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Distressed Properties and Crime in Durham, North Carolina: Revisited by Brian Luo

Following the Great Recession and housing crisis from 2007 to 2009, the United States experienced widespread rates of foreclosure, among other economic turmoil. During this time, new research dedicated to determining the relationship between foreclosure rates and crime rates began to emerge, as researchers and policymakers alike wanted to understand the negative externalities of the falling economy and of the rising foreclosure rates specifically. While there have been a number of studies completed across the country since then, research on the relationship between foreclosure and crime has yet to provide a strong empirical basis for further analysis.

As a result of the relative lack of research, many cities throughout the United States (and abroad), such as Durham, North Carolina, lack a solid foundation on which to model foreclosure and crime relationships. Through this current study, I hope to expand on my previous study and continue analyzing data related to distressed homes in Durham and data related to crime around the sampled homes over the three years from 2012 to 2014. By expanding this study to cover multiple years, I aim to specifically analyze the relationship between crime and a property during the various stages of distress.

Literature Review

The housing crisis that swept the U.S. during the Great Recession contributed to the perceived need to study cities and neighborhoods that have been hit the hardest by poor economic conditions related to foreclosure. Unfortunately, many results of these studies have been inconclusive and mixed. Adding in large part to the confusion is the inconsistency across studies in the units of analysis, definitions and measures of foreclosure and distressed properties, and analytical strategies needed to link these economic events to crime rates (Baumer et al. 2012).

To examine the relationship between foreclosure and crime in a variety of major U.S. cities, Baumer et al. employ foreclosure rates as the primary independent variables and focus on incidents of robbery and burglary as the primary dependent variables. Controlling for neighborhood and prior-crime variables, the researchers found that there was a significant variability across cities. In addition, Baumer et al. find different patterns for their two dependent variables. With respect to robbery, the results suggest that foreclosure is more strongly related to robbery in cities with higher levels of socioeconomic disadvantage and lower overall levels of foreclosure. On the other end, higher rates of foreclosure are more closely associated with higher burglary rates, in addition to other factors such as a shrinking police force and little new housing.

In a similar vein, Katz et al. (2011) focus on the drivers of policy that come from foreclosure-crime research. Namely, the researchers were interested to understand whether and how long foreclosure causes crime rates to rise. Bringing in the context of the most recent U.S. housing crisis, the authors examine whether there is a linear and crisis-specific effect of foreclosure on neighborhood levels of crime and also examine whether there was a time lag in the impact of foreclosure on neighborhood levels of crime. Regarding the first objective, Katz et al. suggest that foreclosed homes may not have long-term negative impacts on crime in a given metropolitan area. Instead, their results show that foreclosure has a short-term impact, typically no more than 3 or 4 months. On the second objective, the authors find that the relationship between the housing crisis and increases in crime are not linear, but rather are characterized by short-term fluctuations.

Jones and Pridemore (2012) contribute to existing research by providing city-level analysis that incorporates nearly 150 major metropolitan areas from across the country. Their approach focuses on data from 2006 forward, so as to capture the trends of foreclosure and crime throughout the period of the Great Recession and in to its aftermath. Jones and Pridemore’s approach differs from previous research in one important aspect: instead of using foreclosure rates as the primary independent variable in their study, the authors instead elect to employ a new indicator, the Housing-Mortgage Stress Index (consisting of three variables: negative equity, loan-to-value ratio, and the monthly mortgage cost-to-income ratio), to capture a more complete and accurate set of information about foreclosure-associated economic stress in a city. Using the Housing-Mortgage Stress Index as the main independent variable, the authors’ results suggest that the housing crisis is not associated with metropolitan rates of serious violent and property crime.

Unlike the previous three teams of researchers, Ellen et al. (2013) employ point-specific and longitudinal crime and foreclosure data to understand the relationship between foreclosure and crime in New York City. Their study uses blockfaces – individual street segments, including properties on both sides of the street – to compare the changes in the crime level of a given blockface before and after homes on the blockface enter foreclosure with the changes in the crime level of a different blockface in the same neighborhood that did not experience a change in foreclosure activity during the same time period. The researchers reason that, as crime trends are likely to be the same along different blockfaces in the same neighborhood, their difference-in-difference model can identify whether foreclosures lead to higher crime rates. They also include estimates that control for future foreclosure notices on a blockface, through which they hope to capture the unobserved trends between blockfaces where foreclosures tend to occur and those where they do not. Their results show that foreclosures on a blockface lead to a small number of additional crimes, with the largest effect on the rate of violent crimes. Furthermore, their results suggest that the effects are largest for foreclosed properties that go all the way through the foreclosure process to an auction. The researchers also note that their methods do not allow for results to determine the net changes in the overall crime in a city, only changes in new crimes around the localized area of a foreclosure.

