Search Results (198)

Urban blight and litter are twin issues that significantly affect the quality of life in city neighborhoods. This paper investigates the relationship between blight and litter, commonly overlooked in urban studies literature. We measure the prevalence of blight and litter across block groups in our mapping with a focus on socioeconomic factors, including income levels, crime rates, and land use types (industrial, commercial, and residential) for our case study, Memphis, Tennessee. Using statistical and spatial analytics, as well as data from the Memphis Data Hub and the City of Memphis, we show the prevalence of blight and litter across block groups. GIS was used to map neighborhood-specific blighted structures and their spatial connection to litter accumulation. We also explore the distribution of blight and litter across different land uses. A Pearson correlation value of 0.639 suggests a strong positive relationship between blight and litter at the block group level. Spatial clustering is assessed by Global Moran’s I and Local Moran’s I, identifying neighborhood-level hotspots. The block group is used as the unit of analysis to capture micro-spatial variation and to enable meaningful equity-based insights at the neighborhood level. Our mapping offers practical insights into urban revitalization strategies in deference to per capita income, crime rate, and land use. The findings contribute to urban policy discussions by promoting the joint consideration of blight and litter, helping guide future community-based interventions aimed at alleviating the negative impacts of blight and litter, particularly in disadvantaged neighborhoods. Full article

This study proposes an intelligent patrol planning framework that leverages reinforcement learning, spatiotemporal crime forecasting, and simulated sensor telemetry to optimize autonomous vehicle (AV) navigation in urban environments. Crime incidents from Washington DC (2024–2025) and Seattle (2008–2024) are modeled as a dynamic spatiotemporal graph, capturing the evolving intensity and distribution of criminal activity across neighborhoods and time windows. The agent’s state space incorporates synthetic AV sensor inputs—including fuel level, visual anomaly detection, and threat signals—to reflect real-world operational constraints. We evaluate and compare three learning strategies: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Experimental results show that DDQN outperforms DQN in convergence speed and reward accumulation, while PPO demonstrates greater adaptability in sensor-rich, high-noise conditions. Real-map simulations and hourly risk heatmaps validate the effectiveness of our approach, highlighting its potential to inform scalable, data-driven patrol strategies in next-generation smart cities. Full article

The presence of people has a complex relationship with public safety—while it is often associated with increased natural surveillance, it can also attract specific types of crime under certain urban conditions. This exploratory study examines this dual relationship by integrating Jane Jacobs’s urban theories and the principles derived from them in Quito, Ecuador. Anchored in Jacobs’s concept of “eyes on the street,” this research assesses four morphological dimensions—density, land use mixture, contact opportunity, and accessibility through nine specific indicators. A binary logistic regression model is used to examine how these features relate to the incidence of street robberies against individuals. The findings indicate that urban form characteristics that foster “eyes on the street”—such as higher population density and a mix of commercial and residential uses—show statistically significant associations with lower rates of street robbery. However, other indicators of “eyes on the street”—such as larger block sizes, proximity to public transport stations, greater street lighting, and a higher balance between residential and non-residential land uses—correlate with increased crime rates. Some indicators, such as population density, block size, and distance to public transport stations, show statistically significant relationships, though the practical effect size compared to residential/non-residential balance, commercial and facility mix, and street lighting is modest. These results underscore the importance of contextualizing Jacobs’s frameworks and offer a novel contribution to the literature by empirically testing morphological indicators promoting the presence of people against actual crime data. Full article

