Homewk 5 - Calculating Indices of Residential Segregation

Data Manipulations

# Get other  US county Hispanic/Ethnicity Race variables from the 5 year (2015-2019) ASC data source.

us <- unique(fips_codes$state)[1:51]


Racetable <- map_df(us, function(x) {get_acs(geography = "tract",
                         year=2019,
                         geometry = F,
                         output="wide",
                         table = "B03002",
                         cache_table =T,
                         state = x)
})

#Merge the socioeconomic variables from the Area resource file 2019-2020 with the other socioeconomic variables from the 5 year (2015-2019) ACS estimates.

tractdata<-Racetable%>%
  mutate(nhwhite=B03002_003E,
         nhblack=B03002_004E,
         nhother= B03002_005E+B03002_006E+B03002_007E+B03002_008E+B03002_009E+B03002_010E,
         hisp=B03002_012E, 
         total=B03002_001E,
         year=2019,
         cofips=substr(GEOID, 1,5))%>%
  select(GEOID,nhwhite, nhblack ,  nhother, hisp, total, year, cofips )%>%
  arrange(cofips, GEOID) 

#We need the county-level totals for the total population and each race group
countydata<-tractdata%>%
  group_by(cofips)%>%
  summarise(co_total=sum(total),
            co_wht=sum(nhwhite),
            co_blk=sum(nhblack),
            co_oth=sum(nhother),
            cohisp=sum(hisp))

#we merge the county data back to the tract data by the county FIPS code
ctytrtdata<-left_join(x=tractdata,
                  y=countydata,
                  by="cofips")

Two groups segregation measures in US counties

I calculated the tract-specific contribution to the county dissimilarity index. Also, calculated the tract-specific contributions within counties. This then used to calculate the dissimilarity index for Non-Hispanic blacks and Non-Hispanic whites is:

Dissimilarity Index

The following codes calculate the dissimilarity index for Non-Hispanic blacks and Non-Hispanic whites.

co.dis<-ctytrtdata%>%
  mutate(d.wb=abs(nhwhite/co_wht - nhblack/co_blk))%>%
  group_by(cofips)%>%
  summarise(dissim=round( .5*sum(d.wb, na.rm=T),1))

Interaction Index

The following codes calculate the interaction index for Non-Hispanic blacks and Non-Hispanic whites. In the calculation, first population is the minority population, the second is a non-minority population.

co.int<-ctytrtdata%>%
  mutate(int.bw=(nhblack/co_blk * nhwhite/total))%>%
  group_by(cofips)%>%
  summarise(inter_bw=round(sum(int.bw, na.rm=T),1))

Isolation Index

The following codes calculate the isolation index Using only the Non-Hispanic blacks population group

co.isob<-ctytrtdata%>%
  mutate(isob=(nhblack/co_blk * nhblack/total))%>%
  group_by(cofips)%>%
  summarise(iso_b= round(sum(isob, na.rm=T),1))

US Segregation Indices Maps

Discriptive summary

This maps show the likelihood of population subgroups(in this case, Non-Hispanic Black and Non-Hispanic White Population groups) interacting with one another using the index of Interaction. On the other hand, the index of Isolation, measures how likely it is that one group (Non-Hispanic Black population group) is isolated, or only surrounded by other members of the same group.

This index ranges between 0 and 1. Higher values (closer to 1), indicate more contact between the two groups, while values near 0 indicate more isolation.