	Tehama County	2020	88	Y	47	140	Y	37	37	N	10	-1	-9	-1	75	Y	28	127	Y	16	0	N	0	64	N	5	136	N	10	128	N	9	104	Y	23	2.0	70.3	-1.0	-1.0	-1.0	-1.0	-1.0	26	-1	-1	-1	-1	-1	5	10.6	9	19.1	-1	-1.0	-1	-1.0	-1	-1.0	0	5
239	6105	CA	California	Trinity County	2020	149	Y	16	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	0	6
240	6107	CA	California	Tulare County	2020	140	Y	516	239	Y	436	43	Y	80	421	Y	20	151	Y	166	134	Y	311	19	Y	17	174	Y	116	223	Y	135	205	Y	104	141	Y	144	9.0	82.1	-1.0	8.0	-1.0	-1.0	-1.0	358	-1	35	-1	-1	-1	77	14.9	42	8.1	5	1.0	-1	-1.0	-1	-1.0	0	3
241	6109	CA	California	Tuolumne County	2020	123	Y	59	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2	-9	-2	-2.0	-2.0	-1.0	-2.0	-1.0	-1.0	-1.0	-2	-1	-2	-1	-1	-1	-2	-2.0	-2	-2.0	-2	-2.0	-1	-1.0	-1	-1.0	1	5
242	6111	CA	California	Ventura County	2020	160	Y	1139	282	Y	987	42	Y	152	414	Y	55	128	Y	426	204	Y	590	24	Y	32	152	Y	173	221	Y	233	219	Y	235	185	Y	466	13.7	84.4	4.6	4.4	4.9	19.1	76.3	833	45	43	48	29	116	164	14.4	74	6.5	26	2.3	19	1.9	7	4.6	0	3
243	6113	CA	California	Yolo County	2020	165	Y	310	292	Y	264	47	Y	46	672	Y	32	166	Y	147	188	Y	106	23	Y	13	158	Y	47	212	Y	56	275	Y	63	254	Y	131	3.3	79.9	-1.0	-1.0	-1.0	-1.0	-1.0	211	-1	-1	-1	-1	-1	47	15.2	28	9.0	-1	-1.0	-1	-1.0	-1	-1.0	0	2
244	6115	CA	California	Yuba County	2020	158	Y	101	220	Y	71	95	Y	30	367	N	9	168	Y	60	170	Y	30	-1	-9	-1	111	Y	14	181	Y	19	354	Y	29	-1	-9	-1	6.0	67.6	-1.0	15.5	-1.0	-1.0	-1.0	48	-1	11	-1	-1	-1	23	22.8	13	12.9	5	5.0	-1	-1.0	-1	-1.0	0	4

	GEO ID	Year	State Abbreviation	State	County Name	New Diagnoses Rate	New Diagnoses Rate Stability	New Diagnoses Cases	New Diagnoses Male Rate	New Diagnoses Male Rate Stability	New Diagnoses Male Cases	New Diagnoses Female Rate	New Diagnoses Female Rate Stability	New Diagnoses Female Cases	New Diagnoses Black Rate	New Diagnoses Black Rate Stability	New Diagnoses Black Cases	New Diagnoses White Rate	New Diagnoses White Rate Stability	New Diagnoses White Cases	New Diagnoses Hispanic Rate	New Diagnoses Hispanic Rate Stability	New Diagnoses Hispanic Cases	New Diagnoses Asian Rate	New Diagnoses Asian Rate Stability	New Diagnoses Asian Cases	New Diagnoses American Indian/Alaska Native Rate	New Diagnoses American Indian/Alaska Native Rate Stability	New Diagnoses American Indian/Alaska Native Cases	New Diagnoses Multiracial Rate	New Diagnoses Multiracial Rate Stability	New Diagnoses Multiracial Cases	New Diagnoses Native Hawaiian/Pacific Islander Rate	New Diagnoses Native Hawaiian/Pacific Islander Rate Stability	New Diagnoses Native Hawaiian/Pacific Islander Cases	New Diagnoses Age 13-24 Rate	New Diagnoses Age 13-24 Rate Stability	New Diagnoses Age 13-24 Cases	New Diagnoses Age 25-34 Rate	New Diagnoses Age 25-34 Rate Stability	New Diagnoses Age 25-34 Cases	New Diagnoses Age 35-44 Rate	New Diagnoses Age 35-44 Rate Stability	New Diagnoses Age 35-44 Cases	New Diagnoses Age 45-54 Rate	New Diagnoses Age 45-54 Rate Stability	New Diagnoses Age 45-54 Cases	New Diagnoses Age 55+ Rate	New Diagnoses Age 55+ Rate Stability	New Diagnoses Age 55+ Cases	New Diagnoses Heterosexual Contact Percent	New Diagnoses Heterosexual Contact Cases	New Diagnoses IDU Percent	New Diagnoses IDU Cases	New Diagnoses Other Transmission Category Percent	New Diagnoses Other Transmission Category Cases	New Diagnoses MSM Rate	New Diagnoses MSM Percent	New Diagnoses MSM Cases	New Diagnoses MSM/IDU Percent	New Diagnoses MSM/IDU Cases	2013 NCHS Urbanicity Code
0	1001	2020	AL	Alabama	Autauga County	17.0	N	8	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-1	0.0	0	0.0	0	-1.0	-1.0	-1	0.0	0	3
1	1003	2020	AL	Alabama	Baldwin County	5.1	N	10	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	3.0	N	5	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	27.5	N	7	-1.0	-9	-1	-1.0	-9	-1	0.0	N	0	-1.0	-1	0.0	0	0.0	0	0.6	-1.0	8	-1.0	-1	4
2	1005	2020	AL	Alabama	Barbour County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	6
3	1007	2020	AL	Alabama	Bibb County	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	2
4	1009	2020	AL	Alabama	Blount County	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	-9	-1	0.0	N	0	-1.0	-9	-1	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	0	0.0	0	0.0	0	0.0	0.0	0	0.0	0	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3216	72145	2020	PR	Puerto Rico	Vega Baja Municipio	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	-9
3217	72147	2020	PR	Puerto Rico	Vieques Municipio	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	-9
3218	72149	2020	PR	Puerto Rico	Villalba Municipio	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	0	0.0	0	0.0	0	-1.0	0.0	0	0.0	0	-9
3219	72151	2020	PR	Puerto Rico	Yabucoa Municipio	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-9	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1.0	-1	-1.0	-1	-9
3220	72153	2020	PR	Puerto Rico	Yauco Municipio	0.0	N	0	0.0	N	0	0.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	-1.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	N	0	0.0	0	0.0	0	0.0	0	-1.0	0.0	0	0.0	0	-9

