GIS Homework 7- IPMUS

Downloaded Dataset from IPMUS Database

#Import the dataset downloaded from IPMUS  database 
ddi <- read_ipums_ddi("usa_00003.xml")
data <- read_ipums_micro(ddi) %>% 
  clean_names() %>% 
  filter(statefip==6, !migrate1%in%c(0,9)) %>% 
  mutate(migrate=ifelse(migrate1%in%c(2:4),0,migrate1))
 
 
#Generate survey design object
des<-svydesign(ids = ~cluster,
               strata = ~ strata,
               weights = ~perwt,
               data = data) 

# Get estimate, percent and standard error for people that migrate or not by PUMA, taking into account the survey design
puma_est_migrate<-svyby( ~I(migrate==1),
                       by = ~puma,
                       design=des,
                       FUN=svymean,
                       na.rm = TRUE ) %>% 
  clean_names() %>% 
  mutate(Pctsh=round((i_migrate_1_true*100),1),Pctnsh=round((i_migrate_1_false*100),1)) %>% 
  rename(propnsh=i_migrate_1_false,
         propsh=i_migrate_1_true,
         Stderr=se_i_migrate_1_true) %>% 
  select(puma,Pctsh,Pctnsh,Stderr)

# Get California Boundary File from Tigris and merge the migrate estimates with it .
pumas<-pumas(state = "CA",
             year = 2019,
             cb = T) %>% 
  mutate(puma=as.numeric(PUMACE10)) %>% 
  left_join(puma_est_migrate, by="puma")

Map showing Percent of PUMA Population Living in the same House, CA, 2015-2019

Interactive Map showing Percent of PUMA Population Living in the same House, CA, 2015-2019