An Exploratory Spatial Analysis of the Urban Crime Environment Around the Next National Geospatial I

An Exploratory Spatial Analysis of the Urban Crime Environment Around the Next National Geospatial-Intelligence Agency West Development Tara A. Smith1 and J.S. Onésimo (Ness) Sandoval2 1 2

Integrated and Applied Sciences Program, Saint Louis University, St. Louis, MO 63108 Department of Sociology and Anthropology, Saint Louis University, St. Louis, MO 63108

*Corresponding author: tara.a.smith@slu.edu ABSTRACT The current study provides a baseline, exploratory spatiotemporal analysis of violent and property crimes around the Next National Geospatial-Intelligence Agency (NGA) West (NNW) development during the pre- and post-demolition periods. Five, 500-meter concentric buffers were created around the NNW site. The geographic distributions and spatial patterns of crime were statistically measured during the pre- and postdemolition periods. We observed that during the post-demolition phase, crime became more concentrated and existing crime hot spots intensified. Crime within the NNW footprint dissolved, but it remained the same in the directly adjacent buffer while the farthest buffer saw higher crime levels. The observations from the current research establish the need for additional studies to examine the social and economic environment associated with the NNW development.

Keywords: exploratory spatial data analysis, Saint Louis, Missouri; crime, urban renewal, urban re-development, distance decay

INTRODUCTION Background On March 31, 2016, the National Geospatial-Intelligence Agency (NGA) announced their decision to build a new facility, termed Next NGA West (NNW), in north St. Louis City.

The NNW will replace the existing NGA facility in south St. Louis City. The north St. Louis City site, with the decision process outlined in the publicly released Record of Decision (ROD) document (NGA 2016), was challenged from various stakeholders. One of the contention points for the north St. Louis City site selection was the high violent crime rate in and around the proposed development location. NGA responded to the crime concern, in the ROD, by focusing on the implementation of various crime detractors (e.g. specialized lighting conditions, security surveillance) and continued police partnerships with the City of St. Louis police force (NGA 2016).

The Geographical Bulletin 61(1): 37-53 ISSN 2163-5900 © The Author(s).

The Geographical Bulletin © 2020 Gamma Theta Upsilon

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The $1.75 billion development is a massive investment in a historically depressed, urban area (Crane 2016). The NNW establishes the framework to observe how large U.S. government developments placed in disadvantaged, urban areas may influence urban growth and renewal efforts. The site development and the subsequent completion will attract additional commercial and residential developments in the area, which will likely result in social and economic growth.

The purpose of the current study was to complete an exploratory spatiotemporal analysis, focused on the spatial crime environment in and around the NNW site. Specifically, examining how the spatial crime environment changed with the demolition of the NNW site. Crime events representing personal or property crimes (auto theft and burglary) and

T.A. Smith and J.S. Onésimo (Ness) Sandoval

violent crimes (robbery and homicide) were collected for two different periods: the pre-demolition of the NNW (May 1, 2015, – February 28, 2017) and the post-demolition (March 1, 2017, – December 31, 2018). A descriptive spatial gravity model was developed to examine how the NNW development influenced crime considering spatial distance. Five, 500-meter concentric buffers were created around the NNW site to assess crime levels at various distances from the development.

The geographic distribution of crime for pre-demolition and post-demolition periods were measured. This yielded a set of descriptive spatial statistics that indicated the mean center, shape and orientation, and dispersal of crime in and around the NNW site. Additionally, the spatial patterns of crime were examined during both time frames by using the average nearest neighbor analysis, Global Moran’s I, Global Differential Moran’s I, and Local Indicators of Spatial Association (LISA) analysis.

The current study observed the immediate influence of the NNW development on spatial crime patterns, while establishing the framework for subsequent exploratory and investigative spatial analyses on the urban crime environment surrounding the NNW site. The spatial methods used in the current study provide a detailed, micro-level representation of the crime environment before, during, and after the NNW demolition activity, which supports future research activities. Additionally, the current research establishes the need for additional studies observing the potential social, economic, or environmental effects associated with the development and associated investment of large U.S. Government buildings located in depressed, urban areas.

RELEVANT LITERATURE Factors Influencing the Urban Crime Environment The urban crime environment can be influenced by different factors associated with the modification and utilization of space. Some of these factors include land use, zoning, and demolition activities. Different types of land use can be observed throughout the urban environment. Specific land use types may correlate with crime prone areas (Twinam 2017; Boessen and Hipp 2015; Kinney, et al. 2008). For example, Boessen and Hipp (2015) observed that residential zoned blocks identified with lower levels of crime. While commercial zoned areas were associated with higher amounts of crime (Boessen and Hipp 2015; Stucky and Ottensmann 2009; Twinam 2017).

Kinney et al. (2008) found in Vancouver, British Columbia, that assaults and motor vehicle thefts were the most prevalent in commercial land use types, with shopping centers having the greatest levels of both crimes.

Mixed land use types may substantially influence the crime environment. Some studies suggest that mixed land use areas may be less criminogenic (Twinam 2017; Anderson, et al.

2013; Zahnow 2018), while others suggest that mixed land 38

use may increase certain crime types (Browning, et al. 2010).

Building on this general trend, Wo (2019) found that mixed land use in affluent neighborhoods increased the likelihood of property crime occurrence. Twinam (2017) observed less crime in mixed land use areas with a greater population density as compared to residential areas alone. Stucky and Ottensmann (2009) suggest that the relationship between certain land use types and crime depends on the socioeconomic environment underlying the spatial area being observed.

The re-zoning of urban areas to change land use types over time may influence the level of crime. When Anderson et al. (2013) compared blocks with analogous crime levels that experienced zoning changes to blocks with stabilized zoning in Los Angeles, California, they observed that increases in zoning modifications were related with decreases in crime. Changes in urban zoning may be associated with the gentrification of an area to modify space by, for instance, increasing residential areas and decreasing industrial areas. While gentrification can be defined several ways, Ding et al. (2016) suggest that it represents “the socioeconomic upgrading of a previously, low-income central city neighborhood, characterized by the influx of residents of a higher socioeconomic status relative to incumbent residents and rising home values and rents” (p. 38).

