Safe and Sustainable City: Exploring the Impact of Urban Factors on Crime Occurrence

Abstract

Safety, a critical component of sustainable development, necessitates an integrated approach in which urban planning assumes a central role. This study investigates the relationship between urban form and crime incidents in public spaces within the center of the city. This study was conducted in the city of Łódź, located in central Poland. Through geolocated data, this research explores crime incidents that, while not the most severe, disrupt public order and impact the overall quality of life. This study fills a gap in the existing literature by analyzing spatial variables such as urban vibrancy and the presence of alcohol outlets, alongside other urban elements. The analysis incorporates a variety of urban form variables, including land development indices, the functional layout of the urban neighborhood, pedestrian infrastructure, public space amenities, and facilities. Urban vibrancy, represented by the density of human activity, is also assessed in relation to crime incidents. The results indicate significant correlations between certain urban features and the occurrence of crime incidents, particularly the presence of public amenities and small businesses. While these findings suggest that urban design can influence crime rates, further panel and time-series regression analysis is needed to confirm these dynamics. Aligned with the 11th Sustainable Development Goal, this study provides insights that could inform urban planning strategies, offering recommendations to enhance both the functionality and safety of city centers. By understanding how urban design elements contribute to public safety, policymakers can develop more effective and targeted spatial planning strategies that promote not only aesthetics and functionality but also the well-being and security of residents.

1. Introduction

Urban planning profoundly influences various aspects of urban life, particularly the incidence and spatial distribution of criminal activity. The interplay between the built environment and crime is a crucial area of study, essential for shaping policies that enhance public safety and urban living conditions. This study investigates the impact of urban design and infrastructure on crime incidents, focusing on crime incidents that are analyzable through geolocated data. While these crime incidents may not be the most severe crimes, they significantly affect the sense of safety and overall quality of life in a given area, disrupting public order.

In the existing literature, consensus is lacking regarding the specific social and environmental characteristics that influence crime incidence. Researchers highlight the importance of surveillance in urban areas, especially in public spaces that foster safe interactions [1]. Studies indicate that poorly designed environments can facilitate criminal behavior. Research also explores critical physical elements of planning and design that contribute to successful urban projects [2], examining how urban design quality impacts the urban structure. This involves considering both the objective expression of space and subjective perceptions in relation to reported street crime [3]. Urban functions, amenities, and infrastructure are inconsistently addressed in their crime context [4].

Distinct research themes include exploring the relationship between urban mobility and crime patterns [5]. Newton [6] also provides evidence for the presence of crime hotspots near clusters of premises based on the Routine Activity Theory of crime patterns. It is worth noting that extensive studies on green spaces and crime often support the hypothesis that access to nature mitigates urban crime [7,8]. Additionally, a positive correlation exists between long-term climate changes such as temperature and crime rates [9,10], suggesting that increasing green areas through resilience efforts could reduce local violence.

The research delves into both micro- and macro-scale aspects, examining linear and nonlinear relationships between urban form indicators and crime types, suggesting their potential coexistence. Macro-scale factors include city size, fragmentation, connectivity, green space integration, population density, and proximity to agricultural areas [11]. Insights gained from this research are crucial for long-term urban planning and policy making aimed at achieving balanced urban and community development [12].

The significance of land management practices and property ownership structures is underscored (G). Another research direction examines how urban planning laws, such as zoning codes and municipal plans, might impact crime rates [13,14]. Time and spatial dynamics in crime contexts are also explored, with spatiotemporal crime patterns analyzed through geoinformatic and geostatistical approaches to understand the influence of urban land use [15,16].

This proposed study complements existing knowledge by enhancing the understanding of behaviors within the core of urban centers. Unlike previous approaches that solely focus on physical spatial correlations, this research also investigates the vibrancy of places and includes factors such as the location of alcohol outlets. By integrating various spatial and functional urban variables, this study aims to generate new insights and recommendations concerning safety within the built environments of city centers.

2. Methodology

Several studies [17,18] were conducted on the relationships between urban form and crime. However, a literature review revealed a lack of comprehensive studies examining the relationship between urban form and crime incidents in public spaces. Moreover, there are no studies investigating the dependence of safety on urban form specifically for the city center of Łódź.

This study, therefore, aims to analyze the dependencies between urban form and crime incidents within Łódź’s special regeneration zone, which is the most vibrant and representative part of the city. Figure 1 shows the location of the research area on the map of the city within its administrative boundaries, while Figure 2 presents the map with the outline of the urban fabric covered by this study. For clarity, the image depicts only buildings and greenery, as these elements most effectively illustrate the structure of the city. The research area was further subdivided into 75 urban blocks, defined as formations of housing units separated on all sides by public spaces (e.g., streets, woonerfs, or public squares). In this context, each urban block is treated as a statistical observation, characterized by both safety and urban form attributes.