	GEO ID	County	County Cases	County PrEP Rate	County PrEP Users	New Diagnoses Rate	New Diagnoses Cases	New Diagnoses Black Rate	New Diagnoses Black Cases	New Diagnoses White Rate	New Diagnoses White Cases	New Diagnoses Hispanic Rate	New Diagnoses Hispanic Cases	New Diagnoses Asian Rate	New Diagnoses Asian Cases	New Diagnoses American Indian/Alaska Native Rate	New Diagnoses American Indian/Alaska Native Cases	New Diagnoses Multiracial Rate	New Diagnoses Multiracial Cases	New Diagnoses Native Hawaiian/Pacific Islander Rate	New Diagnoses Native Hawaiian/Pacific Islander Cases
0	6001	Alameda	6030	131	1857	11.1	157	34.4	50	6.0	27	18.2	54	3.9	18	0.0	0	10.0	5	26.2	3
1	6003	Alpine	-1	-1	-1	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
2	6005	Amador	184	40	14	-1.0	-1	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
3	6007	Butte	249	29	55	5.5	10	0.0	0	6.1	8	-1.0	-1	0.0	0	0.0	0	0.0	0	-1.0	-1
4	6009	Calaveras	50	27	11	-1.0	-1	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
5	6011	Colusa	18	31	5	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
6	6013	Contra Costa	2709	67	646	7.4	72	25.7	22	4.2	18	8.8	21	4.4	8	0.0	0	5.9	2	21.3	1
7	6015	Del Norte	38	67	16	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
8	6017	El Dorado	209	42	69	3.6	6	0.0	0	-1.0	-1	-1.0	-1	0.0	0	0.0	0	0.0	0	0.0	0
9	6019	Fresno	2134	44	344	15.0	120	32.2	12	10.6	26	18.3	75	5.8	5	20.1	1	7.9	1	0.0	0
10	6021	Glenn	23	31	7	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
11	6023	Humboldt	216	63	74	4.3	5	0.0	0	-1.0	-1	-1.0	-1	0.0	0	0.0	0	-1.0	-1	0.0	0
12	6025	Imperial	370	244	349	16.8	24	-1.0	-1	-1.0	-1	17.5	21	0.0	0	0.0	0	-1.0	-1	0.0	0
13	6027	Inyo	24	46	7	-1.0	-1	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
14	6029	Kern	1928	48	339	22.3	160	50.0	19	16.5	41	25.3	95	10.8	4	0.0	0	8.5	1	0.0	0
15	6031	Kings	189	38	46	6.5	8	-1.0	-1	-1.0	-1	-1.0	-1	0.0	0	0.0	0	0.0	0	0.0	0
16	6033	Lake	141	78	42	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1
17	6035	Lassen	23	42	11	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
18	6037	Los Angeles	50243	182	15431	16.4	1382	42.8	295	11.5	265	18.4	724	4.3	56	24.2	4	23.0	37	5.2	1
19	6039	Madera	211	31	38	6.3	8	0.0	0	-1.0	-1	7.1	5	0.0	0	0.0	0	0.0	0	-1.0	-1
20	6041	Marin	789	91	205	6.3	14	-1.0	-1	3.1	5	15.3	5	-1.0	-1	0.0	0	0.0	0	0.0	0
21	6043	Mariposa	19	31	5	-1.0	-1	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
22	6045	Mendocino	173	78	58	6.8	5	0.0	0	-1.0	-1	-1.0	-1	0.0	0	-1.0	-1	0.0	0	0.0	0
23	6047	Merced	347	30	66	14.0	31	0.0	0	17.6	11	12.3	16	-1.0	-1	0.0	0	-1.0	-1	0.0	0
24	6049	Modoc	-1	41	3	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
25	6051	Mono	8	43	5	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
26	6053	Monterey	745	65	227	3.1	11	0.0	0	-1.0	-1	4.1	8	-1.0	-1	0.0	0	0.0	0	0.0	0
27	6055	Napa	287	68	81	4.3	5	0.0	0	0.0	0	-1.0	-1	-1.0	-1	0.0	0	0.0	0	0.0	0
28	6057	Nevada	126	30	27	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1
29	6059	Orange	7092	87	2332	9.8	264	30.2	14	6.5	72	16.9	145	4.5	27	0.0	0	8.0	5	13.3	1
30	6061	Placer	371	41	137	5.6	19	-1.0	-1	4.4	11	-1.0	-1	-1.0	-1	0.0	0	0.0	0	0.0	0
31	6063	Plumas	21	34	6	-1.0	-1	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
32	6065	Riverside	9765	87	1737	11.6	239	24.1	32	9.3	68	12.4	123	6.9	10	0.0	0	14.7	6	0.0	0
33	6067	Sacramento	4519	73	935	11.8	153	43.5	55	7.8	46	11.4	33	5.0	11	15.0	1	11.2	6	6.5	1
34	6069	San Benito	56	81	41	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1
35	6071	San Bernardino	4845	49	863	14.7	263	32.8	48	8.7	44	16.3	154	7.7	11	0.0	0	15.5	5	18.2	1
36	6073	San Diego	13331	131	3686	10.5	296	32.7	44	6.2	81	17.3	157	2.0	7	0.0	0	8.6	7	0.0	0
37	6075	San Francisco	11803	897	7056	19.6	153	69.1	27	14.2	45	54.5	61	5.6	16	130.3	2	4.3	1	35.1	1
38	6077	San Joaquin	1432	28	170	13.3	83	44.1	20	11.1	22	13.3	33	7.6	8	0.0	0	0.0	0	0.0	0
39	6079	San Luis Obispo	385	58	142	4.4	11	0.0	0	4.6	8	-1.0	-1	-1.0	-1	0.0	0	0.0	0	0.0	0
40	6081	San Mateo	1674	134	877	6.9	45	19.7	3	5.4	14	12.9	19	3.9	8	0.0	0	5.2	1	0.0	0
41	6083	Santa Barbara	591	98	365	6.2	23	-1.0	-1	2.9	5	9.5	15	0.0	0	0.0	0	-1.0	-1	0.0	0
42	6085	Santa Clara	3443	99	1606	6.5	105	22.8	9	3.9	20	16.1	61	1.2	8	0.0	0	16.7	7	0.0	0
43	6087	Santa Cruz	503	82	194	5.5	13	0.0	0	5.7	8	6.9	5	0.0	0	0.0	0	0.0	0	0.0	0
44	6089	Shasta	205	37	56	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1
45	6091	Sierra	-1	-1	-1	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
46	6093	Siskiyou	63	38	14	-1.0	-1	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
47	6095	Solano	1287	58	216	12.2	46	24.6	13	6.9	10	11.4	11	9.8	6	-1.0	-1	-1.0	-1	-1.0	-1
48	6097	Sonoma	1438	79	337	8.7	37	-1.0	-1	6.9	19	11.2	12	-1.0	-1	0.0	0	-1.0	-1	0.0	0
49	6099	Stanislaus	824	30	132	6.1	27	-1.0	-1	5.8	11	6.9	14	-1.0	-1	0.0	0	0.0	0	0.0	0
50	6101	Sutter	119	31	24	7.6	6	0.0	0	-1.0	-1	-1.0	-1	0.0	0	0.0	0	0.0	0	0.0	0
51	6103	Tehama	47	37	20	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1
52	6105	Trinity	16	44	5	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
53	6107	Tulare	516	36	132	7.9	29	0.0	0	8.2	9	8.2	19	-1.0	-1	0.0	0	0.0	0	-1.0	-1
54	6109	Tuolumne	59	30	14	0.0	0	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2	-2.0	-2
55	6111	Ventura	1139	68	482	7.7	55	7.5	1	3.0	10	14.9	43	0.0	0	0.0	0	6.6	1	0.0	0
56	6113	Yolo	310	40	75	5.9	11	-1.0	-1	-1.0	-1	12.4	7	-1.0	-1	0.0	0	0.0	0	0.0	0
57	6115	Yuba	101	30	19	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1	-1.0	-1

	date	area	area_type	population	cases	cumulative_cases	total_tests	cumulative_total_tests	reported_tests
0	2020-02-01	Alameda	County	1685886.0	3.0	3.0	4.0	4	NaN
1	2020-02-02	Alameda	County	1685886.0	0.0	3.0	1.0	5	NaN
2	2020-02-03	Alameda	County	1685886.0	0.0	3.0	0.0	5	NaN
3	2020-02-04	Alameda	County	1685886.0	0.0	3.0	0.0	5	NaN
4	2020-02-05	Alameda	County	1685886.0	0.0	3.0	1.0	6	NaN
5	2020-02-06	Alameda	County	1685886.0	1.0	4.0	0.0	6	NaN
6	2020-02-07	Alameda	County	1685886.0	0.0	4.0	0.0	6	NaN
7	2020-02-08	Alameda	County	1685886.0	0.0	4.0	0.0	6	NaN
8	2020-02-09	Alameda	County	1685886.0	1.0	5.0	1.0	7	NaN
9	2020-02-10	Alameda	County	1685886.0	0.0	5.0	0.0	7	NaN

			cases	deaths	total_tests	positive_tests	reported_cases	reported_deaths	reported_tests
date	County	population
2021-01-31	Alameda	1685886.0	18375.0	323.0	336612.0	22026.0	20461.0	278.0	249461.0
	Alpine	1117.0	4.0	0.0	194.0	0.0	4.0	0.0	84.0
	Amador	38531.0	559.0	12.0	16254.0	686.0	699.0	8.0	6509.0
	Butte	217769.0	2373.0	40.0	25887.0	2717.0	2626.0	35.0	21739.0
	Calaveras	44289.0	537.0	13.0	5290.0	596.0	592.0	0.0	3723.0
...	...	...	...	...	...	...	...	...	...
2021-12-31	Tulare	484423.0	3044.0	58.0	58361.0	3187.0	2041.0	61.0	59517.0
	Tuolumne	52351.0	582.0	6.0	8489.0	616.0	401.0	0.0	8236.0
	Ventura	852747.0	16693.0	33.0	180196.0	19430.0	8391.0	20.0	161586.0
	Yolo	223612.0	2500.0	4.0	114002.0	2491.0	1371.0	10.0	110767.0
	Yuba	79290.0	660.0	5.0	8384.0	728.0	446.0	10.0	8002.0