During the early phases of gentrification in Seattle, Kreager et al. (2011) observed that crime increased and then decreased as the process matured. Similarly, Lee (2016) also found in Los Angeles, California that approximately one-year after gentrification, assault, robbery, automobile theft, and thefts from automobiles increased. The phase of gentrification observed in adjacent neighborhoods may impact crime levels. Boggess and Hipp (2014) found that a lone gentrifying neighborhood had higher levels of aggravated assaults compared to multiple adjoining neighborhoods concurrently undergoing gentrification. Additionally, neighborhoods with rising home values that are encompassed by neighborhoods with static or decreased home values corresponded with higher levels of violent crime (Boggess and Hipp 2014).

The demolition of vacant buildings in shrinking cities and blighted areas may impact the associated crime environment.

Across the United States, several cities initiated a vacant lot clearing campaign to reduce associated crime and impose social order. Research on the demolition operations carried out by several U.S. cities suggest an overall decline of total, property, or violent crime found in and near the cleared areas (Larson, et al. 2018; Stacy 2018; Wheeler, et al. 2018; Jay, et al. 2019). Determining the approximate distance demolition activities may exert a significant influence on crime levels is a challenge. Spader et al. (2016) observed reduced crime levels in buffer areas up to 250 feet from demolition sites in Cleveland, Ohio; while Wheeler et al. (2018) observed diminished crime levels up to 1,000 ft from demolition sites. Relating to the approximate distance that demolition may influence crime, Frazier et al. (2013) spatially examined demolition activities in Buffalo, NY and recognized that crime patterns shifted

Spatial Analysis of Urban Crime Environment

closer to the city’s periphery and adjacent suburban areas in relation to the demolition site. The geographic scale used in analysis is critical in indicating and understanding accurate spatial relationships between demolition sites and crime. The current body of knowledge utilizes various levels of spatial scale in examining demolition and the associated impacts on crime levels. Common scales include census tracts (Wheeler, et al.

2018), block groups (Larson, et al. 2018; Stacy 2018; Frazier, et al. 2013; Jay, et al. 2019), and parcel data (Wheeler, et al.

2018). The demolition and crime body of knowledge benefits from the current study’s use of micro-level scales to analyze 2500 meters of area in 500 meter increments and 100 meter raster grids for crime pattern observations.

Relative Spatial Distance Associated with Crime An expansive body of research has examined the distance associated with crime. Gravity models, which are initially based off Isaac Newton’s Law of Universal Gravitation, asserts that as features become larger and closer, they exert greater influence on one another. The gravity model and other simplified variants are inverse distance weighted models which assume that nearby features are more similar than those farther apart (Chen and Huang 2018). Gravity models have a rich, historical application in various research endeavors (Carey 1858; Ravenstein 1885; Stewart 1941; 1948; Zipf 1946; Smith 1976).

In the past and even more recently, gravity models have been applied to study issues in international and urban economics (Robertson and Robitaille 2017; Lee, et al. 2014; Cantore and Cheng 2018; Li, et al. 2019), migration flows (Curiel, et al.

2018; Park, et al. 2018), and travel time, mode, and distance (Xia, et al. 2018; Stefanouli and Polyzons 2017; Delgado and Bonnel 2016; Goh, et al. 2012). While other researchers have examined the application, interpretation, and evaluation of the distance-decay parameters associated with gravity models (Fotheringham 1981; Chen 2015; Cheng and Huang 2018).

Based on the study and explanation of migration flows, Stouffer’s theory of intervening opportunities (1940) can also be applied to examine the distance associated with crime.

Divergent from gravity models, Stouffer proposed that a relationship exists between an individual’s mobility and available opportunities in an area, precluding the connection between mobility and distance. The theory suggests that “the number of persons going a given distance is directly proportional to the percentage increase in opportunities at that distance” (Stouffer 1940, 846). Stouffer refined the model to include directional shifts and competing migrants (1960) and upon examination, the updated model explained more than the distance decay models. Smith (1976) applied both a gravity and intervening opportunities model to Rochester, New York, crime data. A distance decay function was apparent, which supported the gravity models; while no evidence existed in support to the influence of intervening opportunities on residence-to-crime distance. More recently, Elffers et al. (2008) examined the 39

path from an offender’s residence to a crime location using the intervening opportunities model, in The Hague, Netherlands.

The authors found that distance provided more robust results over intervening opportunities. Generally, the findings for both the gravity and intervening opportunity models indicate that several methodological factors may cause variations in results.

METHODS Data The data collected for the study were obtained through open sources. The dates for pre-demolition and post-demolition periods were determined through reviewing news articles and the City of St. Louis website press updates. The City of St. Louis posted that the NNW site demolition began on March 9, 2017 (City of St. Louis 2019). March 1, 2017 was selected as beginning date of the demolition process to establish comparable date ranges for the periods analyzed. The official completion of demolition was signified by the City of St. Louis transferring the NNW land to the United States Air Force, on December 13, 2018 (Petrin 2018). Most of the land was cleared before the transfer to the U.S. Air Force in December 2018. For instance,

by June 2017, 129 of 137 buildings and associated land located within the NNW development site were leveled (City of St. Louis 2019) and by February 2018, all buildings located inside the site were cleared except for the Faultless Healthcare Linen building (Barker 2018). The pre-demolition period considered crimes that occurred May 1, 2015 to February 28, 2017, for a total of 22-months. The post-demolition time period included March 1, 2017 to December 31, 2018, which also resulted in 22-months of crimes (see Table 1). Both periods represented close to four-years’ worth of data to analyze the crime environment and were determined based on data availability.

Crime data were collected from the St. Louis Metropolitan Police Department (SLMPD) publicly accessible website (SLMPD 2019). SLMPD adheres to the U.S. Department of Justice, Federal Bureau of Investigation (FBI) Uniform Crime Reporting (UCR) Program for recording the City of St. Louis’ crime events. Data were obtained for the violent crimes of homicide and robbery and for the property crimes of burglary and auto theft (see Table 1), from May 2015 to December 2018. Crimes were geo-located across the City of St. Louis with a minimum 99.9% location rate. Based on dates of occurrence, the crimes were categorized into a pre-demolition or post-demolition group.

