CrimeStat

1
CrimeStat III

• Susan C. Smith
• Christopher W. Bruce

2
About CrimeStat
3
About CrimeStat

• Spatial Statistics Program
• Analyzes Crime Incident Locations
• Developed by Ned Levine Associates
• Grant 1997-U-CX-0040
• Grant 1999-U-CX-0044
• Grant 2002-U-CX-0007
• Grant 2005-U-CX-K037
• Provides supplemental statistical tools for crime
  mapping

4
About CrimeStat

• Newest version is CrimeStat III (3.0)
• Program inputs incident locations (e.g. robbery
  locations) in .dbf, .shp, ASCII or ODBC-compliant
  formats using either spherical or projected
  coordinates
• Program calculate various spatial statistics and
  writes graphical objects to several GIS programs
  (ArcMap for the purpose of this workbook)

5
About CrimeStat

• The workbook provides copyright information
• The workbook provides information on how to
  correctly cite the program in publications/reports
  
• The workbook provides a link to obtain more
  information on CrimeStat, including the complete
  manual
• Dr. Ned Levines contact information is provided
  in the workbook

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Chapter OneIntroduction and Overview
7
In Chapter One.

• Purpose of CrimeStat III
• Uses of spatial statistics in crime analysis
• CrimeStat III as a tool for analysts
• Statistical Routines
• Hardware and Software requirements
• Downloading sample data
• Chapter Layout and Design

8
Introduction

• Nearly all crimes have a location that can be
  analyzed
• In crime analysis, we can identify patterns by
  looking at the geography of the incidents
• Analyzing crime location is a major part of
  policing from determining police districts to
  response times to determining a tactical
  deployment to an active crime series

9
Geographic Information Systems

• GIS is often synonymous with crime mapping
• Crime mapping
• Geocoding incidents or other police-related data
  and displaying them on a paper or computerized
  map
• Geocoding
• The process of assigning geographic coordinates
  to data records, usually based on the street
  address

10
Geographic Information Systems

• When incidents are geocoded, a list or database
  of crimes is turned into a map of those crimes
• This map can now tell a story about the police
  data
• Thematic maps are created
• Point Symbol maps
• Choropleth maps
• Graduated Symbol maps

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Geographic Information Systems

• Why map crime?
• Identify patterns and problems
• Identify hot spots
• Use as a visual aid
• Shows relationship between geography other
  factors
• Look at direction of movement
• Query data
• Track changes in crime
• Make maps for police deployment
• And many other reasons

13
Geographic Information Systems

• After you create the map, then analyze
• Why?
• To answer questions about data
• Historically, analysts relied heavily on visual
  interpretation of the map to answer the questions
• To identify hot spots
• To draw conclusions
• To recommend responses

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Geographic Information Systems

• Why is visual interpretation not always possible?
• Cant easily pick out hot spots among 1000s of
  data points
• Cant detect subtle shifts in the geography of a
  crime pattern over time
• Cant calculate correlations between two (or
  more) geographic variables
• Cant analyze travel times among complex road
  networks
• Cant apply complicated journey-to-crime
  calculations across tens of 1000s of grid cells
• Spatial Statisticsa need filled by CrimeStat

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CrimeStat III

• First released in August, 1999
• Current version, 3.1, released March 2007
• Not a GIS does not create or display maps
• It reads the files geocoded by a GIS and then
  exports the results into formats the GIS can read
• Effective use of CrimeStat requires a GIS and
  knowledge of its use

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CrimeStat III

• With geocoded crime data, CrimeStat can perform
  calculations and output map layers including (but
  not limited to)
• Mean/center of minimum distance of a group of
  incidents
• An area representing the standard deviation of a
  group of incidents or the entire geographical
  extent of a group of incidents
• Statistics measuring the spatial relationship
  between points (cont next
  slide)

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CrimeStat III

• (cont)
• Statistics that measure the level of clustering
  or dispersion within a group of incidents
• Distance measurements between points
• Identification of hot spots based on spatial
  proximity
• Estimation of density across a geographic area
  through kernel smoothing
• Statistics that analyze the relationship between
  space and time (cont next slide)

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CrimeStat III

• (cont)
• Statistics that analyzed the movement of a serial
  offender
• Routines that estimate the likelihood that a
  serial offender lives at any location in the
  region, based on journey-to-crime research
• And much, much more.