Similarly, Cui and Walsh (2015) focus on understanding the effects of a given foreclosure on crime levels in the immediate vicinity of properties in Pittsburgh, Pennsylvania. They pay special attention to identify the separate impacts of foreclosure duration and vacancy on the rate of crime observed. Cui and Walsh are unique in defining treated neighborhoods as a 250-foot buffer surrounding each foreclosed house, and they define control neighborhoods as an equal area donut surrounding this buffer, which is the area from 250 feet to 353 feet away from the given property. They elect to use these neighborhood definitions to more specifically lessen concerns regarding the common trends assumption when dealing with larger neighborhood definitions. Their results suggest that the foreclosure process can lead to significant increases in violent crime rates within 250 of the property. In addition, the results suggest that crimes associated with foreclosure do not occur during the early stages of foreclosures, but instead by the vacancies that are associated with the foreclosure process. The impact of vacancy on crime increases when the property stays vacant for longer periods, with effects plateauing between 12 and 18 months of vacancy. Their results also suggest that once a home is reoccupied, the crime impacts of the recent vacancy are reduced.

Criminal Activity in Durham

Despite the housing crisis and Great Recession from 2007 to 2009, the total number of crimes per capita in Durham has been trending steadily downward from 2000 to 2014. Looking only at the trend in Figure 1, it would be very difficult to predict any type of economic turmoil around 2007-2009, given the consistent decline in crime rate. However, from 2012 to 2014, the total number of crimes per capita have leveled off slightly, decreasing about 5.9% from 2012 to 2013, but then rising about 12.0% from 2013 to 2014 (which equates to about a 5.4% increase from 2012 to 2014).

bl1Figure 1: Durham crime per 100,000 residents, from 2000-2014

While crime levels appear to be dropping overall since 2000, property crime (defined here as the combination of larceny and burglary) continues to be the type of crime most committed in Durham since 2000, representing at least 80% of the total number of crime incidents in from 2012 to 2014 (see Figures 2-5). Violent crime (defined here as the combination of assault and robbery) is observed in a much smaller proportion, representing from 12-14% of total crime in each of the years from 2012 to 2014.Larceny, which includes any type of theft offense, constitutes over half of the total number of crime incidents from 2012 to 2014. Burglary, which includes any breaking and entering offenses, constitutes over a quarter of the total number of crime incidents during this time period, second only to larceny. The next two most frequent offenses – aggravated assault and robbery – trail significantly, comprising only 8-9% and 5% of total crime incidents, respectively, in the three-year span.

bl2 bl3 bl4Figures 2-5: Durham crime by number of incidents, from 2012-2014

Methodology and Data

Similar to my previous study, I use the Real Property Delinquent Taxpayer list from the Durham County Tax Collector to randomly select 35 delinquent owners and their properties in Durham city. For selection in to the sample, the property must have at least three consecutive years of unpaid (as opposed to partially paid or fully paid) tax bills. The intuition behind this decision is that homes with multiple consecutive years of unpaid bills will be most likely foreclosed by Durham county for failure to pay property taxes; therefore, the homes will be the most distressed.

For each address obtained, I use the Crime Mapper tool from the Durham Police Department (see Figure 6) to show the number of nearby crimes of assault, burglary, larceny, and robbery from January to December in each of the three years from 2012 to 2014. By including data from 2012-2014, I hope to capture the trends in crime over the life of a distressed property. Since most of the sampled properties have been delinquent for exactly three years, the addition of a time characteristic to the data set can provide an understanding about crime rates around a property throughout the distress process.

