

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

Poverty data from ACS(American community survey)

Poverty data is from ACS 'ACS_14_5YR_B17001', 5-year estimate 2010-2014, for all census tracts in California. The first column HD01_VD01 is the total number of people in the census tract. The column HD02_VD01 is the number of people whose income in the past 12 months were below poverty level. 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_B17001/ACS_14_5YR_B17001_with_ann.csv')



In [5]:

    
head(ca_data)









    Out[5]:





GEO.id GEO.id2 GEO.display.label HD01_VD01 HD02_VD01 HD01_VD02 HD02_VD02 HD01_VD03 HD02_VD03 HD01_VD04 ellip.h HD01_VD55 HD02_VD55 HD01_VD56 HD02_VD56 HD01_VD57 HD02_VD57 HD01_VD58 HD02_VD58 HD01_VD59 HD02_VD59

	1 Id Id2 Geography Estimate; Total: Margin of Error; Total: Estimate; Income in the past 12 months below poverty level: Margin of Error; Income in the past 12 months below poverty level: Estimate; Income in the past 12 months below poverty level: - Male: Margin of Error; Income in the past 12 months below poverty level: - Male: Estimate; Income in the past 12 months below poverty level: - Male: - Under 5 years ⋯ Estimate; Income in the past 12 months at or above poverty level: - Female: - 35 to 44 years Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 35 to 44 years Estimate; Income in the past 12 months at or above poverty level: - Female: - 45 to 54 years Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 45 to 54 years Estimate; Income in the past 12 months at or above poverty level: - Female: - 55 to 64 years Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 55 to 64 years Estimate; Income in the past 12 months at or above poverty level: - Female: - 65 to 74 years Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 65 to 74 years Estimate; Income in the past 12 months at or above poverty level: - Female: - 75 years and over Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 75 years and over
	2 1400000US06001400100 06001400100 Census Tract 4001, Alameda County, California 2752 174 137 89 83 51 0 ⋯ 153 59 221 67 338 80 198 56 86 41
	3 1400000US06001400200 06001400200 Census Tract 4002, Alameda County, California 1939 147 78 35 19 25 0 ⋯ 168 47 139 47 97 30 160 36 55 26
	4 1400000US06001400300 06001400300 Census Tract 4003, Alameda County, California 5365 462 460 154 169 84 0 ⋯ 575 140 303 111 380 123 259 99 107 74
	5 1400000US06001400400 06001400400 Census Tract 4004, Alameda County, California 4274 326 307 109 114 61 9 ⋯ 332 89 284 72 198 62 140 58 117 46
	6 1400000US06001400500 06001400500 Census Tract 4005, Alameda County, California 3516 282 530 182 320 133 0 ⋯ 274 70 187 64 210 72 79 51 67 60



In [6]:

    
ca_data = select(ca_data, GEO.id2, GEO.display.label, HD01_VD01,HD01_VD02) %>% slice(-1) 
head(ca_data)









    Out[6]:





GEO.id2 GEO.display.label HD01_VD01 HD01_VD02

	1 06001400100 Census Tract 4001, Alameda County, California 2752 137
	2 06001400200 Census Tract 4002, Alameda County, California 1939 78
	3 06001400300 Census Tract 4003, Alameda County, California 5365 460
	4 06001400400 Census Tract 4004, Alameda County, California 4274 307
	5 06001400500 Census Tract 4005, Alameda County, California 3516 530
	6 06001400600 Census Tract 4006, Alameda County, California 1733 143



In [7]:

    
#convert data type and calculate percentage
ca_data$GEO.id2 = as.character(ca_data$GEO.id2)
ca_data$GEO.display.label = as.character(ca_data$GEO.display.label)
ca_data$HD01_VD01 = as.numeric(as.character(ca_data$HD01_VD01))
ca_data$HD01_VD02 = as.numeric(as.character(ca_data$HD01_VD02))
ca_data$percent = (ca_data$HD01_VD02/ca_data$HD01_VD01)*100
head(ca_data)









