Embed Size (px)
Citation preview
HunchLab: Spatial-Temporal Data Mining Robert Cheetham
10 April 2009 Workshop on Spatial-Temporal Modeling
Geographic
Analysis
Geoprocessing
Web Services &
Solutions
Custom Web
Apps
PhillyHistory.org
Crime Mapping
Connect 211
Connect 211
Weighted Raster Overlay
x 5 x 2x 3x 1
+ ++
=
AfricaMap
AfricaMap
Background
1,500,000 humans
7,000 police
1,000 civilian employees
2,000,000 new incidents / year
Philadelphia Context
3 crime analysts
Weekly Compstat
Lots of maps
Automation of map creation
Web-based systems
What we did
- accelerate the cycle
- proactively notify
- automatically run
… But what if we could
1999 Internal research project at Phila PD
2000 Paper for NIJ Crime Mapping Conference
2004-2005 Prototype
2007-2010 National Science Foundation R & D Grant
Crime Early Warning System
ArcViewVB & MapObjects
MS SQL Server
Crime Incidents
Database
Shapefiles
and
GRIDs
Process
Documentation
.ini
file
Philadelphia Context
Philadelphia Context
Philadelphia Context
Philadelphia Context
Bad News
It was crap…
…sort of
We needed
1. Better Statistics
2. Notification
3. Super-simple
Round 2: Crime Spike Detector
Round 2: Crime Spike Detector
Burglaries, 6/20/2004, 1275ft. Radius, 30 Day Period Each Year
Round 2: What we learned
1. People used it but not the way we expected
2. More flexibility
3. More customized to user needs
� Automatic detection
� Subscribe to search patterns
� Automatically e-mail
� Configurable
� Fast
� Centralized
� Individualized
� Applicable to any space-time event
� No new software or hardware
Round 3: HunchLab Objectives
What is HunchLab?
HunchLab DatabaseCrime
Database
GIS Database
Alerts
Alerting SystemGeostatistical Engine
Crime Mapping Tools
Web Map Service
HunchLab Web Site
A crime spike is an “above average” amount, but:
How do we know when to send an alert?
� How much above average?
� What “average” are we using?
� When? Over what period of time?
� Where?
� What category of event?
1. Location
2. Time
3. Type of Event
Basic Data Requirements
1. Location
2. Time
3. Filter
Setting up a Hunch
Four options:
1) Address + Radius;
2) District/Sector/Beat
3) Custom Polygon
4) Mass Hunch
Hunch Parameters – Location
Time: two parts:
1) Current Data (recent past)
2) Historical Data
Hunch Parameters – Time
Hunch Parameters – Time
Event Filter:
� Category
� Time of Day
� Text (future)
Hunch Parameters – Event Filter
Hunch Parameters – Type of Hunch
� Statistical Hunch
� Event Trigger
Notification
Email Notification
� Too large area or too long time � nothing is a spike.
� Too small area or too short time � everything is a spike.
� Different crime classes require different calibration.
Defining Hunches can be tricky because…
� Doesn’t need lots of detailed data …
� … but has to be accurate data
� Humans still required
� No explanatory or interpretive power
� Not good for low volume events
The small print ….
High volume
databases:
humanly
impossible to track
all the data
When and where
has something
different
happened?
Geographic
Notification
Large space-time events databases
� Public Health
� Real Estate Sales
� Other data sets
Not just for criminals anymore….