	county	county_type	demographic_category	demographic_value	est_population	est_age_12plus_pop	est_age_5plus_pop	administered_date	partially_vaccinated	total_partially_vaccinated	fully_vaccinated	cumulative_fully_vaccinated	at_least_one_dose	cumulative_at_least_one_dose	cumulative_unvax_total_pop	cumulative_unvax_12plus_pop	cumulative_unvax_5plus_pop	suppress_data	cumulative_booster_recip_count	booster_eligible_population
0	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-17	3.0	6287.0	2.0	103277.0	3.0	109564.0	11381.0	11381.0	11381.0	False	59041	102155
1	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-16	0.0	6286.0	0.0	103275.0	0.0	109561.0	11384.0	11384.0	11384.0	False	59041	102155
2	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-15	4.0	6286.0	1.0	103275.0	4.0	109561.0	11384.0	11384.0	11384.0	False	59041	102155
3	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-14	9.0	6283.0	2.0	103274.0	9.0	109557.0	11388.0	11388.0	11388.0	False	59041	102155
4	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-13	2.0	6276.0	1.0	103272.0	2.0	109548.0	11397.0	11397.0	11397.0	False	59041	102155
5	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-12	5.0	6275.0	3.0	103271.0	5.0	109546.0	11399.0	11399.0	11399.0	False	59041	102155
6	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-11	7.0	6273.0	4.0	103268.0	7.0	109541.0	11404.0	11404.0	11404.0	False	59041	102155
7	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-10	4.0	6270.0	2.0	103264.0	4.0	109534.0	11411.0	11411.0	11411.0	False	59041	102155
8	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-09	2.0	6268.0	1.0	103262.0	2.0	109530.0	11415.0	11415.0	11415.0	False	59041	102155
9	Alameda	MIXED	Age Group	12-17	120945.0	120945.0	120945.0	2022-10-08	2.0	6267.0	0.0	103261.0	2.0	109528.0	11417.0	11417.0	11417.0	False	59041	102155

	county	administered_date	partially_vaccinated	fully_vaccinated	booster_recip_count	date
290	Alameda	2021-12-31	45.0	55.0	144	2021-12-31
291	Alameda	2021-12-30	181.0	137.0	539	2021-12-30
292	Alameda	2021-12-29	167.0	218.0	510	2021-12-29
293	Alameda	2021-12-28	205.0	190.0	464	2021-12-28
294	Alameda	2021-12-27	111.0	134.0	424	2021-12-27
295	Alameda	2021-12-26	38.0	34.0	181	2021-12-26
296	Alameda	2021-12-25	1.0	1.0	0	2021-12-25
297	Alameda	2021-12-24	24.0	52.0	192	2021-12-24
298	Alameda	2021-12-23	130.0	139.0	661	2021-12-23
299	Alameda	2021-12-22	168.0	167.0	565	2021-12-22

	date	county	partially_vaccinated	fully_vaccinated	booster_recip_count
0	2021-12-31	Alameda	3777616.0	3903944.0	1794082
1	2021-12-31	Alpine	1953.0	1746.0	665
2	2021-12-31	Amador	64856.0	60826.0	23347
3	2021-12-31	Butte	326051.0	333391.0	128099
4	2021-12-31	Calaveras	74861.0	69925.0	26732
5	2021-12-31	Colusa	36881.0	36354.0	8935
6	2021-12-31	Contra Costa	2659318.0	2718869.0	1218085
7	2021-12-31	Del Norte	36867.0	37096.0	13630
8	2021-12-31	El Dorado	332572.0	336526.0	145605
9	2021-12-31	Fresno	1752383.0	1709869.0	561323
10	2021-12-31	Glenn	42278.0	43653.0	13967
11	2021-12-31	Humboldt	248241.0	256882.0	107666
12	2021-12-31	Imperial	523794.0	453346.0	120293
13	2021-12-31	Inyo	31900.0	32815.0	15545
14	2021-12-31	Kern	1371125.0	1344753.0	385137
15	2021-12-31	Kings	195768.0	191330.0	54654
16	2021-12-31	Lake	100938.0	100824.0	36015
17	2021-12-31	Lassen	22235.0	24596.0	7849
18	2021-12-31	Los Angeles	21122804.0	20920505.0	8057173
19	2021-12-31	Madera	246508.0	238451.0	75904
20	2021-12-31	Marin	645880.0	656920.0	355216
21	2021-12-31	Mariposa	28478.0	21710.0	5766
22	2021-12-31	Mendocino	174319.0	170898.0	72501
23	2021-12-31	Merced	460622.0	403335.0	96301
24	2021-12-31	Modoc	10180.0	11201.0	4346
25	2021-12-31	Mono	25687.0	25534.0	10491
26	2021-12-31	Monterey	880348.0	870430.0	302930
27	2021-12-31	Napa	314734.0	310243.0	143712
28	2021-12-31	Nevada	183325.0	178874.0	77952
29	2021-12-31	Orange	6645164.0	6617632.0	2772794
30	2021-12-31	Placer	764677.0	772712.0	338400
31	2021-12-31	Plumas	28951.0	30855.0	13117
32	2021-12-31	Riverside	4238465.0	4105594.0	1362541
33	2021-12-31	Sacramento	2990218.0	2966984.0	1165767
34	2021-12-31	San Benito	124737.0	124546.0	42121
35	2021-12-31	San Bernardino	3525915.0	3476473.0	1065249
36	2021-12-31	San Diego	6988308.0	7016623.0	2672215
37	2021-12-31	San Francisco	2089662.0	2144298.0	1177199
38	2021-12-31	San Joaquin	1456593.0	1320639.0	371591
39	2021-12-31	San Luis Obispo	510206.0	517929.0	239369
40	2021-12-31	San Mateo	1815337.0	1839145.0	929376
41	2021-12-31	Santa Barbara	879394.0	872777.0	339398
42	2021-12-31	Santa Clara	4645203.0	4755576.0	2340830
43	2021-12-31	Santa Cruz	603595.0	602284.0	270602
44	2021-12-31	Shasta	233655.0	235476.0	87089
45	2021-12-31	Sierra	4325.0	4488.0	1802
46	2021-12-31	Siskiyou	62186.0	60676.0	24271
47	2021-12-31	Solano	888702.0	856535.0	329696
48	2021-12-31	Sonoma	1097284.0	1111186.0	515953
49	2021-12-31	Stanislaus	963030.0	885788.0	230770
50	2021-12-31	Statewide	80966936.0	80161357.0	31686096
51	2021-12-31	Sutter	163389.0	164552.0	51596
52	2021-12-31	Tehama	82520.0	82476.0	27806
53	2021-12-31	Trinity	16861.0	17210.0	7378
54	2021-12-31	Tulare	736805.0	699230.0	207671
55	2021-12-31	Tuolumne	87200.0	82434.0	33788
56	2021-12-31	Ventura	1729287.0	1744257.0	686694
57	2021-12-31	Yolo	447676.0	445754.0	192169
58	2021-12-31	Yuba	105863.0	108672.0	30518

