ArcGIS Space-Time Mining of Crime Data
- 1. Margaret Furr MINING CRIME INCIDENT DATA FOR SPACE-TIME PATTERNS AROUND WASHINGTON, DC CAMPUSES Photo Source: http://www.thecollegefix.com/post/19184/
- 2. DATA • 2012 Crime Incidents; 2013 Crime Incidents; 2014 Crime Incidents • File type: shapefile, points • Variables of interest: X coordinates, Y coordinates, Offense, Report Date, Start Date, End Date • Observations: 109,656 • Projection: Spatial reference = 26985 • Source: Open DC Data • University and College Campuses; Campus Areas Zoning • File type: shapefile, polygons • Variables of interest: Campus names, Campus areas, Campus lengths • Observations: 8 college/university campus zoning areas, 30 college/universities • Projection: Spatial reference = 26985 • Source: Open DC Data • USStates • File type: shapefile, polygons • Observations: 1 for each state + 1 for Washington, DC • Projection: GCS_WGS_1984 GCS • Source: United States Geodatabase 1Photo Source: http://opendata.dc.gov/
- 3. RESEARCH MOTIVATIONS • Crime -- less likely to occur on campuses • Washington, DC -- not a city with the most crime • 2 DC universities - ranked as the most dangerous campuses in some news reports • Gallaudet University • Howard 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 1990s • Understanding crime frequencies and trends across time helps (1) police departments, (2) administrators, (3) policymakers, (4) journalists and (5) students make decisions 2
- 4. RESEARCH MOTIVATIONS • Researchers have analyzed space-time crime patterns • None have looked at DC crimes as they relate to campuses’ locations • I have conducted spatial-regression analyses using Chicago crime data and R spatial packages • I have not conducted space-time analyses, using the time series component of crime data, but I find this component to be an important one • I have not analyzed crime data that occurs in the DC area, but this is where 2 universities are reported to be unsafe • I have not used ArcGIS tools yet! 3
- 5. RESEARCH QUESTIONS • What 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? 4
- 6. RESEARCH APPROACH • Space-Time Analysis of crime within buffered campus areas • Space-Time Cube • Emerging Hot Spot Analysis 5
- 7. • Define Projection and Project • Selection • Merge • Buffer • Frequency Analysis • Convert Time • Create Space-Time Cube • Emerging Hot Spot Analysis TOOLS 6 Photo Sources: https://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining-toolbox/learnmorecreatecube.htm https://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining-toolbox/learnmoreemerging.htm
- 8. GEODATABASE • FinalProjectMargaretFurr.gdb • Crime Incident data • Campus Area data • DC polygon 7
- 9. PROJECTION • Crime Incident data and Campus Area data had no projection • Metadata said this data was 26985 • Defined data as NAD83-Maryland, Projection • Reprojected data to be NAD83 17N, UTM • DC data had WGS_1985 Coordinates and WGS_1984 datum • Reprojected data to be NAD83 17, UTM 8
- 10. MERGE • Merged 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) 9
- 11. DC LAYER: SELECT • Data from DC Open data does not have any shapefiles for the general DC area • UnitedStates.gdb has a USStates shapefile, which includes DC as one state • Selected DC from USStates • Exported selection as its own DC layer (DC_NAD8317) 10
- 15. CREATE BUFFERS • Create buffer around campus areas • 1000 ft. is a standard buffer for schools • 1000 ft = 305m • 305m as the first buffer distance • 1500 ft, 458m and 2000 ft, 610m* as second and third buffer distances • SELECT crime points within buffers • 40,873 incidents with 2000 ft. or 610m* of campus areas 14
- 17. INITIAL TIME SERIES VISUALIZATION 16 Buffer Distance: 2000 ft./610 m
- 18. BUFFERED FREQUENCIES BY TYPE 17
- 19. BUFFERED FREQUENCIES BY TYPE Offense Frequency - all data Frequency - data in buffer 1 Frequency - data in buffer 2 Frequency - data in buffer 3 Arson 95 11 15 21 Assault w/ dangerous weapon 7224 783 1174 1533 Burglary 10151 1470 2247 2991 Homicide 296 28 39 52 Motor vehicle theft 8633 967 1573 2070 Robbery 11470 1568 2437 3341 Sex abuse 862 171 232 269 Theft f/ auto 31299 6283 9650 13162 Theft / other 39269 8540 13561 17414 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Arson Assault w/ dangerous weapon Burglary Homicide Motor vehicle theft Robbery Sex abuse Theft f/ auto Theft / other Frequency - data in buffer 1 Frequency - data in buffer 2 Frequency - data in buffer 3 Frequency Differences: Buffered Data Type Frequencies/All Data Type Frequencies 18