For the purposes of analyzing public safety in the context of the Łódź city center, several safety indicators were taken into account: crimes and crime incidents in public spaces, as well as the subjective feeling of insecurity. The first two types of indicators can be tracked through records from law enforcement agencies, while the latter involves surveys of the local community. The major challenge with surveying public safety is research bias, as the perception of safety is inherently subjective. Additionally, the density of serious crimes, such as murders in public spaces in Polish cities, is relatively low, leading to lower variance and less robust statistical analysis. For these reasons, the focus was placed on crime incidents in public spaces, as defined by the Polish Code of Crime incidents.

To map the urban form variables, the analysis considered not only common land development indices, such as the floor area ratio of housing, building functions, and biologically active areas but also variables reflecting the condition of specific urban elements such as buildings, pavements, and greenery. The set of variables was expanded to include public space amenities, vegetation parameters, and communication infrastructure.

Additionally, following the literature review [19], the relationship between safety and urban vibrancy, understood as the density of human activities in the built environment, was also explored in the context of Łódź’s city center. Referring to the scientific studies on this topic [20,21], three types of vibrancy were identified: vibrancy derived from real-time human activity, vibrancy derived from the consistent evidence of human activity and cultural urban vibrancy. Considering that there are no data on real-time human activities in the research area, urban vibrancy for the purposes of this study was derived from the presence of small enterprises and cultural hotspots as part of the OpenStreetMap POI (point of interest) layer for the corresponding time period.

The initial research hypothesis of this study posits that public order disturbances are strongly related to alcohol consumption, which, in turn, depends on the characteristics of the built environment. The assumption that alcohol consumption can provoke violence in public spaces is directly confirmed by Bromley and Nelson [22] and indirectly supported by investigations into the correlation between alcohol outlets and incidents of violence, as evidenced by other research contributions [23,24]. In this context, penalties for alcohol consumption and public order disturbances serve as records of crime incidents registered by the city guard at various stages of escalation—either immediately before or shortly after the disturbances occur. For this reason, in the quantitative part of this research, a two-equation regression model was employed: the first equation explains public order disturbances based on urban form attributes and alcohol consumption, while the second equation explains alcohol consumption in public spaces based on the characteristics of the built environment. Since both dependent variables represent count data—namely, the number of penalties issued by the city guard for public order disturbances and alcohol consumption in public spaces—this study employs count data models, following established methodologies [25,26,27,28]. Specifically, the models were estimated using negative binomial and zero-inflated Poisson regression. Additionally, the methodology accounts for spatial autocorrelation in the model residuals.

3. Data

This research aimed to gather data on crime incidents in public spaces within the city of Łódź, as well as indicators of urban form parameters. As the mapping technologies are a valuable methodological addition to environmental criminological research [29], our study also uses these technologies to process data but also to present it in a suggestive manner. However, to obtain the first part of the data, access to the public order authority’s database was required. Following the practices outlined in similar studies [30], a public information request was submitted to the Łódź city guard on 3 January 2024. Bearing in mind the initial research hypothesis, which posits that crime incidents in a public space in downtown Łódź depend on the alcohol consumption, the aforementioned request sought the following information:

• Penalties for crime incidents in public spaces in Łódź in 2023.
• Penalties for alcohol consumption in public spaces in Łódź in 2023.

This data request was preceded by consultations with the Local Authority, which included discussions about how data on crime incidents in public spaces are collected by the city guard. It was revealed that the Łódź city guard maintains these records with attributes such as the date of the penalty and the address where the offense occurred. On 27 February 2024, the Łódź city guard responded to the public information request and provided the requested data.

The data were then geolocated using QGIS software (3.34.13) and further visualized in RStudio (2022.12.0 Build 353). Figure 3 presents the aggregation of penalties issued by the city guard in 2023 for public order disturbances and alcohol consumption in public spaces by a particular urban block, along with spatial autocorrelation scatterplots. As shown in the figure, both variables tend to form geographic clusters. To confirm the presence of spatial autocorrelation, Moran’s I tests, on the basis of the nearest neighborhood matrix with the Queen parameter, were conducted. The results indicate a statistically significant spatial autocorrelation for both variables: Moran’s I for penalties related to alcohol consumption in 2023 is 0.13 (p-value < 0.01), while Moran’s I for penalties related to public order disturbances in public spaces in 2023 is 0.14 (p-value < 0.01). A noticeable aspect of Figure 3 heatmaps is the number of the statistical observations with zero value.