	County	population	cases	deaths	total_tests	positive_tests	reported_cases	reported_deaths	reported_tests	partially_vaccinated	fully_vaccinated	boosted
0	Alameda	1685886.0	88478.0	887.0	3625096.0	110982.0	79968.0	949.0	3421039.0	3777616.0	3903944.0	1794082
1	Alpine	1117.0	42.0	0.0	1256.0	32.0	45.0	0.0	1114.0	1953.0	1746.0	665
2	Amador	38531.0	3326.0	46.0	119657.0	3850.0	3400.0	46.0	109341.0	64856.0	60826.0	23347
3	Butte	217769.0	15031.0	231.0	240176.0	17085.0	14880.0	236.0	232849.0	326051.0	333391.0	128099
4	Calaveras	44289.0	3540.0	64.0	55562.0	4143.0	3482.0	72.0	54116.0	74861.0	69925.0	26732
5	Colusa	22593.0	1313.0	12.0	18847.0	1291.0	1345.0	14.0	18367.0	36881.0	36354.0	8935
6	Contra Costa	1160099.0	74071.0	634.0	2040461.0	87392.0	68181.0	722.0	1941843.0	2659318.0	2718869.0	1218085
7	Del Norte	27558.0	3094.0	40.0	120105.0	3545.0	3083.0	41.0	112826.0	36867.0	37096.0	13630
8	El Dorado	193098.0	11837.0	130.0	225776.0	13670.0	11789.0	145.0	221964.0	332572.0	336526.0	145605
9	Fresno	1032227.0	81519.0	1454.0	1407947.0	103136.0	82443.0	1662.0	1361232.0	1752383.0	1709869.0	561323
10	Glenn	29348.0	2024.0	22.0	24602.0	2218.0	2143.0	30.0	22735.0	42278.0	43653.0	13967
11	Humboldt	134098.0	9069.0	99.0	185188.0	10926.0	8598.0	98.0	174099.0	248241.0	256882.0	107666
12	Imperial	191649.0	15042.0	314.0	292679.0	20494.0	14957.0	382.0	269580.0	523794.0	453346.0	120293
13	Inyo	18453.0	2162.0	23.0	20563.0	2001.0	2120.0	29.0	18796.0	31900.0	32815.0	15545
14	Kern	927251.0	69264.0	1318.0	1287274.0	81319.0	70552.0	1308.0	1262587.0	1371125.0	1344753.0	385137
15	Kings	156444.0	18067.0	257.0	383526.0	21848.0	17960.0	278.0	355495.0	195768.0	191330.0	54654
16	Lake	64871.0	5121.0	85.0	80290.0	6172.0	5172.0	93.0	77156.0	100938.0	100824.0	36015
17	Lassen	30065.0	2563.0	43.0	148684.0	3378.0	2712.0	54.0	137275.0	22235.0	24596.0	7849
18	Los Angeles	10257557.0	896443.0	14813.0	34154701.0	1158760.0	820545.0	16856.0	35212183.0	21122804.0	20920505.0	8057173
19	Madera	160089.0	13950.0	178.0	299144.0	16484.0	14250.0	254.0	283661.0	246508.0	238451.0	75904
20	Marin	260800.0	11834.0	97.0	521158.0	13806.0	10654.0	101.0	482077.0	645880.0	656920.0	355216
21	Mariposa	17795.0	1314.0	19.0	26423.0	1564.0	1273.0	3.0	24653.0	28478.0	21710.0	5766
22	Mendocino	88439.0	6140.0	75.0	114111.0	7202.0	6022.0	77.0	104353.0	174319.0	170898.0	72501
23	Merced	287420.0	25371.0	422.0	408417.0	31783.0	25651.0	465.0	387631.0	460622.0	403335.0	96301
24	Modoc	9475.0	341.0	6.0	4226.0	187.0	360.0	6.0	3737.0	10180.0	11201.0	4346
25	Mono	13961.0	1324.0	1.0	21364.0	1602.0	1217.0	1.0	20695.0	25687.0	25534.0	10491
26	Monterey	448732.0	22995.0	377.0	591842.0	26639.0	21786.0	452.0	519254.0	880348.0	870430.0	302930
27	Napa	139652.0	9298.0	78.0	268230.0	10223.0	8540.0	79.0	243740.0	314734.0	310243.0	143712
28	Nevada	98710.0	7834.0	55.0	133055.0	9032.0	7636.0	95.0	126607.0	183325.0	178874.0	77952
29	Orange	3228519.0	191419.0	3281.0	4218004.0	243395.0	174086.0	3997.0	4061425.0	6645164.0	6617632.0	2772794
30	Placer	400434.0	27283.0	327.0	528098.0	30702.0	26870.0	356.0	513462.0	764677.0	772712.0	338400
31	Plumas	18997.0	1568.0	8.0	28957.0	1591.0	1582.0	11.0	26557.0	28951.0	30855.0	13117
32	Riverside	2468145.0	216175.0	3150.0	3340252.0	278870.0	208450.0	3511.0	3177348.0	4238465.0	4105594.0	1362541
33	Sacramento	1567975.0	112565.0	1458.0	2538054.0	134541.0	106606.0	1600.0	2429617.0	2990218.0	2966984.0	1165767
34	San Benito	64022.0	4738.0	55.0	94024.0	6062.0	4547.0	51.0	87026.0	124737.0	124546.0	42121
35	San Bernardino	2217398.0	194632.0	3693.0	3408407.0	239831.0	182627.0	4461.0	3273382.0	3525915.0	3476473.0	1065249
36	San Diego	3370418.0	291421.0	2674.0	6735412.0	384075.0	267016.0	2877.0	6583538.0	6988308.0	7016623.0	2672215
37	San Francisco	892280.0	47010.0	430.0	2153149.0	58717.0	40353.0	495.0	1984056.0	2089662.0	2144298.0	1177199
38	San Joaquin	782545.0	61585.0	1135.0	1233392.0	73338.0	60000.0	1245.0	1193147.0	1456593.0	1320639.0	371591
39	San Luis Obispo	278862.0	21610.0	261.0	537932.0	25116.0	21182.0	271.0	507570.0	510206.0	517929.0	239369
40	San Mateo	778001.0	41680.0	338.0	2232098.0	53689.0	35463.0	387.0	2017530.0	1815337.0	1839145.0	929376
41	Santa Barbara	456373.0	32944.0	392.0	724964.0	40798.0	31796.0	404.0	683898.0	879394.0	872777.0	339398
42	Santa Clara	1967585.0	100671.0	1084.0	5121735.0	121951.0	92643.0	1289.0	4879392.0	4645203.0	4755576.0	2340830
43	Santa Cruz	273999.0	14067.0	123.0	640017.0	16662.0	13834.0	144.0	608319.0	603595.0	602284.0	270602
44	Shasta	177925.0	14865.0	341.0	273979.0	15806.0	15059.0	363.0	260557.0	233655.0	235476.0	87089
45	Sierra	3115.0	180.0	3.0	3179.0	195.0	180.0	0.0	3029.0	4325.0	4488.0	1802
46	Siskiyou	43956.0	2494.0	52.0	30583.0	2762.0	2530.0	58.0	28919.0	62186.0	60676.0	24271
47	Solano	444255.0	31056.0	254.0	798527.0	34925.0	29564.0	282.0	736040.0	888702.0	856535.0	329696
48	Sonoma	496668.0	28517.0	220.0	856175.0	32473.0	26527.0	221.0	808371.0	1097284.0	1111186.0	515953
49	Stanislaus	562303.0	48674.0	807.0	765079.0	59177.0	49389.0	779.0	734288.0	963030.0	885788.0	230770
50	Sutter	105747.0	8184.0	125.0	118877.0	9729.0	8188.0	141.0	115339.0	163389.0	164552.0	51596
51	Tehama	65885.0	5796.0	125.0	64217.0	6378.0	5865.0	130.0	61227.0	82520.0	82476.0	27806
52	Trinity	13354.0	498.0	15.0	6364.0	672.0	589.0	15.0	6879.0	16861.0	17210.0	7378
53	Tulare	484423.0	37326.0	790.0	647145.0	42952.0	37947.0	706.0	641000.0	736805.0	699230.0	207671
54	Tuolumne	52351.0	5321.0	123.0	109808.0	6254.0	5135.0	78.0	103187.0	87200.0	82434.0	33788
55	Ventura	852747.0	70945.0	847.0	1671607.0	88960.0	65966.0	958.0	1626635.0	1729287.0	1744257.0	686694
56	Yolo	223612.0	13861.0	135.0	1005823.0	15058.0	13286.0	150.0	985222.0	447676.0	445754.0	192169
57	Yuba	79290.0	6984.0	83.0	90173.0	8325.0	6999.0	80.0	88194.0	105863.0	108672.0	30518

