Intro

Markdown that I’ll fill with plots and analysis pertaining to the Division / mvmt / segregation project.

Summary plots + tables

Tables, then scatterplots of movement and residential based segregation

Tables

CBSAs

CZs

PLCs

## Scatterplots

Showing association between residential and movment seg

CZs

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).

### CBSAs

## Warning: Removed 34 rows containing missing values (geom_point).

## Warning: Removed 34 rows containing missing values (geom_point).

## Warning: Removed 33 rows containing missing values (geom_point).

## Warning: Removed 34 rows containing missing values (geom_point).

## Warning: Removed 33 rows containing missing values (geom_point).

### Places

Tracts

Incoming vs residential

## Warning: Removed 1676 rows containing missing values (geom_point).

## Warning: Removed 1676 rows containing missing values (geom_point).

Outgoing vs residental

## Warning: Removed 1726 rows containing missing values (geom_point).

## Warning: Removed 1726 rows containing missing values (geom_point).

Outgoing vs Incoming

## Warning: Removed 1726 rows containing missing values (geom_point).

## Warning: Removed 1726 rows containing missing values (geom_point).

“Truly” Isolated

Looks at “Truly isolated” regions where this term is defined as regions that have at least, say, 60% white/black residents and 60% isolation for white/black based on movement.

True isolation counts

Number of CZs, CBSAs, Places that are “truly isolated” by various metrics

# drop tract-level table from list
divrs <- divs[!grepl("ct", names(divs))]


# Inflow -----------------

# inflow black isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  inflow.isol_black_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##         8         3         7
# inflow white isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  inflow.isol_white_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##       827       666       192
# Residential ------------------

# residential black isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  residential.isol_black_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##         9         3         7
# residential white isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  residential.isol_white_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##       828       668       192
# Outflow -----------------------

# outflow black isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  outflow.isol_black_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##         8         3         6
# outflow white isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  outflow.isol_white_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##       767       632       152