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CrimeStat III

• Using CrimeStat statistical routines, an analyst
  is able to
• Identify crime patterns series
• Identify the target area in which a serial
  offender is most likely to strike next
• Identify and triage hot spots
• Conduct a risk analysis across a jurisdiction
  based on known crime locations
• Create a geographic profile to assist in
  investigating suspected offenders
• Optimize patrol routes and response times

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CrimeStat III

• CrimeStat is valuable for
• Tactical Crime Analysis
• Crime Patterns, Crime Series, Forecasting
• Strategic Crime Analysis
• Hot Spots, Problem Solving, Geographic Profiling
• Operations Analysis
• Patrol Routes, Patrol Districts, Response Times

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Spatial Statistics in Crime Analysis

• Some maps are simplistic and require only a
  simple scanning and a limited amount of human
  perception
• Hot Spot Identification, Spatial Forecasting

22
Spatial Statistics in Crime Analysis

• Some map interpretation are impossible without
  spatial statistics
• Geographic Profiling, Density Mapping

23
Spatial Statistics in Crime Analysis

• It would be difficult to see subtle shifts in
  crime incidents (within a series or pattern or
  over years of changes in geography within a
  jurisdiction)

These incidents are actually moving northwestward
over time..
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Spatial Statistics in Crime Analysis

• Other spatial statistics tools available to crime
  analysts
• Those that come with ArcView MapInfo
• ArcViews SpatialAnalyst
• ArcViews Animal Movements extension
• Geographic Profiling software
• Rigel byECRI
• Dragnet from Center for Investigative Psychology
• SPSS
• Microsoft Excel
• CrimeStat puts all of the methods into one
  applicationand its free!

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Hardware and Software

• Windows operating system
• Windows 2000, Windows XP and Vista
• Must have 256 MB of RAM
• Must have 800MHz processor speed
• Best is 1GB of Ram / 1.6MHz processor
• Need a GIS to display the CrimeStat outputs
  (ArcMap used in workbook)

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Notes About the Book Course

• Introductory course only
• Certain routines/techniques most applicable to
  crime analysts
• So much more to learn
• Correlated Walk Analysis
• Journey-to-Crime
• Crime Travel Demand
• Basic GIS background required

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Exploring Lincoln, NE

• Lessons screen shots use data from Lincoln
• Some data has been manipulated or even
  created/invented for lessons
• Outputs / maps should not be taken as an accurate
  representation of crime in Lincoln
• Before starting the CrimeStat lessons, explore
  the Lincoln data in the GIS

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Exploring Lincoln, NE

• Open your GIS
• Add the following data layers
• Streets
• Citylimit
• Cityext
• Streams
• Waterways
• Display in a logical order
• Apply styles and labels as you please

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Exploring CrimeStat

• There are five tab across the top of the
  CrimeStat screen
• Under each tab, additional tabs appear
• They are color coordinated (in case you lose your
  place)
• The five main tabs are
• Data Setup - Spatial Description
• Spatial modeling - Crime travel demand
• Options

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Data Setup

• In CrimeStat
• Screen you specify the files on which you want
  CrimeStat to perform
• The calculations
• The various parameters
• Note CrimeStat does not query data
• You must already have the data queried out
• CrimeStat will perform spatial calculations on
  the entire file

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Data Setup

• CrimeStat requires at least one primary file
  which will likely contain your crime data
• Allows for a secondary file for comparisons in
  some types of spatial statistics
• Like comparing homicides (primary file) to
  poverty rates (secondary file)
• A reference file is either imported or created in
  CrimeStat
• A measurement parameters tab is provided to input
  geographic information on your jurisdiction, the
  length of the street network and the methods for
  calculating distance.

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Spatial Description

• Like descriptive statistics-analyze the data as
  is
• The Spatial Distribution tab includes functions
  that tell us the central tendency and variance in
  our data
• Includes the mean center, standard deviation
  ellipses and convex hulls
• The Distance Analysis I screen has functions to
  measure distances between points
• Nearest Neighbor Analysis Ripleys K help
  determine the significant of the clustering or
  dispersion of the incidents
• Assign primary points to secondary points takes
  the points from one file and connect them to
  their nearest neighbor in another file

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Spatial Description

• Distance Analysis II has functions that create
  matrices of distances between points
• Hot Spot Analysis I and II contains a series of
  routines that help us identify, flag, and triage
  clusters in our incident data

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Spatial Modeling

• Helps create interpolations predictions based
  on our data
• The Interpolation tab contains the options to
  create a kernel density estimation resulting in a
  density map.
• Space-time analysis is about analyzing
  progression in a series of crimes, including the
  moving average (covered) and correlated walk
  analysis (not covered)
• Journey-to-Crime and Bayesian Journey-to-Crime
  Estimation helps determine the likelihood of a
  serial offender living in a certain area based on
  the locations of his offenses (not covered)