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Figure 6: Example of crime mapping tool, Denfield Street property

Results from the Crime Mapper, similar to the previous study, are limited and recorded first within a 500-foot radius (which I label the “inner zone”) of the distressed property and then within a 1,320-foot (1/4-mile) radius (which I label the “total zone”)of the distressed property. While the Crime Mapper is also capable of providing crime information within a 1/2-mile radius of a given property, I elected to forgo collecting the 1/2-mile data, as results from this distance were unhelpful for showing the relationship between a distressed property and nearby crime under the assumptions of uniform and random distribution. In each of the first few cases collected at this distance, the numbers of total incidents of crime observed were too low to produce meaningful estimates. Given that a circle with a 500-foot radius has an area that is about 3.6% of the area of a circle with a 1/2-mile radius, there must be at least 28 observations of crime in the larger area to yield a random estimate of at least 1 crime in the inner zone. This means that for each type of crime, any 1 incident within the inner zone would be categorized as “above expected” if there were fewer than 28 incidents in the 1/2-mile radius circle, which holds true for the majority of the sampled properties. Thus, I have chosen to continue calculating the number of observed crime incidents within the inner zone with the number of observed crime incidents within a 1/4-mile radius total zone.

Assuming a distressed property is not a hub for increased crime and that crime is uniformly and randomly distributed within the total zone without the influence of a distressed property, I aim to compare the number of observed crimes in the inner zone with the number of observed crimes in the total zone. Given the radii of the inner and total zones and assuming that the areas of focus are perfectly flat and circular, a quick calculation shows that the area of the inner zone is roughly 14.3% of the area of the total zone. Given the number of crimes in the total zone and the relative sizes of the inner and total zones, I then calculate the average number of crimes expected within the inner zone, given uniform and random distribution (i.e., without the concentrating “pull” of a distressed property in the center).

Taking the “expected” number of crime incidents in the inner zone, I then compare the number of expected incidents with the number of observed incidents. If the number of observed crimes is higher than the number of expected crimes for more properties than not, then there might be evidence to support the hypothesis that the presence of a distressed property is associated with more crime. However, if the number of observed crimes is lower than the number of expected crimes for more properties than not, then there might be evidence to reject the same hypothesis.

In discussing the results, I will define a property as “above expected” (AE) if the observed number of crimes within the property’s inner zone is above the estimate given by the property’s total zone. For example, if a property’s total zone estimates an expected 1.87 burglary crimes in 2014 in the property’s inner zone, and the observed number of crimes is 3, then we can categorize this property as an AE property with respect to burglary in 2014. The AE designation of a property is dependent on both the type(s) of crime and the time period specified.In general, if the number of AE properties rises from 2012 to 2014, there might be evidence to suggest that crime rates rise as homes become more distressed or remain equally as delinquent.

Results and Discussion

Among the 35 sampled properties over the three years from 2012 to 2014, a total of 5,672 crime incidents were observed, with total crime falling from 1,824 in 2012 to 1,771 in 2013 and rising back to 2,077 in 2014 (see Table 1). With respect to the distribution of crime between the four types – assault, burglary, larceny, and robbery – used in this study, the observed crimes were directionally representative of the annual distribution of crime in Durham overall.Larceny, consistent with the citywide distribution, was the type of crime most observed in this sample, followed by burglary, assault, and robbery. While assault was observed in a slightly smaller proportion to burglary in this sample, it is noteworthy that the proportion of assault incidents in this sample is much higher than that of Durham overall. Over the three years covered in this study, assault makes up about 23.3% of this sample, but only about 8.5% of Durham crime (as a percentage of total assault, burglary, larceny, and robbery incidents total). This finding, while notable for showing disparities between this sample and Durham overall, is also consistent in this sample for each of the three years observed.

In terms of the number of “above expected” (AE) properties found, more interesting patterns arise when analyzing results over the three years. With respect to all crime incidents reported in a given year, the number of AE properties has been fairly consistent from 2012 to 2014 (see Table 2). This trend is also true of AE properties with respect to reported incidents of assault and larceny in each of the three years.

With respect to burglary and robbery, the numbers of AE properties were much less consistent. The number of AE properties in burglary increased from 15 in 2012 to 19 in 2013 and 2014, while the number of AE properties in robbery decreased dramatically from 12 in 2012 to 4 in 2013 and 6 in 2014 (see Table 3). Recall that, as a result of the selection criteria for this sample, most of these properties have been in delinquency and distress for exactly three consecutive years. The change in the number of burglary and robbery-related AE properties can possibly fit in to the distressed narrative of these properties: as a property becomes more financially distressed, the property can become more neglected, which could provide more opportunities for breaking and entering type crimes. In Durham, breaking and entering is categorized as a burglary, which could explain the increase in the number of burglary-related AE properties from 2012 to 2014.At the same time, as a property becomes more neglected, it may be more likely that the property will be vacated or abandoned. With fewer people in and around the property itself, there may be fewer people to target in a robbery. The presence of fewer people also decreases the likelihood of having witnesses who can report crimes, which would also affect the number of observed robberies (though this works against the increased observation of burglaries).

