The exponential rise in urban agglomerations, economic inequalities, fluctuating economies, unpredictable emergencies, and AI technological advancements are a few main characteristics that describe our current world. Accompanied by these factors are rise in crimes. The focus of cities that aim to be ‘smart’ should not just be on sustainability through the effective use of data but also on the safety & security of citizens. Crime rate is one of the crucial factors determining cities’ livability, and unfortunately, this persists in being an open issue today (Pradhan et al., 2019).
The idea of smart cities involves using technology to enhance different aspects of urban life, such as development, safety, and energy. However, the issue of crime remains a significant challenge for cities and affects the quality of life for its residents. In various nations worldwide, law enforcement agencies record numerous crimes daily, which are made available to the public through the Open Data initiative. This aims to increase citizen involvement in decision-making and enable the discovery of valuable insights from the data. Reducing crime remains an essential goal for improving urban life.
This article briefly dwells on numerous urban data tools & methods, their applications in identifying & preventing crime, the significance of open data initiatives, and the impact of crime analysis on the future of city planning.
Urban Data Analytics | Crime Prevention
Referring to methodologies implemented in the collection, processing, and interpretation of urban data across cities, urban data analytics are incredibly helpful in providing detailed insights into changing trends of criminal activities on both neighbourhood and city scales. The main objective is to create a predictive model that can identify the type of crime that is more likely to occur based on various characteristics such as time, location, detailed criminal profiles and their modus operandi.
The following tools provide essential urban data for crime analysis for smart & secure cities.
Geographic Information Systems
GIS (Geographic Information System) can assist in identifying criminal network analysis by enabling the integration and visualization of various spatial data layers, including crime incidents, offender locations, and their connections, to identify spatial patterns and relationships. Additionally, GIS can aid in identifying crime hotspots and visualising the spatial distribution of criminal activities, which can be used to inform law enforcement authorities. Finally, GIS can facilitate network analysis techniques, such as centrality measures, that can be used to identify critical nodes and pathways within criminal networks.
Predictive Analysis
Predictive Analysis uses statistical & machine learning procedures to predict what, when, and where criminal activities are most likely to occur. This will also aid concerned authorities in the effective allocation of resources.
Walkability assessments
Assessment of walkability evaluates and gauges the safety of citizens using pedestrian routes in cities. This helps city planners to identify potentially dangerous areas that are poorly lit & hidden and implement safety measures accordingly.
Urban Satellite Imagery
Urban satellite imagery could contribute to crime prevention by monitoring public places, assessing environmental factors, analyzing traffic patterns, and studying urban growth.
Data Visualization Tools | Crime Prevention
Data visualisation tools like Google Data Studio, Power BI, Tableau, etc. provide access to a wide range of relevant data to analyse urban areas with certain parameters.
Social Media Monitoring Tools
Social media can be a rich source of data for crime analysis. Law enforcement agencies can identify potential criminal activity and respond proactively by monitoring social media platforms via monitoring tools such as Geofeedia and Dataminr.
Applications of urban data in crime prevention
Identification of Crime Patterns
Referring to criminology theories that crime is crucially connected to time & location, urban data could be utilised to ascertain patterns followed in illegal activities via temporal-spatial pattern analysis (Zhao & Tang, 2018).
Criminal Temporal Pattern Analysis
This type of analysis refers to gathering structured information based on region, time & seasonal intervals; pertaining to the tendency and frequency of crime.
Urban Pattern Analysis
This is an analysis of the distribution & intensity of crime and how they differ in urban & rural areas.
Some examples include: Crime Prevention
-
Concentrating Earthquake-like patterns
Similar to the concept of aftershocks post-earthquakes, criminal activities occur in a repeated formation in the same area, such as burglaries. This model aids in applying principles derived from seismology in locating hotspots of urban-level crimes.
-
Spatial-temporal hotspot patterns
Identifying geographic locations and time intervals to deduce the amount and intensity of crimes such as gang violence, organised crimes, etc.
Predicting Crime Rates
Time Series Models
Utilising univariant time models to deduce the frequency of illegal activities concentrated in smaller areas. These models are based on the premise that trends of future criminal activities will occur mostly in similar areas of the past.
One such example of a time series model is ARIMA- Autoregressive Integrated Moving Average Model which utilises past data to predict crime patterns hourly, daily, weekly, and monthly. Similar to ARIMA is the Holt-Winters Model which incorporates seasonal trends, making it simpler to predict rates of crimes during annual holidays.
Climatic Data
Analysing criminal tendencies through environmental patterns is based on the assumption that higher temperatures lead people to spend time outdoors, increasing the urge of criminals and exponentially raising their activities.
Identifying Criminal Network Analysis
Analysing networks of criminals and their plans could be briefly categorised into three techniques:
Geographic Information Systems | Crime Prevention
Studying and analysing geographic data to comprehend the spatial distribution of crime and establish hotspot locations of their networks.
Community Interaction
Employing agents to interact with the public to understand the complexity of people’s behavioural dynamics.
Graph Theory
Using centrality metrics & community detection techniques to identify the central figure of a criminal network disrupting their operations.
Open data initiative
‘Open data initiative’ makes urban data freely available to the public without restricting its accessibility. Law-enforcement authorities, urban planners, and citizens could benefit immensely from this method as it strongly advocates for transparency and accountability.
In the context of smart cities, open data initiatives could enhance the potential of city governance, foster participatory engagement with citizens, and co-creating frameworks of innovative services (Neves et al., 2020).