The data related to urban form consisted of 16 variables, which can be categorized into three distinct types: count data continuous variables and binomial variable. Count data represent the aggregation of particular features of the built environment (e.g., the number of the playgrounds or the number of the benches in a particular urban block). On the other hand, continuous variables represent ratios related to the presence of particular features in public spaces (e.g., floor area ratio of the buildings of a particular function, biologically active area, urban vibrancy). Finally, the regressors set were enlarged with one binomial variable responsible for mapping whether there appears to be a water facility in the urban block or not.

To compile the database for the entire research area, two primary sources were utilized:

• The GIS database of building heights, floor area ratios, and functions, provided by the Łódź City Geodesical Office through a second Public Information request
• The GIS database of urban indices for Łódź city available on OpenStreetMap.

These databases enabled the calculation of urban form ratios as well as some nominal variables related to the functions of urban blocks. The resulting variables, along with their sources and calculation methods, are presented in

Table 1. Resulting database.

No	Variable	Depiction	Formula
1	VioPen	Penalties for the public order disturbances in year 2023 in the particular urban block	$VVa{l}_i={\displaystyle \sum _{j=1}^n}Entit{y}_{ij}$ where $VVa{l}_i$—the value of the particular variable (VioPen, AlcPen, AlcOutl, EqCam, EqBnch, EqPlgnd) in the i-urban block $Entit{y}_{ij}$—the j-entity (penalty, alcohol outlet, camera surveillance, bench or playground) in the i-urban block.
2	AlcPen	Penalties for the alcohol consumption in public space in year 2023 in the particular urban block
3	AlcOutl	The number of the alcohol selling points in the particular urban block
4	EqCam	The number of camera surveillance in the particular urban block
5	EqBnch	The number of benches in the particular urban block
6	EqPlgnd	The number of playgrounds in the particular urban block
7	VibSCB	The Urban Vibrancy as the number of the small businesses in the particular urban block	$VVa{l}_i={\displaystyle \frac{{\sum }_{j=1}^{n}Entit{y}_{ij}}{BlockAre{a}_i}}$ where $VVa{l}_i$—the value of the particular variable (VibSCB, VibClt, VibSCB&Clt) in the i-urban block $Entit{y}_{ij}$—the j-entity (small business or cultural point of interest) in the i-urban block. $BlockAre{a}_i$—the i-urban block area.
8	VibClt	The Urban Vibrancy as the number of the cultural PoIs in the particular urban block
9	VibSCB&Clt	The Urban Vibrancy as the sum of VibSCB and VibSCB&Clt
10	PblSpc	The ratio of the public spaces surface in the particular urban block	$VVa{l}_i={\displaystyle \frac{{\sum }_{j=1}^{n}Entit{yArea}_{ij}}{BlockAre{a}_i}}$ where $VVa{l}_i$—the value of the particular variable (PblSpc, BldFtpt, BActSur) in the i-urban block $Entit{yArea}_{ij}$—the j-entity (public space, building footprint or biologically active area) area in the i-urban block. $BlockAre{a}_i$—the i-urban block area.
11	BldFtpt	The ratio of the buildings footprints in the particular urban block
12	BActSur	The ratio of the biologically active area in the particular urban block
13	WtrFac	The presence of the water facilities in the particular urban block	0—for no water facilities. 1—for no water facilities.
14	HousF	The ratio of the housing function in the particular urban block	$VVa{l}_i={\displaystyle \frac{{\sum }_{j=1}^{n}Entit{yArea}_{ij}\times Entit{yHeight}_{ij} }{BlockAre{a}_i}}$ where $VVa{l}_i$—the value of the particular variable (HousF, CommF, EducF, OffcF, ProdF) in the i-urban block. $Entit{yArea}_{ij}$—the j-entity (Multifamily Housing, Commercial, Educational, Office, Production building) area in the i-urban block. $Entit{yHeight}_{ij}$—the j-entity (Multifamily Housing, Commercial, Educational, Office, Production building) height in the i-urban block. $BlockAre{a}_i$—the i-urban block area.
15	CommF	The ratio of the commercial function in the particular urban block
16	EducF	The ratio of the education function in the particular urban block
17	OffcF	The ratio of the office function in the particular urban block
18	ProdF	The ratio of the production function in the particular urban block

.

