Indonesia faces persistent challenges in crime forecasting and judicial resource management, compounded by chronic underreporting and inconsistent spatial resolution in official crime statistics. In this study, a multi-level spatio-temporal machine l...
Crime forecasting has gained popularity in recent years; however, the majority of studies have been conducted in the United States, which may result in a bias towards areas with a substantial population. In this study, we generated different models c...
As the demand for more accurate crime prediction and risk assessment grows, researchers have been developing smarter models that blend statistical methods with machine learning. This study compares a hybrid ARIMA-ANN model with traditional classifica...
In this article, we introduce the topic of crime forecasting performed in North Rhine-Westphalia, Germany. We give a brief overview of three forecasting methods used in theory and practice: predictive policing, risk terrain modeling, and time series...
Geographical mapping has revolutionized data analysis with the help of analytical tools in the fields of social and economic studies, whereby representing statistical research variables of interest as geographic characteristics presents visual insigh...
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...
In this article, we present the use of risk terrain modeling for the risk assessment of explosive ATM attacks in North Rhine-Westphalia, Germany. We give a brief overview of three methods used for this purpose: risk terrain modeling, recapture rate i...
There is ongoing debate regarding the merits of decriminalization or outright legalization of commercial sex work in the United States. A few municipalities have officially legalized both the selling and purchasing of sex, while others unofficially c...
Ecuador has experienced one of the fastest escalations of lethal violence in Latin America, with annual intentional homicides rising from about 1000 in 2014–2019 to 9283 in 2025. This study evaluates how well such patterns can be forecast, and...
This paper tackles the problem of forecasting real-life crime. However, the recollected data only produced thirty-five short-sized crime time series for three urban areas. We present a comparative analysis of four simple and four machine-learning-bas...
Public governance has evolved in terms of safety and security management, incorporating digital innovation and smart-analytics-based tools to visualize abundant data collections. Urban safety and security are vital social problems that have many bran...
This paper empirically examines the impact of various variables on Perceived Risk of Crime (PRC) for Italian regions in the period of 2004–2022 using data collected in the framework of the Benessere Equo e Sostenibile (BES, Equitable and Sustai...
Accurate spatial–temporal crime prediction is a critical component of proactive public safety governance, yet it remains challenging due to complex dependency structures and severe data sparsity in real-world crime datasets. Most existing metho...
Crime forecasting in heterogeneous urban contexts remains challenging due to the combined effects of territorial heterogeneity and complex temporal dynamics. However, a large portion of the existing literature tends to address territorial segmentatio...
The aim of the present research is to assess the impact of factors such as welfare, infrastructure, security, and the environment on inbound tourism as well as to develop its forecast. Six proxy indicators of the above-mentioned factors were selected...
There has been a significant focus on predictive policing systems, as law enforcement agents embrace modern technology to forecast criminal activity. Most developed nations have implemented predictive policing, albeit with mixed reactions over its ef...
Urban forecasting systems increasingly draw on mobility and other dynamic data, yet improved test accuracy alone cannot show whether an added source lowers an information-constrained error bound or merely benefits a particular model. We develop a fin...
Emergency calls are defined by an ever-expanding utilisation of information and sensing technology, leading to extensive volumes of spatio-temporal high-resolution data. The spatial and temporal character of the emergency calls is leveraged by author...
An event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong...
Ecologically motivated violence that manifests itself in the animal-rights and environmental forms is not a declining phenomenon. The fluctuating increase of the number of ecologically motivated crimes during the last 50 years, the multiplicity of th...
This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short...
Learning transferable region embeddings is fundamental to urban computing, supporting applications from economic forecasting to public safety. Existing multi-view fusion methods predominantly rely on coarse-grained fusion, applying uniform weights ac...
This study compares the predictive performance of traditional multivariate time series models and machine learning (ML) techniques in modelling perceived feelings of safety among South African residents. The analysis uses secondary data from the Gove...
The smart environment hides numerous security challenges that need to be addressed promptly. Smart cities have emerged as a novel concept, integrating emerging technologies and data-driven solutions to improve urban living conditions. Traffic surveil...
This article aims to forecast the information trends related to the most popular cyberattacks, seen as the cyber-crimes’ consequences reflecting on the Internet. The study database was formed based on online users’ search engine requests...
Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such an amount of information is analyzed to discover data-driven models, which can be...