

In [2]:

    
library(dplyr)
library(ggplot2)
library(ggmap)
library(data.table)
library(maptools)
library(rgdal)
options(jupyter.plot_mimetypes = 'image/png')

Census tract data

To map data onto map at the census tract level, need to get California shapefile from census.gov. I chose cartographic boundary shapefiles for small scale mapping projects, at the 500k scale. The function readOGR() from the package rgdal can read shapefile into SpatialPolygonsDataFrame. GEOID has the unique ID for each county and will be used to join the ACS census data.



In [3]:

    
ca_shp = readOGR(dsn="./cb_2014_06_tract_500k", layer ="cb_2014_06_tract_500k")









    



OGR data source with driver: ESRI Shapefile 
Source: "./cb_2014_06_tract_500k", layer: "cb_2014_06_tract_500k"
with 8043 features
It has 9 fields






    



Warning message:
In readOGR(dsn = "./cb_2014_06_tract_500k", layer = "cb_2014_06_tract_500k"): Z-dimension discarded

Insurace coverage data from ACS(American community survey)

Health insurance coverage data is from ACS 'ACS_14_5yr_S2701', 5-year estimate 2010-2014 for all census tracts in California. The column 'HC03_EST_VC01' is percent uninsured. GEO.id2 is the unique ID for each census tract and will be used to join census tract data.



In [4]:

    
ca_data = read.csv('ACS_14_5YR_S2701/ACS_14_5YR_S2701_with_ann.csv')



In [5]:

    
head(ca_data)









    Out[5]:





GEO.id GEO.id2 GEO.display.label HC01_EST_VC01 HC01_MOE_VC01 HC02_EST_VC01 HC02_MOE_VC01 HC03_EST_VC01 HC03_MOE_VC01 HC04_EST_VC01 ellip.h HC01_EST_VC95 HC01_MOE_VC95 HC02_EST_VC95 HC02_MOE_VC95 HC03_EST_VC95 HC03_MOE_VC95 HC04_EST_VC95 HC04_MOE_VC95 HC05_EST_VC95 HC05_MOE_VC95

	1 Id Id2 Geography Total; Estimate; Total civilian noninstitutionalized population Total; Margin of Error; Total civilian noninstitutionalized population Number Uninsured; Estimate; Total civilian noninstitutionalized population Number Uninsured; Margin of Error; Total civilian noninstitutionalized population Percent Uninsured; Estimate; Total civilian noninstitutionalized population Percent Uninsured; Margin of Error; Total civilian noninstitutionalized population Number Insured by  Coverage Type; Estimate; Total civilian noninstitutionalized population ⋯ Total; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care Total; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care Number Uninsured; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care Number Uninsured; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care Percent Uninsured; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care Percent Uninsured; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care Number Insured by  Coverage Type; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care Number Insured by  Coverage Type; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care Percent Insured by  Coverage Type; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care Percent Insured by  Coverage Type; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care
	2 1400000US06001400100 06001400100 Census Tract 4001, Alameda County, California 3385 545 114 63 3.4 1.9 3271 ⋯ 14.4 (X) (X) (X) (X) (X) (X) (X) (X) (X)
	3 1400000US06001400200 06001400200 Census Tract 4002, Alameda County, California 1939 147 105 45 5.4 2.2 1834 ⋯ 8.0 (X) (X) (X) (X) (X) (X) (X) (X) (X)
	4 1400000US06001400300 06001400300 Census Tract 4003, Alameda County, California 5428 466 404 203 7.4 3.8 5024 ⋯ 8.5 (X) (X) (X) (X) (X) (X) (X) (X) (X)
	5 1400000US06001400400 06001400400 Census Tract 4004, Alameda County, California 4274 326 292 100 6.8 2.3 3982 ⋯ 8.5 (X) (X) (X) (X) (X) (X) (X) (X) (X)
	6 1400000US06001400500 06001400500 Census Tract 4005, Alameda County, California 3516 282 555 251 15.8 6.6 2961 ⋯ 9.2 (X) (X) (X) (X) (X) (X) (X) (X) (X)



