Given that a very limited number of empirical studies on the relationship between the urban built environment and crimes focus on measuring the physical spatial structure at the neighborhood level by integrating different spatial components and on the moderating effects of socioeconomic factors in developing countries, this study contributes to the literature by providing new and valuable evidence on the relationship between the urban built environment and different specific types of crimes, leading to a deeper understanding of said relationship. In addition, the UBEIs developed in this study (i.e., POS, RNC, and RegSI; NAge and PFSB) will fill the existing gap in the literature and provide better indicators to studies that highly demand UBEIs such as urban planning, energy efficiency, public health/urban walking [34
], and body mass index [54
5.1. Effect of UBEIs and CUBEI in Crimes
Overall, the correlation results of the individuals’ UBEIs with different crimes types were statistically significant and consistent with previous studies, with the exception of RegSI, which was not statistically significant. Both extrinsic indicators (NAge and PFSB) were positively associated with all type of crimes studied.
An increase in RNC is associated with a consistent decreasing rate of all types of crimes in the study area. However, it was slightly higher in residential robbery and crimes involving weapons scenario. Legibility is strictly related to the principle of surveillance of CPTED, which states that neighborhoods with complex street networks tend to register more crimes because they are difficult to navigate by both pedestrians and security officers [22
]. Conversely, according to rational choice theory [5
], this is a suitable environment from the offenders’ perspective.
Unlike cities of developed countries, in Praia. the denser neighborhoods are old, informal areas that today are relatively close to the city center. These neighborhoods are denser than those planned neighborhoods or those located in the city center. This fact justifies our findings that as POS increases, the crime rates decrease, meaning that very compact urban environments lead to higher crime rates, especially burglary, because compact neighborhoods tend to diminish the visibility between houses and increase social friction [7
]. In opposition, open spaces improve visibility, reduce the fear of crimes, and promote safety. Another fact is that in Praia, very dense neighborhoods generally present deficiency in car parking, leading residents to park their cars on street sidewalks, worsening passive surveillance and offering hiding places for criminals.
As hypothesized, neighborhoods where the average street lengths are larger tend to promote better walkability and wider visibility or passive surveillance. This fact may support the negative relationship found between ETA index and crimes rates.
Regarding the extrinsic indicators, neighborhoods with higher PFSB are associated with an increase in crime rates, especially crimes involving weapons (ρ = 0.844), mugging (ρ = 0.726), and residential robbery (ρ = 0.721). In general, these types of crimes are highly dependent on neighbors’ interactions. For instance, empirical evidence has shown that, in Praia, gangs act more in neighboring neighborhoods than in their own for many reasons; they can be easily recognized by the local population, they have a sense of territoriality and ownership, and they have stronger social interaction with locals. Similarly, offenders committing mugging and residential robbery adopt the same strategy. This fact may generate more social friction between neighborhoods leading to higher crime rates [7
], particularly the types of crimes mentioned above. In fact, some studies [55
] have suggested traffic barriers and road closures as effective crime prevention techniques.
Crimes involving weapons are strongly correlated with CUBEI, which means that the integration of the principles of CPTED and BWT in the urban planning process may result in an overall decrease of this type of crime. In summary, all types of crimes analyzed in this study have shown some degree of association with individuals UBEIs and the CUBEI, which also show some consistency with our expected hypotheses and some previous studies. For example, the relationship between CUBEI and all types of crimes analyzed in this study is supported by the principles of CPTED and the BWT.
From our results (Table 4
), socioeconomic indicators presented a positive relationship and CUBEI a negative relationship with burglary, respectively. This indicates that neighborhoods with high socioeconomic indicators are associated with higher burglary rates. This fact may be because high socioeconomic indicators suggest, on the one hand, high neighborhood accessibility and permeability and, on the other, from the offenders’ perspective, higher probability of succeeding in their activities. In contrast, an improvement in neighborhood planning, represented as CUBEI, is associated with a decrease in burglary rate. In fact, BWT posits that neighborhood physical disorder is a key factor for the reproduction of criminals’ activities. In addition, planned neighborhood has both, better passive and active surveillance compared with non-planned neighborhoods. For example, in Praia, police stations are mostly located in planned neighborhoods, and these neighborhoods also present a mixed land-use (residential, commercial, and services) that, according to principles of CPTED, enforce passive surveillance.
The observed effect of the unemployment rate on both types of crime, robbery and mugging (Table 5
and Table 6
), contrast with what is hypothesized in previous studies. We observed that an increase in unemployment rate is associated with a decrease in robbery and residential robbery. This effect was expected in our study area, given the reduced size of the neighborhoods. This fact strengthens social relations and territoriality and hence decreases the probability of criminals’ success. Moreover, both types of crimes require physical strength and direct contact with the victims, which is much easier toward strangers.
