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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	
 ...
+4	
  

Difference	
  in	
  the	
  percentage	
  of	
  the	
  total	
  population	
  
that	
   is	
   White,	
   non-­‐Hisp...
+25	
  

Difference	
   in	
   the	
   percentage	
   of	
   housing	
   that	
  
is	
   owner-­‐occupied	
   around	
   ea...
Of	
   c ourse	
   n ot,	
   i t’s	
   a 	
   h ack,	
   b ut…	
  
—  PostgreSQL	
  
—  PostGIS	
  
—  Quantum	
  GIS	
  (QGIS)	
  
—  Pandas/Matplotlib	
  
—  iPython	
  Notebook	
  
...
Come	
   t alk	
   t o	
   m e	
  
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan
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 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.

Published in: Technology, Economy & Finance
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Big Gulp Demographics: Using Spatially Weighted Sums in Manhattan

  1. 1. An  Invitation  to  Cease  and  Desist  
  2. 2. “We  need  Census  data!”     -­‐Every  Sales  Manager,  everywhere  
  3. 3. +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. 4. +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)  
  5. 5. +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  
  6. 6. Of   c ourse   n ot,   i t’s   a   h ack,   b ut…  
  7. 7. —  PostgreSQL   —  PostGIS   —  Quantum  GIS  (QGIS)   —  Pandas/Matplotlib   —  iPython  Notebook   —  OpenStreetMaps   datapolitan@gmail.com  /  richard.dunks@nyu.edu     @rdunks1  /  @datapolitan   blog.datapolitan.com  
  8. 8. Come   t alk   t o   m e  

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