mapping and analysis for public safety: an overview

Mapping and analysis for public safety: An Overview

Motivation

Identifying events (e.g. Bar closing, football games) that lead to increased crime.

Crime generators and attractors

Predicting crime events

Identifying location and time where a serial offender would commit his next crime.

Predicting the next target of a burglary offender

Identification of patrol routes

Force deployment to mitigate crime hotspots.

Courtsey: www.startribune.com

http://www.dublincrime.com/blog/wp-content/MappingOurMeanStreets.jpg

Scientific Domain: Environmental Criminology

Courtsey: http://www.popcenter.org/learning/60steps/index.cfm?stepNum=16

Crime pattern theory Routine activity theory and Crime Triangle

Courtsey: http://www.popcenter.org/learning/60steps/index.cfm?stepnum=8

Crime Event: Motivated offender, vulnerable victim (available at an appropriate location and time), absence of a capable guardian.

Crime Generators : offenders and targets come together in time place, large gatherings (e.g. Bars, Football games) Crime Attractors : places offering many criminal opportunities and offenders may relocate to these areas (e.g. drug areas)

What is spatiotemporal data mining ?Process of discovering interesting, useful and non-trivial patterns from spatiotemporal data.

Traditional Data Mining Spatiotemporal data mining (STDM)

Da

ta m

inin

g T

ask

s Frequent patterns (e.g. Associations, Sequential association, frequent graphs)

ST Frequent patterns (e.g. ST Co-occurrence, ST Sequences and Cascading ST patterns)

Clustering Hotspot Analysis

Anomaly detection

Classification/ Regression ST Classification / ST (auto) Regression

ST Outliers

STDM pattern families

Spatial outliers: sensor (#9) on I-35Nest locations Distance to open water

Vegetation durability

Water depth

Location prediction: nesting sites

Co-occurrence Patterns Hotspots

www.sentient.nl/crimeanabody.html

Projects : Mapping and Analysis for public safety

US DoJ/NIJ- Mapping and analysis for Public Safety CrimeStat .NET Libaries 1.0 : Modularization of CrimeStat, a tool for the analysis of crime

incidents. Performance tuning of Spatial analysis routines in CrimeStat CrimeStat 3.2a - 3.3: Addition of new modules for spatial analysis.

US DOD/ ERDC/ AGC – Cascade models for multi scale pattern discovery

Designed new interest measures and formulated pattern mining algorithms for identifying patterns from large crime report datasets.

US DOD – Spatial network hotspot discovery New algorithms to discover hotspots along street networks

CrimeStat

A Spatial statistics software to analyze crime incident locations. It provides modules for spatial statistics, space-time analysis, finding

patterns: Hotspot Analysis Spatial Modeling Crime Travel Demand

Used widely by law enforcement agencies throughout the country. Popular among Public Health agencies and research groups throughout the

country.

CrimeStat

Used by law enforcement all over the country (e.g. Redlands Police Department, Baltimore County)

File down loads: Fall 2010 65,875 (Source: http://www.icpsr.umich.edu/CrimeStat/about.html )

6 Releases since 1999

Our Contributions

• Crime Stat Libraries 1.0[1] – Set of .NET components distributed by NIJ– Credits: http://www.icpsr.umich.edu/CrimeStat/files/Documentation_for_CrimeStat_Libraries_1.0.pdf

• Crime Stat v 3.2-3.3– Statistical Simulation functions for Spatial Analysis Routines– Credits: http://www.icpsr.umich.edu/CrimeStat/files/CrimeStat3.3updatenotesPartI.pdf

• Scalability to Large Datasets– Self-Join Index[2]

[1] http://www.spatial.cs.umn.edu/projects/crimestat-pub/beta/ [2] Pradeep Mohan, Shashi Shekhar, Ned Levine, Ronald E. Wilson, Betsy George, Mete Celik, Should SDBMS support the join index ?:

A Case Study from Crimestat. In Proc. of 16th ACM SIGSPATIAL International Conference on

Advances in Geographic Information Systems (ACM GIS 2008), California, USA,2008.

Real Crime DatasetsLincoln, NE Dataset

Years 2002- 2007 > 40 Crime types > 200 Sub types Average size of each year ~ 40000

Real Data

Cascading spatio-temporal pattern (CSTP)

Output: CSTP

Partially ordered subsets of ST event types.

Located together in space.

Occur in stages over time.

Aggregate(T1,T2,T3)

Time T1

Assault(A) Drunk Driving (C)

Bar Closing(B)

Time T3>T2Time T2 > T1

B A

C

CSTP: P1

a

Input: Crime reports with location and time.

Lincoln, NE crime dataset: Case study Is bar closing a generator for crime related CSTP ?

Observation: Crime peaks around bar-closing!

Bar locations in Lincoln, NE

Is bar closing a crime generator ?

Are there other generators (e.g. Saturday Nights )?

Questions

Bar closing Increase(Larceny,vandalism, assaults)

Saturday Night Increase(Larceny,vandalism, assaults)

K.S Test: Saturday night significantly different than normal day bar closing (P-value = 1.249x10-7 , K =0.41)

Lincoln, NE crime dataset: Case study

{Bar Closing}

{Vandalism}

{Assault}

Spatial Neighborhood

Gen-CPR CPI Max-CPR

1 Mile 0.0386 0.02283 0.0386

2.5 Miles 0.18491 0.04539 0.18491

Temporal Neighbor Size = 1 hrDataset Years 2002-2006


Crimes considered: Assault and Vandalism

Probability of a Bar closing generating a crime in Lincoln City = 0.038

Probability of a Lincoln city downtown Bar closing generating a crime = 0.0862


Only bar closings that also generate assaults Downtown subsetting may decrease/ increase chances.

Probability of a Vandalism after Bar closing in Lincoln City = 0.022

Probability of a Vandalism after a downtown Bar closing = 0.0397


Only bar closings that also generate Vandalism Downtown subsetting may decrease/ increase chances

Probability of an Assault after Bar closing in Lincoln City = 0.029

Probability of an Assault after a downtown Bar closing = 0.021

Spatial Network Hotspots

Geometric HotspotNetwork Hotspot

mapping and analysis for public safety: an overview

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