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The Dynamics of Robbery and Violence Hot Spots  Herrmann The dynamics of robbery and violence hot spots Herrmann Herrmann Crime Sci (2015) 4:33 DOI 10.1186/s40163-015-0042-5 Herrmann Crime Sci (2015) 4:33 DOI 10.1186/s40163-015-0042-5 RESEARCH Open Access The dynamics of robbery and violence hot spots Christopher R. Herrmann* Abstract This paper examines how hot spots shift by hour of day and day of week. Hot spot analysis is more likely to have a substantial impact on crime patterns if spatiotemporal shifts are incorporated into the crime analysis process. Even in some of the highest crime neighborhoods in Bronx County (NY), not all micro-level geographies (e.g. street seg- ments and property lots) contain substantial (if any) amounts of crime over the 5-year study period. Moreover, while there are 168 h in a week, even the hottest hot spots do not contain crime 24 h a day, 7 days a week, and 52 weeks a year. Hot spots shift by both space and time and it is important to illustrate these dual shifts when researching and analyzing different levels of geographies and/or hot spots. Spatiotemporal crime analyses are appearing much more frequently in our academic literature in recent years and have become a principal contributor to the progression of routine activities, crime pattern theory, place-management and situational crime prevention. In addition, spatiotem- poral hot spots provide important subject and opportunity context and can help answer some of the questions about what specific types of crime opportunities are available inside of hot spots based on land-use and people’s movement patterns. When studying geographical hot spots, it becomes important to measure and illustrate the inter- related temporal shifts within each of the specific hot spots (i.e. not all hot spots are the same) that are generated. Similarly, when studying temporal hot spots, it is important to measure and illustrate the interrelated spatial shifts within each of the temporal frame(s) that are examined. Examples of space–time and time–space hot spot analysis are provided using violent crime data from the New York City Police Department. Key findings of this research include significant shifting of hot spots from weekday to weekend and afternoon to evening, as well as decisive spatiotempo- ral pattern variations between school-day robberies vs. non-school day robberies. Keywords: Robbery, Violence, Crime analysis, Spatiotemporal, Routine activities, Hot spots Background hot spots at the micro-level, crime prevention and crime The crime science and crime analysis communities have control specialists can have a greater impact on appre- become proficient in creating, tracking, and handling hending criminals, police resource allocation and plan- hot spots of crime (Chainey and Ratcliffe 2005). Current ning, crime modeling and forecasting, and evaluation research (Weisburd et al. 2012; Groff et al. 2010; Ber- of crime prevention and crime control programs (Boba nasco and Block 2011; Brantingham et al. 2009; Weisburd 2001; Townsley et al. 2003; Ratcliffe 2004; Johnson et al. et al. 2009) indicates that as crime scientists drill down 2007). In our continuing state of shrinking government into the micro-levels of geography (e.g., streets, tax lots, operating budgets, crime scientists and crime analysts buildings), crime hot spots start to form new shapes (e.g. need to consider the interrelatedness of spatial and tem- lines, points, building outlines), sizes, and spatiotemporal poral shifts in crime patterns when creating, tracking, patterns. and handling crime hot spots. By developing a more comprehensive understanding of Many studies indicate that crimes are clustered at the the spatial and temporal variations within violent crime neighborhood level, but the entire neighborhood is rarely (if ever) criminogenic and only specific parts of neighbor- hoods contain high concentrations of crime (Taylor 1997; *Correspondence: [email protected] New York, NY, USA Fagan and Davies 2000; Groff et al. 2010). Prior studies © 2015 Herrmann. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Herrmann Crime Sci (2015) 4:33 Page 3 of 14 incorrectly assume that the relationships between crime, and drug violence reduction programs). Shifting hot- population, land-use, and business establishment types spots research hopes to advance these trends of increas- are both homogenous and spatially stationary (Shaw and ing success for law enforcement crime control strategies, McKay 1969; Bursik and Grasmick 1993; Sampson et al. advancement of current environmental criminology 1997; Morenoff and Sampson 1997). Even at the neigh- theories, and expansion upon existing crime preven- borhood level, crime is highly clustered and the highest tion frameworks. Integration of theory and new analyses crime neighborhoods have significant percentages