Crime Early Warning: Automated Data Mining of CAD and RMS
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Crime Early Warning: Automated Data Mining of CAD and RMS

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The genesis of HunchLab was the idea to mine law enforcement agencies' CAD and RMS databases to detect unusual levels of activity in particular areas and then send alerts to the appropriate police ...

The genesis of HunchLab was the idea to mine law enforcement agencies' CAD and RMS databases to detect unusual levels of activity in particular areas and then send alerts to the appropriate police staff. While crime analysis tools often are aiming to display what has happened, the concept of a geographic early warning system, such as within HunchLab, tries to answer the question: "what is unusual that is happening?"

http://www.azavea.com/products/hunchlab/features/early-warning/

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  • We make custom map requests, provide data for CompStat and support a Web site that is updated daily to support daily functions of districts.

Crime Early Warning: Automated Data Mining of CAD and RMS Crime Early Warning: Automated Data Mining of CAD and RMS Presentation Transcript

  • 340 N 12 th St, Suite 402 Philadelphia, PA 19107 215.925.2600 [email_address] www.azavea.com/hunchlab Crime Early Warning Systems Automated Data Mining of CAD and RMS Databases
  • About Us Robert Cheetham President & CEO [email_address] 215.701.7713 Jeremy Heffner HunchLab Product Manager [email_address] 215.701.7712
  • Agenda
    • Company Background
    • The Backstory
    • HunchLab
      • Concept of Early Warning / Data Mining
      • Demonstration of Hunches
      • Underlying Statistics
    • Q&A
  • About Azavea
    • Founded in 2000
    • 27 people
    • Based in Philadelphia
      • Also Boston & Minneapolis
    • Geospatial + web + mobile
      • Software development
      • Spatial analysis services
  • Clients & Industries
    • Public Safety
    • Municipal Services
    • Public Health
    • Human Services
    • Culture
    • Elections & Politics
    • Land Conservation
    • Economic Development
  • Azavea & Governments
  • The Backstory
  • How Phila PD uses GIS
    • Customized Map Products
    Weekly CompStat Meetings Web Crime Analysis
  • Complainant 911 Operator Radio Dispatcher Police Officer District 48 Desk Daily download & Geocoding Routines Incident Report Completed by Officer Maps distributed Through Intranet, Printing, CompStat INCT & PARS – main database sources  over 5,000 incidents daily, over 2 million annually CAD Verizon 911 INCT District X District Y District Z PARS
  • The Context
    • 1,500,000 people
    • 7,000 police officers
    • 1,000 civilian employees
    • 2,000,000 new incidents / year
    3 crime analysts
  • What we did
    • Weekly Compstat
    • Lots of maps
    • Automation of map creation
    • Web-based systems
  • … but what if we could…
    • Accelerate the cycle
    • Proactively notify
    • Automate the process
  • Prototype ArcView VB & MapObjects MS SQL Server Crime Incidents Database Shapefiles and GRIDs Process Documentation .ini file
  •  
  • … but there was a problem …
  • It was crap … sort of.
  • We needed ….
    • Better Statistics
    • Notification
    • Very Straightforward
  •  
    • web-based crime analysis, early warning, and risk forecasting
    • Crime Analysis
      • Mapping (spatial / temporal densities)
      • Trending
      • Intelligence Dashboard
    • Early Warning
      • Statistical & Threshold-based Hunches (data mining)
      • Alerting
    • Risk Forecasting
      • Near Repeat Pattern
      • Load Forecasting
    • Crime Analysis – What has happened?
      • Mapping (spatial / temporal densities)
      • Trending
      • Intelligence Dashboard
    • Early Warning – What is out of the ordinary?
      • Statistical & Threshold-based Hunches (data mining)
      • Alerting
    • Risk Forecasting – What is likely to happen?
      • Near Repeat Pattern
      • Load Forecasting
  • Early Warning
  • Early Warning
    • Geographic Early Warning System
      • A system to alert staff of an unusual situation in a particular location
      • Ingests data sets to automatically “cook on” and only involves staff when a statistically unusual situation is found
    HunchLab Database Alerting System Geostatistical Engine Operational Database Operational Database Operational Databases
  • Data Mining
    • What do we mean by data mining?
      • The process of “cooking on” the data to reveal something new (unusual)
    • Benefits
      • Automated discovery process
      • Can examine large data sets without additional staff time
        • Major crime incidents
        • Minor crime incidents
      • Near real-time alerts
    • Limitations
      • Can’t determine why something unusual is happening, only that it is happening
  • Early Warning bit.ly/crimespikedetector
    • Demo
  • What is a Hunch?
    • A proposed hypothesis, saved into the system, and continually tested for validity
    • Incident Attribute Requirements
      • Location (x, y)
      • Time (timestamp)
      • Classification
    • Hunch Attributes
      • Location (area)
      • Time (recent / historic periods)
      • Classification
    • Analyses
      • Statistical Hunch
      • Threshold Hunch
  • Hunch Parameters: Location
    • Address & Radius
    • Precinct/County/Country
    • Custom Drawn Area
    • Mass Hunch
  • Hunch Parameters: Time
    • Statistical Hunch
      • Recent Past
      • Historic Past
  • Hunch Parameters: Classification
    • Category
    • Time of Day
    • Narrative
  • Hunch Helper
  • Email Alert
  • Hunch Details
  • The Statistics
  • What do we know?
    • Hunch
      • Geographic region (that we care about)
      • Recent time frame (to alert on)
      • Historic time frame (to compare against)
      • Classification (that we are interested in)
  • What do we know?
    • Hunch
      • Geographic region (that we care about)
      • Recent time frame (to alert on)
      • Historic time frame (to compare against)
      • Classification (that we are interested in)
    Within Hunch Outside of Hunch Recent past ? ? Historic past ? ?
  • Hypergeometric Distribution
    • Arises when selecting items at random from a heterogenous pool without replacement
      • Example
        • A bag contains 45 black marbles and 5 white marbles
        • What is the chance of picking 4 white marbles when we draw 10 marbles?
    Tony Smith University of Pennsylvania Drawn Not Drawn White Marbles 4 1 Black Marbles 6 39
  • Hypergeometric Distribution en.wikipedia.org/wiki/Hypergeometric_distribution Drawn Not Drawn Total White Marbles 4 = k 1 = m – k 5 = m Black Marbles 6 = n-k 39 = N + k – n - m 45 = N – m Total 10 = n 40 = N - n 50 = N
  • What do we know?
    • Hunch
      • Geographic region (that we care about)
      • Recent time frame (to alert on)
      • Historic time frame (to compare against)
      • Classification (that we are interested in)
    Within Hunch Outside of Hunch Recent past ? ? Historic past ? ?
  • What do we know?
    • Valid Hunch
      • The current condition (and all worse conditions) is unlikely to simply be due to chance
    • Demo
  • Research Topics
  • Research Topics
    • Mobile Interfaces
    • Analysis
      • Real-time Functionality
        • Consume real-time data streams & conduct ongoing analysis
  • Research Topics
    • Risk Forecasting
      • Load forecasting enhancements
        • Machine learning-based model selection
        • Weather and special events
      • Combining short and long term risk forecasts
      • Risk Terrain Modeling
  • Q&A
  • Contact Us Robert Cheetham President & CEO [email_address] 215.701.7713 Jeremy Heffner HunchLab Product Manager [email_address] 215.701.7712