Descriptive Spatial Gravity Model A descriptive spatial gravity model was developed to analyze the spatial interaction between the NNW development and the crime environment (see Figure 1). The NNW footprint or boundary was delineated (Ihnen 2015) and five, 500 m buffer intervals surrounding the footprint were developed. The five

T.A. Smith and J.S. Onésimo (Ness) Sandoval

Table 1. Definitions of Relevant Terms Terms

Definitions

Pre-Demolition Period

A period of 22-months prior to the demolition of the Next National Geospatial-Intelligence Agency West, May 1, 2015 to February 28, 2017.

Post-Demolition Period

A period of 22-months that included active and completed demolition, March 1, 2017 to December 31, 2018.

U.S. Department of Justice, Federal Bureau of Investigation (FBI), Uniform Crime Reporting (UCR) Program Part I Offenses Violent Crimes

Offenses that include force or the threat of forcea.

Homicide

The intentional (nonnegligent) killing of a person by another; to include manslaughter by negligence or the killing of a person by means of negligence.

Excluded from the definition are deaths caused by negligence, attempts to kill, assaults to kill, suicides, accidental deaths, self negligence, accidental deaths not associated with negligence, and traffic fatalitiesb.

Robbery

The taking or attempt to taking anything of value from a person(s) by force, threat of force, violence, or instilling fearb.

Property Crimes

The seizure of money or property without force or the threat of force to the victimc.

Auto Theft

The theft or attempted theft of a motor vehicle. A motor vehicle is self-propelled and runs on land surface and not on rails. Motorboats, construction equipment, airplanes, and farming equipment are specifically excluded from this categoryb.

Burglary

The unlawful entry of a structure to commit a felony or a theft. Attempted forcible entry is includedb.

Note: a U.S. Department of Justice. Uniform Crime Reporting Offense Definitions. Retrieved from https://www.ucrdatatool.gov/offenses.cfm b U.S. Department of Justice. (2010). Violent Crime. Retrieved from https://ucr.fbi.gov/crime-in-the-u.s/2010/crime-in-the-u.s.-2010/violent-crime c U.S. Department of Justice. (2010). Property Crime. Retrieved from https://ucr.fbi.gov/crime-in-the-u.s/2010/crime-in-the-u.s.-2010/property-crime

individual 500 m buffer zones represented an average walking distance in approximately 5 to 6 minutes. Violent and property crimes were clipped to the NNW footprint and each 500 m buffer, yielding crime events that occurred in each buffer area for the two-different time periods.

Characteristics of the Geographic Distributions Violent and property crimes that occurred pre- and postdemolition in the NNW footprint and the five buffer areas were each measured using the mean center, standard distance, and directional distribution techniques. These methods provided a comprehensive way to describe the geographic distribution of crimes, specifically to measure the compactness, orientation, and direction of crimes during the pre- and post-demolition periods.

The mean center analysis provided the average X and Y coordinates for crimes pre- and post-demolition (Wong and Lee 2005). The mean center analysis considered all violent and property crimes that occurred in the NNW footprint and the adjacent 2500 m of buffer zones. The mean center values for pre- and post-demolition were compared to determine if the central area of crime concentration changed.

The standard distance measure produced an ellipse that included one-standard deviation or 68% of crime events that occurred around the mean center. This measure considered an average distance of features from the mean center. If the two measures are compared, crime concentration or dispersion can 40

be determined (Mitchell 2009). The standard distance measure was applied to the NNW footprint, the five, 500 m buffer zones, and the entire study area. The area of each standard distance ellipse was calculated per square kilometer.

The directional distribution or standard deviational ellipse provided a measure of orientation and direction of the crime events pre- and post-demolition. Similar to the standard distance measure, the standard deviational ellipse included one standard deviation or 68% of crimes. The standard deviational ellipse provided a greater understanding of the observations and higher precision with the inclusion of the angle of rotation and dispersion along major and minor axes in the measurement (Mitchell, 2009). The area of standard deviational ellipse was calculated per square kilometer.

Since the standard distance ellipse is an average measure based on the feature’s distance to mean center, and the standard deviational ellipse considers the orientation and direction of crime events, the difference between the area for the standard distance and standard deviational ellipses provided further information on the level of concentration or dispersion found in each geographic unit measured. Greater differences between the standard distance and standard deviational ellipses indicated compactness or concentration of crime events; while a smaller difference suggested the dispersion of crimes. With the differences between the standard distance and standard deviational ellipses established, the most compact and dispersed crimes at various distances from the NNW footprint were identified

Spatial Analysis of Urban Crime Environment

Figure 1. Descriptive Spatial Gravity Model. This model provides the framework to analyze the spatial interaction between the NNW development and the surrounding crime environment. Five, 500 m buffer zones surrounding the NNW footprint were developed. The five individual 500 m buffer zones represent an average walking distance in approximately 5 to 6 minutes.

41

T.A. Smith and J.S. Onésimo (Ness) Sandoval

Spatial Patterns of Crime For the observation of crime patterns and concentration of the pre-demolition and post-demolition time frames, the average nearest neighbor statistic, Global Moran’s I, Global Differential Moran’s I, and the Local Indicators of Spatial Association (LISA) analysis were used. These techniques assisted in determining the extent of clustering and concentration of specific crimes at different buffer zones surrounding the NNW development.

The average nearest neighbor statistic considered the center location of a feature and the associated adjacent features to determine the mean distance. The nearest neighbor statistic represents the ratio of the observed mean distance and the expected mean distance. A resultant index of lower than one suggested clustering, while an index of greater than one implied dispersion (Mitchell 2009). The average nearest neighbor statistic was calculated for the NNW footprint, the five buffer zones, and the entire study area to determine the level of crime clustering or dispersion.

The remaining spatial statistical tests utilized 100 m raster grids to analyze crime patterns. The NNW footprint and the five, 500 m buffer zones surrounding the development were divided into a series of 100 m raster grids. A 100 m raster grid cell is comparable in size to approximately one city block. Crimes were assigned to each 100 m grid cell where they occurred.

A Global Moran’s I analysis was used to determine if the crimes were spatially autocorrelated and if the resultant spatial pattern was dispersed, random, or clustered within the NNW footprint, the five, 500 m buffer areas, and the entire study area, for both pre- and post-demolition time frames.

