2004 San Antonio Burglaries: a Spatiotemporal Analysis

2004 San Antonio Burglaries: A Spatiotemporal Analysis

Prepared by the Neo Geo Spatial Detectives May 4, 2005

1. INTRODUCTION

San Antonio had the third highest total crime rate in 2003, among U.S. cities with a population of over one million. This statistic came for the FBI website, and was calculated by crime per 100,000 people. San Antonio ranked third for the highest number of burglaries in 2003, by the same FBI standard.

San Antonio is the ninth largest city in the United States, and the third largest in

Texas. The city is the industrial, commercial, and financial center of a large agricultural area. Its economy is largely dependant upon tourism as well.

In order to ensure the fortitude of the tourist industry in San Antonio, it has become crucial to devise a method of reducing the amount of burglaries committed within San Antonio’s city limits. The first step in resolving this issue was to first identify the hazard regions.

The goal of the Neo Geo Spatial Detectives (NGSD) is to analyze the 2004 burglary data from San Antonio, Texas. Burglaries are divided into two categories: 1) burglary from a vehicle; and 2) burglary from a habitation. The location of the crimes will be mapped and analyzed in order to identify crime clusters, a region with a high occurrence of burglaries. The crimes will also be analyzed temporally, by seasons; to identify shifts within the clusters throughout the year.

After the clusters, or hot spots, have been identified NGSD will attempt to locate crime generators in each region. A crime generator can be defined as a high-traffic

1 location that experiences crime as a by-product of the large number of people who regularly visit there. We hypothesize, based on a study cited by Rossmo, that a significant number (20%) of burglaries are located within 0.25 miles of freeway access.

This hypothesis ties in with crime generators, because of the heavy traffic generated.

The Neo Geo Spatial Detectives will also investigate journey-to-crime, the distance between the criminal’s residence and the location at which they committed their crime. We will compare our results to a study cited in Rossmo, which states the average distance for journey-to-crime is less than a mile, to see if our findings fit this hypothesis.

CrimeStat will be used to conduct the cluster analysis, which will locate the hot spots in San Antonio. The data will then be imported into ArcGIS, through the ‘Select by

Attribute’ function the burglaries will be temporally divided into quarters. ArcGIS will also be utilized to construct all of our maps.

After our analysis, of the 2004 burglaries in San Antonio, is complete we will turn over our findings to the San Antonio Police Department. With this information the

SAPD can adjust their patrol routes to areas experiencing the highest rates of burglary during each season. Not only will they be aware of where the hot spots are located, but also of possible contributing factors (crime generators).

2. LITERATURE REVIEW

2.1 CLUSTER ANALYSIS

In order to give the San Antonio Police Department an accurate depiction of where burglaries are being committed frequently, it was necessary for NGSD to plot the incidents of crime before they could be spatially analyzed.

2 Since the mapping of a large number of data points usually results in a chaotic, unintelligible map, different methods of distinguishing clusters of crime occurrences have been developed. There is an overwhelming consensus that crime mapping, using cluster analysis is crucial to law enforcement. It is the best method for identifying high crime areas.

A crime pattern occurring in a small area is called a cluster or hot spot. A hot spot can be defined as a small area in which crime occurrence is so frequent that it becomes highly predictable over a one year period. The high concentration of crime and the high probability of future crimes occurring within the same area make it a suitable target for crime-suppression strategies. The identification of cluster areas is important in understanding where concentrations of crime exist, as well as a starting point for understanding other factors that may have contributed to the formation of the concentrations.

2.2 NEAREST NEIGHBOR HIERARCHICAL SPATIAL CLUSTERING

Cluster analyses encompass a number of different methods and algorithms for grouping objects of similar kind into respective categories. These analyses discover structures in data without explaining why they exist. There is not a single solution to the identification of hot spots

Hierarchical techniques are like an inverted tree diagram, in which two or more incidences are first grouped on the basis of some criteria (nearest neighbor). Then the pairs are grouped into second-order clusters. The second-order clusters are then grouped into third-order clusters, and this process is repeated until either all incidences fall into a single cluster or else grouping criteria fails.

3 Nearest neighbor hierarchical clustering identifies groups of points that are spatially closer than would be expected to occur by chance alone. It is determined by calculating a mean random distance, and a confidence interval for the mean random distance. Criteria used for clustering points are the lower confidence interval of the random expected nearest neighbor, called the threshold distance. All distance pairs, smaller than the threshold distance, are then selected for clustering. We, the user, were required to provide a one-tailed probability level for defining the threshold distance, and the minimum number of points that were required for each cluster.