Property crime is a pressing issue in maintaining social order and urban sustainability, particularly in regions marked by pronounced socioeconomic disparity. While the link between socioeconomic stress and crime is well established, regional variations in Thailand have not been fully examined. Therefore, the purpose of this research was to examine spatial patterns of property crime and identify the potential associations between property crime and socioeconomic environment across Thailand. Using nationally compiled property-crime data from official sources across all provinces of Thailand, we employed geographic information system (GIS) tools to conduct a spatial cluster analysis at the sub-national level across 76 provinces. Both global and local statistical techniques were applied to identify spatial associations between property-crime rates and neighborhood-level socioeconomic conditions. The results revealed that property-crime clusters are primarily concentrated in the south, while low-crime areas dominate parts of the north and northeast regions. To analyze the spatial dynamics of property crime, we used geospatial statistical models to investigate the influence of socioeconomic variables across provinces. We found that property-crime rates were significantly associated with monthly income, areas experiencing high levels of household debt, migrant populations, working-age populations, an uneducated labor force, and population density. Identifying associated factors and mapping geographic regions with significant spatial clusters is an effective approach for determining where issues concentrate and for deepening understanding of the underlying patterns and drivers of property crime. This study offers actionable insights for enhancing safety, resilience, and urban sustainability in Thailand’s diverse regional contexts by highlighting geographies of vulnerability. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article

The literature shows greenspaces generally increase nearby property values, but in Riyadh, this relationship is complex and understudied. Existing studies lack sector-specific analyses across Riyadh’s neighborhoods, overlook the impact of the Green Riyadh Project launched in 2019, and fail to address negative externalities associated with large greenspaces in an arid, privacy-conscious context. Such paradoxical impact of larger greenspaces bordering major roads at the neighborhood edge, unexpectedly reduce property values by 2–4% due to petty crime, congestion, poor upkeep, and privacy concerns, contrasting with 10–18% premiums for properties abutting greenspaces with restricted access in affluent neighborhoods. Global studies typically report positive greenspace effects, so negative impacts in specific Riyadh sectors are surprising. This highlights the city’s unique arid, cultural, and urban dynamics in addressing this research gap. The research uses purposive quota sampling of Riyadh neighborhood greenspaces and a mixed-methods approach of quantitative hedonic pricing analysis combined with qualitative semi-structured interviews with real estate agents. Findings underscore the need for tailored urban planning (e.g., mitigating petty crime, overcrowding, poor maintenance). This suggests the importance of integrating green infrastructure into urban planning, not only for its ecological and social benefits but also for its tangible positive impact on property values. Poor greenspace upkeep and safety concerns can reduce price premiums of abutting properties. Full article

Objective: Low birth weight is a serious public health problem even in developed countries. The objective of this study was to assess the ability of machine learning to predict low birth weight rates in big cities in the USA on an ecological/population level. Study design: The study was based on publicly available data from the Big Cities Health Inventory Data Platform. The collected data related to the 35 largest, most urban cities in the United States from 2010 to 2022. The model-agnostic approach was used to assess and visualize the magnitude and direction of the most influential predictors. Results: The models showed excellent performance with R-squared values of 0.82, 0.81, 0.81, and 0.79, and residual root mean squared error values of 1.06, 0.87, 1.03, 0.99 for KNN, Best subset, Lasso, and XGBoost, respectively. It is noteworthy that the Best subset selection approach had a high RSq and the lowest residual root mean squared error, with only a four-predictor subset. Influential predictors that appeared in three/four models were rate of chlamydia infection, racial segregation, prenatal care, percentage of single-parent families, and poverty. Other important predictors were the rate of violent crimes, life expectancy, mental distress, income inequality, hazardous air quality, prevalence of hypertension, percent of foreign-born citizens, and smoking. This study was limited by the unavailability of data on gestational age. Conclusions: The machine learning algorithms showed excellent performance for the prediction of low birth weight rate in big cities. The identification of influential predictors can help local and state authorities and health policy decision makers to more effectively tackle this important health problem. Full article

Lack of green spaces, citizen insecurity, and crime are the primary issues afflicting the Pueblo Libre district. This research aims to propose public spaces that revalue the cultural and historical landscape of Pueblo Libre. The methodology involves a literature review, urban analysis, and climate analysis, incorporating sustainability strategies supported by digital tools (AutoCAD, Revit, and Sketch-Up). The resulting design features an ecological park with vegetation capable of capturing carbon and emitting oxygen, absorbing up to 3544.99 kg of CO₂ annually. It also includes installing 26 solar-powered lights to illuminate necessary spaces efficiently and using eco-friendly materials. Additionally, the park incorporates an artificial wetland with a capacity to process 38,500 L of water using plants that remove toxic elements and capture nutrients. In conclusion, the ecological park seeks to revalue the cultural landscape and counteract environmental degradation by creating a green lung that purifies the air, fosters social connectivity, and integrates users with nature, enhancing their quality of life. Full article