While these intuitions about the crime behavior around distressed properties can help begin to uncover the underlying mechanism responsible for these changes in observations and the change in the number of AE properties, they do not help explain the relatively smaller change from 2013 to 2014 (when compared to the change from 2012 to 2013). With respect to robbery, the number of AE properties drops dramatically from 12 in 2012 to 4 in 2013, but rises slightly to 6 in 2014. While the change is significant within the sample, it is also important to note that, in 2013, the Durham Police Department reports that the number of robberies were at a 23-year low in 2013. What remains unclear is whether this change is due to changes in prevention tactics, reporting policies, the climate of crime and criminals, or something else entirely.

In considering all of these results, it is also important to note that results are based on crimes that must first be observed by a witness, and then reported and filed by the Durham Police Department. Clearly, it is unreasonable to assume perfect crime observation within any given area, but as mentioned before, the inability to observe and report crime may be increased in areas of high home distress or foreclosure. This is consistent with the idea that distressed and foreclosed homes result in temporary (or sometimes permanent) decreases in the population of the immediate area, particularly within the defined inner zone of 500 feet around a property. Additionally, the crime data used in this study do not completely account for cases in which a crime is catalogued under multiple types. Inclusion of these results affects interpretations about the distribution of the crime types that are observed within a given area.

For improvement on this study in the future, additional research that compares the results of this study to a sample of properties in good financial standing may highlight differences in the level of AE properties found. Further research can also improve upon this study by following the sampled properties throughout the process of foreclosure to monitor the changes in crime levels. Continuing in research similar to the work of Cui and Walsh (2015), this type of research would provide further insight in to the crime behaviors near a foreclosed property through vacancy and re-occupancy. Unfortunately, studies such as these are limited by data related to specific foreclosed properties in Durham (and other cities). In terms of policy implications, understanding the relationship between crime and a property throughout its foreclosure lifecycle can help shape legislated foreclosure programs and processes to mitigate the increasing rates of crime.

Conclusion

To begin understanding the relationship between property distressed and nearby crime in Durham, North Carolina, this study used publicly available data to map incidents of observed crime from 2012 to 2014 on a sample of distressed properties. Analyzing crime over each of the three years, the study took data related to incidents of larceny, burglary, assault, and robbery to determine an expected number of crimes within an inner zone, representing a 500-foot circle around the property, and compared these figures with the number of observed crimes.

During the three years overall, results showed that the number of observed crimes is higher than the number of expected crimes with respect to only burglary, while the same is untrue for assault, larceny, and burglary. However, with respect to burglary and robbery, there were more dramatic shifts in the number of AE properties from 2012 to 2013, which could reflect a number of factors including the criteria and opportunity for reporting. Ultimately, this study can serve as the foundation for further research, given additional data related to the foreclosure process and crime behaviors of Durham properties.

References

Baumer, Eric P., Kevin T. Wolff, and Ashley N. Arnio, 2012, “A multicity neighborhood analysis of foreclosure and crime,”Social Science Quarterly 93(3), 577-601

Cui, Lin, and Randall Walsh, 2015, “Foreclosure, vacancy, and crime,” Journal of Urban Economics 87, 72-84

Durham County NC, Real Property Delinquent Taxpayer List, Accessed Durham 18 March 2015, http://www.ustaxdata.com/nc/durham/durhampolicy.cfm

Durham Police Department, 2014 Annual Report, Accessed Durham 18 March 2015, http://durhamnc.gov/ich/op/DPD/Documents/2014AnnualReportMarch2.pdf

Durham Police Department, 2013 Annual Report, Accessed Durham 15April 2015, http://durhamnc.gov/ich/op/DPD/Documents/2013AnnualReport.pdf

Durham Police Department, 2012 Annual Report, Accessed Durham 15April 2015, http://durhamnc.gov/ich/op/DPD/Documents/2012AnnualRepor0301FINAL.pdf

Durham Police Department, Crime Mapper, Accessed Durham 18 March 2015, http://gisweb.durhamnc.gov/gis_apps/CrimeData/dsp_entryform.cfm

Ellen, Ingrid G., Johanna Lacoe, and Claudia A. Sharygin, 2013, “Do foreclosures cause crime?,” Journal of Urban Economics 74, 59-70