35
Crime Travel Demand

• Helps analyze travel patterns of offenders over
  large metropolitan areas
• Emerging and potentially valuable analysis
• Very complex
• Not included in this workbook

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Summary of CrimeStat Functions

• Refer to Table 1-1, pages 12-13
• Note the functions included in the workbook
• Chapter 3 Mean Center, Standard Deviation
  Ellipse, Median Center, Center of Minimum
  Distance, Convex Hull
• Chapter 4 Nearest Neighbor Analysis, Assign
  primary points to secondary points
• Chapter 5 Mode (Hot Spot), Fuzzy Mode, Nearest
  Neighbor Hierarchical Spatial Clustering,
  Spatial and Temporal Analysis of Crime
• Chapter 6 Kernel Density Estimate
• Chapter 7 Spatial-Temporal Moving Average

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Chapter TwoGetting Data into (and out of)
CrimeStat
38
In Chapter Two...

• File formats understood by CrimeStat
• Projection and coordinate system considerations
• Associating your data with values needed by
  CrimeStat
• Accounting for missing values
• Creating a reference grid
• Measurement parameters
• Getting data out of CrimeStat

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Introduction

• Data must already be created, queried and
  geocoded
• If your RMS or CAD automatically assigns
  geographic coordinates, you can import the data
  without going thru a GIS first
• CrimeStat can read many formats, including .txt.,
  .dat, .dbf, .shp, .mdb and ODBC data sources

40
Introduction

• No matter the format, for CrimeStat to analyze
  the data, the attribute table must contain X and
  Y coordinates
• X and Y coordinates X coordinate value denotes a
  location that is relative to a point of reference
  to the east or west and the Y coordinate to the
  north or south
• Exception ArcGIS shapefiles which CrimeStat
  will interpret automatically and add the X and Y
  coordinates as the first columns in the table

41
Introduction

• Coordinate Systems
• Longitude (X) and Latitude (Y) data (spherical
  coordinates)
• Can be determined easily because the X coordinate
  will be a negative number (well, in North and
  South America)
• If data is in this format, CrimeStat doesnt need
  anything else
• CrimeStat only reads long/lat data in decimal
  degrees (used by most systems)
• U.S. State Plane Coordinates, North American
  Datum of 1983 (projected coordinates)
• Specific to each state based on an arbitrary
  reference point to the south and west of the
  state boundaries.
• CrimeStat needs to know measurement units
  (feet/meters)

42
Entering Your First Primary File

• Open basemap in ArcView
• Add burglary series shapefile
• Check projection and coordinate system
• Launch CrimeStat
• Add shapefile to CrimeStat
• Direct CrimeStat to X and Y coordinates
• Select coordinate system and data units

43
Other Settings and Options

• These are not required
• Intensity tells CrimeStat how many times to
  count each point.
• Default is to count each point once
• Weight allows us to apply different statistical
  calculations to different points
• Rarely used but will see in a future chapter
• Time is used in several CrimeStat space-time
  calculations
• Must be input as integers or decimal numbers
  will see in a future chapter

44
Other Settings and Options

• The missing value column allows us to account for
  bad data
• Tell CrimeStat which records to ignore when
  performing calculations
• Default is blank which excludes blank fields
  and those with nonnumeric values
• Users often choose 0
• Enter each missing value (-1, 99, 999)
• Cannot enter ranges

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Other Settings and Options

• Directional and distance fields are used if your
  data uses polar coordinate systems
• This is rare
• The secondary file screen allows us to enter a
  second file to relate to the first
• Must use the same coordinate system and data
  units as the primary files
• Cannot include a time variable

46
Creating a Reference Grid

• CrimeStat needs to know the extent of the
  jurisdiction
• The reference file is a grid that sits over the
  entire study area
• Can be imported or created by CrimeStat
• To have CrimeStat create the grid
• Specify coordinates of lower left and upper right
  extremities of the jurisdiction
• Coordinates must be in the same system as the
  primary file

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Creating a Reference Grid

• Select the Reference File tab create grid
• Enter values for Lower Left Upper Right
• Specify grid parameters
• Either distance for each cell, or
• Number of columns desired
• Save LincolnGrid

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Measurement Parameters

• Final bits of data for certain routines
• Total area of jurisdiction (88.19 square miles in
  Lincoln)
• Length of street network is the sum of all of the
  individual lengths of the streets (1283.61 miles
  in Lincoln)
• The distance measurement tells CrimeStat how we
  want to see the distances calculated
• Direct (as the crow flies), Indirect or Manhattan
  (along a grid) or Network (uses actual road
  network)

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Entering Measurement Parameters

• Select the Measurement Parameters tab
• Enter values for Area Length of street Network
• Choose Indirect (Manhattan) for type of
  distance measurement

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Getting Data Out of CrimeStat

• If the routine results in calculations for a
  number of records, it exports as a .dbf
• If the routine results in one or more sets of
  coordinates, exports as
• a .shp for ArcView
• a .mif for MapInfo
• a .bna for Atlas GIS boundary file

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Chapter ThreeSpatial Distribution
53
In Chapter Three...