4. Quantitative Modeling

The first step of a quantitative part of this research involves calculating the Pearson correlation coefficient for the data collected. Figure 4 presents the correlation matrix between potential regressors and the classes of the binomial variable VioBin which takes 1 for statistical observations where public order disturbances have occurred and 0 in the opposite scenario. It should be noticed that penalties for alcohol consumption show the strongest correlation with the ratio of public spaces available for pedestrians. Moreover, attention is drawn to other variables that correlate with potential response variables. Given that the analysis of scatter plots for all 16 variables and the dependent variables did not reveal any visible nonlinear (curved) relationships, it was decided to include in the regression analysis only those potential regressors that are not collinear with other regressors. Hence, the regressor set was shrunk by (1) the ratio of public spaces, (2) the level of SCB and cultural PoIS-derived urban vibrancy, and (3) the ratio of the office function in the particular urban block. The reduced regressor set in a linear regression against the number of crime incidents in public spaces revealed no regressor with a variance inflation factor exceeding 10, thus providing evidence for the lack of significant collinearity in the data.

Presenting quantitative data, it is important to consider certain qualitative aspects. Activity in the urban space of Łódź’s city center changes dynamically, primarily depending on the time of day, which influences the way that different places and establishments included in this study are used. In the morning, bakeries and grocery stores serve customers making quick purchases. There is noticeable increased movement around educational institutions. Subsequently, small service points become more active, while shopping malls gain popularity, particularly after standard working hours. At the same time, the number of public services increases. As the day transitions into the evening, the activity of small 24 h stores and alcohol outlet rises. It is also important to note the variation in operation depending on the day of the week. Entertainment venues and cultural institutions experience their highest attendance during weekends.

Another routine aspect is the movement patterns of people. Piotrkowska Street, the central axis of the studied area, is generally closed to car traffic, with exceptions for deliveries. As a result, pedestrians and cyclists dominate the space. Two-way vehicular traffic prevails on the major roads surrounding Piotrkowska Street and on the intersecting streets. Public transport is organized using buses and trams.

Routines related to the function of specific places play a secondary role since the city center is not zoned in the same way as suburban areas of Łódź. The downtown area essentially offers every type of function except for large-scale industry, extensive medical services, or large sports complexes. Instead, residential and service functions predominate.

To assess the causal relationships between urban form and alcohol consumption, as well as between alcohol consumption and public order disturbances, a two-stage cross-sectional count data regression model was estimated. The modeling strategy at each stage involved the step-by-step execution of the following procedures: overdispersion tests (to choose between the Poisson and negative binomial regression models), zero-inflation tests (to choose between the zero-inflated and regular versions of the count data model), the selection of the best model subset, the assessment of spatial autocorrelation in the residuals, and the potential modeling of spatial spillovers.

Following the aforementioned methodology, in the first stage of modeling, overdispersion tests were conducted. For this purpose, the best subset of negative binomial and Poisson models, obtained as a result of the stepwise backward algorithm, were compared. The Pearson chi-squared overdispersion test statistic equals 1.03, which, being very close to 1, provided evidence to choose the Poisson distribution. However, the zero-inflation ratio test provided evidence in favor of the zero-inflated model, indicating that the expected number of zeros in the distribution equals 0.62, while the actual number of zeros is 60.

In the next step, the best subset model was estimated. This step was divided into two subparts: the zero-inflation part and the Poisson part. In the first subpart, the best subset of the binary logit regression, accounting for whether the number of crime incidents in the selected region equals 0, was identified. The best model specification was chosen by comparing the likelihood ratio of the models, the AIC criterion, and the best subset of statistically significant estimates. However, the best subset of the zero-inflated component included only the VibSCB variable.

In the second subpart of this step, a stepwise forward zero-inflated Poisson regression with a fixed number of zero-inflated independent variables was conducted. The stepwise forward algorithm was applied, considering the main research hypothesis that the number of crime incidents depends on the number of alcohol consumption penalties, incorporating AlcPen as the first regressor. However, the algorithm did not identify a better subset of the zero-inflated Poisson model in which the subset of x-values included only AlcPen, and the subset of z-values (responsible for modeling the zero inflation) included only VibSCB.

