Data
The data scientists at CITYDATA.ai take a look at the presidential matchup that we all wish could happen. Excited at the prospect of rambunctious contested conventions? Giddy with anticipation as major political parties splinter and disintegrate? Can't resist binge-watching the House of Cards? Then you must read
visualization
The U.S. Census Bureau [https://www.census.gov/en.html] provides a very intricate and sometimes unstructured hierarchy of geospatial geometries that link back to the U.S. demographic data. This post dives deeper into the geometries or polygons, as a follow up up to our previous post [http:
govtech
Intro What neighborhood related datasets are openly available, how good are they, and how can I use them? These are essential questions for any project looking to use or understand neighborhoods. This first of a 4-part series delves into the geometries of neighborhoods. The following three parts of this series
Data
The data scientists at CITYDATA.ai have conjured up their analytical super powers to make bold predictions for Super Tuesday. Today’s the day. Can anyone take down Trump? Will Bernie's populist revolution flare up or flame out? The primaries of Super Tuesday create a unique political moment
govtech
Our neighborhood rolls down the hills Through happy farms to the old mill Green oaks, red doors, enchanting collage The occasional tremor and a foot massage The old downtown's still smart and fine Inclusive, affluent, spiritual, divine Sumptuous restaurants, boisterous bars Drones, hoverboards and electric cars Kids, teens,
govtech
Pinpointing the composition, coordinates, and concept of a single neighborhood is a tough proposition. To understand these elements, we need more than simple data collection - we need a philosophical and sociological approach toward the definition. Neighborhood Composition Philosophically, something deeper than location ties a neighborhood together. After all, the
govtech
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