Jones, Roderick W., and William Alex Pridemore, 2012, “The Foreclosure Crisis and Crime: Is Housing‐Mortgage Stress Associated with Violent and Property Crime in US Metropolitan Areas?,” Social Science Quarterly 93(3), 671-691

Katz, Charles M., Danielle Wallace, and E. C. Hedberg, 2011, “A longitudinal assessment of the impact of foreclosure on neighborhood crime,” Journal of Research in Crime and Delinquency 000(00), 1-31

Figures and Tables

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Figure 1: Durham crime per 100,000 residents, from 2000-2014

The figure depicts the trend of violent and property crimes since 2000 in Durham, notably showing the overall downward trend (despite the economic shocks of 2007-2009), though property crime continues to occur more frequently than property crime.

Durham Police Department, 2014 Annual Report

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Figures 2-5: Durham crime by number of incidents, from 2012-2014

The figures depicts the breakdown of the six most frequent types of crime from 2012-2014, showing that the property crimes of larceny and burglary have consistently total for about 80% of total crime for each of the last three years.

Durham Police Department, 2012 Annual Report, 2013 Annual Report, and 2014 Annual Report

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Figure 6: Example of crime mapping tool, Denfield Street property

The figure depicts the results from the Crime Mapper tool available publicly online by the Durham Police Department. For a given address, the tool maps incidents of crime that can by a variety of criteria, including distance, time period (up to 12 months), and type of crime.

Durham Police Department, Crime Mapper

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Table 1: Observed crimes in sample by type

This table shows the number of observed crimes by type in each of the three years from 2012 to 2014, along with the percentage of each type observed within a given span of time.

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Table 2: Expected number of crimes within 500 ft. of distressed properties, given number of crimes observed within ¼ mile, total totals from 2012-2014

This table shows the number of expected crimes within the inner zone by type of crime. Data points bolded in red text and highlighted pink represent cases in which the number of observed crimes is higher than the number of expected crimes for a given property. Data points in green text represent the opposite. Data points with grey text had zero total observations for the given property.

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Table 3: Expected number of crimes within 500 ft. of distressed properties, given number of crimes observed within ¼ mile

This table shows the number of expected crimes within the inner zone by type of crime. Data points bolded in red text and highlighted pink represent cases in which the number of observed crimes is higher than the number of expected crimes for a given property. Data points in green text represent the opposite. Data points with grey text had zero total observations for the given property.

A SAFER ALTERNATIVE? CUL-DE-SACS AND CRIME IN DURHAM, NC

by Chris, Whittaker  DP_WHITTAKERCHRISTOPHER

 

INTRODUCTION

Urban planners and economists often debate the merits of cul-de-sacs, or circular, dead end streets that serve adjacent dwellings. Proponents claim that cul-de-sacs reduce urban congestion, improve community relations and reduce crime. However, economists occasionally challenge such claims, asserting that cul-de-sacs do not provide additional safety benefits or that such benefits are negligible. This research explores the relationship between crime and the existence of cul-de-sacs in the city of Durham, North Carolina. By utilizing Geographical Information Systems (GIS) and publically available crime data, I compare the spatial differences in crime rates between communities built on cul-de-sacs and two-way streets. Though crime in cul-de-sacs appears to be markedly lower than crime on nearby two-way streets, further economic analysis is necessary to separate the spatial effects of cul-de-sacs from other socioeconomic factors.

 

CURRENT CUL-DE-SAC RESARCH

The cul-de-sac is a hallmark of suburban sprawl. The term itself comes from a French expression that means “the bottom of the bag” (Lonngren). It is a dead end street with only one entrance for vehicle traffic. Further, cul-de-sacs tend to differ from typical dead end streets such that they end in a circular turn-around space that permits vehicles to exit by making a wide U-turn. An aerial view of a typical cul-de-sac is provided in the appendix (figure 1).

Cul-de-sacs have a number of important benefits. For suburban developers, cul-de-sacs allow them to place more homes in oddly shaped tracts of land (Nielsen and Lonngren).  For residents, cul-de-sacs provide privacy and limit the noise from traffic while still remaining a part of the larger suburban community. Their one entrance and exit naturally help to constrain the speed of traffic, as well as the frequency of unknown vehicles passing through. Because of lower traffic, they encourage walking, bicycle use and outdoor activity by children. Unsurprisingly, homebuyers perceive houses surrounding cul-de-sacs to be safer than those located on two-way streets, and are willing to pay a premium for them (Neilsen).