Finally, the residuals from the best subset Poisson model were tested for spatial autocorrelation. In the case of the first model, the null hypothesis in the Moran’s I test was not rejected, with a p-value of 0.11 and a Moran’s I statistic of 0.06. Consequently, the final specification of the first-stage model equation is presented below:

$$P\left(VioPen=y|VibSCB, AlcPen\right)=\left\{\begin{array}{c}\pi \left(VibSCB\right)+\left(1-\pi \left(VibSCB\right)\right)e^{-\mu \left(AlcPen\right)}, for y=0,\\ \left(1-\pi \left(VibSCB\right)\right){\displaystyle \frac{{\mu \left(AlcPen\right)}^y{e}^{-\mu \left(AlcPen\right)}}{y!}}, for y>0,\end{array}\right.$$

where

$$\pi \left(VibSCB\right)={\displaystyle \frac{\exp \left({\mathsf{\gamma }}_0+{\mathsf{\gamma }}_{1}VibSCB\right)}{1+\exp \left({\mathsf{\gamma }}_0+{\mathsf{\gamma }}_{1}VibSCB\right)}}$$

$$\mu \left(AlcPen\right)=\mathrm{e}\mathrm{x}\mathrm{p}({\mathsf{\beta }}_0+{\mathsf{\beta }}_{1}AlcPen)$$

• ${\mathsf{\gamma }}_0,{\mathsf{\gamma }}_1$—Coefficients for 0 generation process;
• ${\mathsf{\beta }}_0,{\mathsf{\beta }}_1$—Coefficients for crime incident generation process.

In the second-stage model, the overdispersion test, conducted using the same methodology as in the first stage, provided evidence in favor of the negative binomial model (chi-square statistic = 15.53). However, the zero-inflation test for the second-stage model did not support the presence of zero inflation: the expected number of zeros was 46.76, while the actual number of zeros was 46.

In this case, the best subset of the model could be obtained by running a simple stepwise backward algorithm for the full set of regressors and comparing the AIC and likelihood ratio (LR) of all models. Following this approach, the best subset of regressors included AlcOutl, VibSCB, and BActSur.

Similarly to the first-stage modeling, the residuals of the second-stage model showed no spatial autocorrelation: the p-value of Moran’s I statistic was 0.28. The final specification of the second-stage model equation is presented below:

$$\mathrm{P}\left(\mathrm{AlcPen}=\mathrm{y}|\mathsf{\mu },\mathsf{\alpha }\right)={\displaystyle \frac{\mathsf{\Gamma }(\mathrm{y}+{\mathsf{\alpha }}^{-1})}{\mathsf{\Gamma }\left({\mathsf{\alpha }}^{-1}\right)\mathsf{\Gamma }(\mathrm{y}+1)}}{\left({\displaystyle \frac{1}{1+\mathsf{\alpha }\times \mathsf{\mu }}}\right)}^{{\mathsf{\alpha }}^{-1}}{\left({\displaystyle \frac{\mathsf{\alpha }\times \mathsf{\mu }}{1+\mathsf{\alpha }\times \mathsf{\mu }}}\right)}^{\mathrm{y}} ,$$

where

$$\mathsf{\mu }=\exp \left(\ln \right(\mathrm{t})+{\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{A}\mathrm{l}\mathrm{c}\mathrm{O}\mathrm{u}\mathrm{t}\mathrm{l}+{\mathsf{\beta }}_2\mathrm{V}\mathrm{i}\mathrm{b}\mathrm{S}\mathrm{C}\mathrm{B}+{\mathsf{\beta }}_3\mathrm{B}\mathrm{A}\mathrm{c}\mathrm{t}\mathrm{S}\mathrm{u}\mathrm{r})$$

5. Results

The econometric modeling considered a range of variables representing urban form attributes, both from a sociological perspective (e.g., urban vibrancy) and a physical perspective (e.g., the building footprint in each urban block). The results can be divided into two major groups: variables that affect alcohol consumption in public spaces and variables that have a statistically significant influence on penalties for crime incidents in public spaces. The second category can be further subdivided into two subcategories: factors influencing the generation of zero values and factors affecting the occurrence of non-zero penalties for crime incidents in public spaces.

Table 2. Model estimation results.

Variable	Depiction	Estimate (Standard Error)	Odds Ratio	Average Marginal Effects (Standard Error)
Ist equation—count model coefficients
(Intercept)	-	−0.133 (0.323)	0.875	-
AlcPen	Penalties for the alcohol consumption in public space in year 2023 in the particular urban block.	0.035 **** (0.008)	1.036	0.014 (<0.001)
Ist equation—zero inflation model coefficients
(Intercept)	-	2.602 **** (0.323)	13,491	-
VibSCB	The urban vibrancy as the number of the small businesses in the particular urban block	−0.353 *** (0.136)	0.703	0.055 (<0.001)
II nd equation
(Intercept)	-	−2.530 **** (0.538)	0.080	-
AlcOutl	The number of the alcohol selling points in the particular urban block	0.124 ** (0.052)	1.132	0.886 (1.005)
VibSCB	The urban vibrancy as the number of the small businesses in the particular urban block	0.224 **** (0.068)	1.251	1.605 (1.235)
BActSur	The ratio of the biologically active area in the particular urban block	0.164 **** (0.035)	1.178	1.175 (0.966)

Significance: ** p < 0.05, *** p < 0.01, **** p < 0.001.

presents the results of the econometric modeling.

