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OpenNews/hackdays/insideroutsider: Difference between revisions
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* [https://dl.dropboxusercontent.com/u/6682410/FY%202013%20Schedule%20C%20-%20Merge%20Final1.pdf 2013 New York City Council budget document (warning large PDF download)] | * [https://dl.dropboxusercontent.com/u/6682410/FY%202013%20Schedule%20C%20-%20Merge%20Final1.pdf 2013 New York City Council budget document (warning large PDF download)] | ||
* [http://www.nyc.gov/html/nypd/html/traffic_reports/motor_vehicle_accident_data.shtml NYPD Motor Vehicle Accident Data] | * [http://www.nyc.gov/html/nypd/html/traffic_reports/motor_vehicle_accident_data.shtml NYPD Motor Vehicle Accident Data] | ||
'''From Daniel X O'Neil, Smart Chicago Collaborative/Everyblock:''' | '''From Daniel X O'Neil, Smart Chicago Collaborative/Everyblock:''' | ||
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* [ftp://66.97.146.93/ Dallas FTP Bulk Crime Database] | * [ftp://66.97.146.93/ Dallas FTP Bulk Crime Database] | ||
This is an enormous, underutilized cache of crime data. Chicago gets lots of attention and plaudits for their crime data, but the Dallas stuff goes even farther back (2000!) and contains narrative that will make your eyes bleed. They have the actual comments typed into the system by actual police officers, including graphic details about horrible crimes and a huge amount of profanity. This is a researcher's treasure chest. | This is an enormous, underutilized cache of crime data. Chicago gets lots of attention and plaudits for their crime data, but the Dallas stuff goes even farther back (2000!) and contains narrative that will make your eyes bleed. They have the actual comments typed into the system by actual police officers, including graphic details about horrible crimes and a huge amount of profanity. This is a researcher's treasure chest. | ||
'''From Matt MacDonald, NearbyFYI:''' | |||
* [http://said.nearbyfyi.com/docs/V1/ NearbyFYI - local government documents] | |||
What would you do with 100,000+ documents and extracted text from 170 city and town municipalities in Vermont? We collect city and town documents from select board meeting minutes, planning and zoning committees and other local government legislation. These are often published as PDFs and difficult to scrape HTML. We classify, extract entities [People, Companies, Locations], terms and make them searchable. This is a corpus of partially structured raw text from hundreds of cities and towns. | |||
=== Tools & APIs === | === Tools & APIs === | ||