Some means of implementing open data would be:
Predictive policing | Crime Prevention
Open data can be used to train machine learning algorithms to predict the time and location where crimes are likely to occur. By analysing past crime data, law-enforcement authorities could identify patterns and use this information to allocate resources more effectively.
Crime mapping
Open data can be used to create interactive maps that show crime trends and patterns in different neighbourhoods, helping citizens and law enforcement agencies to identify high-risk areas and take appropriate measures.
Resource allocation
Alongside optimising the deployment of police officers & other resources, city planners can make informed decisions about where to allocate resources to prevent crime and improve public safety by analyzing open data. Crime statistics, public transportation data & demographic data aid urban planners in identifying areas with higher crime rates and safe transit needs. This information can be used to allocate resources more efficiently, such as increasing police patrols or improving public transportation in certain areas.
Impact of crime analysis on the Future of city planning
Crime analysis significantly impacts the future of city planning by providing a data-driven approach to the development of urban spaces. City planners could make informed decisions in planning secure urban spaces by using data from the above means on crime patterns & trends. By identifying high-risk areas, city planners and designers could work alongside law enforcement agencies to implement interventions to deter illegal activities through the following measures:
Informed design of public spaces | Crime Prevention
City planners and architects could design public areas such as parks, squares, plazas & transit stations to be less vulnerable to illegal activities. By comprehending patterns of illicit activities, spaces can be designed or re-designed with minimal hiding spots, high visibility areas, and clear sightlines that are well-lit & maintained.
Safe Urban Mobility
Areas with high incidents of pickpocketing or theft on public transportation routes can be targeted with increased patrols and security measures. Crime analysis can inform the design of transportation infrastructure to reduce risks. Through optimised placements of lighting, security cameras, etc., based on crime hotspots. Additionally, the layout and design of transportation hubs can be modified to reduce opportunities for criminal activities, such as improving visibility and reducing isolated areas.
Moreover, crime analysis can inform policies and programs to address the underlying social and economic factors that contribute to crime, such as unemployment, through social programs and investments in education and housing that can reduce crime risks associated with urban mobility.
Implementing Theory of Defensible Spaces & Crime Prevention through Environmental Design
Developed by architect and urban planner Oscar Newman in the 1970s, this theory mainly speaks about achieving a safe environment & reduce fear of crime by implementing design features such as Territoriality (Defining physical boundaries via landscaping), Natural Surveillance (Increasing visibility through windows and lighting), Access Control through openings and gates & Constant Maintenance of public areas.
Creating defensible areas will induce an additional sense of responsibility amongst residents within their immediate surroundings by encouraging them to participate in spatial maintenance actively.
On similar lines is the concept of Crime Prevention through Environmental Design (CPED) which is based on idea that the design of the physical environment (buildings, public areas & neighbourhoods) could reduce crime by making it challenging for potential offenders.
Security in Smart Cities | Crime Prevention
Smart cities rely on integrating various technologies, including Internet of Things (IoT) devices, sensors, and artificial intelligence (AI) systems, on improving citizens’ quality of life. By incorporating crime analysis into the development of smart cities, urban planners can leverage these technologies to understand crime patterns and trends better.
Deploying smart cameras and sensors can detect unusual activities and alert law enforcement agencies in real-time, enabling them to respond quickly to potential criminal activities. Furthermore, integrating AI systems can analyse the data from various sources to detect patterns that may take time to be apparent through traditional crime analysis methods.
Resource Allocation
Crime analysis via urban data could also inform appropriate segregation & allocation of resources within cityscapes. In identifying high-risk areas, planners & other concerned authorities could prioritise allocating police patrol and social & emergency healthcare services in these spaces.
Conclusion
In summary, the use of urban data analytics has the potential to enhance crime prevention strategies and improve public safety in urban areas. Through crime patterns and trends analysis, law enforcement agencies and urban planners can more effectively allocate resources and develop evidence-based interventions.
Moreover, urban data analytics can inform the development of smart cities by promoting the creation of safer urban environments. By integrating data-driven approaches into public infrastructure design, urban planners can mitigate crime risks and enhance the quality of life for urban residents.
Additionally, urban data analytics can revolutionise urban planning and law enforcement practices, leading to safer and more livable cities. As smart city programs continue to advance in various nations accompanied by growth in population, the application of urban data analytics will become increasingly crucial in creating sustainable, secure and smart nations.
References | Crime Prevention
- Zhao, X. and Tang, J. (2018) “Crime in Urban Areas: A Data Mining Perspective,” ACM SIGKDD Explorations Newsletter, 20(1), pp. 1–12. Available at: https://doi.org/10.1145/3229329.3229331.
- Pradhan, I. et al. (2019) “Exploratory Data Analysis and crime prediction for Smart Cities,” Proceedings of the 23rd International Database Applications & Engineering Symposium on – IDEAS ’19 [Preprint]. Available at: https://doi.org/10.1145/3331076.3331114.
- Neves, F.T., de Castro Neto, M. and Aparicio, M. (2020) “The impacts of Open Data Initiatives on Smart Cities: A Framework for evaluation and monitoring,” Cities, 106, p. 102860. Available at: https://doi.org/10.1016/j.cities.2020.102860.
- Crime Prevention Through Environmental Design (CPTED) (no date) Menlopark.gov. Available at: https://menlopark.gov/Government/Departments/Police/Crime-safety-and-prevention/Crime-Prevention-Through-Environmental-Design#:~:text=CPTED%20works%20by%20eliminating%20criminal,that%20undesirable%20behavior%20occurs%20here (Accessed: April 22, 2023).
