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crime

Public Safety in Troy, NY

Description: 
This demo integrates information on public safety from Troy Police department and RPI's public safety department and display it using geographical data taken from Google Maps.
Contributor:

Introduction

This project aims to help residents of Troy, New York to visualize different events related to public safety. The original objective was to cover only crimes such as robberies. However, based on the reports from Department of public safety from RPI and Troy Police Department we obtained several other types of incidents, including fires, and medical events. You can check regularly the events of interest. You can also use the RDF link in the results to mash that data up with other datasets. You can also use RSS feed (so you can suscribe with an RSS reader) soon . If you are searching, you may choose from 2 differente data sources: The first one are the events reported by the Department of Public Safety. The second one is events reported by the Troy Police Department. You can choose to seach for any (or both) data sources.

Technology

The implementation of this experiment is based in several different technologies: As a backend I used semantic technologies, such as RDF and SPARQL. Also, JSON and AJAX has been used to register new events. For visualization I used Exhibit and Google maps. Finally JQuery has been used to wrap the interface.
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US and UK Crime Data

Description: 
This demo uses data available at Dataset 311 from data.gov and from http://PoliceAPI.rkh.co.uk for UK data and compare number of crimes in cities in the US and UK.
Contributor:

Description

We compare crime in cities and several interesting things are noticed: First, the classification of crimes is different for each country. There are certain similarities, such as 'Burglary' and 'Robbery', however others are not easy to be matched (for example, 'Vehicle theft' and 'Vehicle crimes' are similar but not the same). Another point is the granularity of data available in the UK: It is possible to select a whole police area force, a city or a particular subarea. For the case of the US, we considered only cities. We also allow users to divide the number of events occurred by the location's population. In this way we can do a comparison of events per person instead of considering only totals.
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