• Spatial Forecasting
• Mean and median centerpoints
• Measures of variance
• Analyzing a cluster
• Limitations of spatial distributions

54
Introduction

• Introducing Spatial Distribution
• Forecasting
• Part Art / Part Science
• Probability
• Of being right
• Of being wrong
• Forecasting is inherent in any spatial or
  temporal analysis

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Spatial Forecasting

• Two Step Process
• Identify the target area for the next incident
• Identify potential targets in the target area

57
Spatial Forecasting

• Targets
• Consider availability of targets in any given
  area
• Banks, restaurants, convenience stores (vs.)
• Pedestrians, parked cars, houses

58
Spatial Forecasting

• Three types of spatial patterns in tactical crime
  analysis
• Those that cluster
• Concentrated in an area, but randomly dispersed
• Those that walk
• Offender moving in a predictable manner in
  distance direction
• Hybrids
• Multiple clusters with predictable walks, or
• Cluster in which the average points walks

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Types of Spatial Patterns in Tactical Analysis
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Spatial Distribution

• How are the crimes distributed?
• Average location?
• Greatest volume / concentration?
• Boundaries?
• Questions can be answered by looking at (points)
• Mean Center - Geometric Mean
• Harmonic Mean - Median Center
• Center of Minimum Distance

61
Spatial Distribution

• Questions can be answered by looking at (areas)
• Standard Deviation of X Y Coordinates
• Standard Distance Deviation
• Standard Deviation Ellipse
• Two Standard Deviation Ellipse

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Measures of Spatial Distribution

• Mean Center
• Intersection of the mean of the X coordinates and
  the mean of the Y coordinates
• Mean Center of Minimum Distance
• The points at which the sum of the distance to
  all the other points is the smallest
• Median Center
• Intersection between the median of the X
  coordinates and the median of the Y coordinates
• Great if you have outliers!

63
Measures of Spatial Distribution

• Geometric Mean Harmonic Mean
• Alternate measures of the mean center
• Just rely on the mean

64
Measures of Concentration

• Standard Deviation of the X and Y coordinates
• A rectangle encloses the area in which four lines
  intersect one s/d above the mean of the X axis,
  one s/d below the mean on the X axis, one s/d
  above the mean on the Y axis and one s/d below
  the mean on the Y axis
• Standard Distance Deviation
• Calculates the linear distance from each point to
  the mean center point, then draws a circle around
  one s/d from the center point.

65
Measures of Concentration

• Standard Deviational Ellipse
• Similar to the standard distance deviation but
  accounts for skewed distributions, minimizing any
  extra space that might appear in a circle
• Convex Hull Polygon
• Encloses the outer reaches of the series.
• No points fall outside of the polygon
• Outliers may greatly increase the size of the
  polygon

66
Analyzing a Cluster

• Open burglary series in ArcView
• Click on Spatial Description tab in CrimeStat
• Select appropriate checkboxes
• Save results for burglaryseries
• Compute
• Ten (10) ArcView shapefiles will be created
• Open each, format and compare

67
Exercises Page 33 34
68
Cautions Caveats

• You generally cant do this by hand
• Wouldnt account for multiple incidents at a
  single location
• Larger series or large volumes of crime would be
  nearly impossible to interpret on your own
• CrimeStat can be precise you cannot (usually)
• Nothing should replace your experience, intuition
  and the obvious (see Figure 3-7)

69
Figure 3-7 An unhelpful spatial distribution.
The mean center, standard deviation ellipse, and
standard distance deviation circle are
technically correct, but they miss the point of
the pattern, which is that it appears in two
clusters. The analyst in this case would probably
want to create a separate dataset for each
cluster and calculate the spatial distribution on
them separately.
70
Chapter FourDistance Analysis
71
In Chapter Four...

• Nearest neighbor analysis
• Comparing relative clustering and dispersion for
  multiple offense types
• Assigning points from one dataset to their
  nearest neighbor in another dataset

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Introduction

• Distance Analysis statistics for describing
  properties of distances between incidents
  including nearest neighbor analysis, linear
  nearest neighbor analysis and Ripleys K
  statistic
• Answers questions about the dispersion of
  incidents
• Answers questions to help us identify where
  crimes concentrate

73
Nearest Neighbor Analysis

• With random crimes scattered in a jurisdiction,
  its normal to have small cluster and wide gaps,
  but youd still have an average distance
• CrimeStat compares the actual average distance
  between points and their nearest neighbors with
  what would be expected in a random distribution
• Now you can identify if your incidents are
  significantly clustered or dispersed.

