Using IPUMS USA for Estimation of Population Characteristics in Various Geographic Areas
Assogba Albert
4/20/2021
Load some other packages
library(survey)
library(dplyr)
library(car)
library(ggplot2)
library(tigris)
library(classInt)
library(tmap)
library(ipumsr)## Warning: package 'ipumsr' was built under R version 4.0.5ddi <- read_ipums_ddi("C:/Users/chris/University of Texas at San Antonio/SPRING 2021/DEM 7093 GEOGRAPHIC INFORMATION FOR POPULATION SCIENCE/Homeworks/GIS HMWK/HOMEWORK 7/usa_00002.xml")
data <- read_ipums_micro(ddi)## Use of data from IPUMS USA is subject to conditions including that users should
## cite the data appropriately. Use command `ipums_conditions()` for more details.data<-haven::zap_labels(data) #necessary to avoid problems with "labeled" dataDownload geographic data for Public Use Microdata Areas
options(tigris_class = "sf")
pumas<-pumas(state = "CA", year = 2019, cb = T)##
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|======================================================================| 100%plot(pumas["GEOID10"], main = "Public Use Microdata Areas in California")
names(data)<-tolower(names(data))Recode variables
#weight variables, these have implied decimal places so we have to divde by 100, following the codebook
data$pwt <- data$perwt/100
data$hwt <- data$hhwt/100
# Migration
data$migration <- Recode(data$migrate1, recodes ="1=0; 2:4=1; else=0")
#race/ethnicity
data$hisp <- Recode(data$hispan, recodes = "9=NA; 1:4='Hispanic'; 0='NonHispanic'")
data$race_rec <- Recode(data$race, recodes = "1='White'; 2='Black'; 3='Other'; 4:6='Asian'; 7:9='Other'")
data$race_eth <- interaction(data$hisp, data$race_rec, sep = "_")
data$race_eth <- as.factor(ifelse(substr(as.character(data$race_eth),1,8) == "Hispanic", "Hispanic", as.character(data$race_eth)))
data$race_eth <- relevel(data$race_eth, ref = "NonHispanic_White")
#sex
data$male <- ifelse(data$sex == 1,1,0)
#education
data$educ_level<- Recode(data$educd, recodes = "2:61='0LT_HS';62:64='1_HSD/GED';65:80='2_somecoll';90:100='2_somecoll'; 81:83='3_AssocDegree';101='4_bachelordegree'; 110:116='4_BAplus_GradDegree'; else=NA")
#education
data$educ_level<- Recode(data$educd, recodes = "2:61='0LT_HS';62:64='1_HSD/GED';65:80='2_somecoll';90:100='2_somecoll'; 81:83='3_AssocDegree';101='4_bachelordegree'; 110:116='4_BAplus_GradDegree'; else=NA")Survey design
Here we identify the person weights and the survey design variables.
des<-svydesign(ids = ~cluster,
strata = ~ strata,
weights = ~pwt,
data = data)Survey Estimation for PUMAs
puma_est_edu<-svyby(formula = ~educ_level,
by = ~puma,
design = subset(des, age>25),
FUN=svymean,
na.rm = TRUE )
puma_est_migrate<-svyby(formula = ~migration,
by = ~puma,
design=des,
FUN=svymean,
na.rm = TRUE )
head(puma_est_edu)## puma educ_level0LT_HS educ_level1_HSD/GED educ_level2_somecoll