The Global Moran’s I parameters included: the number of crimes in each raster grid for the input field, continuity edges and corners for the conceptualization of spatial relationships, the Euclidean distance method, and standardization by row.

Additionally, a global Differential Moran’s I analysis was performed to observe the crime change between the pre- and post-demolition periods. Specifically, the global differential Moran’s I test indicates if and how the crime events changed over time considering statistically significant associations with neighboring areas within the study area. Statistically significant differences in the clusters of crime events for the pre- and post-demolition periods are indicated. In the Global Differential Moran’s I, the post-demolition crime events were deducted from the pre-demolition crime events, this difference underwent a Global Moran’s I test to determine the statistically significant levels of change (Anselin 2019). The number of crimes that occurred in each 100 m grid cell for pre- and post-demolition periods served as the two time periods in the analysis.

Global statistical models indicate statistically significant areas of spatial similarity for an entire study area, while LISA statistics provide local measures of spatial association within a study area. LISA statistics can also indicate spatial outliers 42

and local clustering (An, et al. 2015; Anselin 1995; Guo, et al.

2013; Lee & Li 2017; Siordia, et al. 2012). The LISA analysis indicates local areas of concentrations where crime spatially varies, referred to as hot and cold spots. Robbery was selected as a case study for the LISA analysis due to the significant decrease of events during the post-demolition period and the significant, clustered spatial pattern. The number of robberies that occurred in each of the 100 m grid cells were used to perform the LISA analysis. A resultant cluster category (Anselin 1995) for each 100 m grid cell was provided; the current study focused on analyzing the high – high or hot spot clusters and low – low or cold spot clusters. The cluster results for robbery were mapped for a visual comparison of the various buffer areas and the pre- and post-demolition time frames.

RESULTS Descriptive Spatial Gravity Model Table 2 displays the number of crimes that occurred in the NNW footprint and the five, 500 m buffer zones the for the pre- and post-demolition time frames, as well as the difference between them. Both increases and decreases of crime around the NNW development occurred at different spatial levels. The pre-demolition footprint area had a total of 14 crimes, while same area experienced no crime during the post-demolition period. The pre- and post-demolition time frames at the 0 – 500 m buffer zone had the same total of number crimes, with some minor variation that occurred for each crime type. The post-demolition period for the 500 – 1000 m buffer area saw a total decrease by 32 crimes; however, the amount of burglary increased. The post-demolition period at the 1000 – 1500 m buffer zone witnessed a decrease in auto theft, burglary, and robbery; while the number of homicides increased. The post-demolition time frame at the 1500 – 2000 m buffer area indicated a decrease in robbery and homicide, an unchanged amount of burglary, and an increase in auto theft. The post-demolition period at the 2000 – 2500 m buffer zone underwent a considerable increase in the total amount of crime; with notable increases in auto theft and homicide events. The total number of crimes decreased by 58 from the pre-demolition to the post-demolition period.

The violent and property crime types examined in the current study also exhibited some variation. The violent crime types (robbery and homicide) decreased from the pre-demolition to post-demolition periods. While the property crimes (auto theft and burglary) increased from the pre-demolition to postdemolition periods. Robberies showed the greatest decrease and auto thefts had the greatest increase from the pre-demolition to post-demolition periods.

Characteristics of the Geographic Distributions The location shift of the mean center and the different sizes and positions of the standard distance and standard

Spatial Analysis of Urban Crime Environment

Table 2. Number of Crimes Pre- and Post-Demolition for 500 Meter Interval Buffers in and Around the New National GeospatialIntelligence Agency (NGA) Footprint Pre-Demolition (5/1/2015 - 2/28/2017) Study Area

Auto Theft

Burglary

Robbery

Homicide

Post-Demolition (3/1/2017 - 12/31/2018)

Total

Auto Theft

Burglary

Robbery

Homicide

Difference between Pre- and Post-Demolition

Total

Auto Theft

Burglary

Robbery

Homicide

Total Difference

Footprint

2

9

2

1

14

0

0

0

0

0

-2

-9

-2

-1

-14

0–500 m Buffer

56

41

26

7

130

52

46

27

5

130

-4

5

1

-2

0

500–1000 m Buffer

194

164

110

27

495

189

184

75

15

463

-5

20

-35

-12

-32

1000–1500 m Buffer

287

217

231

24

759

286

208

210

29

733

-1

-9

-21

5

-26

1500–2000 m Buffer

299

234

252

24

809

305

234

194

22

755

6

0

-58

-2

-54

2000–2500 m Buffer

282

205

147

8

642

334

207

152

17

710

52

2

5

9

68

Total

1120

870

768

91

2849

1166

879

658

88

2791

46

9

-110

-3

-58

deviational ellipses provided an indication of how the spatial distribution of crime changed during the pre- and post-demolition periods near the NNW development. Table 3 displays the differences in the pre- and post-demolition mean center locations. On average and considering all crimes, the 2000 – 2500 m buffer zone had the most distant mean center, considering pre- and post-demolition periods. For the property crime of auto theft, the 500 – 1000 m buffer showed the least movement, with a difference of 49.451 m; while the 2000 – 2500 m buffer zone had a difference of 620.149 m between the mean centers. The post-demolition mean center was located approximately six and a half city blocks (one city block is approximately 100 m) southeast of the pre-demolition mean center. Burglary, the other property crime examined in the study, had minimal variation in the mean centers within the various buffer zones. The maximum difference between pre- and post-demolition mean centers for burglary was nearly two city blocks, while the average was a one city block difference. The post-demolition mean centers had an identifiable southwestern and southeastern shift in directionality.

The pre- and post-demolition period mean centers varied for the violent crime of robbery. The robbery mean center differed at the 0 – 500 m buffer by 39.521 m, which was less than one city block. The 1000 – 1500 m and 2000 – 2500 m buffer zones had greater mean center distance shifts of approximately three to three and a half city blocks. The postdemolition robbery mean centers deviated to the northwest, except for the southwestern shift apparent in the 0 – 500 m buffer and a southeastern trajectory observed in the 1500 – 2000 m buffer. The violent crime of homicide exhibited the greatest difference in pre- and post-demolition mean center distances. As the buffer distance increased from the NNW footprint, the mean center differences for the pre- and post43

demolition periods became greater. The 0 – 500 m buffer had a four city block mean center difference, the 1000 – 1500 m buffer had a five-and-a-half city block mean center difference, and the 2000 – 2500 m buffer had a six city block mean center difference. When the homicide mean center for the entire study area was analyzed, the pre- and post-demolition difference was 220.765 m (a little over two city blocks). The directionality of the mean center ranged in the various buffer areas, but all scales exhibited eastern tendencies.