The reason NGSD chose to use the nearest neighbor index is because it is one of the oldest distance statistics. This type of analysis returns three different clusters: first-, second- and third-order clusters. This allows for analysis at both a macro and micro level. This index is particularly useful, being that it is simple to understand and calculate.

2.3 JOURNEY- TO-CRIME

Journey-to-crime is the distance traveled by the offender, from their nominal home address to the place the offense was committed. Research has concluded that the majority of crimes appear to take place within a mile of the criminal’s residence.

Through the relation of an offender’s last know residence to an arrest location, likely roads of travel can be identified.

2.4 MANHATTAN DISTANCE

In order to generate a figure for journey-to-crime distance must be calculated.

Due to difficulties NGSD had with our road network, we were unable to calculate a direct path. We were required to calculate indirect distance to find journey-to-crime.

4 Indirect distance is an approximation of travel on a grid road network, also called

Manhattan distance, referring to the grid-like structure of Manhattan. San Antonio’s road network is not a perfect grid, but is close enough to suffice this form of calculation. The sum of the horizontal and vertical distances traveled between the two points is the

Manhattan distance. This calculation approximates an actual travel pattern, rather than assuming the shortest distance between points. Distances measured over a street network are always longer than a direct line or arc.

2.5 ANALYSIS SOFTWARE

Over thirty percent of police agencies, with a force of more than 100, use computer-mapping software. The International Association of Crime Analysts estimates the need for GIS experts in law enforcement has grown ten-fold over the last 15 years. In a survey of 2004 United States police departments, 85% of respondents stated that mapping was a valuable tool and reported both increasing interest and implementation

(Rossmo 2000).

CrimeStat is a spatial statistical application designed specifically for the analysis of crime. It is a standalone application that contains a variety of spatial statistics, producing outputs that can be exported to GIS software for display (Hirschfield and

Bowers 2001). CrimeStat was used to conduct hot spot analysis, which determines statistically significant concentrations of point patterns.

3. DATA

The Bexar county boundary datum was downloaded from the U.S. Census

Bureau’s website. The datum was projected into the State Plane Coordinate System:

South Central Texas 4204 using the NAD_1983 datum. The boundary file was used as a

5 backdrop for all of the other data used in the project. All other data points became spatially oriented based upon this datum layer. Because this datum was obtained from a government website it maintains high quality and reliability.

The San Antonio police service areas were acquired from the San Antonio police department website. The data were projected the same as the Bexar county boundary file.

The service areas were mapped in order to allow the SAPD to see which patrol district the hot spots lie within. Because the data are in a shapefile, which was obtained from the

San Antonio police department, the quality and reliability of the data are not compromised.

The street data for San Antonio were obtained from the U.S. Census Bureau

TIGER files. In order for any journey-to-crime analysis to be done, the data were projected as were the other data layers. The street data is necessary to compute the journey-to-crime, as well as to create the data layer of San Antonio exits.

The location of the crimes, burglary of vehicles and burglary of habitation, as well as the location of the criminal’s residences, were obtained in spreadsheet format from the

San Antonio Police Department. These data were collected in order to perform the hot spot and journey-to-crime analyses. Although these data were collected from the SAPD, the data quality is not assured to be completely accurate. It may have been possible for errors to be introduced during data gathering and entry stages, due to human error.

Before the data could be brought in for analysis, they were first projected into the State

Plane Coordinate System: South Central Texas 4204 using the NAD_1983 datum.

A datum layer of the San Antonio freeway exits was created in order to identify the percent of burglaries occurring within 0.25 miles of freeway access. The data were

6 created in ArcMap using the road data layer. Each point was added manually in the

‘Editor’ toolbar. The quality of this data is not the highest, due to the fact our only option was to create the layer ourselves, human error greatly compromised the reliability of this layer.

4. METHODS

A flowchart representing the steps in our methodology can be seen in Appendix I.

4.1 HOT SPOT ANALYSIS

The first step in any analysis is to collect the data. All data layers, county boundary, roads and police service areas were imported into ArcMap after being projected. Because our data came from different sources, we projected each layer into the State Plane Coordinate System: South Central Texas 4204 and the NAD_1983 datum.

The point data for the burglaries were geocoded.

The burglaries were divided into two categories: 1) burglary from a vehicle; and

2) burglary from a habitation. The ‘Select by Attribute’ function in ArcMap, was used on each data set to divide them temporally. The first quarter included the months of

January, February and March. The second quarter included the months of April, May and June. The third quarter included the months of July, August and September. The forth quarter included the months of October, November and December.