First of all, the results of this study align with the initial research hypothesis: a one-unit increase in alcohol penalties leads to a 3.6% increase in the expected count of penalties for crime incidents in public spaces. The marginal effect indicates that one additional alcohol penalty corresponds to an average increase of 0.014 public order disturbance penalties. This supports the thesis—and the intuition behind it—that alcohol consumption fosters violence.

On the other hand, the zero-inflation process shows statistical significance in relation to urban vibrancy. Each additional small business decreases the probability of having zero disturbance penalties by 29.7%. The marginal effect suggests that one additional business reduces the probability of zero inflation by 5.5 percentage points. This could be explained by the fact that alcohol-related violence in city centers mainly occurs in the most vibrant urban neighborhoods, as these areas are the primary locations for nighttime attractions for students and young people.

Conversely, alcohol consumption in the city center can be explained by several factors. As in the case of violence in public spaces, alcohol consumption tends to occur in more vibrant urban areas: higher urban vibrancy is associated with a 25.1% increase in penalties for alcohol consumption in public spaces. The marginal effect indicates that a one-unit increase in vibrancy leads to an average of 1.605 additional alcohol penalties. This once again supports the thesis that highly dense urban areas in the city center attract young people, who, in certain cases, spend their time drinking alcohol, which may lead to violent behavior followed by penalties from the city guard.

Not surprisingly, alcohol outlets show a statistically significant relationship with alcohol consumption: each additional alcohol-selling point increases the expected count of alcohol penalties by 13.2% in the same urban block. The marginal effect means that one additional alcohol outlet corresponds to an average of 0.886 more alcohol penalties per year in an urban block. This could be explained by the fact that penalties for alcohol consumption are issued by the city guard in the same blocks where the density of alcohol outlets is higher.

Finally, there is a noticeable relationship between penalties for alcohol consumption and biologically active areas. A higher ratio of biologically active areas is associated with a 17.8% increase in penalties for alcohol consumption in public spaces. The marginal effect implies that a one-unit increase in a biologically active area leads to an average of 1.175 more alcohol penalties.

6. Discussion

The results of this study are consistent with the expanding body of literature exploring the complex relationships between urban factors and crime. The assumptions of this paper often align with the findings of the Ecological School of [31,32]. According to research related to this center, the crime is not randomly distributed in the city but rather concentrated in specific areas, especially in rapidly urbanizing and industrializing cities. The patterns of behavior are influenced by the urban environment. It also emphasized that a city’s structure and organization could foster conditions that encourage actions contrary to the law. Specifically, our research on the availability of alcohol outlets in the city center and the subsequent rise in crime incidents provides new insights into this domain. Malek-Ahmadi and Degiorgio [33] highlight divergent behaviors in rural versus urban settings, demonstrating a higher risk of alcohol-related crimes in non-urban areas. However, the present study attempts to elucidate the factors influencing these behaviors.

A key determinant of crime identified in this article is urban vibrancy, a variable that has not been extensively explored by previous researchers in the context of its impact on crime rates. Despite the popular perception of “green criminology” as a result of long-term top-down actions detrimental to society [34], this specialized knowledge should serve to prevent the development of common crime in green areas within the city. Additionally, apart from the concept of green criminology, which addresses crimes against the environment, including soil, water, air, and living beings, there is also environmental criminology [35,36], which focuses specifically on space and place. Our study adheres to the framework of so-called computational criminology. It is noticeable that the existing literature has focused, among other things, on the relationship between crime and green spaces. Stevens et al. [8] suggest that a higher proportion of overall vegetation is significantly associated with lower rates of violent outdoor crimes. Although our analysis reveals a weak correlation between biologically active areas and the number of crime incidents, our findings do not contradict those of Stevens et al. [8], where the authors demonstrate that an increase in the average maximum temperature in a city is linked to a rise in violent crimes. Green areas, on the other hand, have the ability to cool down spaces, making them more comfortable, which can improve the criminal situation. In their further conclusions, the authors themselves emphasize that the factors influencing violence are complex. Therefore, any inference that more green spaces automatically lead to fewer crime incidents is unfounded. The situation studied in Łódź concerns minor crime incidents, primarily related to disturbing public order. These crime incidents do not involve serious crimes or violent acts.