The differences in area (sq km) between the standard distance ellipse and the standard deviational ellipse for all crimes and both time periods, indicated varied degrees of crime elongation and compaction. The standard distance ellipse served as the baseline average of where crimes concentrated, and the standard deviational ellipse provided more precision.

The greater the total difference between the standard distance ellipse area and the standard deviational ellipse area indicated an increased compaction or concentration of crime. While smaller differences in the two measures suggested minimal compaction or increased dispersion in crime. The greatest difference between the standard distance ellipse and standard deviational ellipse for the pre- and post-demolition periods occurred in the 1500 – 2000 m and 2000 – 2500 m buffer areas, which suggested an increased level of crime compaction. For the pre-demolition period, auto theft in the 2000 – 2500 m buffer area was the most compact (-4.279 sq km), while burglary in the NNW footprint (-0.002 sq km) and the 500 – 1000 m buffer (-0.002 sq km) had minimal changes in crime concentrations. For the post-demolition period, robbery at the 2000 – 2500 m buffer (-3.457 sq km) showed the greatest increase in compaction, and burglary at the 1000 – 1500 m buffer (-0.003 sq km) indicated the least amount of change in concentration. For the entire study area, auto thefts that occurred pre-demolition (-1.313 sq km) and

T.A. Smith and J.S. Onésimo (Ness) Sandoval

robbery during the post-demolition (-1.372 sq km) were the most compact. Auto thefts for the entire study area became more dispersed during the post-demolition time period, while the other crimes became more compact.

Figure 2 depicts the locations of the pre- and post-demolition mean centers, standard distance ellipses, and standard deviational ellipses. Figure 2 considered all robberies, homicides, burglaries, and auto thefts that occurred during the pre- and post-demolition periods, within the entire study area. The difference between the pre- and post-demolition mean centers was 54.252 m; both occurred within the same city block located in the south-central portion of the NNW footprint. When the difference between the standard distance and standard deviational ellipses for the pre- and post-demolition periods are compared, the increased precision of the standard deviational ellipses is apparent by the compaction and elongation of the polygons that contained one standard deviation (68%) of crime events. The numeric and visual differences of the standard distance and standard deviational ellipses suggested that during both periods, all crimes were more compact than average, for the entire study area. This finding coincides with the increased compaction observed in the individual crime types within the five, 500 m buffer zones during post-demolition period.

Spatial Patterns of Crime The results of the average nearest neighbor statistic on the pre-demolition crimes suggested significant clustering (p ≤ 0.001) in the individual 500 m buffer zones and the entire study area (see Table 3). Auto theft, burglary, and robbery that occurred in the NNW footprint area were significantly dispersed (p ≤ 0.001). The average nearest neighbor statistic for the post-demolition crimes of auto theft and burglary indicated significant clustering (p ≤ 0.001) in the each of the five buffer areas. Robbery was significantly clustered (p ≤ 0.001) in the 500 – 1000 m; 1000 – 1500 m; 1500 – 2000 m; 2000 – 2500 m buffers, and the entire study area. Homicide exhibited some variation with significant dispersion (p ≤ 0.001) at the 0 – 500 m buffer zone and significant clustering (p ≤ 0.001) at the 1000 – 1500 m; 1500 – 2000 m; 2000 – 2500 m buffer zones, and the entire study area.

A Global Moran’s I analysis examined the location and number of crimes in each 100 m grid cell for spatial autocorrelation, which resulted in a dispersed, random, or clustered pattern. In Table 4, the Global Moran’s I for the NNW footprint, the five, 500 m buffer areas, and the entire study area, yielded variation during the pre- and post-demolition periods. The Global Moran’s I for the NNW footprint indicated a dispersed pattern for pre-demolition crimes. Due to the absence of crime in the footprint during the post-demolition period, spatial autocorrelation could not be measured. In the pre-demolition 0 – 500 m buffer zone, auto thefts were significantly clustered (p ≤ 0.001), while the remaining crimes were 44

dispersed. Homicide was significantly clustered (p ≤ 0.05) during post-demolition period at the same spatial scale. Auto theft (p ≤ 0.01), burglary (p ≤ 0.001), and robbery (p ≤ 0.01) exhibited a significant clustered pattern at the 500 – 1000 m buffer zone during the pre-demolition period; while auto theft and burglary (p ≤ 0.001) were significantly clustered during the post-demolition period. Auto theft, burglary, and robbery exhibited significant (p ≤ 0.001) clustering for the remaining buffer zones (1000 – 1500 m; 1500 – 2000 m; 2000 – 2500 m) and the entire study area. Pre-demolition homicide for each spatial scale was not significant and dispersed. During the post-demolition period, homicide was clustered (p ≤ 0.05) at the 0 – 500 m buffer zone and for the entire study area.

The Global Moran’s I analysis for the specific crime types suggested that for the pre-demolition time frame, auto theft was clustered at every geographic scale considered in the analysis (excluding the NNW footprint). Burglary and robbery events started to cluster in the 500 – 1000 m buffer zone and continued to cluster in the remaining buffer zones and the entire study area. Homicide did not exhibit a discernible spatial pattern for all geographies examined. During the postdemolition period, auto theft, burglary, and robbery exhibited an observable clustered pattern, beginning at the 1000 – 1500 m buffer. Generally, homicide was dispersed in the five buffer zones. Pre- and post-demolition pattern differences are apparent at the 0 – 500 m and 500 – 1000 m buffers. Auto theft at the 0 – 500 m buffer during the pre-demolition period had a significant (p ≤ 0.001) clustered pattern and during the postdemolition period it was dispersed. Post-demolition homicide at the 0 – 500 m buffer zone established a clustered pattern (p ≤ 0.05) that was not evident during the pre-demolition period.

At the 500 – 1000 m buffer zone, robbery was clustered (p ≤ 0.01) during pre-demolition; however, robbery was dispersed during the post-demolition time frame.

