Driving Forces of Houston s Burglary Hotspots During Hurricane Rita

Transcription

1 Driving Forces of Houston s Burglary Hotspots During Hurricane Rita Marco Helbich Department of Geography University of Heidelberg Heidelberg, Germany & Michael Leitner Department of Geography and Anthropology Louisiana State University Baton Rouge, USA 26 th Annual Louisiana Remote Sensing & GIS Workshop Lafayette, LA Wednesday, April

2 RESEARCH QUESTIONS What impact does a natural disaster (e.g., hurricane) have on crime? Does the evacuation and/or displacement of people result in an increase, decrease, or no change of criminal activities? Are these changes in criminal activity short - and/or long term? Do these changes show regional differences in criminal activity?

3 Deterrence framework THEORIES Criminal activities will increase because police is occupied with non-criminal activities (e.g., evacuation) rather than direct crime control Routine activities (or opportunity) theory Unguarded possessions are left behind by evacuating and/or displaced residents; this creates opportunities for criminals Theories of social disorganization Absence or breakdown of formal and informal social controls leads to increased criminal activity

4 WHY DO THIS RESEARCH? Impact of small-scale targeted police operations on crime (e. g., video surveillance) has been studied many times BUT, impacts of large-scale natural disasters or on crime are largely unexplored

5 IMPACT OF TARGETED POLICE OPERATIONS ON CRIME E.g., Video surveillance of area with known problems of drug dealing and drug distribution Compare drug activities before and after start of video surveillance Visualize changes in drug activities within the surveyed area and in neighboring areas Spatial displacement of drug activities?

6 IMPACTS OF LARGE SPORTING EVENTS ON CRIME UEFA EURO 2008

7 HURRICANES KATRINA AND RITA Hurricane Katrina Landfall: August Landfall location: Between Louisiana and Mississippi Strength at landfall: Category 3 (205km/h) Little impact on crime in Houston Hurricane Rita Landfall: September Landfall location: Between Texas and Louisiana Strength at landfall: Category 3 (185km/h) Largest evacuation in U.S. history (3 million people); 40% of Houston s residents evacuated

8 IMPACT OF HURRICANE KATRINA ON CRIME IN LOUISIANA Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Model Non-Violent Crime Rate Apr-03 Jul-03 Oct-03 Jan-04 Apr-04 Hurricane Katrina Jul-04 Oct-04 Jan-05 Actual Non-Violent Crime Rate Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Region hit by a natural disaster: (1) Mitigation (2) Preparedness/planning (3) Emergency/recovery Crime trends downward (4) Reconstruction Crime returns to predisaster levels Example: Orleans Parish Region receiving large numbers of evacuees from disaster area: (3) Emergency/recovery and (4) Reconstruction: Crime remains unchanged or trends downward Example: Rapides Parish, non-violent crime rate Leitner, M., Barnett, M., Kent, J., and T. Barnett. The Impact of Hurricane Katrina on Crime in Louisiana A Spatial and Temporal Analysis. In LeBeau, J. L. and M. Leitner (eds.) Spatial Methodologies for Studying Crime. Special Issue of the Professional Geographer (forthcoming 2011)

9 EVALUATING THE IMPACT OF HURRICANES (KATRINA AND) RITA ON CRIME IN HOUSTON, TX Houston Police Department (HPD) serves 2 million people Approx 600 square miles large More than 5,500 sworn full-time police officers More than 1,500 non-sworn full-time employees

10 CRIME DATA Crime data were downloaded from the HPD Website (

11 CRIME DATA The following eleven Crime Types were included in this study: Aggrevated Assault Auto Theft Burglary Burglary of Motor Vehicle Driving While Intoxicated Forcible Rape Manslaughter by Negligence Murder & Nonnegligent Manslaughter Narcotic Drug Law Violations Robbery

12 CRIME DATA Crime data were geo-coded to the street block level using U.S. Census TIGER (Topologically Integrated Geographic Encoding and Referencing system) street network data ( Time-Frame: August 1 October