74
Measures for Distance Analysis

• Two primary measures for distance analysis in
  CrimeStat
• Nearest neighbor analysis
• Ripleys K statistic
• (not covered in this workbook)

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Nearest Neighbor Analysis

• Nearest Neighbor Analysis
• Measures the distance of each points to its
  nearest neighbor, determines the mean distances
  between neighbors and compared the mean distance
  to what would have been expected in a random
  distribution
• Can run routine to nearest, second nearest,
  third, etc.
• User define whether distance is
• Direct (standard)
• Indirect (linear)
• Based on a Network

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Nearest Neighbor Analysis

• NNA produces the Nearest Neighbor Index (NNI)
• Score of 1 no discrepancy between expected
  distance and measured distance
• Score lower than 1 incidents are more clustered
  than would be expected
• Scores higher than 2 incidents are more
  dispersed than would be expected

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Nearest Neighbor Analysis

• Most crime types show clustering
• Geography plays a significant role
• No business burglaries in places without
  businesses
• No residential burglaries in places without
  residences
• No bank robberies in cities with no banks
• Primary value for analysts
• Conduct distance analysis for several crimes and
  compare the results to each other
• You can then determine which offenses are most
  clustered into hot spot and which are more
  disperse

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Comparing Distances for Three Offenses

• Set up data in new CrimeStat session
• On Measurement Parameters, enter jurisdiction
  information and type of distance measurement
• Check Nearest Neighbor Analysis box on Spatial
  description/Distance Analysis I tab
• Compute and examine results
• Repeat for other files
• Examine findings

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Crime Actual Expected NNI
Robberies 1066.8578 1874.1078 0.56926
Residential Burglaries 348.7187 636.7427 0.54766
Thefts from Autos 236.2937 447.2314 0.52835
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Cautions, Caveats and Notes

• We are computing single nearest neighbor
• You can change to another value, but not higher
  than 100
• Significance is only calculated on single nearest
• Limited utility for doing this
• Nearest Neighbors may occur on borders
• NNA overestimates in this case, compensating for
  the edge effect if Border correction option
  is chosen

83
Assigning Primary Points to Secondary Points

• Two ways to conduct
• Nearest Neighbor Assignment
• Assigns each point in the primary file to the
  nearest point in the secondary file
• Point-in-polygon Assignment
• CrimeStat interprets the geography of a polygon
  rile (like police beats) and calculates how many
  points fall within each file, regardless of
  anything a point is technically closest to
• ArcGIS MapInfo can perform this easily

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Assigning Primary Points to Secondary Points

• Set up CrimeStat for afternoonhousebreaks
• Add schools on the Secondary File tab
• On Spatial Description / Distance Analysis I tab,
  check Assign Primary Points to Secondary Points
  box
• Save results
• Compute and examine results

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Chapter FiveHot Spot Analysis
87
In Chapter Five...

• Summary of different hot spot routines
• Mode and fuzzy mode
• Nearest neighbor hierarchical spatial clustering
• Spatial and Temporal Analysis of Crime

88
Introduction

• Identifying hot spots
• A spatial concentration of crime, or
• A geographic area representing a small percentage
  of the study area which contains a high
  percentage of the studied phenomenon
• Can be on a variety of scales
• A hot address
• A hot office building
• A hot block
• A hot area

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Hot Spot Routines

• Mode
• Identifies the geographic coordinates with the
  highest number of incidents
• Fuzzy Mode
• Identifies the geographic coordinates, plus a
  user-specified surrounding radius, with the
  highest number of incidents
• Nearest-Neighbor Hierarchical Spatial Clustering
• Builds on NNA by identifying clusters of
  incidents

90
Hot Spot Routines

• Spatial Temporal Analysis of Crime (STAC)
• Alternate means of identifying clusters by
  scanning the point and overlaying circles on
  the map until the density concentrations are
  identified
• K-Means Clustering
• User specifies the number of clusters and
  CrimeStat positions them based on the density of
  incidents

91
Hot Spot Routines

• Aneslins Local Moran statistic
• Compares geographic zones to their larger
  neighborhoods and identifies those that are
  unusually high or low
• Kernel Density Interpolation
• A spatial modeling technique

92
Mode

• Just counts the number of incidents at one spot
• Note same address vs. X Y coordinates
• Which is your records management or CAD system
  receiving?
• How would this effect the mode?