The Global Differential Moran’s I analysis indicated some variation in the change between the pre- and post-demolition period crimes and buffer zones. Table 5 indicates that auto theft, burglary, and homicide events underwent statistically significant changes from pre-demolition to post-demolition.

Statistically significant changes in auto theft events occurred at the 0 – 500 m buffer (p ≤ 0.05), 1500 – 2000 m buffer (p ≤ 0.05), and 2000 – 2500 m buffer (p ≤ 0.001). Changes in burglary events were statistically significant at the 0 – 500 m (p ≤ 0.05), 1000 – 1500 m (p ≤ 0.01), and the entire study area (p ≤ 0.01). While homicide events only displayed statistically significant changes at the 1500 – 2000 m buffer (p ≤ 0.001).

The LISA analysis provided locations of robbery hot spotss around the NNW development. Figure 3 depicts the pre-demolition buffer areas with 100 m grids cells and the associated LISA cluster category for robbery. In the NNW footprint, cold spots are observed in the northwest and southeast corners. Scattered robbery hot spots were apparent at

Spatial Analysis of Urban Crime Environment

Table 3. Characteristics of the Geographic Distribution of Crimes Pre-Demolition and Post-Demolition of the Next National Geospatial-Intelligence Agency (NGA) West (NNW) Development Pre-Demolition Study Area

Average Nearest Neighbor Ratio

Footprint†

40.882

Standard Distance Ellipse (Area Sq Km)

Post-Demolition

Directional Distribution Ellipse (Area Sq Km)

Standard Distance and Directional Distribution Ellipses Difference (Area Sq Km)

Standard Distance Ellipse (Area Sq Km)

Average Nearest Neighbor Ratio

Directional Distribution Ellipse (Area Sq Km)

Standard Distance and Directional Distribution Ellipses Difference (Area Sq Km)

Difference between PreDemolition and PostDemolition Mean Center‡ (Euclidean Distance in Meters)

Auto Theft ***

0–500 m Buffer

0.492

***

1.467

1.203

-0.264

0.513

***

1.280

1.167

-0.113

198.384

500–1000 m Buffer

0.494

***

4.215

4.157

-0.058

0.416

***

4.301

4.266

-0.035

49.451

1000–1500 m Buffer

0.402

***

8.503

8.396

-0.107

0.410

***

8.522

8.457

-0.065

123.190

1500–2000 m Buffer

0.368

***

13.142

11.212

-1.930

0.325

***

13.569

12.026

-1.543

147.694

2000–2500 m Buffer

0.274

***

22.616

18.338

-4.279

0.286

***

20.617

17.949

-2.668

620.149

Entire Study Area

0.541

***

12.374

11.061

-1.313

0.512

***

12.561

11.674

-0.886

185.476

1.467

1.456

-0.011

167.054

Burglary Footprint†

1.377

***

0.185

0.183

-0.002

0–500 m Buffer

0.637

***

1.262

1.214

-0.048

0.512

***

500–1000 m Buffer

0.462

***

3.806

3.804

-0.002

0.393

***

3.810

3.759

-0.051

29.205

1000–1500 m Buffer

0.423

***

7.933

7.695

-0.238

0.401

***

7.594

7.590

-0.003

123.834

1500–2000 m Buffer

0.343

***

13.825

11.532

-2.293

0.319

***

14.465

11.369

-3.096

174.265

2000–2500 m Buffer

0.299

***

17.794

16.667

-1.127

0.342

***

17.650

16.060

-1.589

65.021

Entire Study Area

0.540

***

11.532

10.690

-0.842

0.510

***

11.610

10.479

-1.131

54.038

1.316

1.281

-0.035

39.521

Robbery Footprint†

40.774

***

0–500 m Buffer

1.111

***

1.147

1.138

-0.009

0.916

500–1000 m Buffer

0.623

***

4.159

4.102

-0.057

0.645

***

4.277

4.182

-0.095

143.525

1000–1500 m Buffer

0.429

***

8.063

7.733

-0.330

0.431

***

8.212

8.069

-0.143

307.193

1500–2000 m Buffer

0.371

***

13.604

11.120

-2.483

0.371

***

13.406

10.920

-2.485

113.758

2000–2500 m Buffer

0.363

***

20.943

18.737

-2.206

0.293

***

22.232

18.776

-3.457

350.974

Entire Study Area

0.629

***

11.817

10.647

-1.170

0.568

***

12.608

11.236

-1.372

148.591

Homicide Footprint† 0–500 m Buffer

1.340

*

1.471

0.827

-0.643

2.622

500–1000 m Buffer

0.734

**

3.436

3.265

-0.171

0.954

***

0.364

0.122

-0.242

395.727

4.405

3.649

-0.756

466.643

1000–1500 m Buffer

0.521

***

5.586

5.398

-0.187

0.615

***

8.076

7.988

-0.088

553.250

1500–2000 m Buffer

0.630

***

9.150

7.978

-1.172

0.317

***

10.087

9.089

-0.998

506.713

2000–2500 m Buffer

1.630

***

15.086

14.420

-0.666

0.452

***

19.772

16.718

-3.054

602.334

Entire Study Area

0.803

***

7.150

6.679

-0.471

0.682

***

10.467

9.544

-0.923

220.765

Note: Statistical significance levels, two-tailed: * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; †For the pre-demolition timeframe: The footprint areas for auto thefts, robberies, and homicides had too few vertices for the standard distance and directional distribution analysis. The footprint area for homicides had too few vertices for the nearest neighbor analysis; For the post-demolition timeframe: No crimes reported in the Next National Geospatial-Intelligence Agency West footprint area. ‡Difference in mean center values derived from the mean center X, Y coordinates for pre- and post-demolition time periods.

45

T.A. Smith and J.S. Onésimo (Ness) Sandoval

Figure 2. Characteristics of the Geographic Distribution of Crimes Pre- and Post-Demolition. This figure depicts the locations of the pre- and post-demolition mean centers, standard distance ellipses, and standard deviational ellipses while considering all robberies, homicides, burglaries, and auto thefts that occurred during the pre- and post-demolition periods. The numeric and visual differences of the standard distance and standard deviational ellipses suggested that during both periods, all crimes were more compact than average, for the entire study area.