of low at micro-levels (e.g. street segments and risky facilities) or zero crime streets within the neighborhood(s). will help crime scientists, police departments, and policy A substantial body of research has identified that a makers better understand the spatial and temporal pro- small percentage of crime locations (i.e. hot spots) con- cesses in the ‘magma’ that fuels today’s crime hot spots. tain a significant percentage of crime (Sherman et al. 1989; Block and Block 1995; Eck and Weisburd 1995; Data and methodology Brantingham and Brantingham 1999). One of the cur- The objective of shifting hot spots research is to explore, rent trends in environmental criminology and crime measure, and illustrate the various spatiotemporal shifts analysis is the study of crime at more ‘micro’ level places that occur within and between violent crime hot spots. (i.e., buildings, properties, block faces, street segments), a Specifically, this research focuses on spatiotemporal geographic scale/level well below the neighborhood level. crime shifts within violent crime ‘hot spots’ in the Bronx. Part of this concept of micro-level analysis is known as The Bronx was selected because it contains a higher per the 80/20 rule or Pareto’s Principle (Eck et al. 2007; Weis- capita violent crime rate (crimes per 1000 residents), as burd et al. 2012), and reigns true not only in crime pre- well as a higher crime density (crimes per square mile), vention and crime control, but other areas of the criminal compared to the other four counties that comprise the justice field as well [this concept has also been branded City of New York. While much of the spatiotemporal as the ‘law of crime concentrations’ by Weisburd et al. variation(s) or hot spot ‘shiftiness’ occurs as a result of (2012)]. routine activities and land-use heterogeneity-very few The 80/20 rule suggests that by targeting or focusing hot spots (if any) appear to be both spatially and tempo- on the highest 20 % of crime hot spots can have a dra- rally stationary. matic influence on the majority (approximately 80 %) of The research area and data for this study are comprised total crime in the study area. According to the 80/20 rule, of various Geographic Information Systems (GIS) data- the net impact of crime prevention and crime control sets for Bronx County, including violent crime datasets strategies targeting the 20 % would be much higher than (2006–2010) from the New York City Police Department. attempting to target an entire neighborhood (or whatever The Bronx is geographically organized into 38 neighbor- the larger study area is that is being examined). hoods (note, 36 of the 38 neighborhoods are ‘residential’, Environmental criminologists using Pareto’s 80/20 the other two are categorized as parks/open space and rule have pointed out that not all parks are full of drug industrial), 12 Police Precincts, 355 Census Tracts, 987 users/dealers (Wilcox et al. 2004; Eck et al. 2007; Groff Census Block Groups, 10,781 street segments, 89,211 and McCord 2012), not all high schools have high rates tax/property lots, and 101,307 buildings (NYC Depart- of delinquency (Wikstrom et al. 2012; Glover 2002; ment of City Planning 2010; NYPD 2010; NYC Depart- LaGrange 1999), not all bars contain high rates of assault ment of Finance 2010; NYC City Department of Buildings (Ratcliffe 2012; Newton and Hirschfield 2009; Eck et al. 2010). The Bronx is 42 square miles in area, which makes 2007; Gorman et al. 2001), and not all parking lots have it 14 % of New York City’s total geographical land area high rates of auto theft (Rengert 1997; Clarke and Gold- (NYC Department of City Planning 2010). According stein 2003). In fact, even ‘high crime’ neighborhoods con- to the US Census (2000), the population of the Bronx is tain hot spots (high density crime areas) and cold spots 1,332,650 which comprises 17 % of the total New York (zero/low crime areas), high crime streets and zero/low City population. crime streets, and both ‘good’ (i.e. crime protectors) and One of the reasons the Bronx is an ideal place to study ‘bad’ (i.e. crime generators) streets and businesses. crime is because it is the third most densely populated Violent crime (i.e., murder, rape, robbery, assault, and county in the United States (behind Manhattan & Brook- shootings) has declined 73 % in the Bronx since 1990 lyn) and about a quarter of its land area is uninhabited (NYPD Compstat 2010). In his book, Zimring (2011) open space or industrial areas. Interestingly, the US Cen- suggests that the historic crime drop in New York City sus (2000) indicates that the Bronx is the most diverse is a result of better policing (i.e. CompStat, crime analy- county in the US: 15 % Non-Hispanic White, 31 % Non- sis, hot spot policing, zero tolerance, stop and frisk) and Hispanic Black, 49 % Hispanic, and 5 % other.
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