Each quarter, for both categories of burglary, were loaded into CrimeStatv2.0.

Here the function ‘nearest neighbor hierarchical cluster routine (Nnh)’ was conducted on each data set. The threshold distance was set to the default value of 50% (setting seven); the minimum number of points to create a cluster was set at five.

7 The output for each quarter was imported into ArcMap as a shapefile, where the projection was rechecked. The maps were then compared in order to identify any temporal shift among the hot spots.

The freeway access layer was then created in ArcMap using the road layer, obtained from the U.S. Census Bureau’s TIGER file, as a guide. Information on San

Antonio exits was acquired from: http://home.att.net/~texhwyman/san.htm. “This site covers the San Antonio freeway system in-depth.” A complete list of exits in both directions, as well as the major streets each exit accessed, was available for each freeway.

There were 7 freeways: I-10, I-35, I-37, I-410, US-90, US-281, and Loop 1604.

The point data for each exit was added to the map using the ‘Editor’ toolbar.

Only the exits connecting to streets, as opposed to other freeways, were included. The streets listed for each exit were ‘Selected by Attribute’. The exits were plotted after zooming in on the freeway and the connecting streets, and estimating the location. After all relevant exits were plotted we used the ‘Select by Location’ function, in order to identify all burglaries from 2004, which were within 0.25 miles from freeway access. We calculated the percent of burglaries committed within the specified distance in order to compare our results to Rossmo’s findings.

4.2 JOURNEY-TO-CRIME

The point data of the burglaries were given to us by the San Antonio Police

Department in a spreadsheet. The table was opened in Excel and converted into .dbf file so that it could be imported into ArcMap. In ArcMap the points were geocoded and projected to the State Plane Coordinate System: South Central Texas 4204 using the

8 NAD_1983 datum. The x, y coordinates were given to us in decimal degrees, we converted them into meters.

The burglary table was joined to the criminal’s residence table by case number.

The joined table was brought back into Excel. A Manhattan distance formula was used to calculate distance. The unit of measurement was converted to miles and rounded to one decimal place. A table was created to display the results.

5. RESULTS

5.1 HOT SPOTS

Of all of the burglaries committed in San Antonio in 2004 22.8% were located within 0.25 miles of the plotted freeway exits.

5.1.1 Burglary from Habitations

The total number of burglaries from habitations was 10,322. The totals of the first-order clusters were: 119 for the first quarter, 125 for the second quarter, 139 for the third quarter, and 148 for the forth quarter.

The second-order clusters were compiled from first-order clusters which were close in proximity, resulting in fewer clusters. The totals for the second-order clusters were: 12 for the first quarter, 11 for the second quarter, 14 for the third quarter, and 16 for the forth quarter.

5.1.2 Burglary from Vehicles

The total number of burglaries from vehicles was 25,744. The totals of the first- order clusters were: 275 for the first quarter (January-March), 298 for the second quarter

(April-June), 316 for the third quarter (July-September), and 328 for the forth quarter

(October-December).

9 The second-order clusters were compiled from first-order clusters which were close in proximity, resulting in fewer clusters. The totals for the second-order clusters were: 26 for the first quarter, 29 for the second quarter, 32 for the third quarter, and 37 for the forth quarter.

10 The average distance traveled for journey-to crime was 4.2 miles (Graph 1).

Journey to Crime Distance: Burglary 2004

0 250 0 2 : e c n a r 200 u c c O

y 150 c n e u q e r 100 F

0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 Distance from Offenders Home

11 6. DISCUSSION

6.1 HOT SPOTS

6.1.1 Burglary from Habitations

The first-order clusters (Figures 1-4) are concentrated inside Loop 410, around the downtown area, and north of Loop 410. The forth quarter of 2004 habitation burglaries is most significant, 1,517 out of the 2,644 (57%) incidences were contained in the first- order clusters; each cluster is roughly the size of a neighborhood.

The highest occurrence of burglary, 986 incidences, from habitation in 2004 was in May. July also experienced a large number of burglaries, only 27 fewer than May.

6.1.2 Burglary from Vehicles

The highest concentration of first-order clusters, of burglaries from vehicles, is located on Loop 410, from Callaghan Rd., west 1.5 miles, to Evers Rd. (Figures 5-8).

The second-order clusters appear across freeways and the downtown central business district, from Hildebrand to Durango, and Zarzamora to New Braunsfels.