93
Mode

• Set up a new CrimeStat session
• Check Mode on Spatial description / Hot Spot
  Analysis I tab
• Click compute
• Top 45 locations, ordered by frequency
• Save result to (.dbf)
• (You could then import to GIS)

94
Fuzzy Mode

• User can specify a search radius around each
  point
• Hence, it will include all of the points within
  that radius in the count
• For agencies with GPS data, may be only way to
  find hot spots
• Unlikely two incidents will have identical X Y
  coordinates

95
Figures 5-3 and 5-4 Accidents at several
intersections. The agency has been ultra-accurate
in its geocoding, assigning the accidents to the
specific points at the intersections where they
occur. The mode method (left) would therefore
count each point only once, whereas the fuzzy
mode method (right) aggregates them based on
user-specified radiuses
96
Fuzzy Mode

• Return to CrimeStat screen
• Uncheck Mode / Check Fuzzy Mode
• Search radius of 500 feet
• Save result to
• Compute
• Note different results from Mode
• Create proportional symbol map based on frequency
  in ArcView

97
Nearest Neighbor Hierarchical Spatial Clustering

• Builds on NNA (NNA determines if a particular
  crime was more clustered than might be expected
  by random chance)
• NNH takes the analysis to the next level by
  actually identifying those clusters
• CrimeStat clusters groups of pairs that are
  unusually close together
• It creates first order, second order etc.
  clusters
• Continues until it cannot locate any more
  clusters
• Creates both s/d ellipses convex hulls

98
Nearest Neighbor Hierarchical Spatial Clustering

• Options that can be used when running NNH
• Fixed distance vs. threshold distance
• Becomes a subjective measure vs. probability
• Minimum points per cluster
• Default is 10
• Alter depending on volume type of crime
• Search Radius Bar
• Adjust threshold distance and associated
  probability
• Left smallest distance, but 99.999 confidence
• Right greatest distances, but only .1
  confidence

99
Nearest Neighbor Hierarchical Spatial Clustering

• Options (cont)
• Number of standard deviations for the ellipses
• Single s/d is the default/norm
• Can make small ellipses that are hard to view at
  a small scale
• Another option is two s/ds
• May exaggerate the size of the hot spot
• Convex hull vs. ellipse
• Convex hull has greater accuracy
• Convex hull has a higher density than an ellipse
• Convex hulls are defined by the data

100
Nearest Neighbor Hierarchical Spatial Clustering

• Data Setup Measurement parameters
• Spatial description, Hot Spot Analysis I, uncheck
  Fuzzy Mode, check NNH
• Adjust minimum number points size of ellipses
• Save ellipses to.
• Save convex hulls to.
• Compute
• Add to ArcView project evaluate
• Experiment with other NNH settings

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Spatial and Temporal Analysis of Crime (STAC)

• Originally a separate program integrated into
  CrimeStat in Version 2
• Produces ellipses and convex hulls
• STACs algorithm scans the data by overlaying a
  grid on the study area and applying a search
  circle to each node of the grid
• Size is specified by user
• Routine counts the number of points in each
  circle to identify the densest clusters

103
Spatial and Temporal Analysis of Crime (STAC)

• Un-check NNH option check STAC option
• Set STAC Parameters
• Note reference file From data set option
• Save ellipses to.
• Save convex hulls to.
• Compute
• Open in ArcView
• Examine results
• Run with other parameters

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Final Notes on Hot Spot Identification

• Clusters are identified based on volume, not risk
• Two areas of town
• 3 burglaries in rural area vs. 20 burglaries in
  midtown
• Technique to normalize hot spots available
• Risk-Adjusted Nearest Neighbor Hierarchical
  Spatial Clustering (RNNH)
• Relies on a secondary file with a denominator
• Number of houses, parking spots, etc
• In all of these routines, subjectivity plays a
  role

106
Chapter SixKernel Density Estimation
107
In Chapter Six...

• How kernel density estimation works
• Understanding different interpolation methods
• Guidelines for kernel size and bandwidth
• Creating and mapping a kernel density estimation
• Uses and misuses of kernel density

108
Introduction

• Crime Analysts most often create
• Pin maps
• Kernel density maps
• AKA surface density maps
• AKA continuous surface maps
• AKA density maps
• AKA isopleth maps
• AKA grid maps
• AKA hot spot maps

109
Introduction

• Kernel Density Estimation (KDE)
• Generalizes data over larger regions
• As opposed to volumes of incidents at specific
  locations
• Good image to show estimation
• Comparative to weather maps
• What is going on here is probably going on
  there
• Question on accuracy in crime analysis
• Provides a risk surface more than an actual
  picture of what is occurring

110
How KDE Works

• Every point on the map has a density estimate
  based on its proximity to crime incidents
• Done by overlaying a grid on top of the map
• Calculates the density estimate for the
  centerpoint of each grid cell
• Number of cells in the grid is defined by the user

111
How KDE Works

• CrimeStat measures the distance between each grid
  cell centerpoint and each incident data point and
  determines the cell weight for that point
• Sums the weights received from all points into
  the density estimate
• But the weight of each cell depends on three
  things.