Our study corroborates the findings of Jaeyoung, Dennis, and Lindsay [37], who argue that the spatial distribution, quality, and management of green spaces are more significant in relation to crime than the mere quantity of urban green space. Analyzing metropolitan cities in the United States, these researchers demonstrated that the composition and configuration of urban green spaces are correlated with crime. Specifically, tree canopies were negatively correlated with crime rates, while grassy areas were positively correlated. Similarly, our study indicates that the composition of green spaces, including trees and areas designed for children’s activities, is linked to crime rates. We also concur with Jaeyoung, Dennis, and Lindsay [37] that effective urban green space planning can be a viable strategy for crime reduction.

Our results, like those of Yuika and Naoko [38], suggest that thematic green areas may influence crime levels. In our study, the Pearson correlation for water facilities in the vegetation category reached up to 0.35. In the Yuika and Naoko [38] study, the correlation was identified with dog-friendly areas in city parks, where visitors adhered to park rules and morality, and the park was well managed.

The findings of this study also support those of Ogletree et al. [39], who noted that, while green spaces generally enhance the quality of urban life, they can also lead to unexpected and undesirable consequences. The authors point out that the relationship between green spaces and crime in cities is not straightforward, and none of the cities that they studied exhibited a significant positive correlation. They suggest a need for further analysis of the mechanisms driving trends and the connections between different types of crime and specific components and seasonal changes in green spaces.

Other conclusions related to these issues concern adapting urban communities to the effects of global warming and future urban densification [10]. Authors recognize the need for future studies to include more vegetation data, especially in areas undergoing housing development, and suggest prioritizing this in future research endeavors.

Adzande [1] emphasizes the lack of consensus in the existing literature regarding the social and environmental characteristics of spaces that influence crime. This assertion is reflected in our findings as well. Adzande [1] identified buildings, population density, fences, the age of residents, and the “age” of residential areas as key determinants of crime. Our research found correlations with variables such as the building footprint. Although individual variables showed low correlations, the coexistence of these variables—i.e., the sequence of specific variables together—appears to be critical. Urban space is a complex composition of various factors coexisting within a single context. As with Adzande’s [1] study, certain determinants facilitate the “eyes on the street” strategy, underscoring the importance of formal and spontaneous monitoring of urban spaces. We agree with Adzande [1] that design principles supporting the surveillance of urban spaces, such as the introduction of outdoor communal spaces to encourage interaction and the development of social networks, can enhance urban safety.

A similar research trajectory was pursued by Woldetsadik and Beyene [40], who investigated the relationship between crime and the built environment, including land use, streets, and buildings. Their research demonstrated that crime is correlated with land use mismatches, inadequate street design, and improper building orientation. The findings of Woldetsadik and Beyene [40] align with our analysis, highlighting the critical importance of urban vibrancy. In our study, we also examined the correlation of individual functions, building conditions, and facilities, but we found that individual variables exhibited minimal correlations. Thus, the configuration of variable sequences in a specific urban context appears to be crucial.

In concluding this discussion, it is essential to consider the research on the spatiotemporal patterns of crime conducted by Mukherjee, Saha, and Karmakar [41]. Utilizing geoinformatics and geostatistics, their study identified spatial clusters, significant crime hotspots, and their shifts from 2015 to 2020 while also analyzing the impact of urban land use on crime rates. Our study extends and updates this research by focusing on data from 2023 and introducing a broader range of variables. Crime, including minor crime incidents, poses a global challenge with significant implications for quality of life. Our findings confirm that diverse urbanization processes influence the occurrence, types, and intensity of crimes. Therefore, urban design and management play a pivotal role in ensuring the safety of city inhabitants.

Enriching the urban fabric with well-designed public spaces aligns with Leon Krier’s *Res Publica, Res Economica, Civitas* ideogram, which serves as a foundational principle of New Urbanism. This concept underscores the idea that authentic towns and cities are composed of two distinct yet interdependent realms—the public and the private. A well-balanced proportion between these spheres is essential for fostering both social cohesion and economic vitality.

In accordance with Krier’s [42] recommendations, the share of public spaces within the urban structure should range between 25% and 35%, ensuring a harmonious distribution that neither overwhelms nor diminishes the integrity of the built environment. Equally important is the integration of diverse functions and the activation of ground floors—an aspect that Gehl [43] identifies as a critical determinant of urban safety and vibrancy. The presence of engaged and dynamic street-level spaces fosters a sense of security, encourages social interaction, and enhances the overall quality of urban life. These principles should therefore constitute fundamental guidelines for contemporary urban planners seeking to create inclusive, resilient, and human-centered cities.