46

Spatial Analysis of Urban Crime Environment

the 500 – 1000 m buffer area and cold spots also appeared around the inner and outer edges of the buffer. Robbery hot spot concentrations occurred to the west and southeast of the NNW footprint, in the 1000 – 1500 m buffer zone.

Robbery cold spots were found in the northern and southern portions of the buffer area. The 1500 – 2000 m buffer area indicated robbery hot spots in the northern portion of the NNW development, while a large concentration of robbery appeared southeast of the NNW footprint. At the 2000 – 2500 m buffer zone, robbery hot spots occurred in the north and southwest of the NNW development and the large robbery hot spot observed in the previous buffer area continued to expand. Robbery cold spots were absent in the 0 – 500 m; 1500 – 2000 m; and 2000 – 2500 m buffer areas.

The LISA analysis for the post-demolition buffer areas showed discernible spatial patterning with robbery cold spots and hot spots. In Figure 4, the robbery cold spot locations appeared around the inner and outer edges of the 0 – 500 m; 500 – 1000 m; and 1000 – 1500 m buffer zones. Robbery cold spots were absent in the 1500 – 2000 m and 2000 – 2500 m buffer areas. The spatial patterns of robbery hot spots were observed southeast of the NNW development (centered on Washington Avenue and Tucker Boulevard) and a linear concentration north and northwest of the NNW development (along North Grand Boulevard). A few robbery hot spots were also scattered east of the NNW development in the 0 – 500 m; 500 – 1000 m; and 1000 – 1500 m buffer areas.

The pre-demolition LISA analysis showed greater spatial variation in the robbery cold spot and hot spot locations; while the post-demolition LISA analysis indicated distinct spatial patterning of robbery cold spots and hot spots. A discernible loose framework for the post-demolition spatial patterning of robbery could be observed in the pre-demolition LISA analysis. Robbery hot spots were absent in the pre-demolition 0 – 500 m buffer area; however, a small hot spot appeared in the post-demolition LISA analysis. Fewer robbery hot spots were indicated in the post-demolition 500 – 1000 m buffer zone. The post-demolition LISA analysis at the 1000 – 1500 m buffer zone suggested the accumulation of robbery hot spots into larger spatial concentrations or patterns.

The remaining outer-most buffer areas displayed an increased concentration of robbery hot spots. A small robbery hot spot concentration located in the southwestern portion of the 2000 – 2500 m buffer zone was viewable in the pre-demolition LISA analysis. This area was not identified as a robbery hot spot during the post demolition LISA analysis.

DISCUSSION AND CONCLUSIONS The exploratory spatiotemporal analysis of crimes in and around the NNW location for the pre- and post-demolition periods yielded some interesting observations. We examined how distance from the NNW development influenced crime and observed that the number of crimes decreased during the 47

post-demolition period in all the buffers except the 0 – 500 m and 2000 – 2500 m zones. The NNW development footprint was cleared of auto theft, burglary, robbery, and homicide events during the post-demolition period. The post-demolition crime environment in the study area increased with approximately 70 crimes the outermost 2000 – 2500 m buffer and an saw an average decrease of 37 post-demolition crimes in the middle buffer zones (500 – 1000 m, 1000 – 1500 m, and 1500 – 2000 m). This observation warrants additional analysis to determine how spatial distance from the NNW demolition site impacted crime levels.

In exploring the NNW development’s impact on the surrounding crime environment, the 2000 – 2500 m buffer zone and homicides had the farthest pre- and post-demolition distance between mean centers but were quite similar overall.

The difference between the standard distance and directional distribution ellipses suggested that for the entire study area, all crimes became more compact than average during the post-demolition period. This compactness is also apparent within the individual crime types and buffer zones. The crimes of auto theft, robbery, and homicide were the most compact at the 2000 – 2500 m buffer zone during the postdemolition period. For the average nearest neighbor ratio, the post-demolition period indicated greater clustering in the individual crimes and buffers. This finding concludes that the average distance and direction of crime changed between the two-time frames being analyzed. Furthermore, crime became more concentrated and clustered during the post-demolition period.

Investigating the change of crime patterns between the pre- and post-demolition periods, the results of the Global Moran’s I suggested that spatial autocorrelation was present at a similar capacity in both periods. In the 0 – 500 m buffer zone, the most notable change between both periods was the insignificant random pattern of auto theft and significant (p ≤ 0.05) clustered pattern of homicide that occurred during the post-demolition period. The post-demolition robbery in the 500 – 1000 m buffer was also random, while it appeared clustered during the pre-demolition period. These two observations suggest that different spatial patterns existed for various crime types. The Global Differential Moran’s I provided the statistically significant spatial autocorrelation changes between the pre- and post-demolition periods. Statistically significant changes were observed with auto theft, burglary, and homicide.

Auto theft and burglary saw greater geographic variability in the significant changes of spatial autocorrelation, while significant changes in homicide were apparent in the 1500 – 2000 m buffer zone only.

The LISA analysis that used robbery events as a case study during the pre- and post-demolition periods, indicated spatially varied cold spot and hot spot locations. The pre-demolition period LISA analysis appeared to outline the beginning framework for more discernible spatial patterning of post-demolition robbery cold spots and hot spots. Robbery hot spots were not

T.A. Smith and J.S. Onésimo (Ness) Sandoval

Table 4. Global Moran’s I of Crimes in 100 Meter Grids for Pre- and Post-Demolition Activities for the Next National GeospatialIntelligence Agency (NGA) West (NNW) Global Moran’s I for Crimes Pre-Demolition Study Area

Auto Theft

Footprint†

-0.031

0–500 m Buffer

0.101

Burglary

***

Robbery

Global Moran’s I for Crimes Post-Demolition Homicide

Auto Theft

0.050

-0.041

-0.023

-0.018

-0.028

-0.033

0.015

Burglary

Robbery

0.002

Homicide

-0.017

0.057

500–1000 m Buffer

0.060

**

0.097

***

0.066

**

-0.012

0.087

***

0.115

***

0.028

1000–1500 m Buffer

0.088

***

0.061

***

0.121

***

0.023

0.076

***

0.140

***

0.160

***

0.023

1500–2000 m Buffer

0.259

***

0.170

***

0.220

***

-0.001

0.213

***

0.173

***

0.209

***

0.011

2000–2500 m Buffer

0.111

***

0.143

***

0.159

***

-0.006

0.193

***

0.146

***

0.127

***

-0.012

Entire Study Area

0.148

***

0.138

***

0.178

***

0.018

0.165

***

0.162

***

0.174

***

0.022

*

-0.013

*

Note: Statistical significance levels, two-tailed: * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; †No crimes reported in the Next National Geospatial-Intelligence Agency (NGA) West footprint during the post-demolition period.