	Overall HIV Cases	PrEP Rate	PrEP Users	New Diagnoses Rate	New Diagnoses Cases	New Diagnoses Black Rate	New Diagnoses Black Cases	New Diagnoses White Rate	New Diagnoses White Cases	New Diagnoses Hispanic Rate	New Diagnoses Hispanic Cases	New Diagnoses Asian Rate	New Diagnoses Asian Cases	New Diagnoses American Indian/Alaska Native Rate	New Diagnoses American Indian/Alaska Native Cases	New Diagnoses Multiracial Rate	New Diagnoses Multiracial Cases	New Diagnoses Native Hawaiian/Pacific Islander Rate	New Diagnoses Native Hawaiian/Pacific Islander Cases	COVID Cases	COVID Deaths	Total COVID Tests	Positive COVID Tests	Partially Vaccinated	Fully Vaccinated	Boosted	COVID Cases_Rate	COVID Deaths_Rate	Total COVID Tests_Rate	Positive COVID Tests_Rate	Partially Vaccinated_Rate	Fully Vaccinated_Rate	Boosted_Rate
count	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	5.800000e+01	5.800000e+01	5.800000e+01	5.800000e+01	5.800000e+01	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000	58.000000
mean	2300.603448	74.017241	719.120690	5.520690	66.965517	8.451724	10.655172	2.587931	14.775862	6.048276	32.568966	0.610345	2.672414	2.579310	-0.551724	1.760345	0.706897	1.444828	-0.568966	52353.034483	760.500000	1.496490e+06	6.558221e+04	1.372719e+06	1.364270e+06	5.412361e+05	7.322823	0.100458	172.836819	8.590406	177.230966	174.912079	68.303968
std	7010.663457	117.514985	2255.385812	6.058074	191.102247	17.252859	40.583702	5.260047	38.803166	10.284791	100.788142	3.297244	9.209812	17.710575	1.157317	5.592066	5.412813	7.135879	1.109956	127223.298231	2054.377955	4.581755e+06	1.644255e+05	3.071887e+06	3.051943e+06	1.198178e+06	1.793165	0.048063	88.974333	2.316866	37.576817	37.439916	25.493021
min	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	42.000000	0.000000	1.256000e+03	3.200000e+01	1.953000e+03	1.746000e+03	6.650000e+02	3.598945	0.000000	44.601583	1.973615	73.956428	81.809413	26.106769
25%	56.750000	31.750000	14.500000	0.000000	0.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	-2.000000	3839.500000	55.000000	9.113575e+04	4.622750e+03	7.677575e+04	7.305225e+04	2.700050e+04	5.979265	0.057974	126.346934	6.945495	152.793772	148.794261	48.228354
50%	268.000000	44.000000	71.500000	5.550000	9.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	14008.500000	156.500000	2.959115e+05	1.614500e+04	3.293115e+05	3.349585e+05	1.241960e+05	7.435202	0.097817	150.457430	8.827808	172.550632	167.552679	63.654310
75%	1395.750000	78.000000	342.750000	8.500000	45.750000	16.650000	2.500000	6.075000	13.250000	12.400000	20.500000	0.900000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	45677.500000	583.000000	1.176500e+06	5.746000e+04	1.302665e+06	1.268276e+06	3.817505e+05	8.605224	0.141821	195.245248	10.398319	200.162406	198.141022	81.615725
max	50243.000000	897.000000	15431.000000	22.300000	1382.000000	69.100000	295.000000	17.600000	265.000000	54.500000	724.000000	10.800000	56.000000	130.300000	4.000000	23.000000	37.000000	35.100000	3.000000	896443.000000	14813.000000	3.415470e+07	1.158760e+06	2.112280e+07	2.092050e+07	8.057173e+06	11.716252	0.234953	494.541826	13.965381	273.309018	251.886503	136.202454

