Space-Time Analysis of Crime Near and Far from Washington, DC Colleges and Universities

Margaret FurrMining Crime incident DATA for space-time patterns around washington, dc campuses

Photo Source: http://www.thecollegefix.com/post/19184/

DATA2012 Crime Incidents; 2013 Crime Incidents; 2014 Crime Incidents File type: shapefile, pointsVariables of interest: X coordinates, Y coordinates, Offense, Report Date, Start Date, End DateObservations: 109,656Projection: Spatial reference = 26985 Source: Open DC DataUniversity and College Campuses; Campus Areas ZoningFile type: shapefile, polygonsVariables of interest: Campus names, Campus areas, Campus lengthsObservations: 8 college/university campus zoning areas, 30 college/universitiesProjection: Spatial reference = 26985 Source: Open DC DataUSStatesFile type: shapefile, polygonsObservations: 1 for each state + 1 for Washington, DCProjection: GCS_WGS_1984 GCS Source: United States Geodatabase

1Photo Source: http://opendata.dc.gov/

Research MotivationsCrime -- less likely to occur on campusesWashington, DC -- not a city with the most crime

2 DC universities - ranked as the most dangerous campuses in some news reports Gallaudet UniversityHoward University (7 forcible rapes, 90 robberies, 27 aggravated assaults, 160 burglaries, 43 car thefts)

In general -- a growing concerns about students safety on campuses since 1990sUnderstanding crime frequencies and trends across time helps (1) police departments, (2) administrators, (3) policymakers, (4) journalists and (5) students make decisions 2

Research MotivationsResearchers have analyzed space-time crime patternsNone have looked at DC crimes as they relate to campuses locationsI have conducted spatial-regression analyses using Chicago crime data and R spatial packagesI have not conducted space-time analyses, using the time series component of crime data, but I find this component to be an important oneI have not analyzed crime data that occurs in the DC area, but this is where 2 universities are reported to be unsafeI have not used ArcGIS tools yet!

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Research QuestionsWhat is the frequency of each crime type at locations near campuses, and how do these frequencies compare to the frequencies of each type far from campuses?Looking at the start datetimes of 2012-2014 crime reports, are there any patterns in when each incident occurs near campuses?Do these patterns reveal emerging trends? If so, how do trends differ by crime type?What trends are emerging near the two most dangerously ranked DC campuses?

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Research ApproachSpace-Time Analysis of crime within buffered campus areasSpace-Time CubeEmerging Hot Spot Analysis5

Define Projection and ProjectSelectionMergeBufferFrequency AnalysisConvert TimeCreate Space-Time Cube Emerging Hot Spot Analysis

Tools6Photo Sources: https://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining-toolbox/learnmorecreatecube.htmhttps://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining-toolbox/learnmoreemerging.htm

GeodatabaseFinalProjectMargaretFurr.gdbCrime Incident dataCampus Area dataDC polygon7

ProjectionCrime Incident data and Campus Area data had no projection Metadata said this data was 26985Defined data as NAD83-Maryland, ProjectionReprojected data to be NAD83 17N, UTM

DC data had WGS_1985 Coordinates and WGS_1984 datumReprojected data to be NAD83 17, UTM8

MERGEMerged Crime Incidents 2012 Crime Incidents (NAD83-17N)2013 Crime Incidents (NAD83-17N)2014 Crime Incidents (NAD83-17N)Merged Campus Areas University College Campuses (NAD83-17N)Campus Areas Zoning (NAD83-17N)

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DC Layer: SelectData from DC Open data does not have any shapefiles for the general DC areaUnitedStates.gdb has a USStates shapefile, which includes DC as one stateSelected DC from USStatesExported selection as its own DC layer (DC_NAD8317)

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Initial Data Visualizations11

Initial Data Visualizations

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Frequencies by Type

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Create BuffersCreate buffer around campus areas1000 ft. is a standard buffer for schools1000 ft = 305m 305m as the first buffer distance1500 ft, 458m and 2000 ft, 610m* as second and third buffer distances

SELECT crime points within buffers40,873 incidents with 2000 ft. or 610m* of campus areas

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Buffered Areas

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Initial Time Series visualization16

Buffer Distance: 2000 ft./610 m

Buffered Frequencies by Type

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Buffered Frequencies by typeOffenseFrequency - all dataFrequency - data in buffer 1Frequency - data in buffer 2Frequency - data in buffer 3Arson95111521Assault w/ dangerous weapon722478311741533Burglary10151147022472991Homicide296283952Motor vehicle theft863396715732070Robbery11470156824373341Sex abuse862171232269Theft f/ auto312996283965013162Theft / other3926985401356117414

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Summarizes a set of points into a netCDF data structure by aggregating them into space-time bins.Within each bin, the points are counted.For all bin locations, the trend for counts over time are evaluated.

Space-Time Pattern Mining Toolbox: create space Time Cube19Photo Sources: https://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining-toolbox/learnmorecreatecube.htm

Identifies trends in the clustering of point densities (counts) or summary fields in a space time cube created.Categories: (1) new, (2) consecutive, (3) intensifying, (4) persistent, (5) diminishing, (6) sporadic, (7) oscillating, and (8) historical hot and cold spots.

Space-Time Pattern Mining Toolbox: Emerging Hot spot analysis

20Photo Sources: https://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining-toolbox/learnmorecreatecube.htm

Convert timeOriginal date variable = String typeDate type is required for Space-Time Pattern Mining ToolsReport date, Start date, End date

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Incorrect Values

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Create space time cubeTime Step Alignment: start time, end time, reference timeEnd time; eliminates bias from choosing a reference timeTime Step Interval: 1 WeeksDistance Interval:Calculated optimal interval based on algorithm that considers spatial distribution (histogram bin-width optimization)Template Cube:did not use

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Photo Sources: https://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining-toolbox/learnmorecreatecube.htm

Statistics from space time cubeMann-Kendall StatisticStatistical question: are the events represented by the input points increasing or decreasing over time?Answer: the number of points for all locations in each time-step interval, analyzed as a time series of count valuesRank correlation analysis for the bin count or value and their time sequence +1 if 1st bin < 2nd bin-1 if 1st bin > 2nd bin0 if 1st bin = 2nd bin)Results are summedObserved sum compared to expected sum

p-valuesmall p-value: the trend is statistically significant

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Statistics from space time cubeAll DataBuffered Data 3Total Number of Locations89898400Locations with at least one point29451814Associated Bins52892203198082% Non-zero Sparseness1.541.03Time Step Interval1 week1 weekDistance Interval201m134mNumber of Time Steps1796Cube Extent Across SpaceMin Y836904.0313m837838.3256mMin X4303610.9737m4309021.9271mMax X854777.2083m849063.8549mMax Y4323770.1568m4322418.4232mRows101100Columns8984Total bins1614424414809200Overall Data TrendTrend Direction IncreasingTrend Statistic24.378823.9058Trend p-value00

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Theft/Other: Increasing Trend, SignificantTheft f/Auto: Increasing Trend, SignificantRobbery: Decreasing Trend (>-5), SignificantBurglary: Increasing Trend (18), Significant (> 0 though unlike 0)Motor Vehicle Theft: Increasing Trend (