Understanding the complexity of urban systems remains a significant challenge for researchers and practitioners in urban planning and governance. Cities function as multifaceted systems composed of interconnected subsystems with nonlinear interactions, making the design of effective interventions to enhance sustainability and liveability particularly challenging. Spatial modelling has gained prominence in recent decades, fuelled by advances in digital technologies and the advent of digital twins as decision support tools. To fully harness these innovations, it is essential to grasp their underlying principles, strengths, and limitations, and to select the most suitable modelling approach for specific applications. This paper examines two contrasting spatial modelling paradigms: top-down and bottom-up. Specifically, it focuses on Space Syntax and Agent-Based Modelling as representative tools of each approach, analyzing their potential applications in urban planning. This discussion delves into the effectiveness of the proposed methodologies in analyzing crime dynamics—selected as a representative application field—at the micro-urban scale. It highlights the insights each approach offers, emphasizing their contributions to understanding the spatial and environmental factors influencing crime patterns. Finally, this paper explores the potential for integrating these methodologies to develop hybrid models that capture both spatial structure and emergent behaviours, offering enhanced support for sustainable urban policies and planning. Full article

In 21st-century American cities, urban crime remains a critical public safety concern influenced by complex social, political, and environmental structures. Crime is not randomly distributed and built-environment characteristics, such as street network configuration, impact criminal activity through spatial dependence effects at multiple scales. This study investigates the cross-sectional, multi-scale spatial effects of street network configuration on property crime across neighborhoods in Charlotte, North Carolina. Specifically, we examine whether the fundamental characteristics of a neighborhood’s street network contribute to variations in its property crime. Using a novel and granular spatial approach, incorporating spatial econometric models (SAR, CAR, and GWR), several street network characteristics, including density, connectivity, and centrality, within five nested buffer bands are measured to capture both local and non-local influences. The results provide strong and consistent evidence that certain characteristics of the neighborhood street network, such as connectivity and accessibility, significantly influence the occurrence of property crime. Impacts are also found to be spatially heterogenous, manifesting themselves at the mid-range scale rather than hyper-locally. The integration of comprehensive measures of street network configuration into spatially explicit models offers new opportunities for advancement in environmental criminology literature. Such spatial dynamics further contribute to urban safety policy by informing decision-makers so that they can ensure a defensively built environment design. Full article

Rising global temperatures and increasing extreme weather events pose challenges to social stability and public security. This study examines the relationship between extreme weather and crime in China using fixed-effects quasi-Poisson and negative binomial regression models, along with a generalized additive model to explore nonlinear effects. The results show that extreme heat significantly increases crime, following an “S” shaped pattern. This intense heat heightens emotional instability and impulsivity, leading to a crime surge. While moderate heat reduces crime, extreme cold and heavy rainfall have no significant effects. These findings highlight the need for stratified policy interventions. Based on empirical evidence, this study proposes three key recommendations: (1) developing a weather warning and public security risk coordination system, (2) promoting community-based crime prevention through mutual assistance networks and infrastructure improvements, and (3) enhancing psychological interventions to mitigate mental health challenges linked to extreme weather. Integrating meteorological data, law enforcement, and interventions to help potential perpetrators can strengthen urban resilience and public safety against climate-induced crime risks. Full article

