24  San Diego Tracts

This dataset contains an extract of a set of variables from the 2017 ACS Census Tracts for the San Diego (CA) metropolitan area.

import contextily
import geopandas
import cenpy

acs = cenpy.products.ACS(2017)

24.1 Download Data

  • Set variables to download
vars_to_download = {
    "B25077_001E": "median_house_value",  # Median house value
    "B02001_002E": "total_pop_white",     # Total white population
    "B01003_001E": "total_pop",           # Total population
    "B25003_003E": "total_rented",        # Total rented occupied
    "B25001_001E": "total_housing_units", # Total housing units
    "B09019_006E": "hh_female",           # Female households
    "B09019_001E": "hh_total",            # Total households
    "B15003_002E": "total_bachelor",      # Total w/ Bachelor degree
    "B25018_001E": "median_no_rooms",     # Median number of rooms
    "B19083_001E": "income_gini",         # Gini index of income inequality
    "B01002_001E": "median_age",          # Median age
    "B08303_001E": "tt_work",              # Aggregate travel time to work
    "B19013_001E": "median_hh_income"     # Median household income
}
vars_to_download_l = list(vars_to_download.keys())
  • Download geometries and attributes
%%time
db = acs.from_msa("San Diego, CA",
                  level="tract",
                  variables=vars_to_download_l
                 )

24.2 Metadata

We will also write a companion file with the names of each variable:

var_names = acs.variables\
               .reindex(vars_to_download)\
               [["label", "concept"]]\
               .reset_index()\
               .rename(columns={"index": "var_id"})
var_names["short_name"] = var_names["var_id"].map(vars_to_download)

24.3 Process data

While the ACS comes with a large number of attributes, we are not limited to the original variables at hand; we can construct additional variables. This is particularly useful when we want to compare areas that are not very similar in some structural characteristic, such as area or population. For example, a quick look into the variable names shows most variables are counts. For tracts of different sizes, these variables will mainly reflect their overall population, rather than provide direct information about the variables itself. To get around this, we will cast many of these count variables to rates, and use them in addition to a subset of the original variables.

  • Replace missing values with columns mean
filler = lambda col: col.fillna(col.mean())
db.loc[:, vars_to_download] = db.loc[:, vars_to_download]\
                                .apply(filler)
  • Replace variable codes with short names
db = db.rename(columns=vars_to_download)
db["area_sqm"] = db.to_crs(epsg=5070).area / 1e6
  • Percentage of renter occupied units
db["pct_rented"] = db["total_rented"] / \
                  (db["total_housing_units"] + \
                   (db["total_housing_units"]==0) * 1
                  )
  • Percentage of female households
db["pct_hh_female"] = db["hh_female"] / \
                  (db["hh_total"] + \
                   (db["hh_total"]==0) * 1
                  )
  • Percentage with a Bachelor’s degree
db["pct_bachelor"] = db["total_bachelor"] / \
                  (db["total_pop"] + \
                   (db["total_pop"]==0) * 1
                  )
  • Percentage of white population
db["pct_white"] = db["total_pop_white"] / \
                  (db["total_pop"] + \
                   (db["total_pop"]==0) * 1
                  )
  • Generate indicator for subset of contiguous 30 tracts
tract_geoids = [
     '06073000100',
     '06073000201',
     '06073000202',
     '06073000300',
     '06073000400',
     '06073000500',
     '06073000600',
     '06073000700',
     '06073000800',
     '06073000900',
     '06073001000',
     '06073001100',
     '06073001200',
     '06073001300',
     '06073001400',
     '06073001500',
     '06073001600',
     '06073001700',
     '06073001800',
     '06073001900',
     '06073002001',
     '06073002002',
     '06073002100',
     '06073002201',
     '06073002202',
     '06073002301',
     '06073002302',
     '06073002401',
     '06073002402',
     '06073002501'
]
db["sub_30"] = False
db.loc[db["GEOID"].isin(tract_geoids), "sub_30"] = True
ax = db.plot(alpha=0.5, color="k")
db[db["sub_30"]].plot(ax=ax, color="yellow")
contextily.add_basemap(ax, crs=db.crs);

24.4 Write Out

db.info()
  • Dataset
! rm -f sandiego_tracts.gpkg
db.to_file("sandiego_tracts.gpkg", driver="GPKG")
  • Metadata
! rm -f sandiego_tracts_varnames.json
var_names.to_json("sandiego_tracts_varnames.json")