The Relationship Between Part I Crimes and Public High School Proximity. A study by Mike B. Ahn

Project Focus OBJECTIVE: to see if there is a statistically higher incidence of Part I Crimes near public high schools in Arlington, Texas. PART I CRIMES ARE… Murder, Rape, Assault (Aggravated), Robbery, Burglary, Theft, Motor Vehicle Theft, and Arson. SUBOBJECTIVE: if a statistical significance is observed, then strength of the discovered relationship will be compared with other traditional crime influencing spatial factors (e.g. population, race, age, etc.).

Prior Studies… Previous research proved that there is a relationship between nearness to public high schools and higher instances of Part I crimes. In San Diego, California it was found that residential areas that were adjacent to public high schools had more crime than areas that were more than one city block away from these schools (Roncek and LoBosco 1983). In Cleveland, Ohio to be next to a public high school meant that there was an additional five Part I crimes per city block annually compared to the rest of the city (Roncek and Faggiani 1985).

DATA NCTCOG: 2000 Census blocks of Arlington, Texas NCTCOG: 2000 Census SF1 block data The City of Arlington Police Department: Point shapefile of all crime reports from June 1, 2006 to May 31, 2007. GPS: Public high school points. (Shaofei Chen later offered Texas School Points shapefile to verify)

METHODS PREPARATION Locate School Blocks with GPS. Had to edit Arlington data, filter out non-Part I crimes, and consolidate crimes that were a sub-category of Part I crimes. Make comparable layers: School blocks, Adjacent Blocks, and City Blocks. 12 Layers of blocks surrounding the school blocks in Arlington for multiple regression. Had to delete Ft. Worth. Hawths Tools to count points per polygon

METHODS T-TEST average number of each Part I crime on school blocks with the average number of Part I crimes on the adjacent blocks. ^If no difference, combine the blocks. T-test the average number of each Part I crime on combined blocks with the average number of Part I crimes on the rest of the city blocks. Run t-test for secondary adjacent blocks and all other outside blocks. Utilized a Microsoft Excel based program called StatistiXL to run t-tests.

…METHODS continued. MULTIPLE REGRESSION: for each Part I crime category to define the relationship between crime and school block distance while taking into account other variables that have been known to be related to crime. The independent or control variables per block being… total population % home renters % vacant houses % age 15-24 % Black % Hispanic % single women head of household SUBOBJECTIVE: multiple regression also defines the strength of the school proximity indicator against other crime indicators (control variables).

Problems Encountered Finding data. Crime data did not line up with census data. Block data was scarce. Bad Communication: it might not be a user/technical issue but it was a project issue. A few types of Part I crimes had disruptive spatial bias (construction burglaries, bank robberies, etc.), so they were removed from the study. Could not create an address locator. Disagreement between literary resources and advising. SF1 Census block data was limited so I could not find all the variables to account for in multiple regression. Thus it’s not likely to be as valid as the original studies.

Mean on Adjacent Blocks COMPARING THE AVERAGE NUMBER OF CRIMES ON BLOCKS CONTAINING SCHOOLS WITH THE AVERAGE ON ADJACENT BLOCKS   Mean on School Blocks Mean on Adjacent Blocks t-statistic Murder not significant Rape 0.333 0.172 0.478 Assault 3.167 2.184 0.635 Robbery 0.368 0.105 Burglary 8.500 5.149 0.896 Theft 3.667 3.172 0.27 Auto Theft 2.000 0.782 0.887 Arson All Crimes 18 11.828 0.833 df t-critical 6 Blocks 87 Blocks 91 1.66 @ 95% CI t-statistic > t-critical for significant difference* No statistical significant difference, so blocks containing high schools was combined with adjacent blocks to compare with the remainder of the city blocks. click for analysis

