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«Measuring Patterns of Crime in Durham, North Carolina Timothy Mulrooney, North Carolina Central University I. Introduction A Geographic Information ...»

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(1000)( NumberofCrimes ) CrimeRate = PopOfBlockGroup Crime rates from 2010 for each census block group were subtracted from the value for 2007 to compute the crime change in that time period. A quintile classification scheme was used to group the values into 1 of 5 groups based on these crime rates where the red values represent increases in crime during this time period. This quintile scheme makes each color or group represented the same number of times on a map and is used to counteract the effects that outliers may have on the data and subsequently the map. A higher scale map of the study area as well as surrounding block groups is highlighted in Map 4. Areas closer to downtown Durham have experienced major decreases in crime in this time period. This may be due to their location within the bulls-eye as part of ‘Project Bulls-Eye’, an initiative within Durham to focus on a 2-square mile portion of Durham that is generally considered as the epicenter for crime in the city. Through ‘Project BullsEye’, a 31% decrease in crime within the bulls-eye helped yield Durham’s lowest crimes rate in 10 years (Sharma 2011). Using the GIS analysis from this project, only 2 of 16 census block groups within the bulls-eye region experienced increases in crime during this time period. The boundary of this bulls-eye is shown in purple in Map 4.

While some of this news may be encouraging, an exploration of Part II crimes in the study area may yield other news for parts of the study area outside of the bulls-eye. By Querying and Summarizing Part II crimes that occur within the study area, one can explore the types of less serious/violent crimes that occur with an area that contribute to neighborhood safety and quality of life. Of note is that drug crimes increased from 202 in 2007 to 357 in 2010. For 2010, there were 16.5 incidents of vandalism (an indicator of gang activity) and 12.3 drug crimes per 1,000 people within the study area. By comparison, the rate for vandalism outside of study area was 9.7 per 1,000 and for drugs it was 4.5. This is not good news.

VI. GIS at North Carolina Central University Data provided by the Durham Police Department, as well as other GIS data accrued at NCCU, are stored on a server dedicated for that purpose. Metadata is currently being developed. The use and application of this influx of crime data has taken students research in various directions. While NCCU has traditionally offered GIS coursework revolving around the earth and environmental sciences, students have begun working with this crime data as part of their coursework at NCCU. The DEEGS at NCCU offers an Introductory GIS (GEOG 3435) and Applied GIS (GEOG 4010) courses. Students have leeway about the types of project and research that they will present in front of their colleagues at the end of the semester. Some of the projects, which vary their depth of

analysis because of the course, using this crime data include:

Prostitution in Durham Exploring the relationship between DUI Visualization of DUI Arrests in Durham arrests and the location of ABC stores in Homicide in Durham Durham VII. Discussion The crime data provided signify those offenses reported by the Durham Police Department. These data represent a reflection of criminal activity in Durham processed by the author. The Durham Police Department is in no way responsible for the quality of this analysis or the maps created from these analyses. Please contact the author with any questions about the analyzed data or maps.

In many cases, data preparation and the various forms of accuracy such as horizontal, temporal, attribute and semantic, must be addressed. The Durham Police Department graciously provided the data with their exact northing and easting, as well as attribute information that can be queried. In cases where personal privacy needed to be maintained for a crime (such as forcible rape), a random offset was applied to the northing and easting by the Durham Police Department beforehand. All other crime values are provided with a northing and easting to the nearest foot.

Care must be taken when determining an appropriate aggregation unit in which to display data. The aggregation unit used in this study is the census block group. It is within these block groups that various metrics are displayed in Maps 2 - 4. Given a goal of thematic choropleth maps such as these is to highlight regional differentiation, the use of different scale units which may show different patterns may tell completely contradictory stories. Openshaw (1984) coined this term as the ‘Modifiable Areal Unit Problem’ (MAUP). For example, there may be a cluster of high crime at the census block group level that can be detected. However, these blocks may lie in different census tracts and their rates’ interactions other census block groups within the same tract may obfuscate these high values and more importantly this cluster. It is important that issues of MAUP be addressed by using a scale that adequately dictates and explains transparency between results rendered at various scales. In looking at crime density at different scales (block, block group, tract, zip code), they show the same general trends as highlighted in this research.

