{"id":420,"date":"2014-01-06T21:31:57","date_gmt":"2014-01-07T02:31:57","guid":{"rendered":"http:\/\/homepages.uc.edu\/~yaozo\/wordpress\/?p=420"},"modified":"2014-01-06T21:31:57","modified_gmt":"2014-01-07T02:31:57","slug":"using-contour-lines-to-map-density-of-nypds-stop-and-frisk-data","status":"publish","type":"post","link":"https:\/\/zhuoyao.net\/index.php\/2014\/01\/06\/using-contour-lines-to-map-density-of-nypds-stop-and-frisk-data\/","title":{"rendered":"Using Contour Lines to Map Density of NYPD\u2019s Stop and Frisk Data"},"content":{"rendered":"

Using Contour Lines to Map Density of NYPD\u2019s Stop and Frisk Data<\/h2>\n
\n
July 13 2012\u00a0by\u00a0\u00a0Chris Herwig<\/a><\/div>\n<\/div>\n
\n

Using elevation contour lines is one of several ways to visualize density from vector point data. I recently used this approach to map \u201cstop and frisks\u201d – the practice of a police officer stopping an individual for reasonable suspicion of criminal activity – in New York City. This practice is often criticized as happening more often in neighborhoods with higher numbers of minority residents, so showing the density of such stops is critical to the story behind this data. If you would like to know more about Stop and Frisk, check out the research conducted by the\u00a0Center for Constitutional Rights<\/a>and the\u00a0New York Civil Liberties Union<\/a>.<\/p>\n

In this post I\u2019ll discuss how I visualized the point density of stop and frisk occurrences using theNYPD\u2019s Stop and Frisk databases<\/a>. To get an idea of some of the others challenges faced and approaches in mapping this data, check out the\u00a0Data Without Borders Data Dive NYC wiki page<\/a>\u00a0or my\u00a0precinct<\/a>\u00a0github repo.<\/p>\n

<\/a><\/p>\n

From points to heat map to contour lines<\/h1>\n

From the larger Stop and Frisk database, which contains more than 4.2 million records, I selected only stops that occurred in 2008 where the person stopped was either \u201cBlack\u201d, \u201cLatino,\u201d or \u201cBlack Latino\u201d, and which contained coordinates for the stop.<\/p>\n

 ogr2ogr -f SQLite \/path\/to\/outfile\/sf-race-geo.sqlite PG:\"dbname='' host='localhost' port='5432'\" -sql \"SELECT distinct ogc_fid, lat_lon, year, race, pct FROM public.\"new_all\" where year = '2008' AND xcoord <> '' AND (race = 'B' OR race = 'Q' OR race = 'P')\"\n<\/code><\/pre>\n
This resulted in 432,515 records, which are\u00a0available as an sqlite file<\/a>.<\/p>\n
For some uses, you can visualize density using only the point data and manipulating the opacity and size of markers. I used this method to create\u00a0this map, which shows all of the stops from 2008<\/a>.<\/p>\n