a spatial analysis of random gunfire incidents in dallas, tx
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A Spatial Analysis of Random Gunfire Incidents in Dallas, TX. Chad Smith Geography Undergraduate [email protected]. Objectives of Research. Determine the difference between incidents of random gunfire (RGF) and reports of RGF. Determine the demographic profile of the RGF incident locations. - PowerPoint PPT PresentationTRANSCRIPT
A Spatial Analysis of Random Gunfire Incidents in Dallas, TX.
Chad SmithGeography [email protected]
Objectives of Research
Determine the difference between incidents of random gunfire (RGF) and reports of RGF.
Determine the demographic profile of the RGF incident locations.
Determine the neighborhoods where clusters of RGF occur.
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Definitions
Random Gunfire Incident: – Firing a gun into the air, primarily at night. – Offender does not have intent to harm. – Usually in celebration.
Report: – Call placed to 9-1-1 as a result of RGF.– Does not distinguish between unique incidents
and incidents previously reported.
Dallas Police Department 9-1-1 RFG Calls Received
Assumptions
Reports made within a half mile (2,640 ft.) and fifteen minutes are the same incident of RGF.
The first call received is closest to the incident location.
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Data Sets
Dallas Police Department 9-1-1 RGF calls received from August 1, 2006 to November 31, 2006.
– 4,707 distinct records.– 4,702 records geocoded to a known address.– 5 records were excluded for lack of a valid address.
U.S. Census Data for the 2000 Census.– Block group data– Census Tract data
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Methodology for determining incidents from reports.
9-1-1 RGF records were geocoded with ESRI’s ArcView (9.2) software, using line segment approximation.
Each record was reviewed for concurrent records within the half mile and fifteen minute threshold.
The calls were assigned a value based on the order the RGF call was received.
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Incident
Date Time
1 10/15/2006
1:00 AM
2 10/15/2006
1:00 AM
3 10/15/2006
1:01 AM
4 10/15/2006
1:03 AM
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Sequence of Reports
Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Sequence of Reports
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Results of Classification Process
4,702 reports of RGF were classified as 3,285 incidents.
The typical RGF incident generates 1.43 calls to 9-1-1.
The highest number of reports for a single incident, based on the time/distance threshold, was 31 calls to 9-1-1.
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0
250
500
750
1000
1250
1500
1750
2000
2250
2500
2750
3000
3250
3500
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Reports Made to 9-1-1
Inc
ide
nts
Distribution of Reports to RGF Incidents
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Demographic Profile Methodology
Census block group data was added to the RGF incidents using ESRI’s ArcView 9.2.
A count of incidents was tallied for each block group and that data was added to the attributes of the block groups.
SPSS, statistical analysis software, was used to define correlations between demographic characteristics and RGF incidents.
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Demographic Profile Results
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Pearson Correlations for RGF
College Graduate -.270
Median Household Income -.180
Without HS Diploma .125
Persons Below Poverty .349
Percent Renter Occupied .133
Gun Crimes & Arrests .437
All correlations are statistically significant to 1% (p=0.01).
Methodology for Spatial Statistics
RGF data was spatially weighted by the number of incidents occurring within the block group.
The Moran’s I model measures the distance between each point and compares that with an expected distance within the threshold (clustering).
The model assigns a Z-score based on standard deviations and an I-score based on similarity between points.
RGF incidents can be ranked by cluster and similarity. Targets: Z-scores >= 1.96 (2 standard deviations).
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Conclusions
RGF can be isolated into specific incidents using a GIS with a time/space threshold.
RGF is correlated with levels of education, median household income, levels of owner-occupied housing and total population.
RGF is significantly clustered in neighborhoods with specific demographic profiles.
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Further Research Needed
Develop algorithm or GIS tool to automatically identify duplicate reports of the initial RGF incident.
Explore the placement of the analysis point for the incident.
Use remote sensing data to validate social disorder near clusters of RGF.
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Further Research Needed
Further Research Needed
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Further Research Needed
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Further Research Needed
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Further Research Needed
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Discussion
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