However, others criticize cul-de-sacs. By definition, these communities are not well connected to other streets and they are often far from the central business district (CBD) and other areas of economic activity and community participation (Jagannath). On one hand, they encourage automobile use, as public transportation services are unable to accommodate their select residents. This, in turn, produces urban congestion in other parts of the community as well as a host of environmental problems associated with increased vehicle use. On the other hand, they create a number of inefficiencies with respect to the provision of public goods and services. It is more difficult to sweep streets or plow snow in cul-de-sacs. Further, it is more difficult to patrol cul-de-sacs and emergency vehicle access can sometimes be limited (Lonngren).

Much of the common knowledge regarding the popular appeal of cul-de-sacs is based on the idea of safety. As the logic goes, criminals try to avoid areas that lack easy entrance and exit. Further, if a crime is committed in a cul-de-sac, criminals will have a more difficult time escaping. Since residents of cul-de-sacs tend to be more familiar with each other, they are more likely to report and deter suspicious activity on behalf of their neighbors. Further, since fewer people frequently pass through cul-de-sacs, potential criminals would be unaware of opportunities in those communities. However, as noted by Lonngren, it is more difficult to police cul-de-sacs; these homes may actually provide better targets for criminals. Neilsen further notes that cul-de-sac statistics reveal some of the highest rates of traffic accidents involving young children. According to William Lucy, a professor of environmental studies at the University of Virginia, “the actual research about injuries and deaths to small children under five is that the main cause of death is being backed over, not being driven over forward” (Neilsen).

Little academic research exists regarding criminal activity with relation to the spatial layout of cul-de-sacs, and the majority that does remains inconclusive. As Hillier and others admit, it is difficult to untangle additional socioeconomic variables from the spatial layout of such communities (Hillier). However, it remains a useful exercise to conduct natural experiments that try to minimize such socioeconomic concerns in order to better understand our communities. Though it may not be possible to understand the true magnitude of the additional safety benefit provided by cul-de-sacs, it is certainly feasible to look at direction the answers is pointing. As such, it is possible to evaluate the following question: is living on a cul-de-sac in Durham safer than on a two-way street? To better answer this question, it is worth taking a brief look at recent criminal activity in Durham.

 

CRIMINAL ACTIVITY IN DURHAM

According to FBI statistics, the year 2012 marked an all-time low in violent crime (murder, rape, robbery and aggravated assault) and property crime (burglary, larceny and motor vehicle theft) committed in Durham (Durham Police Department). Crime continued its downward trend from the year 2000; this is shown in the appendix, figure 2. In particular, property crime reached its lowest level since 1988, due to a decrease in larceny and burglaries. Perhaps most striking, property crime is down 44% since the year 2000, with burglaries falling by 15% and larceny by 7% since 2011 (Durham Police Department). Still, larceny and burglary are the two largest contributors to Durham crime, constituting 80% of all criminal activity. These statistics are demonstrated graphically in the appendix, figure 3.

Though criminal activity is at a 23-year low, there were still over 15,000 crimes committed in Durham last year, approximately 80% of which are related to property. Consequently, analyzing the differences in property crime between housing communities on cul-de-sacs and two-way streets may prove useful. Thus far, no specific research exists regarding Durham spatial differences and crime rates. I attempt a first pass at demystifying and exploring these differences below.

 

METHODOLOGY

I begin by identifying communities in Durham that are built around cul-de-sacs using satellite imaging tools available through Google Maps. I selected 36 suitable cul-de-sacs that are relatively similar with respect to spatial layout. I then pair each of these cul-de-sacs with a nearby two-way street comprised of similarly priced houses. While pairings differ in terms of relative pricing, each pair itself reflects a comparatively equivalent level of housing quality. I then compare each pairing with approximately 15,000 pieces of data regarding criminal activity in the city from the year 2012; such data is publically available from the Durham Police Department and their Geographical Information Systems (GIS) software. I then determine the total number of crimes committed in cul-de-sacs and on adjacent two-way streets. Further, I narrow criminal activity to single-family dwellings to remove possible biases from high traffic commercial properties and high-density apartment complexes. I conclude by separating crimes into three categories: crimes related to larceny, crimes related to assault, and crimes related to burglary/breaking and entering. This provides a more detailed assessment of the common types of crime that occur in cul-de-sacs and on two-way streets.

