big gulp demographics: using spatially weighted sums in manhattan
DESCRIPTION
Presentation delivered on 13 Nov 2013 at LocationTech NYC on hacking census tract data to generate spatially weighted demographic data for user generated polygons. There's a few digs at the Census Bureau, a hidden secret to quickly finding vanilla demographics at various levels of aggregation (spoiler alert: it's the Demographic Profile table with pre-joined demographic data to geospatial features), and some thoughts on modeling residential patterns in census tracts.TRANSCRIPT
An Invitation to Cease and Desist
“We need Census data!” -‐Every Sales Manager, everywhere
-‐4
Difference in the percentage of the total population that is male around each location with all of Manhattan (47%) (percentage points)
Difference in the percentage of the total population that is female around each location with all of Manhattan (53%) (percentage points)
-‐4
+10
-‐8
+6
Difference in the percentage of the total population that is White, non-‐Hispanic around each location with all of Manhattan (48%) (percentage points)
Difference in the percentage of the total population that is Black-‐African American, non-‐Hispanic around each location with all of Manhattan (13%) (percentage points)
-‐5
+4
-‐12
0
Difference in the percentage of housing that is owner-‐occupied around each location with all of Manhattan (22%) (percentage points)
Difference in the percentage of housing that is renter-‐occupied around each location with all of Manhattan (74%) (percentage points)
-‐20
+25
-‐20
+25
Of course not, it’s a hack, but…
� PostgreSQL � PostGIS � Quantum GIS (QGIS) � Pandas/Matplotlib � iPython Notebook � OpenStreetMaps
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