Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
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Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan

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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 ...

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.

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    Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan Presentation Transcript

    • An  Invitation  to  Cease  and  Desist  
    • “We  need  Census  data!”     -­‐Every  Sales  Manager,  everywhere  
    • +10   Difference   in   the   percentage   of   the   total   population  that  is  male  around  each  location  with   all  of  Manhattan  (47%)  (percentage  points)   -­‐4   +6   -­‐8   -­‐4   Difference   in   the   percentage   of   the   total   population   that   is   female   around   each   location   with  all  of  Manhattan  (53%)  (percentage  points)  
    • +4   Difference  in  the  percentage  of  the  total  population   that   is   White,   non-­‐Hispanic   around   each   location   with  all  of  Manhattan  (48%)  (percentage  points)   -­‐5   0   -­‐12   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)  
    • +25   Difference   in   the   percentage   of   housing   that   is   owner-­‐occupied   around   each   location   with   all  of  Manhattan  (22%)  (percentage  points)   -­‐20   +25   Difference   in   the   percentage   of   housing   that   is  renter-­‐occupied  around  each  location  with   all  of  Manhattan  (74%)  (percentage  points)   -­‐20  
    • Of   c ourse   n ot,   i t’s   a   h ack,   b ut…  
    • —  PostgreSQL   —  PostGIS   —  Quantum  GIS  (QGIS)   —  Pandas/Matplotlib   —  iPython  Notebook   —  OpenStreetMaps   datapolitan@gmail.com  /  richard.dunks@nyu.edu     @rdunks1  /  @datapolitan   blog.datapolitan.com  
    • Come   t alk   t o   m e