112
How KDE Works

• Weight of each cell depends on
• Distance from the grid cell centerpoint to the
  incident data point
• Size of the radius around each incident data
  point
• Method of interpolation

113
How KDE Works

• Method of Interpolation
• KDE places a symmetrical surface called a kernel
  over each point (size specified by user, shape
  specified by method of interpolation)
• the value is then smoothed throughout the kernel
• finally, overlay a grid

114
How KDE Works

• In a map, the grid cells are color-coded based on
  the density
• Often reds for hottest area and blues for coolest

115
KDE Parameters

• Many parameters involved
• Analyst must use experience judgment
• Single versus dual kernel density estimates
• Single is usually used in crime analysis
• Dual can help normalize data for population or
  other risk factors or calculate change from one
  time to the next
• Bandwidth
• Refers to the size of the cone specified by user

116
KDE Parameters

• Methods of interpolation (shape of bandwidth)
• Normal (bell curve)
• peaks declines rapidly
• No defined radius continues across entire grid

117
KDE Parameters

• Methods of interpolation (shape of bandwidth)
• Uniform (flat) distribution
• Represented by cylinder all points in radius
  equal

118
KDE Parameters

• Methods of interpolation (shape of bandwidth)
• Quartic (spherical) distribution
• Gradual curve density highest over point falls
  to limit of radius

119
KDE Parameters

• Methods of interpolation (cont)
• Triangular (conical) distribution
• Peaks above the point falls off in a linear
  manner to edges of radius

120
KDE Parameters

• Methods of interpolation (cont)
• Negative exponential distribution
• Curve that falls off rapidly from the peak to a
  specified radius

121
KDE Parameters

• Each method will produce different results
• Triangular negative exponential produce many
  small hot and cold spots
• Quartile, uniform and normal distribution
  functions smooth data more

Negative exponential Normal
Distribution
122
KDE Parameters

• Parameter to specify size of bandwidth
• Choice of Bandwidth
• Minimum Sample Size
• Interval
• With adaptive, CrimeStat will adjust the size
  of the kernal until its large enough to contain
  the minimum sample size
• With fixed interval bandwidth, you specify the
  size

123
KDE Parameters

• Output units (any will work fine)
• Absolute densities
• Sum of all the weights received by each cell, but
  re-scaled so the sum of the densities equal the
  total number of incidents (default)
• Relative densities
• Divides the absolute densities by the area of the
  grid
• Red represents X points per square mile, not
  per grid cell
• Probabilities
• Divides the density by the total number of
  incident
• Chance that any incident occurred in that cell

124
KDE Parameters

• Deciding which parameters to use for a particular
  dataset
• Across how great an area is this incident likely
  to have an effect
• Adjust interval distance (bandwidth size)
• How much of this effect should remain at the
  original location how much dispersed?
• Adjust method of interpolation

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Incident Type Interval Interpolation Method Reasoning
Residential burglaries 1 mile Moderately dispersed quartic or uniform Some burglars choose particular houses, but most cruise neighborhoods looking for likely targets. A housebreak in any part of a neighborhood transfers risk to the rest of the neighborhood.
Domestic violence 0.1 mile Tightly focused negative exponential Domestic violence occurs among specific individuals and families. Incidents at one location do not have much chance of being contagious in the surrounding area.
Commercial robberies 2 miles Focused triangular or negative exponential A commercial robber probably chooses to strike in a commercial area, and then looks for preferred targets (banks, convenience stores) within that area. The wide area may thus be at some risk, but the brunt of the weight should remain with the particular target that has already been struck.
Thefts from vehicles 0.25 mile Dispersed uniform If a parking lot experiences a lot of thefts from vehicles, your GIS will probably geocode them at the center of the parcel. This method ensures that the risk disperses evenly across the parcel and part of the surrounding area (which probably makes sense)but not too far, since we know that parking lots tend to be hot spots for specific reasons.
126
Creating a KDE

• Data setup add ArcView SHP file theftfromautos
• Create reference grid on Reference File tab
• On Spatial modeling tab, Interpolation sub-tab,
  chose Single KDE adjust bandwidth and select
  interpolation method
• Save result to compute
• Open KLFA shapefile in ArcView and create a
  choropleth map
• Experiment with different settings

