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 8 rows containing missing values (geom_point).

## Warning: Removed 11 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 8 rows containing missing values (geom_point).

## Warning: Removed 11 rows containing missing values (geom_point).

## Warning: Removed 3 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 34 rows containing missing values (geom_point).

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

Places

Tracts

Incoming vs residential

## Warning: Removed 1611 rows containing missing values (geom_point).

## Warning: Removed 1611 rows containing missing values (geom_point).

Outgoing vs residental

## Warning: Removed 1656 rows containing missing values (geom_point).

## Warning: Removed 1656 rows containing missing values (geom_point).

Outgoing vs Incoming

## Warning: Removed 1656 rows containing missing values (geom_point).

## Warning: Removed 1656 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_bl_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##         8         3         7
# inflow white isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  inflow.isol_wh_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##       704       567        77
# Residential ------------------

# residential black isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  residential.isol_bl_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##         8         3         7
# residential white isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  residential.isol_wh_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##       704       569        78
# Outflow -----------------------

# outflow black isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  outflow.isol_bl_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##         8         3         5
# outflow white isolation
divrs %>%
  map_int(
    ~nrow( filter(.,
                  outflow.isol_wh_truly_isolated) )
  )
## cbsa.divs   cz.divs  plc.divs 
##       689       560        74