Brennon bortz marcos carzolio andrew hoegh shashidhar sundareisan
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Brennon Bortz , Marcos Carzolio, Andrew Hoegh , Shashidhar Sundareisan. CrimeScore. What is CrimeScore ?. CrimeScore is the predicted number of violent crimes per month within a 1km radius of a given location in Washington, D.C. Training Data.

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CrimeScore

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Brennon bortz marcos carzolio andrew hoegh shashidhar sundareisan

BrennonBortz, Marcos Carzolio, Andrew Hoegh, ShashidharSundareisan

CrimeScore


What is crimescore

What is CrimeScore?

  • CrimeScore is the predicted number of violent crimes per month within a 1km radius of a given location in Washington, D.C.


Training data

Training Data

  • Uniform random sample points throughout Washington, D.C.

  • Data collected within a 1km radius of samples

    • Barbershops, bus stops, gas stations, schools, registered property, liquor establishments, and more

    • Distance to nearest police station, distance to nearest public housing project, etc.


Data aggregation

Data Aggregation

  • Parsed crime data from DC Data Catalog

  • Classified crime data

    • Violent and non-violent crimes

    • Focused on violent crimes, consisting of homicide, robbery, assault with a deadly weapon, and sexual abuse


Implementation goals

Implementation Goals

  • Simple, elegant and familiar

  • User Interface like Google maps

  • Dynamic; easily accommodates multiple queries

  • Represent crime score as a color and a number with an associated interpretation

  • Pack as much information as possible

  • Make queries fast and display results faster


Data flow

Data Flow


Implementation

Implementation

  • Query a search or listen to a click on the map

  • Use Google maps API to get positions of the search on the map

  • Feed the results to R-script to calculate CrimeScoreusing Shiny

  • Use the CrimeScore to display color coded markers on the map


Crimescore

Rook

  • Wraps R environment

  • Bootstraps R’s internal web server

  • Maintains environment

  • Finnicky!


Why java script

Why Java Script?

  • Omnipresent in HTML scripting

  • Prevalent support and acceptance

  • Ability to write asynchronous functions so that the queries over the internet and to the database does not halt the web-page

  • Google Maps Java Script API v3 is heavily documented

  • Supports JSON data interchange format


Google maps api

Google Maps API

  • Use URL requests to access geocoding, directions, elevation, place and time zone information.

  • Embed an interactive Google Map in the webpage using JavaScriptby creating markers, infowindows etc.

  • The JavaScript Maps API V3 is a free service, available for any web site that is free to consumers


Google m aps api

Google Maps API

  • Map

  • MapOpions

  • Geocoder

  • Marker

  • Infowindow

  • PlacesService

  • LatLng

  • Events


Google maps api1

Google Maps API

  • Place map at the center of Washington DC

  • Restrict queries up to a 10 km radius

  • Retrieve latitude and Longitude values for results

  • Place markers with appropriate colors depending upon crime score

  • Place infowindow on all markers to show satellite information

  • Allow option to manually give a Lat/Lon by clicking


Data storage

Data Storage

  • The data for the project were stored in a centralized database using MySQL

  • The main use of the database was to store Latitude, Longitude and details of places, as well as crimes relevant to the mining process

  • Data collected from the crime data set and the DC data catalog


Challenges

Challenges

  • Incomplete or missing data

  • Dealing with spatial data

  • Simultaneously dealing with polygons and points in the dc catalog

  • Finding the distances to the nearest barber shop, schools, churches, police stations, bus stops etc. is time consuming


Challenges1

Challenges

  • Limit over number of queries in Google maps API

  • Using radarsearch over textsearch

  • Can’t specify the boundary of a search query other than a rectangle or a circle in google maps API

  • Maximum of 200 results per query


Improving implementation

Improving Implementation

  • Make results appear faster

  • Instead of calculating distances from every place to calculate crime score divide the city into a grid with pre-calculated values of crime scores

  • A query now will only find what grid the place belongs to and return the appropriate crime score


Random forest regression

Random Forest Regression

  • Each tree trains on a bootstrapped subset of data

  • At each node on all trees, algorithm randomly chooses predictors on which to build a regression model and create a split in feature space

  • Response in regression model is actual (observed) CrimeScore

  • Excellent predictions; difficult interpretations

  • Analysis done with randomForest R package


Random forest regression1

Random Forest Regression

Regression Tree 1

Original Data

Bootstrap 1

Regression Tree 2

Bootstrap 2

Random Forest

Regression Tree 3

Bootstrap 3

Regression Tree 4

Bootstrap 4


Model validation

Model Validation

  • Algorithm holds out 20% of data to test against model

  • Performance at each node measured by mean squared error and mean decrease in accuracy


Results

Results


Results1

Results


Crimescore functionality

CrimeScore Functionality

  • Travelers seeking a safe place to stay

  • City planners choosing locations for parks, etc.

  • Police mapping out patrol routes

  • Homebuyers selecting a new residence

  • Hotel and real estate advertising


Crimescore

CrimeScore


Future work

Future Work

  • Implement CrimeScore in other cities

  • Develop interface within travel websites

  • Improve interactivity for city planner


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