While the approach is simplistic, it has several advantages. First, pairing cul-de-sacs with nearby (often adjacent) two-way streets removes certain spatial biases: geographically close streets create a sensible natural experiment such that we can assume many spatial variables are held constant. Second, it mitigates socioeconomic biases by evaluating housing communities of similar economic qualities. Third, the approach applies uncomplicated economic principles to thinking about the relationship between criminal activity in cul-de-sacs and two-way streets. It ultimately provides a foundation for more detailed future research.

 

FINDINGS AND ANALYSIS

The data is summarized in the table below (table 1):

DP_WHITTAKERCHRISTOPHER-1

 

Table 1: Summary statistics of crime rates between cul-de-sacs and two-way streets

In total, there were 89 crimes committed within the selected communities, with 74 occurring on properties located on two-way streets and 15 within communities located on cul-de-sacs. This yields a 1-to-4.93 cul-de-sac to two-way street crime ratio. Stated differently, for every crime committed in a community based around a cul-de-sac, there are nearly 5 committed in a related community along a two-way street.

Compared to total Durham crime, this sample reflects less than 1% (approximately .60%) of total crime committed in Durham. However, this appears reasonable. Given a number of generous assumptions such that there are 20-30 homes per selected pairing, and approximately 107,000 total households in Durham County according to the U.S. Census Bureau, assuming that crime is evenly distributed among only households we would expect this to approach a 1% crime rate (30*36/107,000 = 1.01%). Knowing that crime is not evenly distributed and that much criminal activity occurs in highly frequented public spaces, dense residential communities and commercial areas, we can generally infer that this sample appears appropriate to provide a picture of residential criminal activity.

It is important to interpret these results in the context of total Durham crime in 2012 (see appendix, figure 3). While larceny was the largest category of criminal activity, constituting 53% of total Durham crime in 2012, it only comprises 25.83% of crime in the 36 selected communities. There are several notable considerations that may explain the difference. According to the Durham Police Department Annual Report, shoplifting constituted 25% of all larcenies (4). Further, the single largest percentage of larcenies at 40% consisted of the theft of motor vehicles and motor vehicle parts (4). Since this survey only compared single-family homes, and not criminal activity regarding places of commerce or high-density vehicle parks, these results appear more reasonable.

Burglary constitutes the majority of criminal activity in the 36 paired communities at 58%. This again appears intuitively reasonable as the sample reflects only single-family homes that, by their very nature, are more spatial isolated. This isolation and lower population density makes these homes better targets for potential criminals. The final 15.7% of crimes come in the form of assault. This is a bit higher than the Durham average for violent crime. However, approximately 80% of these assault offenses are deemed simple assault, a misdemeanor, and are not captured in the aggravated assault numbers that figure 3 highlights. Thus, aggravated assault rates are actually found to be lower in the 36 selected communities than Durham on the whole.

 

DISCUSSION

Though it would appear that cul-de-sacs in Durham are less prone to criminal activity than adjacent two-way streets, taking this result at face value would be misleading and overshadows several notable concerns. First, the relative breakdown of types of crime between two-way streets and cul-de-sacs is not markedly different; larceny, assault and burglary are all proportionally similar. As it looks, there is simply more crime by volume on two-way streets, perhaps pointing to housing volume concern.

I assume that the selected pairings of cul-de-sacs and two-way streets have a relatively similar number of housing units. While a handy approximation, this is most likely not the case. Cul-de-sacs tend to have fewer housing options than comparable two-way streets, given the nature of their short circular design. Additionally, housing on two-way streets may be more densely concentrated with smaller lot sizes. Adjusting for lot size is necessary to smooth out these crime rates. This is a systematic limitation of relying on GIS data; it is not specific enough to determine and adjust for property sizes. However, in order to claim that crime differences are completely negligible between cul-de-sacs and two-way streets purely on the grounds of total housing units, it would be necessary to assume nearly five houses on each two-way street for every one in each cul-de-sac. This seems unlikely, as plot sizes appear comparable and such housing communities were carefully selected. Thus, cul-de-sacs do likely maintain some spatial advantage with respect to criminal activity holding housing volume constant and lot size constant.

Johnson and Bowers raise another relevant point in their study of cul-de-sac safety: it is possible that the type of people who live on cul-de-sacs differ from those who live on two-way streets in ways that might increase their risk of victimization (107). Such people may be of a different socioeconomic status, age, marital status or ethnicity. Perhaps such households predominantly have young children. Given these considerations, it is possible that risk profiles differ among residents and may contribute to a difference in criminal activity exposure that is not linked to the spatial design of such communities.