The second quarter (Figure 6), April-June, experienced the highest rates of burglaries from vehicles. The total number of incidences during the second quarter was

6,828, 288 more incidences than experienced during the third quarter, July-September. In

12 June alone 2,522 burglaries from vehicles were committed, 37 percent of the burglaries committed that quarter.

6.1.3 Crime Generators

A crime generator is a high-traffic location that experiences crime as a by-product of the large number of people who frequent the area. The most apparent crime generator is the freeways. There is an urban, socioeconomic environment that occurs off of every exit. In these large business corridors lay shopping centers, fast food establishments, and other industries that experience high rates of turnover, amounting in heavy flow of traffic.

San Antonio’s economy is largely based on tourism, which flourishes in the forth quarter. The holiday season increases the number of crime generators. The downtown region of San Antonio is dense with hotels and shopping areas, locations that draw crime, especially burglaries. Burglaries also increase in the residential areas during the holiday season, because of the heavy amount of traffic due to out of town guests.

The Neo Geo Spatial Detectives hypothesized that the average distance traveled for journey-to-crime would be less than one mile. However, our result showed that the average distance traveled in San Antonio, during 2004, was 4.7 miles. This discrepancy can be attributed to urban sprawl.

6.3 RECOMMENDATIONS TO THE SAPD

After spatially and temporally analyzing the 2004 burglaries in San Antonio, the

Neo Geo Spatial Detectives have located hot spots and identified crime generators. This information will be given to the San Antonio Police Department as a whole, but specific statistics can be given to each police service area, or patrol district.

13 The SAPD should concentrate more of their efforts on areas within close proximity to freeway exits. As a majority of the crime occurs within Loop 410, police efforts should be concentrated in this region. Efforts should be made to maximize security efforts in areas with a high influx of traffic, i.e. shopping malls. With the maps created by the NGSD the SAPD can get a visual overview of how crime trends shift throughout the year, and can now shift their focus accordingly.

Finally, with the knowledge that a criminal in San Antonio travels, on average, less than five miles from their residence, the SAPD can identify repeat offenders’ residence and patrol those areas more frequently.

7. CONCLUSION

The spatiotemporal analysis conducted by the Neo Geo Spatial Detectives will assist the San Antonio Police Department focus their efforts on locations were crime hot spots are prevalent. These data will be of assistance when the force is planning their patrol routes. Perhaps the areas in downtown San Antonio, within hot spots, could be better served with an increase in police bike patrol. The utilization of the NGSD results by the SAPD will allow them to focus their efforts, while minimizing the cost.

Future analysis, from a more micro-level temporal standpoint, would also be of great benefit to the San Antonio Police Department. With this datum they would know specific times of the day and week (temporal hot spots) to patrol the regions experiencing elevated levels of burglary. Analyzing the locations of the criminals’ residence may also be of use to the SAPD. A hot spot analysis could be conducted on these locations in order to identify any areas which may lend themselves to the occurrence of crime.

14 GIS should continue to be used for the analysis of crime by spatial analysts as well as police departments. The technology allows respective parties to visualize their data, allowing them to get a more in depth grasp on the data.

8. REFERENCES

FBI. 2003. 2003 Crime Statistics. Federal Bureau of Investigations, Washington D.C. http://www.dallascrime.com/pages/955140/page955140.html?refresh=1086144767257. Last accessed 3 May 2005.

Goldsmith, Victor, Philip G. McGuire, John H. Mollenkopf, and Timothy A. Ross, eds. 2000. Analyzing Crime Patterns. Thousand Oaks: Sage Publications.

Hirschfield, Alex, and Kate Bowers, eds. 2001. Mapping and Analysing Crime Data: Lessons from research and practice. London: Taylor and Francis.

Leipnik, Mark R., and Donald P. Albert, eds. 2003. GIS in Law Enforcement: Implementation issues and case studies. London: Taylor and Francis.

Levine, Ned. 1996. Spatial statistics and GIS: software tools to quantify spatial patterns. Journal of the American Planning Association. 62 (3): 381-392.

NACJD. 2005. Chapter 5: Data Analysis. CrimeStat. http://www.icpsr.umich.edu/NACJD/crimestat/CrimeStatChapter.5.pdf Last accessed 3 May 2005.

Rossmo, D. Kim. 2000. Geographic Profiling. Boca Raton: CRC Press.

Statistica. 2004. Cluster Analysis. Electronic Textbook Stat.Soft Inc. http://www.statsoft.com/textbook/stcluan.html. Last accessed 3 May 2005.

Weisburd, David, and Tom McEwen, eds. 1997. Crime Mapping and Crime Prevention: Crime Prevention Studies Vol. 8. Monsey: Criminal Justice Press.