	ST	STATE	ST_ABBR	STCNTY	COUNTY	FIPS	LOCATION	RPL_THEMES	AREA_SQMI	E_TOTPOP	M_TOTPOP	E_HU	M_HU	E_HH	M_HH	E_POV150	M_POV150	E_UNEMP	M_UNEMP	E_HBURD	M_HBURD	E_NOHSDP	M_NOHSDP	E_UNINSUR	M_UNINSUR	EP_POV150	MP_POV150	EP_UNEMP	MP_UNEMP	EP_HBURD	MP_HBURD	EP_NOHSDP	MP_NOHSDP	EP_UNINSUR	MP_UNINSUR	EPL_POV150	EPL_UNEMP	EPL_HBURD	EPL_NOHSDP	EPL_UNINSU	SPL_THEME1	RPL_THEME1	E_AGE65	M_AGE65	E_AGE17	M_AGE17	E_DISABL	M_DISABL	E_SNGPNT	M_SNGPNT	E_LIMENG	M_LIMENG	EP_AGE65	MP_AGE65	EP_AGE17	MP_AGE17	EP_DISABL	MP_DISABL	EP_SNGPNT	MP_SNGPNT	EP_LIMENG	MP_LIMENG	EPL_AGE65	EPL_AGE17	EPL_DISABL	EPL_SNGPNT	EPL_LIMENG	SPL_THEME2	RPL_THEME2	E_MINRTY	M_MINRTY	EP_MINRTY	MP_MINRTY	EPL_MINRTY	SPL_THEME3	RPL_THEME3	E_MUNIT	M_MUNIT	E_MOBILE	M_MOBILE	E_CROWD	M_CROWD	E_NOVEH	M_NOVEH	E_GROUPQ	M_GROUPQ	EP_MUNIT	MP_MUNIT	EP_MOBILE	MP_MOBILE	EP_CROWD	MP_CROWD	EP_NOVEH	MP_NOVEH	EP_GROUPQ	MP_GROUPQ	EPL_MUNIT	EPL_MOBILE	EPL_CROWD	EPL_NOVEH	EPL_GROUPQ	SPL_THEME4	RPL_THEME4	SPL_THEMES	E_DAYPOP	E_NOINT	M_NOINT	E_AFAM	M_AFAM	E_HISP	M_HISP	E_ASIAN	M_ASIAN	E_AIAN	M_AIAN	E_NHPI	M_NHPI	E_TWOMORE	M_TWOMORE	E_OTHERRAC	M_OTHERRAC	EP_NOINT	MP_NOINT	EP_AFAM	MP_AFAM	EP_HISP	MP_HISP	EP_ASIAN	MP_ASIAN	EP_AIAN	MP_AIAN	EP_NHPI	MP_NHPI	EP_TWOMORE	MP_TWOMORE	EP_OTHERRA	MP_OTHERRA	F_POV150	F_UNEMP	F_NOHSDP	F_THEME1	F_AGE65	F_AGE17	F_DISABL	F_SNGPNT	F_THEME2	F_MINRTY	F_LIMENG	F_THEME3	F_MUNIT	F_MOBILE	F_NOVEH	F_GROUPQ	F_THEME4	F_TOTAL	SHAPE_STAr	SHAPE_STLe	geometry
0	06	California	CA	06001	Alameda	06001	Alameda County, California	0.3860	737.461520	1661584	0	605767	422	573174	1395	233543	5366	43126	1796	147454	2797	132307	2763	70389	2810	14.3	0.3	4.7	0.2	25.7	0.5	11.2	0.2	4.3	0.2	0.1228	0.1930	0.8947	0.3684	0.1228	1.7017	0.2281	231186	0	341591	0	151635	3012	29079	1353	119494	3061	13.9	0.0	20.6	0.0	9.2	0.2	5.1	0.2	7.6	0.2	0.2632	0.3509	0.1053	0.3509	0.6140	1.6843	0.0702	1153001	6526	69.4	0.4	0.8596	0.8596	0.8596	135019	2273	7291	571	45236	1588	53539	1379	31878	1199	22.3	0.4	1.2	0.1	7.9	0.3	9.3	0.2	1.9	0.1	0.9298	0.0351	0.7544	0.9825	0.4211	3.1229	0.8772	7.3685	1715160	110366	4585	167316	1387	369546	0	515105	2453	4942	249	12924	533	76404	3004	6764	6764	6.8	0.0	10.1	0.1	22.2	0.0	31.0	0.1	0.3	0.1	0.8	0.1	4.6	0.2	0.4	0.1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	2	2	0.198466	2.756594	POLYGON ((-122.34225 37.80556, -122.33412 37.8...
1	06	California	CA	06003	Alpine	06003	Alpine County, California	0.3333	738.340000	1159	172	1794	120	397	79	227	92	45	30	46	20	41	24	88	53	19.6	7.4	10.0	6.4	11.6	4.5	4.7	2.6	7.6	4.5	0.4211	0.9474	0.0000	0.0000	0.6667	2.0352	0.3684	337	62	252	64	182	65	8	8	17	47	29.1	5.2	21.7	4.5	15.7	4.9	2.0	2.0	1.6	4.3	1.0000	0.5088	0.7018	0.0000	0.2105	2.4211	0.5263	564	353	48.7	29.6	0.4912	0.4912	0.4912	450	100	21	19	15	10	13	8	35	18	25.1	5.3	1.2	1.1	3.8	2.4	3.3	2.1	3.0	1.5	0.9649	0.0351	0.2807	0.0526	0.7018	2.0351	0.1930	6.9826	1299	114	245	10	23	183	111	6	9	333	41	0	13	32	23	0	0	10.1	1.6	0.9	2.0	15.8	7.9	0.5	0.8	28.7	4.2	0.0	3.4	2.8	2.1	0.0	3.4	0	1	0	1	1	0	0	0	1	0	0	0	1	0	0	0	1	3	0.199004	2.156769	POLYGON ((-120.07239 38.70277, -120.06762 38.7...
2	06	California	CA	06005	Amador	06005	Amador County, California	0.1754	594.583866	39023	0	18246	170	14844	451	5416	776	853	197	4398	586	2711	314	1817	421	15.3	2.2	5.6	1.3	29.6	3.8	8.8	1.0	5.1	1.2	0.1754	0.3333	0.2105	0.1579	0.2105	1.0876	0.0877	10537	98	5962	67	6336	679	493	144	418	131	27.0	0.3	15.3	0.2	17.9	1.9	3.3	1.0	1.1	0.3	0.8596	0.0175	0.8596	0.0526	0.1053	1.8946	0.1579	9062	436	23.2	1.1	0.1754	0.1754	0.1754	908	374	1349	216	381	138	1009	383	3578	380	5.0	2.0	7.4	1.2	2.6	0.9	6.8	2.6	9.2	1.0	0.3860	0.5789	0.1579	0.7895	0.9474	2.8597	0.6667	6.0173	37659	4304	883	867	125	5591	0	352	104	205	70	30	35	1768	244	249	249	12.1	0.1	2.2	0.3	14.3	0.0	0.9	0.3	0.5	0.2	0.1	0.1	4.5	0.6	0.6	0.6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0.161967	2.874887	POLYGON ((-121.02741 38.50354, -121.02747 38.5...
3	06	California	CA	06007	Butte	06007	Butte County, California	0.5965	1636.488963	223344	0	93968	223	83879	885	63024	2406	7687	783	29129	1348	15042	962	13336	1015	28.9	1.1	7.4	0.8	34.7	1.6	10.3	0.7	6.0	0.5	0.8246	0.6667	0.5965	0.3158	0.3684	2.7720	0.5789	40663	32	44865	0	37565	1531	4313	487	5615	637	18.2	0.1	20.1	0.0	17.0	0.7	5.1	0.6	2.7	0.3	0.6140	0.2982	0.8070	0.3509	0.2982	2.3683	0.4386	64420	2000	28.8	0.9	0.2456	0.2456	0.2456	7581	740	11432	727	2905	424	5564	594	6245	543	8.1	0.8	12.2	0.8	3.5	0.5	6.6	0.7	2.8	0.2	0.5789	0.8596	0.2632	0.7368	0.6491	3.0876	0.8596	8.4735	211098	19835	1877	3778	316	37585	0	10350	507	1725	323	487	138	10160	792	335	335	9.1	0.0	1.7	0.1	16.8	0.0	4.6	0.2	0.8	0.1	0.2	0.1	4.5	0.4	0.1	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.455943	4.175462	POLYGON ((-122.06874 39.84222, -122.06694 39.8...