127
Dual KDE

• KDE based on two files
• Primary Secondary
• Primary use is to normalize for risk
• In single KDE, hot spots are based on volume
• In dual KDE, hot spots are based on risk
• Four things to keep in mind
• Sometimes you just want volume
• Data for secondary file is hard to come by
• The point data in the secondary file is
  interpolated just like the primary file
• You cannot use a different interpolation method
  for numerator and denominator (but you can use an
  adaptive bandwidth)

128
Dual KDE

• Set up Secondary File like Primary File except
• Ratio of Densities
• Divides the density in the primary file with the
  density in secondary file
• Log ratio of densities
• Helps control extreme highs and lows
• Valuable in strongly skewed distributions
• Absolute difference in densities
• Subtracts the secondary file densities from the
  primary file densities
• Valuable in analyzing one time period to the next

129
Dual KDE

• Set up Secondary File like Primary File except
  (cont)
• Relative difference in densities
• Option divides primary and second files densities
  by area of the cells before subtracting them
  (just like absolute difference)
• Sum of densities
• Adds two densities together
• Useful to show combined effects of two types of
  crime
• Relative sum of densities
• Divides primary and second files by the area of
  the cells before adding them

130
Dual KDE

• On Data Setup, remove larcey from autos and add
  resburglaries.shp file
• On Secondary File, select censusblocks.dbf, set
  variables, including Z (Intensity)
• On Spatial Modeling, Interpolation tabs, select
  Dual box (check weighting variable option)
• Save Result to (.shp)
• Open ArcView, add layer, create choropleth map

131
Dual KDE Uses and Cautions

• KDE is a hot spot technique, but it is part
  theoretical
• KDE maps are interpolations
• Meaning incidents did not occur at all of the
  locations within the hottest color
• Creates a uniform risk surface (which is rare)
• You can only have bank robberies where there are
  banks
• Hence, interpret a KDE in reference to where
  suitable targets may exist within the risk surface

132
Chapter SevenSpatial Temporal Moving Average
133
In Chapter Seven...

• Understanding the Spatial Temporal Moving Average
• Using a time variable in CrimeStat

134
Introduction

• Spatial-Temporal Moving Average (STMA)
• Set of points in robbery series
• But mean, SD, SDE doesnt represent the series
• Something is off
• Recall two types of crime patterns (Chpt 3)
• Those that cluster
• Those that walk

135
Introduction

• This one walks

136
Introduction

• STMA calculates the mean center at each point in
  the series
• Tracks how it moves over time
• User specific how many point are included in each
  calculation using the span parameter
• A span of 3 means it calculates the average for
  that point and the two points on either side of
  it in the sequence
• Final result is a series of moving average points
  tied together to create a path

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139
Introduction

• Span is the only parameter in the STMA
  calculation
• Use an odd number for the center observation to
  fall on an actual incident
• Default is five (5)
• Use caution when changing it
• Too high wont see much movement
• Too low just viewing changes from one incident
  to the next

140
Introduction

• All of the Space-Time analysis routines require
  a time variable
• STMA needs it so it will know how the incidents
  are sequenced.
• CrimeStat will not accurately calculate actual
  date/time fields like 06/09/2008 or 1510.
• Instead, it requires actual numbers.
• It doesnt matter where the numbers start as long
  as the intervals are accurate, so if your data
  goes from June 1, 2008 to July 15, 2008, you
  could assign 1 for June 1, 2 for June 2, 31
  for July 1, and so onor you could assign 3000
  for June 1 and 3031 for July 1.
• Its really only the intervals that matter.

141
Introduction

• Microsoft makes date/time conversions easy
• It stores dates as the number of days elapsed
  since January 1, 1900 and times as proportions of
  a 24-hour day
• In either Access or Excel, we can convert date
  values to these underlying numbers, so June 1,
  2008 becomes 39600, and 1510 becomes 0.6319
• We have already used Excel to figure the
  Microsoft date from the actual date, and the
  field is labeled MSDate

142
STMA

• New CrimeStat session using CSRobSeries.shp file
• Add Time setting
• Note it needs a number, not an actual date/time
• Already calculated in MSExcel use MSDate
• Time Unit Days
• Spatial Modeling, space-time analysis tab, check
  STMA

143
STMA

• Save Output as .dbf
• CSRobSeries
• Save Graph as ArcView SHP
• CSRobSeries
• Compute
• Examine results
• Offender moving which way?
• What targets are available?
• Forecast next offense

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CrimeStat III