7. Research Limitations

This study is confined to data derived exclusively from the city of Łódź, which inherently limits the generalizability of the findings. While an in-depth analysis of a single urban area facilitates a comprehensive understanding of local dynamics, the inclusion of data from multiple cities would likely enhance the robustness and universality of the results. Such an expansion, however, falls outside the current study’s scope. Moreover, the investigation did not incorporate variables that capture the social stratification emerging from the post-industrial transformation of urban centers, nor did it explicitly examine the potential moderating influence of city size on the observed phenomena. Similarly, other urban factors—such as the revitalization status of specific neighborhoods—were not considered. Additionally, this research did not integrate detailed demographic variables, notably, the age distribution and educational attainment levels of the population within the examined area. Given that these factors may play a critical role in shaping social dynamics, their omission represents a limitation that warrants further exploration in future studies. In summary, while this study contributes valuable insights into the subject matter, the limitations outlined above should be carefully considered when interpreting the results and in the design of subsequent research projects.

8. Implications for Further Research

In addition to addressing the constraints associated with structural variables and other factors discussed earlier, subsequent investigations should expand their focus to encompass other geographical contexts and urban typologies.

A particularly promising direction involves contrasting the outcomes obtained in city centers with those observed in peripheral areas, where the residential function predominates. In these zones, variations in the proportion of residential land use, as well as differences in the built environment and spatial organization, may yield distinct patterns that offer valuable insights into urban transformation processes.

Furthermore, while the current study focuses solely on Łódź, future research could benefit from incorporating data from multiple cities. A comparative analysis across diverse urban areas—each influenced by unique socio-economic conditions—would not only test the generalizability of the present findings but also contribute to the development of a more robust theoretical framework for understanding urban change and fostering a sense of safety.

By extending the geographical scope and considering additional urban variables, subsequent studies can provide a more comprehensive assessment of how various factors interplay in shaping urban environments, thereby offering a richer context for interpreting the dynamics observed in this research.

9. Conclusions

The 2030 Agenda for Sustainable Development, adopted by the United Nations, emphasizes the importance of sustainable urban development, with Goal 11 focusing on making cities inclusive, safe, resilient, and sustainable. Urban planning plays a crucial role in achieving this objective, particularly through the design and management of public spaces, which significantly enhance environmental quality, livability, and sustainability [44]. This issue is especially relevant for post-industrial cities such as Łódź [45]. This research contributes to sustainability through its interdisciplinary approach, particularly in relation to urban safety. The results provide valuable insights for addressing challenges related to sustainability, such as socio-economic stability and quality of life.

This study on the impact of urban form on crime in Łódź confirmed the complexity of the relationship between spatial planning and crime incidents in public spaces. The results of the analysis showed significant links between various urban elements and crimes. In particular, this concerns the presence of alcohol sale points and urban vibrancy, understood as the density of human activity in a given area unit. The quantitative modeling confirmed the qualitative hypothesis that the number of alcohol outlets in a particular urban block increases the likelihood of higher penalties for alcohol consumption in the same block, which in turn increases the chances of public order disturbances occurring. This may have important implications for urban policies.

Also, several less significant relationships were noted, particularly concerning the typology of buildings. Although the correlation coefficients for these relationships range from −0.25 to +0.25 and thus hover close to 0, they should still be considered within the scope of this study. Despite the relatively weak correlation shown here, it can be inferred that research on building typology in the context of legal violations may form a basis for more advanced studies that take into account a larger number of variables. Furthermore, in the analysis of building purposes, it was noted that residential structures are a type that correlates with the phenomenon being studied. Additionally, an important finding is the inverse correlation with the presence of industrial buildings. This should be interpreted as meaning that the greater the presence of industry, the fewer violations are recorded in the given area. Therefore, introducing an internal typology within these two issues, combined with multilayered analyses, may potentially provide new insights in future studies on the impact of changes on sustainable development.

It is important to note that the spatial representation of offense distribution presented here pertains to places with unique characteristics. These are often distinct urban interiors (e.g., passages, squares, green spaces) located in city centers near the main promenade Piotrkowska Street. Additionally, these areas are sometimes accompanied by services (e.g., a 24 h gas station integrated with a convenience store). All of this suggests that crime incidents in such locations may be influenced by additional factors.

In summary, this study provides new insights into the relationship between urban form and safety in cities. The results may contribute to the implementation of more effective and tailored spatial planning strategies that promote not only aesthetics and functionality but also the safety of residents.

The results can be used to develop more effective urban policies, including socio-economic aspects of urban life. With the aim of improving the quality of life for people in city centers, it is essential to consider verifying the number of alcohol sale points or consciously locating them within the urban fabric.