4	06	California	CA	06009	Calaveras	06009	Calaveras County, California	0.0526	1020.018688	45828	0	28096	63	16958	557	9067	1157	949	241	5956	645	3641	530	2424	628	20.0	2.5	5.2	1.3	35.1	3.6	10.3	1.5	5.3	1.4	0.4386	0.2982	0.2456	0.3158	0.2456	1.5438	0.1579	12840	244	7618	170	9146	915	726	242	312	217	28.0	0.5	16.6	0.4	20.1	2.0	4.3	1.4	0.7	0.5	0.9298	0.0877	0.9825	0.2105	0.0351	2.2456	0.3509	9048	453	19.7	1.0	0.0526	0.0526	0.0526	429	188	2108	342	315	136	269	99	514	154	1.5	0.7	7.5	1.2	1.9	0.8	1.6	0.6	1.1	0.3	0.0175	0.6140	0.0526	0.0000	0.1053	0.7894	0.0000	4.6314	35355	5807	1258	450	159	5710	0	864	152	271	146	18	21	1691	284	44	44	12.8	0.1	1.0	0.3	12.5	0.0	1.9	0.3	0.6	0.3	0.0	0.1	3.7	0.6	0.1	0.1	0	0	0	0	1	0	1	0	2	0	0	0	0	0	0	0	0	2	0.276242	2.931103	POLYGON ((-120.99359 38.22558, -120.99161 38.2...
5	06	California	CA	06011	Colusa	06011	Colusa County, California	0.5439	1150.712427	21491	0	8153	58	7329	163	4958	748	425	149	2152	285	3728	276	1815	360	23.3	3.5	4.2	1.5	29.4	3.8	27.2	2.0	8.5	1.7	0.5439	0.0702	0.1228	0.9298	0.8421	2.5088	0.4912	3142	73	5863	59	2861	309	596	157	2821	379	14.6	0.3	27.3	0.3	13.4	1.4	8.1	2.1	14.2	1.9	0.3684	0.8772	0.5789	0.8421	0.9649	3.6315	0.9825	14009	228	65.2	1.1	0.7368	0.7368	0.7368	155	73	612	114	434	130	324	110	202	65	1.9	0.9	7.5	1.4	5.9	1.8	4.4	1.5	0.9	0.3	0.0526	0.6140	0.5614	0.2456	0.0175	1.4911	0.1053	8.3682	22483	2700	495	263	61	12840	0	160	85	149	71	32	35	512	120	53	53	12.7	0.0	1.2	0.3	59.7	0.0	0.7	0.4	0.7	0.3	0.1	0.2	2.4	0.6	0.2	0.3	0	0	1	1	0	0	0	0	1	0	1	0	0	0	0	0	0	2	0.312219	3.171298	POLYGON ((-122.78509 39.38298, -122.73906 39.3...
6	06	California	CA	06013	Contra Costa	06013	Contra Costa County, California	0.2456	716.929062	1147788	0	415067	303	398299	1065	156345	5795	32666	1747	98841	2767	80257	2339	57133	2864	13.7	0.5	5.5	0.3	24.8	0.7	10.1	0.3	5.0	0.3	0.0877	0.3158	0.8421	0.2807	0.1754	1.7017	0.2281	181578	0	260191	0	127957	2672	23103	1256	65454	2634	15.8	0.0	22.7	0.0	11.2	0.2	5.8	0.3	6.0	0.2	0.4912	0.6491	0.2632	0.5088	0.4912	2.4035	0.4912	658653	5096	57.4	0.4	0.6316	0.6316	0.6316	53171	1691	6533	606	19927	1241	21003	1402	9828	752	12.8	0.4	1.6	0.1	5.0	0.3	5.3	0.4	0.9	0.1	0.7719	0.0877	0.4561	0.4912	0.0175	1.8244	0.1228	6.5612	998459	56576	3642	94463	1509	295791	0	196904	2267	2198	370	5269	370	59875	3550	4153	4153	5.0	0.0	8.2	0.1	25.8	0.0	17.2	0.2	0.2	0.1	0.5	0.1	5.2	0.3	0.4	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.201868	2.710243	POLYGON ((-122.42976 37.96541, -122.41859 37.9...
7	06	California	CA	06015	Del Norte	06015	Del Norte County, California	0.7544	1006.227386	27692	0	11349	117	9805	313	6747	948	625	182	2990	410	4008	359	1370	405	27.4	3.8	6.4	1.8	30.5	4.1	20.3	1.8	5.5	1.6	0.7719	0.5088	0.1930	0.8246	0.2632	2.5615	0.5088	4924	103	6003	114	4622	446	613	207	411	164	17.8	0.4	21.7	0.4	18.5	1.8	6.3	2.1	1.6	0.6	0.5789	0.5088	0.8947	0.6140	0.2105	2.8069	0.6842	10764	1934	38.9	7.0	0.4035	0.4035	0.4035	433	178	2220	262	395	131	847	262	2790	289	3.8	1.6	19.6	2.3	4.0	1.3	8.6	2.6	10.1	1.0	0.2632	0.9474	0.3333	0.9474	0.9825	3.4738	0.9649	9.2457	27006	3099	601	723	126	5552	0	851	174	1867	228	50	36	1380	358	341	341	12.4	0.1	2.6	0.5	20.0	0.0	3.1	0.6	6.7	0.8	0.2	0.1	5.0	1.3	1.2	1.0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	3	3	0.284585	2.946211	MULTIPOLYGON (((-124.25599 41.78301, -124.2548...
8	06	California	CA	06017	El Dorado	06017	El Dorado County, California	0.0175	1707.848728	190345	0	91569	158	73078	874	25790	2223	4197	443	20187	1110	8300	724	8174	918	13.7	1.2	4.7	0.5	27.6	1.5	6.0	0.5	4.3	0.5	0.0877	0.1930	0.4737	0.0702	0.1228	0.9474	0.0351	40418	98	37911	42	24758	1123	2508	389	2722	419	21.2	0.1	19.9	0.0	13.1	0.6	3.4	0.5	1.5	0.2	0.7368	0.2632	0.5439	0.0702	0.1579	1.7720	0.1228	43426	1198	22.8	0.6	0.1404	0.1404	0.1404	4353	499	4861	528	1864	331	3117	438	1766	356	4.8	0.5	5.3	0.6	2.6	0.5	4.3	0.6	0.9	0.2	0.3684	0.4211	0.1579	0.1754	0.0175	1.1403	0.0702	4.0001	165238	14889	1283	1439	203	24773	0	9023	470	864	294	439	100	6579	624	309	309	7.9	0.0	0.8	0.1	13.0	0.0	4.7	0.2	0.5	0.2	0.2	0.1	3.5	0.3	0.2	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.479630	3.753053	POLYGON ((-121.14159 38.71194, -121.13451 38.7...
9	06	California	CA	06019	Fresno	06019	Fresno County, California	0.9649	5958.379845	990204	0	333357	328	310097	1237	316814	7716	39697	2052	110585	2808	138814	2658	76302	3248	32.6	0.8	8.9	0.4	35.7	0.9	22.7	0.4	7.8	0.3	0.9298	0.8246	0.8596	0.8421	0.7368	4.1929	0.9474	121129	59	281391	0	127456	2547	27574	1225	99823	3270	12.2	0.1	28.4	0.0	13.0	0.3	8.9	0.4	10.9	0.4	0.0877	0.9298	0.5088	0.9123	0.8596	3.2982	0.8947	706035	5152	71.3	0.5	0.9123	0.9123	0.9123	28072	1469	12429	899	29905	1591	24446	1188	16855	867	8.4	0.4	3.7	0.3	9.6	0.5	7.9	0.4	1.7	0.1	0.5965	0.2456	0.8947	0.9123	0.3509	3.0000	0.7719	11.4034	1028337	134740	4571	43660	1101	528293	0	102986	1291	4459	483	1305	188	23353	1829	1979	1979	13.8	0.0	4.4	0.1	53.4	0.0	10.4	0.1	0.5	0.1	0.1	0.1	2.4	0.2	0.2	0.1	1	0	0	1	0	1	0	1	2	1	0	1	0	0	1	0	1	5	1.571180	8.914543	POLYGON ((-120.90942 36.74770, -120.90724 36.7...

