Spatio-temporal change in crime hotspots

Abstract

Instances of criminal activity and the fear of harassment not only impede individuals'access to public spaces but also infringe upon their fundamental right to the city,instigating a sense of disconnection (Beebeejaun, 2017; Bhattacharyya, 2016; Harvey,2012; Mahadevia, 2016; Mehta, 2014). A stark 19.7% increase in crimerates has been observed in India, culminating in 0.406 million reported cases as of 2022, a rise achieved within a span of three years (National Crime Records Bureau [NCRB], 2019;NCRB, 2022). This underscores the significance of addressing crime prevention within the framework of urban planning, as highlighted by the role of security in Maslow'shierarchy of needs, the Liveability Index, and Sustainable Development Goal 11.Oscar Newman's assertion in 1972 encapsulates this perspective succinctly: "Crime is not inevitable. It is the byproduct of a poorly designed environment that breeds despair and hopelessness." The environmental approach, known as Crime Prevention Through Environmental Design (CPTED), focuses on leveraging the urban environment's capacity to deter criminal behavior and instil confidence among inhabitants, guided by foundational principles (Coppola, 2021; Soomeren, 2021; Mihinjac and Saville, 2019; Fasolino et al., 2018; Cozens and Love, 2015; Cardia and Bottigelli, 2011; Crowe, 2013; Saville and Cleveland, 2008; Newman, 1972; Jacobs,1961). It is imperative that policies are enacted to ensure that CPTED is not merely a temporary measure but rather a standard for future urban development (Mitchell and Mitchell, 1969; Etzioni, 2006). This study aimed to explore the impacts of spatial and temporal configurations of Delhi’s urban crime environment for enhanced crime prevention through urban planning. To achieve this aim, three objectives for the research were highlighted: 1. To understand the need for crime prevention through urban planning 2. A. To analyse the temporal configuration of Delhi’s urban crime environment. B. To analyse the spatial configuration of Delhi’s urban crime environment. 3. To suggest policy interventions for crime prevention through urban planning This study delves into the spatial and temporal patterns of urban crime environment from 2018 to 2023. It primarily focuses on five specific crime categories: dacoity (violent theft), robbery (theft with force or the threat of force), snatching (theft by sudden taking), motor vehicle theft (MVT) and murder. By examining both the “Spatio-temporal change in crime hotspots: A case of Delhi” geographical distribution and the time-based trends of these crimes, the study seeked to gain a deeper understanding of Delhi's crime landscape. This knowledge is then used to inform strategies for improving public safety and potentially reducing criminal activity within the city. The present study focuses on the environmental approach to crime prevention, commonly known as crime prevention through environmental design (CPTED). The CPTED approach to crime prevention outlines various relevant theories which were highlighted in the thesis- like temperature aggression theory, general strain theory, enclosure hypothesis, encounter hypothesis, eyes on street, routine activity approach Tobler’s law of geography and crime pattern theory. As per crime pattern theory there are various factors known as crime predictor variables that boost the probability of crime occurrences at certain locations. For the purpose of the study these crime predictor variables were identified from the review of the relevant literatures and theories, and were classified as built environment, land use, accessibility, temporal, and crime data patterns. Further, the crime data consisting of crime point data and monthly crime counts were used to perform the seasonal trend decomposition using LOESS for the temporal analysis, spatial point pattern test for the spatio temporal analysis, and the spatial autocorrelation test using Global Moran’s I and LISA clustering for hotspot identification. Additionally, a geographically weighted regression analysis was done to determine the correlation of crime predictor variables to the crime hotspots. Moreover, the current policies evaluated and the need for corrections were identified. And lastly, suitable policies were recommended for the Delhi master plan, thereby contributing to crime prevention through an evidence based urban planning framework. The seasonal trend decomposition using LOESS unveiled that, while trends and residuals fluctuated across crime types and years, the seasonal component remained consistent throughout the five-year period. This validates the presence of statistically significant temporal patterns in Delhi's crime rates. Moreover, the spatial point pattern test compared the point distribution of crime incidents between 2018 and 2022. The analysis revealed a notable resemblance across major crime types, indicating statistically significant spatial clustering of crime within Delhi from 2018 to 2022. Building upon these findings, the LISA cluster analysis conducted for the years 2018 and 2022 revealed statistically significant clusters of crime hotspots and coldspot “Spatio-temporal change in crime hotspots: A case of Delhi”within the city of Delhi. Subsequently, the LISA index was employed as the dependentvariable in a spatial lag model utilizing maximum likelihood estimation. This model aimed to examine the intricate relationships between the LISA index and 40 selected exploratory variables. Through regression analysis, the study delineated parameters that either deterred or attracted criminal activity. Elevated values pertaining to proximity to commercial areas, high-traffic intersections, police stations, auto stands, metro stations, areas with high pedestrian traffic, mixed land use, building density, and population density within the parent ward were found to be correlated with a reduction in crime incidents. Conversely, high proximity to greenbelts, water bodies, residential areas, busy traffic corridors, taxi stands, and higher average monthly temperatures were identified as factors that attracted criminal activity. Synthesizing the research findings, four key policy implications emerge. Firstly, there is a need for interventions aimed at integrating a mix of land uses. Secondly, specific interventions tailored to different land uses are warranted. Thirdly, efforts should be directed towards enhancing accessibility within urban areas. Lastly, there is a necessity for policy implications at the local level. Each of these policy implications is accompanied by a comprehensive set of policy packages designed to achieve the desired outcomes. Notably, local level policy implications encompass four distinct policy packages: designing with density, promoting increased community wellbeing, fostering placemaking initiatives, and initiating flexible approaches. However, it is evident that solely recommending physical interventions to reduce crime rates would be insufficient. Instead, a more holistic approach is essential. As such, the recommendations are categorized into three main areas: physical interventions, nudging initiatives, and technological interventions. These strategies draw upon global best practices while also considering the specific context of crime rates in Delhi. This research primarily examines the role of environmental design in crime prevention through the lens of urban planning. It notably excludes considerations of social and economic factors, as well as predictive policing, although these areas hold promise for future studies. The proposals presented herein predominantly leverage urban planners' tools such as Development Control Regulations (DCR) and Integrated Roadside Control (IRC) codes. However, future research endeavors may explore additional avenues, including behavioral science approaches such as nudging techniques and advancements in technology.