The observed positive relationship between PFSB and residential burglary suggests that neighborhoods with more connections with closer neighborhoods facilitate the entrance of strangers, which may result in more residential robberies since in theory criminals have their own search radius. On the other hand, this shows that external influence might exist even in cases where Moran I index shows evidence of no spatial dependence. Therefore, there is a need to develop indicators that count for spatial relationship in areas where the irregularity of surface topography masks existing spatial relationship and boundary adjacency.
As previously explained, the contrasting effect of the unemployment rate in robbery and mugging is yet observed in crimes involving weapons. We were expecting to have more crimes involving weapons in neighborhoods with higher unemployment rates, especially because a significant number of this type of crime has its origin from gangs called “thugs” in the Praia suburbs. However, as reported by the US Department of State [56
] in Praia many street crimes are not reported to the authorities and therefore are not included in official statistics, especially muggings where locals see low probability in recovering their stolen belongings. This fact might be verified more often in the suburbs than in other areas.
] refers to Praia as a “broken city, where poverty is engraved in the urban morphology”; however, our results showed a non-statistically significant relationship between poverty (measured by average income and socioeconomic indicators) and the neighborhoods’ UBE characteristics. The results demonstrate some of the advantages of the proposed indicators and argue in favor of their use in the urban built environment studies and many other interrelated fields.
5.2. Methodological Discussion
As previously mentioned, one of the regression analysis assumptions is the independence of the observations; however, geographic data generally presents spatial dependency, which is states in Tobler’s law, that “everything is related to everything else, but near things are more related than distant things.” Since preliminary exploratory data analysis pointed out an absence of spatial autocorrelation of crime intensities, which is reasonable in our study area, where the irregular surface topography weakens the spatial contiguity and connectivity among neighborhoods, ordinary least squares regression was chosen instead of spatial regression.
This study provides a new methodological approach to normalize crime data by using the neighborhood’s built-up area as a variable in the denominator, which is more realistic than population or neighborhood area itself. However, using built-up areas in the denominator assumes that all the crimes happened within its geographical extent, which may not be true in many circumstances for certain types of crime. Despite this limitation, this technique avoids the overestimation of crime intensity in the neighborhood with large administrative boundary but small built-up area. We extended the concept of the urban built environment by introducing new and more complete and realist measurements which are able to capture the spatial arrangement of building footprints and roads providing a sense of the degree of planning (i.e., intrinsic-POS, RegSI, RNC, ETA and extrinsic-Nage, PFSB); filling the existing knowledge gap on the literature, since the physical form of the neighborhood was underestimated and simplified.
Although we developed the CUBEI and we found its effect on different types of crimes, particularly in the city of Praia, Cape Verde, this trend may not be necessarily observed in other geographical regions, especially in developed countries, where there are different urban development processes, socio-economic lifestyles, and social relations. Therefore, we strongly recommend similar studies to be conducted by using a larger number of observations that might produce more accurate and reliable results by including several individual UBEIs as independent variables. The limited number of observations limit the number of independent variables which might be incorporated in the model, leading to model overestimation parameters [50
]. Moreover, this study is easy to replicate since we have provided sufficient methodological details to calculate both UBEIs and CUBEI. Individual UBEIs are more useful for larger sample size studies, while CUBEI is more valuable in studies conducted in a small study area with a reduced sample size.
5.3. Implications for Healthy City Planning
This study provides an important clue to urban planners and policy-makers to incorporate CPTED principles and BWT in the conceptualization of their urban plans, both in policy and practical terms, in order to reduce the fear and the incidence of crime and to promote safety in the neighborhoods, by designing and arranging the elements of the urban tissue (e.g., roads and buildings, public spaces, etc.) that leads to a healthier urban built environment which promote well-being and quality of life. Specifically incorporating this paradigm encourages walkability, improves air circulation, and promotes mental and sexual health, which positively influence people’s behaviors and attitudes [58
Furthermore, effective holistic urban planning that provides a physical environment for efficient passive surveillance, grid street network design, high legibility, open spaces, regulated routine maintenance and management of urban spaces, and discouragement of under-used streets either in the early design stage or in the modification of the existing urban environment may prevent or minimize the consequences of many others social issues, such as crime and deficient public health. Public health is strictly linked with the built environment. Therefore, urban spaces planned to encourage walkability are beneficial, especially for those more vulnerable to a sedentary life (e.g., the elderly, children, young parents, the unemployed, and the immobile), providing them opportunities to engage in regular daily walking and cycling, thus reducing the risk of diseases and promoting well-being and, consequently, supportive social contact [58
However, especially in developing countries that are experiencing rapid and intense urbanization processes, effort must be taken so that road network design anticipates the construction of the building in the early planning stage since it establishes the definitive physical structure of the neighborhood that may be crucial for many future interventions. Likewise, urban rehabilitation and interventions should promote land-use diversity, walkability, open spaces and recreational facilities, and safer boundaries between neighborhoods.