Lastly, the time intervals used in time-series analysis (2007 – 2010) and displayed in Map 4 merely show the general trends over this 4 year time period. If different intervals (2008 – 2009, for example) were used, they may show different trends. However, there are many different permutations of intervals that could be shown. As with the issues of MAUP, all efforts were taken to display transparency with these data. The interval shown in Map 4 was used because it encompassed all data from both a spatial and time aspect. Please contact the author if you wish to view or see data from other time periods besides those shown.

Works Cited Andresen, M. A. 2006. Crime measures and the spatial analysis of criminal activity.

British Journal of Criminology 46: 258-285 Bodnar, P. M. 2007. A new approach to geographic profiling. — Paper presented at the Ninth Crime Mapping Research Conference, National Institute of Justice, Pittsburgh, PA Boggs, S. L. 1965. Urban Crime Patterns. American Sociological Review, 30: 899–908.

Brower, A.M and L. Carroll. 2007. Spatial and temporal aspects of alcohol-related crime in a college town. Journal of American College Health 55(5): 267 – 275.

Erlanger, H.S. 1976. Is there a “subculture of violence” in the South? Journal of Criminal Law and Criminology 66:483–490.

Flowers, Barri. 2009. College Crime: A Statistical Study of Offenses on American Campuses. Jefferson: Macfarland & Company Inc.

Harries, K. 1997. Social Stress and Trauma: Synthesis and Spatial Analysis, Social Science and Medicine, 45, 1251–1264.

Harries, K. 1999. Mapping Crime: Principles and Practice. Washington, D.C.:

National Institute of Justice.

Howard, G. Newman, G. and W. Pridemore. 2000. Theory, Method and Data in Comparative Criminology. Measurement and Analysis of Crime and Justice.

Washington, D.C.: U.S. Department of Justice.

Levine, N. 2004. CrimeStat III: A spatial statistics program for the analysis of crime incident locations Ned Levine and Associates and the National Institute of Justice, Version 3.0. Houston TX, and Washington, DC Levine, N. and R. Block. 2011. Bayesian Journey-to-Crime Estimation: An Improvement in Geographic Profiling Methodology. The Professional Geographer 63(2): 213 – 229.

Li, J. and Rainwater, J. 1999. The Real Picture of Land-Use density and Crime: a GIS Application. Retrieved October 5, 2008, from http://gis.esri.com/library/userconf/proc00/professional/papers/PAP508/ p508.html.

Mennis, J, Harris, J., Obradovic, Z, Izenman, A, Grunwald, H and B. Lockwood. 2011.

The Effect of Neighborhood Characteristics and Spatial Spillover on Urban Juvenile Delinquency and Recidivism. The Professional Geography 63(2): 174 – 192.

Neapolitan, J. 1996. Cross-national crime data: Some unaddressed problems. Journal of Criminal Justice 19: 95 – 112.

Openshaw, S. 1984. The Modifiable Areal Unit Problem. Concepts and Techniques in Modern Geography, 38, 41.

Ousey, G. 2000. Explaining Regional and Urban Variation in Crime: A Review of

Research. The Nature of Crime: Continuity and Change. Washington, D.C.:

United States Department of Justice.

Sharma, Chinmayi. 2011. ‘Bull’s Eye’ credited for 10-year crime low. The Chronicle.

[website] http://dukechronicle.com/article/bull-s-eye-credited-10-year-crime-low Spencer Daniel Stewart, "The impact of selected higher education institutions on single family home values: A hedonic approach with GIS application" (January 1, 2010).

Dissertations available from ProQuest. Paper AAI3410486.

http://repository.upenn.edu/dissertations/AAI3410486 Spielman, Seth and J.C. Thill. 2008. Social Area Analysis, Data Mining, and GIS.

Computers, Environment and Urban Systems 32: 110 – 122.

Steinberg, Steven and S. Steinberg. 2006. Geographic Information Systems for the Social Sciences: Investigating Place and Space. Thousand Oaks, CA: Sage Publishing.

United States Department of Justice. 2004. Uniform Crime Reporting Handbook.

Washington D.C.: Federal Bureau of Investigations.

Dr. Timothy J. Mulrooney is an Assistant Professor in the Department of Environmental, Earth and

Geospatial Sciences at North Carolina Central University, Durham, NC, 27707. (Email:

tmulroon@nccu.edu, Phone: 919-530-6575). His research interests include Geographic Information Systems (GIS), including GIS data development standards, GIS education and subject areas in which GIS can be used as an analytical tool at the college and high school levels.

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