13 SUCCESS RATE OF GEO-CODING ALL CRIME TYPES

14 SPATIAL DISTRIBUTION OF CRIMES IN HOUSTON - ALL CRIME TYPES August 2005 September 2005 October 2005

15 TEMPORAL DISTRIBUTION OF CRIMES IN HOUSTON PER MONTH ALL CRIME TYPES August 8057 September % October %

16 TEMPORAL DISTRIBUTION OF CRIMES IN HOUSTON DIFFERENTIATED BY CRIME TYPE Landfall Hurricane Katrina: Aug 29 Landfall Hurricane Rita: Sept 24

17 COMPARING CRIMINAL ACTIVITIES DURING HURRICANE RITA SHORT-TERM IMPACT

18 COMPARING CRIMINAL ACTIVITIES BEFORE AND AFTER HURRICANE RITA LONG-TERM IMPACT

19 KERNEL DENSITY ESTIMATION Source: Fischer, Leitner, & Staufer-Steinnocher 2001 Source: Levine 2007, Chapter 8, pg. 5.

20 KERNEL DENSITY ESTIMATION The result of the Kernel Density Estimation depends on the following three parameters 1. Cell size of regular raster overlaid on the study area 2. The kernel function type: Normal, Quartic, Triangular, Negative-Exponential, Uniform 3. Bandwidth of kernel function: Exerts the most influence 3.a Fix bandwidth 3.b Adaptive bandwidth Quartic kernel function, 2km fixed bandwidth, burglary of motor vehicles, Vienna 2007 Quartic kernel function, 500m fixed bandwidth, burglary of motor vehicles, Vienna 2007

21 BURGLARY DENSITIES FOR THREE SELECTED DAYS BEFORE, DURING, AND AFTER HURRICANE RITA August 17 September 22 October 18

22 DIFFERENCES IN BURGLARY DENSITIES BETWEEN AUG 17 SEPT 22 AND SEPT 22 OCT 18 August 17 to September 22 September 22 to October 18

23 SELECTED U.S. CENSUS VARIABLES % Whites (2000) % African Americans (2000) % Hispanics (2000) % Asians (2000) % of Civilian Labor Force that is Unemployed (2000) Median Household Income in $1000 (1999) % of Persons Below the Poverty Level (1999) % Housing Units Vacant (2000) Rental Vacancy Rate (2000) % Owner Occupied Housing Units (2000) % Renter Occupied Housing Units (2000)

24 GLOBAL SPATIAL AUTOCORRELATION Variables Moran s I p-value Dependent variable: Sept 22 Burglaries Minus Aug 17 Burglaries Independent variables: % Whites (2000) % African Americans (2000) % Hispanics (2000) % Asians (2000) % Owner Occupied Housing Units (2000) % Renter Occupied Housing Units (2000) Median Household Income in $1000 (1999) % of Civilian Labor Force that is Unemployed (2000) % Housing Units Vacant (2000) % of Persons Below the Poverty Level (1999) Rental Vacancy Rate (2000) Distance to police station

25 GLOBAL REGRESSION MODELS OLS models are biased positive spatial autocorrelation in variables/residuals Spatial lag model outperforms OLS models Sept 22 Burglaries Minus Aug 17 Burglaries shows a: Positive relationship with % of occupied housing units that are owner-occupied (p < 0.05) Positive relationship with % of persons below the poverty level (p < 0.1) Positive relationship with Euclidean distance to police stations (p < 0.01)

26 GEOGRAPHICALY WEIGHTED REGRESSION SLPbPov99 SLdistPolice X.Intercept. SOHown Sept 22 Burglaries Minus Aug 17 Burglaries (independent variable) (dependent variables)

27 SUMMARY OF RESULTS Hurricane Katrina did not impact crime in Houston Hurricane Rita led to dramatic short-term increase in Burglaries from Sept Increase in burglaries showed a positive relationship with % of occupied housing units that are owner-occupied % of persons below the poverty level Euclidean distance to police stations Relationships change across space (+ & -)

28 LIMITATIONS OF THIS STUDY Crime data are not official Uniform Crime Report (UCR) statistics Difference in collection date between crima data (2005) and socio-economic & housing variables (1999/2000) 6.2% of crime data not geo-coded Edge effects were not considered for crime data

29 Many Thanks! Questions?