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IS6833 Homicide Prediction 2011 Michelle Bergesch Jeff Stahlhuth.

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Presentation on theme: "IS6833 Homicide Prediction 2011 Michelle Bergesch Jeff Stahlhuth."— Presentation transcript:

1 IS6833 Homicide Prediction 2011 Michelle Bergesch Jeff Stahlhuth

2 Issue: Determine Next Homicide Homicide: Includes murder and non-negligent manslaughter which is the willful killing of one human being by another Key Considerations: – Generally independent non-related events – Too easy to jump to conclusion – Data granularity (Region, District, Ward, Zip, Neighborhood) – Victim to Offender relationship

3 Causation Vs. Correlation Homicide Contributing Factor Assumptions: 1.Economy 2.Abandoned property 3.Census demographics Sex, Race, Education, Income 4.Homicide as a result of another crime 5.Previous conviction type and frequency 6.Felon location

4 Causation Vs. Correlation 1.Economy: – Presumption is bad economy = more crime – Most research is inclusive on this relationship – Available census data was from 1990 2.Abandoned Property – Presumption increased number of abandoned properties would be used for drugs and crime – Property data organized by address not neighborhood, required extensive

5 Causation Vs. Correlation 3.Census Demographics: – Needed data formatted by neighborhood – Available census data was from 1990 4.Homicide as a result of another crime – FBI expanded homicide data sort data by relationship, sex, weapon, related crime, etc. – Related crime categories do not completely match UCR categories

6 Causation Vs. Correlation 5.Previous conviction type and frequency – Strongest predictive factor to predict murders is previous offender history (Berk) – Court case data provides frequency and timing of previous offenses 6.Felon location – Need a mechanism to identify previous offender location (i.e. sex offender registry) – No research to support proximity of murder to offender dwelling

7 What Data Was Considered? Missouri Census Data – http://mcdc.missouri.edu/ http://mcdc.missouri.edu/ St. Louis Police Dept. UCR Data – http://www.slmpd.org/ http://www.slmpd.org/ St. Louis Circuit Court (Cases & Protective Orders) – http://www.stlcitycircuitcourt.com/circuitclerk.html http://www.stlcitycircuitcourt.com/circuitclerk.html – http://www.stlcitycircuitcourt.com/search.php http://www.stlcitycircuitcourt.com/search.php Neighborhood Background Data – http://stlouis.missouri.org/neighborhoods/ http://stlouis.missouri.org/neighborhoods/ Missouri Highway Patrol UCR – http://www.mshp.dps.missouri.gov/MSHPWeb/SAC/data_and_statistics_ucr.html http://www.mshp.dps.missouri.gov/MSHPWeb/SAC/data_and_statistics_ucr.html FBI UCR Crime Statistics – http://www2.fbi.gov/ucr/cius2009/offenses/expanded_information/homicide.html http://www2.fbi.gov/ucr/cius2009/offenses/expanded_information/homicide.html Berk Crime Prediction Tool – Univ. of Pennsylvania – http://www.smartplanet.com/technology/blog/science-scope/in-philadelphia- prediction-and-probability-in-crime-patterns/3598/ http://www.smartplanet.com/technology/blog/science-scope/in-philadelphia- prediction-and-probability-in-crime-patterns/3598/

8 What Data Was Used? St. Louis Police Dept. UCR Data Neighborhood Background Data FBI UCR Crime Statistics

9 Methodology FBI Expanded data breaks down annual homicides by contributing circumstances (Rape, Burglary, Robbery, etc.) Data granularity does not match standard UCR categories, however 5 crimes do match

10 Methodology FBI Expanded Data

11 Methodology Develop a holistic scoring number to predict homicides and related crime trend for each neighborhood Predicted Homicides (PH) – linear trend analysis of homicide rates by neighborhood. Predicted Crime Index (PCI)- predicted sum of projected related crimes based on reported UCR data for each neighborhood Report then outlines where the next homicide will be AND a related crime index. Patrol deployment recommendation based on sum of PH + PCI

12 Conclusion Projected Homicides by Neighborhood NEIGHBORHOOD TOTAL MurderTOTAL Rape TOTAL ROBBERY Burglary TOTAL Larceny TOTALAUTO Theft ANCILLARY HOMICIDEPHPCI Jeff-Vanderlou Average11.270.014.860.830.260.146.1011.2717.37 Mark-Twain Average9.070.002.770.790.140.113.819.0712.88 Kingsway-West Average7.930.011.560.590.080.072.327.9310.25 Wells-Goodfellow Average7.870.025.141.080.240.106.597.8714.46 Baden Average6.800.025.101.580.300.167.166.8013.96 Penrose Average6.730.013.480.910.200.134.736.7311.46 Fairground Average5.870.001.330.150.100.031.625.877.49 North-Point Average5.530.002.040.530.130.092.785.538.32 O'Fallon Average5.400.013.350.960.130.114.555.409.95 Hyde-Park Average5.000.001.350.560.080.072.055.007.05 Dutchtown Average4.870.036.933.670.460.1411.234.8716.10 College-Hill Average4.470.001.080.340.050.031.514.475.97 Hamilton-Heights Average4.400.012.130.440.090.062.744.407.14 Walnut-Park-West Average4.330.012.640.750.080.063.544.337.87 Tower-Grove-South Average3.670.007.402.410.670.2110.693.6714.36 Gravois-Park Average3.600.013.321.410.260.075.063.608.66 Downtown-West Average3.530.024.070.230.710.115.143.538.67 Academy 2011 prediction3.530.011.820.540.110.032.513.536.05 Benton-Park-West Average3.470.002.811.040.140.064.053.477.52 Old-North-St.-Louis Average3.400.001.550.310.090.072.013.405.41

13 Conclusion Resource Deployment by Neighborhood NEIGHBORHOOD TOTAL MurderTOTAL Rape TOTAL ROBBERY Burglary TOTAL Larceny TOTALAUTO Theft ANCILLARY HOMICIDEPHPCI Jeff-Vanderlou Average11.270.014.860.830.260.146.1011.2717.37 Dutchtown Average4.870.036.933.670.460.1411.234.8716.10 Wells-Goodfellow Average7.870.025.141.080.240.106.597.8714.46 Tower-Grove-South Average3.670.007.402.410.670.2110.693.6714.36 Baden Average6.800.025.101.580.300.167.166.8013.96 Mark-Twain Average9.070.002.770.790.140.113.819.0712.88 Penrose Average6.730.013.480.910.200.134.736.7311.46 Kingsway-West Average7.930.011.560.590.080.072.327.9310.25 O'Fallon Average5.400.013.350.960.130.114.555.409.95 The-Greater-Ville Average2.730.014.521.490.150.116.282.739.02 Downtown-West Average3.530.024.070.230.710.115.143.538.67 Gravois-Park Average3.600.013.321.410.260.075.063.608.66 North-Point Average5.530.002.040.530.130.092.785.538.32 Downtown Average2.070.014.750.330.910.166.162.078.23 Carondelet Average2.870.013.521.250.380.155.322.878.18 Walnut-Park-West Average4.330.012.640.750.080.063.544.337.87 Benton-Park-West Average3.470.002.811.040.140.064.053.477.52 Fairground Average5.870.001.330.150.100.031.625.877.49 Central-West-End Average1.870.003.860.480.790.175.301.877.17 Hamilton-Heights Average4.400.012.130.440.090.062.744.407.14

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