Table 5. Global Differential Moran’s I of Crimes in 100 Meter Grids During the Pre- and Post-Demolition Periods for the Next National Geospatial-Intelligence Agency (NGA) West (NNW) Global Differential Moran’s I (Pre-Demolition and Post-Demolition Timeframes) Study Area

Auto Theft

Burglary

Robbery

Homicide

-0.024

-0.027

0.005

-0.022

0.013

0.028

Footprint† 0–500 m Buffer

0.080

500–1000 m Buffer

-0.026

*

-0.055 0.014

1000–1500 m Buffer

-0.030

0.052

* **

1500–2000 m Buffer

0.046

*

0.006

-0.020

-0.074

2000–2500 m Buffer

0.059

***

0.005

0.020

-0.025

Entire Study Area

0.015

-0.001

-0.014

0.027

**

***

Note: Statistical significance levels, two-tailed: * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; †The Global Differential Moran’s I could not be determined for the footprint area due to no crimes reported in the Next National Geospatial-Intelligence Agency (NGA) West (NNW) footprint during the post-demolition period. apparent in the pre-demolition 0 – 500 m buffer area, while a small robbery hot spot appeared in the post-demolition 0 – 500 m buffer area. There were fewer robbery hot spots in the postdemolition 500 – 1000 m buffer zone while the 1000 – 1500 m buffer zone indicated the accumulation of robbery hot spots into larger spatial concentrations or patterns. The remaining outermost buffer areas displayed an increased concentration of robbery hot spots. These findings also coincide with a greater degree of crime compaction and concentration observed during the post-demolition time frame.

The current study offered contributions to the spatial crime body of research. Specifically, through a series of exploratory spatial tests, greater concentrations of crime during the post-demolition activities were observed. These specific crime-dense areas within the study area could be attributed to the NNW demolition activity or other influential factors that were not considered in 48

the current study. While it is likely that the demolition activity removed the crime events from the NNW footprint during the post-demolition period, the innermost and spatially adjacent buffer zone to the NNW footprint may have been influenced by the development, indicated by the same number of crimes in both time periods analyzed. Theoretically, this observation contrasts with the gravity model, where the 0 – 500 m buffer should have sustained a drastic decrease in crime during the postdemolition period. Furthermore, with the exploratory nature of the current research, additional analysis is required to determine if the pre-demolition crime environment offered the beginning framework of specific crime patterns and concentrations that further materialized during the post-demolition period. The idea that other factors, such as varied socioeconomic conditions or targeted policing activities may have exerted a stronger influence on the observed crime patterns, are also plausible.

Spatial Analysis of Urban Crime Environment

Figure 3. Local Indicator of Spatial Association (LISA) Analysis of Pre-Demolition Robbery Events. This figure depicts the five, 500 m pre-demolition buffer areas with 100 m grids cells and the associated LISA cluster category for robbery. The blue grid cells indicate cold spots or grids with lower than the expected number robberies and surrounded by grids of lower than expected robberies, the red grid cells show hot spots or areas of higher than the expected number of robberies, and the grey areas are not significant. Hot and cold spot clustering is readily apparent with specific concentrations in various parts of the study area.

49

T.A. Smith and J.S. Onésimo (Ness) Sandoval

Figure 4. Local Indicator of Spatial Association (LISA) Analysis of Post-Demolition Robbery Events. This figure depicts the five, 500 m post-demolition buffer areas with 100 m grids cells and the associated LISA cluster category for robbery. The blue grid cells indicate cold spots or grids with lower than the expected number robberies and surrounded by grids of lower than expected robberies, the red grid cells show hot spots or areas of higher than the expected number of robberies, and the grey areas are not significant. A change in the pre- and post-demolition hot and cold spot clustering is readily apparent with greater concentrations in hot spots in the distant buffer zones.

50

Spatial Analysis of Urban Crime Environment

While the current study provided an exploratory spatiotemporal analysis of the crime environment associated with the demolition of the NNW development, there are some limitations to disclose. The intent of the research was to provide a baseline, exploratory spatial analysis of the crime environment in and around the NNW development. The periods used for the current study supported an exploratory analysis only, as 44 months of data (22-months before demolition and 22-months during and after demolition) were available to perform the analysis. Due to this shortened time period for analysis, observations may be associated with organic crime shifts or the impact of crime detractors or mitigating factors. The current study area considered the NNW development site and five, 500 m buffer zones; the observations made only reference this micro-sized study area and cannot be applied to larger geographic scales, such as for the entire City of St. Louis or the St. Louis Metropolitan Statistical Area (MSA).

The current study provided the framework for future research regarding the influence of the NNW development on various urban renewal issues. We suggest that future studies research the effect of land use change on crime, in and near the NNW development site, with the potential for increases in housing, retail, and business developments. The increase of security and police presence, along with a geographically segregated perimeter from the rest of the nearby urban environment of the NNW development site could produce interesting results in future studies researching how the NNW development acts as a gated community, especially regarding crime. With additional data available in the future analysis, prospective research activities may consider modelling the spatial relationships between the socioeconomic and criminogenic environments in and around the NNW development, before and after completion.

ACKNOWLEDGEMENTS The authors have no funding or conflicts of interest to report.

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Tara A. Smith is a PhD candidate at Saint Louis University in the Integrated and Applied Sciences Program. Her research interests include spatial statistics, crime analysis, and Exploratory Spatial Data Analysis (ESDA).

Dr. J.S. Onésimo (Ness) Sandoval is an Associate Professor of Sociology in the Department of Sociology and Anthropology at Saint Louis University. He currently runs the Computational Spatial Demography Lab. His primary research interests focus on the intersection of demographic techniques and computational spatial science.

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