- 20. • “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 CUBE 19 Photo Sources: https://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining- toolbox/learnmorecreatecube.htm
- 21. • “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 20 Photo Sources: https://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining- toolbox/learnmorecreatecube.htm
- 22. CONVERT TIME • Original date variable = String type • Date type is required for Space-Time Pattern Mining Tools • Report date, Start date, End date 21
- 24. CREATE SPACE TIME CUBE • Time Step Alignment: start time, end time, reference time • End time; eliminates bias from choosing a reference time • Time Step Interval: 1 Weeks • Distance Interval: • Calculated optimal interval based on algorithm that considers spatial distribution (histogram bin-width optimization) • Template Cube: • did not use 23 Photo Sources: https://desktop.arcgis.com/en/desktop/latest/tools/space-time-pattern-mining- toolbox/learnmorecreatecube.htm
- 25. STATISTICS FROM SPACE TIME CUBE • Mann-Kendall Statistic • Statistical 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 values” • Rank correlation analysis for the bin count or value and their time sequence • +1 if 1st bin < 2nd bin • -1 if 1st bin > 2nd bin • 0 if 1st bin = 2nd bin) • Results are summed • Observed sum compared to expected sum • p-value • small p-value: the trend is statistically significant 24
- 26. STATISTICS FROM SPACE TIME CUBE All Data Buffered Data 3 Total Number of Locations 8989 8400 Locations with at least one point 2945 1814 Associated Bins 5289220 3198082 % Non-zero Sparseness 1.54 1.03 Time Step Interval 1 week 1 week Distance Interval 201m 134m Number of Time Steps 1796 Cube Extent Across Space Min Y 836904.0313m 837838.3256m Min X 4303610.9737m 4309021.9271m Max X 854777.2083m 849063.8549m Max Y 4323770.1568m 4322418.4232m Rows 101 100 Columns 89 84 Total bins 16144244 14809200 Overall Data Trend Trend Direction Increasing Trend Statistic 24.3788 23.9058 Trend p-value 0 0 25
- 27. • Theft/Other: Increasing Trend, Significant • Theft f/Auto: Increasing Trend, Significant • Robbery: Decreasing Trend (>-5), Significant • Burglary: Increasing Trend (<5 though unlike others >18), Significant (> 0 though unlike 0) • Motor Vehicle Theft: Increasing Trend (<6), Significant • Assault with Dangerous Weapon: Decreasing Trend, Insignificant • Sex Assault: Increasing Trend, Significant • Homicide: too few records for analysis • Arson: too few records for analysis STATISTICS FROM SPACE TIME CUBES BY TYPE 26
- 32. CONCLUSIONS • Around Howard University there are significant patterns of theft/auto crimes and also sexual assault • Theft/auto incidents are emerging consecutively • Sexual Assault incidents emerging sporadically • These types are two reported (among with others) in the media • Around George Washington University and Georgetown University there are also significant patterns of theft/other crimes • Theft/other incidents are emerging consecutively to the non-river, and also Howard University side of George Washington University • Theft/other incidents are emerging sporadically on the river-side of Georgetown University and George Washington University • Both types of theft are emerging consecutively around other US colleges’ and universities’ DC campuses • Cornell in Washington, University of California Washington Center 31
- 33. FUTURE WORK 32 • More years of crime data? • Experiment with ArcGIS Pro or ArcGIS’s multidimensional toolbox to visualize the space time box and understand it better • Develop a Python script to tune, or optimize the time and distance intervals as well as the buffer distance for more accurate results • Experiment with the template cube for potentially more consistent results across types
- 34. 33 QUESTIONS?