		partially_vaccinated	fully_vaccinated	booster_recip_count
date	county
2021-01-31	Alameda	300759.0	87910.0	0
	Alpine	549.0	26.0	0
	Amador	8222.0	2039.0	0
	Butte	53463.0	14946.0	0
	Calaveras	8783.0	1839.0	0
...	...	...	...	...
2021-12-31	Tulare	30296.0	28187.0	78491
	Tuolumne	2415.0	1578.0	9368
	Ventura	50370.0	54577.0	263047
	Yolo	13747.0	19232.0	76949
	Yuba	3786.0	3834.0	13093

	ST	STATE	ST_ABBR	STCNTY	COUNTY	FIPS	LOCATION	RPL_THEMES	AREA_SQMI	E_TOTPOP	E_HU	M_HU	E_HH	M_HH	E_POV150	M_POV150	E_UNEMP	M_UNEMP	E_HBURD	M_HBURD	E_NOHSDP	M_NOHSDP	E_UNINSUR	M_UNINSUR	EP_POV150	MP_POV150	EP_UNEMP	MP_UNEMP	EP_HBURD	MP_HBURD	EP_NOHSDP	MP_NOHSDP	EP_UNINSUR	MP_UNINSUR	EPL_POV150	EPL_UNEMP	EPL_HBURD	EPL_NOHSDP	EPL_UNINSU	SPL_THEME1	RPL_THEME1	E_AGE65	M_AGE65	E_AGE17	M_AGE17	E_DISABL	M_DISABL	E_SNGPNT	M_SNGPNT	E_LIMENG	M_LIMENG	EP_AGE65	MP_AGE65	EP_AGE17	MP_AGE17	EP_DISABL	MP_DISABL	EP_SNGPNT	MP_SNGPNT	EP_LIMENG	MP_LIMENG	EPL_AGE65	EPL_AGE17	EPL_DISABL	EPL_SNGPNT	EPL_LIMENG	SPL_THEME2	RPL_THEME2	E_MINRTY	M_MINRTY	EP_MINRTY	MP_MINRTY	EPL_MINRTY	SPL_THEME3	RPL_THEME3	E_MUNIT	M_MUNIT	E_MOBILE	M_MOBILE	E_CROWD	M_CROWD	E_NOVEH	M_NOVEH	E_GROUPQ	M_GROUPQ	EP_MUNIT	MP_MUNIT	EP_MOBILE	MP_MOBILE	EP_CROWD	MP_CROWD	EP_NOVEH	MP_NOVEH	EP_GROUPQ	MP_GROUPQ	EPL_MUNIT	EPL_MOBILE	EPL_CROWD	EPL_NOVEH	EPL_GROUPQ	SPL_THEME4	RPL_THEME4	SPL_THEMES	E_DAYPOP	E_NOINT	M_NOINT	E_AFAM	M_AFAM	E_HISP	E_ASIAN	M_ASIAN	E_AIAN	M_AIAN	E_NHPI	M_NHPI	E_TWOMORE	M_TWOMORE	E_OTHERRAC	M_OTHERRAC	EP_NOINT	MP_NOINT	EP_AFAM	MP_AFAM	EP_HISP	EP_ASIAN	MP_ASIAN	EP_AIAN	MP_AIAN	EP_NHPI	MP_NHPI	EP_TWOMORE	MP_TWOMORE	EP_OTHERRA	MP_OTHERRA	F_POV150	F_UNEMP	F_NOHSDP	F_THEME1	F_AGE17	F_DISABL	F_SNGPNT	F_THEME2	F_MINRTY	F_LIMENG	F_THEME3	F_CROWD	F_NOVEH	F_GROUPQ	F_THEME4	F_TOTAL	SHAPE_STAr	SHAPE_STLe	geometry
53	06	California	CA	06107	Tulare	06107	Tulare County, California	0.9298	4823.896942	463955	150079	175	139044	793	164222	5818	20214	1385	49855	2054	76949	2070	35869	2526	35.9	1.3	9.9	0.6	35.9	1.5	28.1	0.8	7.8	0.5	0.9825	0.9298	0.7018	0.9649	0.7368	4.3158	1.0000	52826	45	142777	0	53760	2610	13115	1082	57319	2173	11.4	0.1	30.8	0.0	11.7	0.6	9.4	0.8	13.4	0.5	0.0526	1.0000	0.3509	0.9649	0.9298	3.2982	0.8947	335204	2920	72.2	0.6	0.9298	0.9298	0.9298	8159	882	9096	626	14201	991	7394	669	5595	452	5.4	0.6	6.1	0.4	10.2	0.7	5.3	0.5	1.2	0.1	0.4035	0.4561	0.9298	0.4912	0.1754	2.4560	0.4561	10.9998	466703	73770	3504	5923	395	301919	15857	498	2592	316	528	120	6913	804	1472	1472	16.1	0.0	1.3	0.1	65.1	3.4	0.1	0.6	0.1	0.1	0.1	1.5	0.2	0.3	0.2	1	1	1	3	1	0	1	3	1	1	1	1	0	0	1	8	1.256098	5.346579	POLYGON ((-119.56647 36.49434, -119.56366 36.4...
54	06	California	CA	06109	Tuolumne	06109	Tuolumne County, California	0.2281	2220.910147	54147	31572	121	22937	637	8819	899	1646	308	7519	718	3729	557	3137	462	17.4	1.8	7.3	1.3	32.8	3.0	9.0	1.3	6.1	0.9	0.3333	0.6140	0.2807	0.1930	0.4211	1.8421	0.3333	14109	116	8986	49	10250	750	931	280	262	130	26.1	0.2	16.6	0.1	20.0	1.5	4.1	1.2	0.5	0.3	0.8421	0.0877	0.9649	0.1930	0.0175	2.1052	0.2982	11008	811	20.3	1.5	0.0877	0.0877	0.0877	757	228	2789	377	561	167	1400	362	3422	264	2.4	0.7	8.8	1.2	2.4	0.7	6.1	1.5	6.3	0.5	0.1228	0.7018	0.1053	0.6667	0.9123	2.5089	0.5439	6.5439	52976	6807	1084	979	117	6810	730	118	765	149	146	29	1476	186	102	102	13.4	0.1	1.8	0.2	12.6	1.3	0.2	1.4	0.3	0.3	0.1	2.7	0.3	0.2	0.2	0	0	0	0	0	1	0	1	0	0	0	0	0	1	1	2	0.604459	5.007781	POLYGON ((-120.65324 37.83282, -120.64865 37.8...
55	06	California	CA	06111	Ventura	06111	Ventura County, California	0.4561	1840.788448	845599	289425	380	271639	1129	133661	4348	22114	905	77020	2169	82261	2143	70449	2941	16.0	0.5	5.1	0.2	28.4	0.8	14.4	0.4	8.4	0.4	0.2105	0.2632	0.7895	0.5965	0.8246	2.6843	0.5439	131672	76	193847	0	92514	2501	13366	961	71885	2424	15.6	0.1	22.9	0.0	11.0	0.3	4.9	0.4	9.0	0.3	0.4561	0.6667	0.2456	0.3158	0.7193	2.4035	0.4912	465628	2689	55.1	0.3	0.5614	0.5614	0.5614	31444	1212	11241	652	16793	1007	11671	807	13099	1007	10.9	0.4	3.9	0.2	6.2	0.4	4.3	0.3	1.5	0.1	0.7544	0.2807	0.5965	0.1754	0.2807	2.0877	0.2281	7.7369	800107	71112	4406	14321	611	361648	59761	1174	1847	311	1655	158	24692	1367	1704	1704	8.5	0.0	1.7	0.1	42.8	7.1	0.1	0.2	0.1	0.2	0.1	2.9	0.2	0.2	0.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.471564	3.509455	MULTIPOLYGON (((-119.47784 34.37942, -119.4737...
56	06	California	CA	06113	Yolo	06113	Yolo County, California	0.4737	1014.732376	218774	78565	273	74614	619	54468	2581	5953	647	22623	1179	16124	911	9433	1194	25.8	1.2	5.6	0.6	30.3	1.6	12.5	0.7	4.3	0.5	0.7368	0.3333	0.5088	0.5088	0.1228	2.2105	0.4211	27314	112	45977	0	22154	1172	4260	579	12433	1231	12.5	0.1	21.0	0.0	10.2	0.5	5.7	0.8	6.0	0.6	0.1228	0.4211	0.1930	0.4912	0.4912	1.7193	0.1053	118516	1558	54.2	0.7	0.5263	0.5263	0.5263	12667	839	2908	357	4336	562	5934	586	8103	731	16.1	1.1	3.7	0.5	5.8	0.8	8.0	0.8	3.7	0.3	0.8596	0.2456	0.5439	0.9298	0.7719	3.3508	0.9123	7.8069	234275	16979	2085	5137	386	69341	31237	787	768	216	928	155	10490	982	615	615	8.1	0.0	2.3	0.2	31.7	14.3	0.4	0.4	0.1	0.4	0.1	4.8	0.4	0.3	0.1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0.274403	3.341287	POLYGON ((-122.42149 38.90233, -122.42190 38.9...
57	06	California	CA	06115	Yuba	06115	Yuba County, California	0.6667	632.005678	77524	28632	122	26434	401	18954	1551	2257	423	8883	725	8724	757	5344	739	25.0	2.0	7.1	1.2	33.6	2.7	17.8	1.5	7.1	1.0	0.6667	0.5789	0.3333	0.6667	0.5789	2.8245	0.5965	9588	150	21355	0	11451	804	1898	360	3228	484	12.4	0.2	27.5	0.0	15.3	1.1	7.2	1.4	4.5	0.7	0.1053	0.9123	0.6667	0.7368	0.4211	2.8422	0.7018	35675	928	46.0	1.2	0.4561	0.4561	0.4561	1932	347	2677	417	1219	244	1757	329	1471	349	6.7	1.2	9.3	1.5	4.6	0.9	6.6	1.2	1.9	0.5	0.4737	0.7368	0.4386	0.7368	0.4211	2.8070	0.6140	8.9298	74261	8149	1134	2533	311	22318	5134	314	596	196	296	54	4664	543	134	134	10.7	0.1	3.3	0.4	28.8	6.6	0.4	0.8	0.3	0.4	0.1	6.0	0.7	0.2	0.2	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0.174089	2.803283	POLYGON ((-121.63631 39.24941, -121.63581 39.2...

Preprocessing, Spatial Analysis Exportation and Visualization: Intersection of HIV and COVID-19 Epidemics in California¶

Data Pre-Processing¶

HIV Data¶

Load HIV Data Sets¶

Current HIV Data (Invididuals Living with HIV)¶

PrEP Users Data¶

Combined HIV Data:¶

COVID Data¶

Load COVID Data Sets¶

COVID Cases, Tests, and Deaths Data¶

Vaccinations Data¶

Combined COVID & Vaccine Data¶

Feature Engineering¶

Exploratory Data Analysis (EDA)¶

Merge HIV and COVID Datasets + Check Data Quality¶

Summary Statistics and Feature Correlations¶

Visualize Social Vulnerability Index (Spatial Data)¶

Exploratory Spatial Data Analysis (ESDA)¶

Get Latitude and Longitude for Counties from Geocoder¶

Create Geographic Data using Latitude and Longtidue Points¶

Spatial Join¶

Visualize HIV and COVID Spatially by County¶

HIV and PrEP¶

COVID and Vaccines¶

Spatial Clusters, Spatial Outliers, and Local Indicator of Spatial Autocorrelation (LISA)¶

Construct Spatial Weights and Calculate Local Moran's I¶

Local Moran's I for New Diagnoses Rate¶

Local Moran's I for PrEP Rate¶

Local Moran's I for COVID Cases Rate¶

Local Moran's I for Total COVID Tests Rate¶

Local Moran's I for Fully Vaccinated Rate¶

Local Moran's I for EP_MINRTY¶

Local Moran's I for EP_UNINSUR¶

Statistically Signifciant Spatial Clusters/Outliers¶

Significant Clusters for New Diagnoses Rate¶

Significant Clusters for PrEP Rate¶

Significant Clusters for COVID Cases Rate¶

Significant Clusters for Total COVID Tests Rate¶

Significant Clusters for Fully Vaccinated Rate¶

Significant Clusters for EP_MINTRY¶

Significant Clusters for EP_UNINSUR¶

Interactive LISA (Local Indicator of Spatial Autocorrelation) Map¶

Clustering Analysis¶

Agglomerative Non-Spatial Clustering¶

Agglomerative Clustering with Spatial Contraints¶

Plot and View Cluster Maps¶

6-Clusters¶

7-Clusters¶

Regionalization with Features¶