    Out[7]:





GEO.id2 GEO.display.label HD01_VD01 HD01_VD02 percent

	1 06001400100 Census Tract 4001, Alameda County, California 2752 137 4.978198
	2 06001400200 Census Tract 4002, Alameda County, California 1939 78 4.022692
	3 06001400300 Census Tract 4003, Alameda County, California 5365 460 8.574091
	4 06001400400 Census Tract 4004, Alameda County, California 4274 307 7.182967
	5 06001400500 Census Tract 4005, Alameda County, California 3516 530 15.07395
	6 06001400600 Census Tract 4006, Alameda County, California 1733 143 8.251587



In [8]:

    
summary(ca_data$percent)









    Out[8]:





   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   7.296  13.470  16.640  23.410 100.000      67

Merge census tract data with ACS poverty data

To use census tract data, need to convert the SpatialPolygonsDataFrame back into data frame with fortiy(). 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 [9]:

    
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 [10]:

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









    Out[10]:





	330321
	12



In [11]:

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









    Out[11]:





	2980
	12



In [12]:

    
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 Poverty Rate\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 [14]:

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









    Out[14]:





	8398
	12



In [15]:

    
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 Poverty Rate\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 [16]:

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









    Out[16]:





	5235
	12



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 Poverty Rate\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 [19]:

    
#zoom in on oakland and san leandro
basemap <-get_map('San Leandro', zoom=11) 
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 Poverty Rate\n ACS 2010-2014 data')









    



Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=San+Leandro&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=San%20Leandro&sensor=false



In [ ]:

	GEO.id	GEO.id2	GEO.display.label	HD01_VD01	HD02_VD01	HD01_VD02	HD02_VD02	HD01_VD03	HD02_VD03	HD01_VD04	ellip.h	HD01_VD55	HD02_VD55	HD01_VD56	HD02_VD56	HD01_VD57	HD02_VD57	HD01_VD58	HD02_VD58	HD01_VD59	HD02_VD59
1	Id	Id2	Geography	Estimate; Total:	Margin of Error; Total:	Estimate; Income in the past 12 months below poverty level:	Margin of Error; Income in the past 12 months below poverty level:	Estimate; Income in the past 12 months below poverty level: - Male:	Margin of Error; Income in the past 12 months below poverty level: - Male:	Estimate; Income in the past 12 months below poverty level: - Male: - Under 5 years	⋯	Estimate; Income in the past 12 months at or above poverty level: - Female: - 35 to 44 years	Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 35 to 44 years	Estimate; Income in the past 12 months at or above poverty level: - Female: - 45 to 54 years	Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 45 to 54 years	Estimate; Income in the past 12 months at or above poverty level: - Female: - 55 to 64 years	Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 55 to 64 years	Estimate; Income in the past 12 months at or above poverty level: - Female: - 65 to 74 years	Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 65 to 74 years	Estimate; Income in the past 12 months at or above poverty level: - Female: - 75 years and over	Margin of Error; Income in the past 12 months at or above poverty level: - Female: - 75 years and over
2	1400000US06001400100	06001400100	Census Tract 4001, Alameda County, California	2752	174	137	89	83	51	0	⋯	153	59	221	67	338	80	198	56	86	41
3	1400000US06001400200	06001400200	Census Tract 4002, Alameda County, California	1939	147	78	35	19	25	0	⋯	168	47	139	47	97	30	160	36	55	26
4	1400000US06001400300	06001400300	Census Tract 4003, Alameda County, California	5365	462	460	154	169	84	0	⋯	575	140	303	111	380	123	259	99	107	74
5	1400000US06001400400	06001400400	Census Tract 4004, Alameda County, California	4274	326	307	109	114	61	9	⋯	332	89	284	72	198	62	140	58	117	46
6	1400000US06001400500	06001400500	Census Tract 4005, Alameda County, California	3516	282	530	182	320	133	0	⋯	274	70	187	64	210	72	79	51	67	60