In [6]:

    
ca_data = select(ca_data, GEO.id2, GEO.display.label, HC03_EST_VC01) %>% slice(-1) 
head(ca_data)









    Out[6]:





GEO.id2 GEO.display.label HC03_EST_VC01

	1 06001400100 Census Tract 4001, Alameda County, California 3.4
	2 06001400200 Census Tract 4002, Alameda County, California 5.4
	3 06001400300 Census Tract 4003, Alameda County, California 7.4
	4 06001400400 Census Tract 4004, Alameda County, California 6.8
	5 06001400500 Census Tract 4005, Alameda County, California 15.8
	6 06001400600 Census Tract 4006, Alameda County, California 22.1



In [7]:

    
str(ca_data)









    



'data.frame':	8057 obs. of  3 variables:
 $ GEO.id2          : Factor w/ 8058 levels "06001400100",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ GEO.display.label: Factor w/ 8058 levels "Census Tract 1, Colusa County, California",..: 3838 3839 3844 3847 3851 3853 3857 3858 3860 3867 ...
 $ HC03_EST_VC01    : Factor w/ 477 levels "-","0.0","0.1",..: 234 411 451 441 78 151 110 103 414 26 ...



In [8]:

    
#convert data types
ca_data$GEO.id2 = as.character(ca_data$GEO.id2)
ca_data$GEO.display.label = as.character(ca_data$GEO.display.label)
ca_data$percent = as.numeric(as.character(ca_data$HC03_EST_VC01))
str(ca_data)









    



Warning message:
In eval(expr, envir, enclos): NAs introduced by coercion





    



'data.frame':	8057 obs. of  4 variables:
 $ GEO.id2          : chr  "06001400100" "06001400200" "06001400300" "06001400400" ...
 $ GEO.display.label: chr  "Census Tract 4001, Alameda County, California" "Census Tract 4002, Alameda County, California" "Census Tract 4003, Alameda County, California" "Census Tract 4004, Alameda County, California" ...
 $ HC03_EST_VC01    : Factor w/ 477 levels "-","0.0","0.1",..: 234 411 451 441 78 151 110 103 414 26 ...
 $ percent          : num  3.4 5.4 7.4 6.8 15.8 22.1 19 18.3 5.7 10.6 ...



In [9]:

    
summary(ca_data$percent)









    Out[9]:





   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00    9.10   15.20   16.68   22.85   65.50      62

Merge census tract data with ACS insurance coverage data

To use census tract data, need to convert the SpatialPolygonsDataFrame back into data frame with fortify(). Join census tract data with insurace coverage data based on id(unique id for each census tract). Select three counties in the bay area to map, San Francisco, Alameda, and San Mateo.



In [10]:

    
ca_tract<-fortify(ca_shp,region = "GEOID") 
str(ca_tract)









    



'data.frame':	330321 obs. of  7 variables:
 $ long : num  -122 -122 -122 -122 -122 ...
 $ lat  : num  37.9 37.9 37.9 37.9 37.9 ...
 $ order: int  1 2 3 4 5 6 7 8 9 10 ...
 $ hole : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ piece: Factor w/ 6 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ id   : chr  "06001400100" "06001400100" "06001400100" "06001400100" ...
 $ group: Factor w/ 8112 levels "06001400100.1",..: 1 1 1 1 1 1 1 1 1 1 ...