## 101 101 0.03771076 0.06569605 0.1166131
## 102 102 0.16054167 0.15357159 0.1579987
## 103 103 0.05152757 0.08422222 0.1280499
## 104 104 0.31012651 0.24563983 0.2083160
## 105 105 0.13347136 0.18646181 0.1905626
## 106 106 0.14333595 0.23360220 0.2146757
## educ_level3_AssocDegree educ_level4_bachelordegree
## 101 0.04322687 0.3299605
## 102 0.06094609 0.2850304
## 103 0.05430587 0.3451345
## 104 0.05510831 0.1160381
## 105 0.07115222 0.2688053
## 106 0.07644186 0.2248899
## educ_level4_BAplus_GradDegree se.educ_level0LT_HS se.educ_level1_HSD/GED
## 101 0.40679279 0.003905639 0.004448080
## 102 0.18191149 0.006521332 0.006326229
## 103 0.33675991 0.004472209 0.005309984
## 104 0.06477123 0.011587978 0.009193415
## 105 0.14954676 0.006885012 0.007559093
## 106 0.10705441 0.007508320 0.008802248
## se.educ_level2_somecoll se.educ_level3_AssocDegree
## 101 0.005691604 0.003405890
## 102 0.005625829 0.003542165
## 103 0.005791771 0.004021030
## 104 0.008470772 0.004609917
## 105 0.006335683 0.004081108
## 106 0.007564748 0.004487001
## se.educ_level4_bachelordegree se.educ_level4_BAplus_GradDegree
## 101 0.008030658 0.008809076
## 102 0.007069620 0.005803563
## 103 0.008150823 0.008192403
## 104 0.006230842 0.004944989
## 105 0.007454848 0.005514219
## 106 0.008090311 0.005358965head(puma_est_migrate)## puma migration se
## 101 101 0.25381149 0.009312584
## 102 102 0.17249132 0.006723822
## 103 103 0.12378144 0.006596332
## 104 104 0.10786312 0.008221215
## 105 105 0.11013835 0.006718592
## 106 106 0.07652232 0.005998268Join to geography
pumas$puma<-as.numeric(pumas$PUMACE10)
geo1<-geo_join(pumas, puma_est_migrate, by_sp="puma",by_df= "puma")## Warning: `group_by_()` was deprecated in dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more helphead(geo1)## Simple feature collection with 6 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -124.4096 ymin: 33.46296 xmax: -116.8412 ymax: 41.46584
## Geodetic CRS: NAD83
## STATEFP10 PUMACE10 AFFGEOID10 GEOID10
## 1 06 09702 7950000US0609702 0609702
## 2 06 02300 7950000US0602300 0602300
## 3 06 08506 7950000US0608506 0608506
## 4 06 06506 7950000US0606506 0606506
## 5 06 08900 7950000US0608900 0608900
## 6 06 06103 7950000US0606103 0606103
## NAME10
## 1 Sonoma County (South)--Petaluma, Rohnert Park & Cotati Cities
## 2 Humboldt County
## 3 Santa Clara County (East)--Gilroy, Morgan Hill & San Jose (South) Cities
## 4 Riverside County (Southwest)--Hemet City & East Hemet
## 5 Shasta County--Redding City
## 6 Placer County (East/High Country Region)--Auburn & Colfax Cities
## LSAD10 ALAND10 AWATER10 puma migration se rank
## 1 P0 344472696 7555813 9702 0.1453726 0.009588211 1
## 2 P0 9241426488 1253864712 2300 0.1960088 0.009523201 1
## 3 P0 2152449674 13432167 8506 0.1210222 0.009195527 1
## 4 P0 645481741 4500965 6506 0.1653621 0.010414257 1
## 5 P0 9778407493 186302040 8900 0.1410088 0.007415338 1
## 6 P0 3094034997 246117939 6103 0.1249433 0.009398838 1
## geometry
## 1 MULTIPOLYGON (((-122.7418 3...
## 2 MULTIPOLYGON (((-124.4086 4...
## 3 MULTIPOLYGON (((-121.8558 3...
## 4 MULTIPOLYGON (((-117.1456 3...
## 5 MULTIPOLYGON (((-123.0688 4...
## 6 MULTIPOLYGON (((-121.4104 3...Migration rates by PUMA
tmap_mode("plot")## tmap mode set to plottingtm_basemap("OpenStreetMap.Mapnik")+
tm_shape(geo1)+
tm_polygons("migration",
style="kmeans",
n=8,
legend.hist = TRUE) +
tm_layout(legend.outside = TRUE,
title = "Migration rate in California PUMAs \n 2015-2019")## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1
## [1] "#FFFDDB" "#FEF1AF" "#FED97B" "#FEB441" "#F68820" "#DB5D0A" "#AF3E03"
## [8] "#7A2A05"