COMPARING THE AVERAGE NUMBER OF CRIMES WITHIN 1 BLOCK OF SCHOOL PROXIMITY WITH THE AVERAGE ON ALL OTHER BLOCKS   Mean Near School Proximity Mean On All Other Blocks t-statistic Murder <0.01 not significant* Rape 0.183 0.104 1.787 significant Assault 2.247 1.284 2.702 Robbery 0.366 0.176 2.33 Burglary 5.366 2.641 2.947 Theft 3.204 1.691 3.143 Auto Theft 0.860 0.523 2.001 Arson All Crimes 12.226 6.425 3.375 df t-critical 92 Blocks 3473 Blocks 3565 1.645 @ 95% CI click for analysis all crimes analysis

COMPARING THE AVERAGE NUMBER OF CRIMES INSIDE THE SECONDARY ADJACENT BLOCKS WITH THE AVERAGE ON ALL OTHER BLOCKS   Secondary Adjacent City Blocks t-statistic Murder 0.013 0.004 1.124 not significant Rape 0.094 0.105 0.443 Assault 1.237 1.289 0.249 Robbery 0.181 0.176 0.145 Burglary 2.682 2.637 0.120 Theft 1.712 1.689 Auto Theft 0.609 0.515 0.890 Arson 0.003 0.001 0.613 All Crimes 6.532 6.415 0.134 df t-critical 299 Blocks 3175 Blocks 3472 1.645 @ 95% CI *discluding school blocks and their directly adjacent blocks. click for analysis

Verification with Multiple Regression Variable Slope\Coefficient\Beta Murder < 0.001 Rape -0.003 Robbery Assault 0.001 Burglary 0.06 Theft 0.018 Auto Theft 0.002 Arson Violent Crimes -0.002 Property Crimes 0.081 All Crimes 0.078 *All signs reversed on the beta results for the block proximity independent variable. click for multiple regression analysis tables

Comparing the Variables Related to Crime Standardized Betas Variable  All Crimes  rank Violent rank  Property Block Proximity 0.63 1 -0.004 6 0.506 Total Population of Block 0.005 5 < 0.001 4 0.003 % Home Renters 0.036 3 0.002 2 0.02 % Vacant Households 0.152 0.009 0.088 % Population Age 15-24 -0.362 8 -0.023 -0.207 % Population Black 0.024 0.013 % Population Hispanic -0.001 -0.002 % Women Head of Household with No Husband -0.173 7 -0.011 -0.1 *All signs reversed on the beta results for the block proximity independent variable. click for multiple regression analysis tables

SUMMARY OF RESULTS The t-tests show that there is a definite relationship between close proximity to public high schools and higher incidences of crime. All relevant categories of Part I crime have higher incidence near schools. (except arson, murder) The influence high schools have on crime does stretch out to all the city blocks for property crimes and the total of Part I crimes, but it is a very weak relationship. The t-tests finds that the notable range of influence by public high schools extends just through the blocks immediately adjacent to school blocks.

Literature Roncek, D.W. and D. Faggiani. 1985. High Schools and Crime: A Replication. The Sociological Quarterly 26(4): 491-505. Roncek, D.W. and A. LoBosco. 1983. The Effect of High Schools on Crime in Their Neighborhood. Social Science Quarterly 64: 598-613. Hirschi, T. and M. Gottfredson. 1983. Age and the Explanation of Crime. The American Journal of Sociology 89 (3): 552-584.

Software ArcGIS: www.esri.com Hawths Tools: www.spatialecology.com/htools/pntpolycnt.php Multiple Regression Analysis and Forecasting: www.business-spreadsheets.com/regfor.htm StatistiXL: www.statistixl.com

Thank you for your support… Cynthia Bauman Crime Analyst Arlington Police Department Dr. Ronald Briggs Professor of Geography and Political Economy The University of Texas at Dallas Shaofei Chen Geographic Information Systems Teacher Assistant Dr. Michael Tiefelsdorf Associate Professor of Geography and Geographic Information Systems

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