In [11]:

    
ca_data$id = ca_data$GEO.id2
ca_tract2 = left_join(ca_tract,ca_data, by=c('id'))
dim(ca_tract2)









    Out[11]:





	330321
	11



In [12]:

    
ca_tract_sf = ca_tract2[grep('San Francisco',ca_tract2$GEO.display.label),]
dim(ca_tract_sf)









    Out[12]:





	2980
	11



In [13]:

    
basemap <-get_map('San Francisco', zoom=12) 
ggmap(basemap) +
geom_polygon(data = ca_tract_sf , aes(x=long, y=lat, group = group, fill=percent)) +
scale_fill_gradient(low = "#ffffcc", high = "#ff4444") +
ggtitle('San Francisco Percent Uninsured\n ACS 2010-2014 data')









    



Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=San+Francisco&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=San%20Francisco&sensor=false



In [15]:

    
ca_tract_alameda = ca_tract2[grep('Alameda',ca_tract2$GEO.display.label),]
dim(ca_tract_alameda)









    Out[15]:





	8398
	11



In [16]:

    
basemap <-get_map('Hayward', zoom=10) 
ggmap(basemap) +
geom_polygon(data = ca_tract_alameda , aes(x=long, y=lat, group = group, fill=percent)) +
scale_fill_gradient(low = "#ffffcc", high = "#ff4444") +
ggtitle('Alameda Percent Uninsured\n ACS 2010-2014 data')









    



Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Hayward&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Hayward&sensor=false



In [17]:

    
ca_tract_sanmateo = ca_tract2[grep('San Mateo',ca_tract2$GEO.display.label),]
dim(ca_tract_sanmateo)









    Out[17]:





	5235
	11



In [18]:

    
basemap <-get_map('San Mateo', zoom=10) 
ggmap(basemap) +
geom_polygon(data = ca_tract_sanmateo , aes(x=long, y=lat, group = group, fill=percent)) +
scale_fill_gradient(low = "#ffffcc", high = "#ff4444") +
ggtitle('San Mateo Percent Uninsured\n ACS 2010-2014 data')









    



Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=San+Mateo&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=San%20Mateo&sensor=false



In [ ]:

	GEO.id	GEO.id2	GEO.display.label	HC01_EST_VC01	HC01_MOE_VC01	HC02_EST_VC01	HC02_MOE_VC01	HC03_EST_VC01	HC03_MOE_VC01	HC04_EST_VC01	ellip.h	HC01_EST_VC95	HC01_MOE_VC95	HC02_EST_VC95	HC02_MOE_VC95	HC03_EST_VC95	HC03_MOE_VC95	HC04_EST_VC95	HC04_MOE_VC95	HC05_EST_VC95	HC05_MOE_VC95
1	Id	Id2	Geography	Total; Estimate; Total civilian noninstitutionalized population	Total; Margin of Error; Total civilian noninstitutionalized population	Number Uninsured; Estimate; Total civilian noninstitutionalized population	Number Uninsured; Margin of Error; Total civilian noninstitutionalized population	Percent Uninsured; Estimate; Total civilian noninstitutionalized population	Percent Uninsured; Margin of Error; Total civilian noninstitutionalized population	Number Insured by Coverage Type; Estimate; Total civilian noninstitutionalized population	⋯	Total; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care	Total; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care	Number Uninsured; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care	Number Uninsured; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care	Percent Uninsured; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care	Percent Uninsured; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care	Number Insured by Coverage Type; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care	Number Insured by Coverage Type; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care	Percent Insured by Coverage Type; Estimate; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care	Percent Insured by Coverage Type; Margin of Error; PERCENT IMPUTED - Health insurance coverage - Public coverage - VA Health Care
2	1400000US06001400100	06001400100	Census Tract 4001, Alameda County, California	3385	545	114	63	3.4	1.9	3271	⋯	14.4	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)
3	1400000US06001400200	06001400200	Census Tract 4002, Alameda County, California	1939	147	105	45	5.4	2.2	1834	⋯	8.0	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)
4	1400000US06001400300	06001400300	Census Tract 4003, Alameda County, California	5428	466	404	203	7.4	3.8	5024	⋯	8.5	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)
5	1400000US06001400400	06001400400	Census Tract 4004, Alameda County, California	4274	326	292	100	6.8	2.3	3982	⋯	8.5	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)
6	1400000US06001400500	06001400500	Census Tract 4005, Alameda County, California	3516	282	555	251	15.8	6.6	2961	⋯	9.2	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)	(X)