Spatial permeability is another concern. Johnson and Bowers reference a study by Armitage (2007) that showed crime rates were typically lower in communities built on cul-de-sacs, except when such communities were connected to other streets or public areas by footpaths or trails (107). This is a spatial concern that is directly linked to crime rates and is notably absent from the findings above. Future research would need to account for such connectivity differences in cul-de-sacs; available GIS data is again limiting.

Though cul-de-sacs appear moderately safer than two-way streets, ultimately, correlation does not prove causality. While there appears a strong correlation between lower crime rates and spatial housing layout, one does not dictate the other. Further research is necessary to better evaluate such claims.

 

EXTENSIONS AND APPLICATIONS

It is difficult if not impossible to accurately assess the magnitude that spatial differences play in determining criminal activity. A variety of socioeconomic variables, as well as serendipity, are often present and problematic to untangle from pertinent spatial differences. Others biases may yet be at play. For example, residents of cul-de-sacs tend not to be random; they are self-selecting and seek the benefits and style of living that cul-de-sacs provide. Further, they may also be more affluent. Given two ideal communities that exist ceteris paribus, the one located on a cul-de-sac will command a higher economic premium than the one on a two-way street. Therefore, a comprehensive analysis of spatial differences in crime rates remains illusive. Moreover, even if such an analysis were possible, it would be questionable to extend the findings to communities outside of a given region; local political and socioeconomic differences appear too nuanced to realistically do so.

            However, that is not to say that such research is fruitless. The careful public policy strategist or city planner may continue to utilize GIS data to better understand local spatial differences in order to craft germane policy. Further, such spatial criminal research may be used to identify high-crime communities and lead to better policing of public spaces. A good example comes from the East Weaver Street public housing community. Though East and West Weaver Street are over one mile in length, 6 out of every 7 crimes committed in the area occur in a small public housing community that constitutes a tenth of mile. Insights like this may be useful to help local authorities increase policing and to direct city planners’ time to addressing the causes of criminal activity in the area.


 

APPENDIX

 

 

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Figure 1: A typical cul-de-sac as shown from Google Maps.

 

 

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Figure 2: Index Crime Rate per 100,000 Residents by Year for Durham, North Carolina (Durham Police Department).

 

 

 

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Figure 3: Crime Breakdown for 2012 in Durham, North Carolina (Durham Police Department).

 

 

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Figure 4: Durham Crime Mapper Software screenshot. Provides a graphical layout of crime locations as well as police and sheriff tables that detail the exact location of criminal offenses. Red dots indicate police responses while yellow stars indicate sheriff responses.


 

CITATIONS

 

Durham Police Department. Annual Report: 2012 Durham Police Department. Durham, 2012.             http://durhamnc.gov/ich/op/DPD/Documents/2012AnnualRepor0301FINAL.pdf.

 

Durham Police Department. Crime Mapper Online Software. Durham, 2012.    http://gisweb.durhamnc.gov/gis_apps/crimedata/dsp_entryform.cfm

 

Hillier, Bill. “Can Streets Be Made Safer?” Palgrave Macmillan 9.1 (2004): n. pag. ProQuest. Apr. 2004.           Web.    http://proxy.lib.duke.edu/login?url=http://search.proquest.com.proxy.lib.duke.edu/advanc  ed?url=http://search.proquest.com.proxy.lib.duke.edu/docview/194522636?accountid=105            98.

 

Jagannath, Thejas. “Do We Need Cul-de-sacs?” Urban Times RSS. N.p., 25 Jan. 2013. Web. 01 Apr.             2013. <http://urbantimes.co/2013/01/do-we-need-cul-de-sacs/>.

 

Johnson, Shane D., and Kate J. Bowers. “Permeability and Burglary Risk: Are Cul-de-Sacs Safer?”    Journal of Quantitative Criminology 26.1 (2010): 89-111. Print.

 

Lonngren, Betty. “Cul-de-sacs Unproven As Deterrent To Crime.” Chicago Tribune. N.p., 25 Apr.      1993. Web. <http://articles.chicagotribune.com/1993-04-25/business/9304250091_1_sacs-      cul-chicago-neighborhoods>.

 

Nielsen, John. “Cul-de-Sacs: Suburban Dream or Dead End?” NPR. NPR, 07 June 2006. Web. 07   Apr. 2013. <http://www.npr.org/templates/story/story.php?storyId=5455743>.