Social cinema is a cinematographic expression used to denounce current and historical problems, as well as to identify social limits and promote the transformation of society itself. To this end, works included in social cinema depict aspects of reality to critically influence it. The objective of this study is to examine the evolution of social cinema, as well as its scientific, economic and political bases and its main consequences for the general population. Method: This study was conducted by the application of the technique of qualitative research thematic analysis as a procedure in the process of the execution of the scientific task, related to a historical trend study of the research object. The search was carried out in the databases of IMDB and the Culture Ministry of Spain. The end of the dictatorship and the beginning of democracy (1975) was placed as the historical cutting point in the analysis. Various psychosocial variables were used as categories of analysis, including poverty, work, substance use, crime, urban and rural contexts, violence, etc. Results: Spanish social cinema has evolved since its origins in the 1950s as a reflection of Spanish society. These developments, including both progressions and regressions, have been connected with social, political and economic factors. Conclusions: While the shape of Spanish social cinema has changed over time, its themes have remained similar since the origin: poverty, work and hopelessness. The evolution has not been continuous, as it has fluctuated in response to the claims and requests from the context. The Spanish social cinema has reflected topics and images from Spanish society, even when the sociopolitical context avoided them. Finally, the legitimacy of Spanish social cinema is based on its cultural strength and social/political commitment. Full article

This study advances crime analysis methodologies in Maryland by leveraging sophisticated machine learning (ML) techniques designed to cater to the state’s varied urban, suburban, and rural contexts. Our research utilized an enhanced combination of machine learning models, including random forest, gradient boosting, XGBoost, extra trees, and advanced ensemble methods like stacking regressors. These models have been meticulously optimized to address the unique dynamics and demographic variations across Maryland, enhancing our capability to capture localized crime trends with high precision. Through the integration of a comprehensive dataset comprising five years of detailed police reports and multiple crime databases, we executed a rigorous spatial and temporal analysis to identify crime hotspots. The novelty of our methodology lies in its technical sophistication and contextual sensitivity, ensuring that the models are not only accurate but also highly adaptable to local variations. Our models’ performance was extensively validated across various train–test split ratios, utilizing R-squared and RMSE metrics to confirm their efficacy and reliability for practical applications. The findings from this study contribute significantly to the field by offering new insights into localized crime patterns and demonstrating how tailored, data-driven strategies can effectively enhance public safety. This research importantly bridges the gap between general analytical techniques and the bespoke solutions required for detailed crime pattern analysis, providing a crucial resource for policymakers and law enforcement agencies dedicated to developing precise, adaptive public safety strategies. Full article

This study examined the relationship between urban spatial hierarchy and crime rates in Busan using space syntax. This research study investigated the correlation between crime frequency and Busan’s urban space structure. The findings are as follows. Crime concentrated in areas near downtown Busan. High-control and globally integrated areas showed a strong link between city center crimes and spatial usage patterns and pedestrian routes. A weak positive correlation was found between Busan’s urban spatial hierarchy and crime frequency, indicating that urban spatial hierarchy influences crime patterns. However, the regression model’s independent variables had low explanatory power for the dependent variable, suggesting external factors influence crime occurrence beyond urban spatial hierarchy. This study provides an empirical analysis of the relationship between crime incidence and urban spatial structure in Busan, serving as essential data for future crime prevention policies. Full article

Background: Cargo theft represents a persistent challenge to last-mile logistics in the peripheral regions of São Paulo, Brazil, compromising transportation security and increasing operational costs. These high-crime areas disrupt supply chain stability and hinder e-commerce growth. Traditional security methods often fail to address the complexity and uncertainty present in these environments, necessitating adaptive approaches. Methods: This study applies an Expert System based on Paraconsistent Annotated Evidential Logic Eτ to assess the effectiveness of security interventions. Logic Eτ is particularly suited for analyzing uncertain, incomplete, and contradictory data in complex logistics settings. A mixed-methods approach was employed, integrating evaluations from nine experts representing different hierarchical levels within a logistics company. Six key security measures, including GPS tracking, armed escorts, optimized delivery windows, and the hiring of local drivers, were analyzed using favorable degrees and unfavorable degrees for each parameter. Results: The results demonstrated that five measures were effective, contributing to a 58% reduction in security costs in Arujá and 75% in Cajamar, two major distribution hubs. Conclusions: This study highlights the potential of combining Expert Systems and Eτ Logic to enhance cargo transport security, offering a scalable decision support framework for companies operating in high-risk urban regions. Full article