Figure 4: P lot over 100 epochs with 3 - Agents F. Ablation Study - Impact of the LearningOptimizerAgent To quantify the OptimizerAgent's effect on the system, we conducted an ablation study that set up two different configurations. Baseline (3 - Agent Framework): CrimeAnalysisAssistant, FeedbackAgent, and CrimePredictorAgent. Extended (4 - Agent Framework): All of the above, with the OptimizerAgent that could oversee and control how the other agents worked. Both settings were tested using the same protocol, working with the same data for 100 epochs and evaluated according to the already mentioned metrics described in Section V - B. Importantly, during the extended framework tests the OptimizerAgent did not have access to the ground truth and its actions reflected those of a real - world supervisor trying to be efficient with resources . The main aim was to bring more stability and better learning curve using our framework LUCID - MA. Table 2: 4 - Aegnts Observed Improvement Metric Baseline (3 agents) With OptimizerAgent Improvement CrimeAnalysis Assistant Final Score 0.94 0.96 +0.02 FeedbackAgent Final Score 0.89 0.92 +0.03 CrimePredictorAgent Final Score 0.85 0.91 +0.06 Avg. Redundancy Across Epochs 14.2% 6.8% - 7.4% Using the OptimizerAgent resulted in a marked increase in the variety and quality of final system outputs . Visual Result: The final plot demonstrates that agent - level meta - control, As a result, the model exhibits higher consistency, greater variety in its results and more reliable improvement over time -- all accomplished without any need for further model fine - tuning. Figure 5: P lot over 100 epochs with 4 - Agents In addition to standard performance comparison metrics, our system portrayed advanced behavioral dynamics pointing to the pre sence of emergent intelligence capabilities which we delve into in the next section in great detail.

Modeling urban crime is an important yet challenging task that requires understanding the subtle visual, social, and cultural cues embedded in urban environments. Previous work has mainly focused on rule-based agent-based modeling (ABM) and deep learning methods. ABMs offer interpretability of internal mechanisms but exhibit limited predictive accuracy. In contrast, deep learning methods are often effective in prediction but are less interpretable and require extensive training data. Moreover, both lines of work lack the cognitive flexibility to adapt to changing environments. Leveraging the capabilities of large language models (LLMs), we propose CrimeMind, a novel LLM-driven ABM framework for simulating urban crime within a multi-modal urban context. A key innovation of our design is the integration of the Routine Activity Theory (RAT) into the agentic workflow of CrimeMind, enabling it to process rich multi-modal urban features and reason about criminal behavior. However, RAT requires LLM agents to infer subtle cues in evaluating environmental safety as part of assessing guardianship, which can be challenging for LLMs. To address this, we collect a small-scale human-annotated dataset and align CrimeMind's perception with human judgment via a training-free textual gradient method. Experiments across four major U.S. cities demonstrate that CrimeMind outperforms both traditional ABMs and deep learning baselines in crime hotspot prediction and spatial distribution accuracy, achieving up to a 24% improvement over the strongest baseline. Furthermore, we conduct counterfactual simulations of external incidents and policy interventions and it successfully captures the expected changes in crime patterns, demonstrating its ability to reflect counterfactual scenarios. Overall, CrimeMind enables fine-grained modeling of individual behaviors and facilitates evaluation of real-world interventions.

As various types of crime continue to threaten public safety and economic development, predicting the occurrence of multiple types of crimes becomes increasingly vital for effective prevention measures. Although extensive efforts have been made, most of them overlook the heterogeneity of different crime categories and fail to address the issue of imbalanced spatial distribution. In this work, we propose a Spatial-Temporal Mixture-of-Graph-Experts (ST-MoGE) framework for collective multiple-type crime prediction. To enhance the model's ability to identify diverse spatial-temporal dependencies and mitigate potential conflicts caused by spatial-temporal heterogeneity of different crime categories, we introduce an attentive-gated Mixture-of-Graph-Experts (MGEs) module to capture the distinctive and shared crime patterns of each crime category. Then, we propose Cross-Expert Contrastive Learning(CECL) to update the MGEs and force each expert to focus on specific pattern modeling, thereby reducing blending and redundancy. Furthermore, to address the issue of imbalanced spatial distribution, we propose a Hierarchical Adaptive Loss Re-weighting (HALR) approach to eliminate biases and insufficient learning of data-scarce regions. To evaluate the effectiveness of our methods, we conduct comprehensive experiments on two real-world crime datasets and compare our results with twelve advanced baselines. The experimental results demonstrate the superiority of our methods.

Extracting relevant urban patterns from multiple data sources can be difficult using classical clustering algorithms since we have to make a suitable setup of the hyperparameters of the algorithms and deal with outliers. It should be addressed correctly to help urban planners in the decision-making process for the further development of a big city. For instance, experts' main interest in criminology is comprehending the relationship between crimes and the socio-economic characteristics at specific georeferenced locations. In addition, the classical clustering algorithms take little notice of the intricate spatial correlations in georeferenced data sources. This paper presents a new approach to detecting the most relevant urban patterns from multiple data sources based on tensor decomposition. Compared to classical methods, the proposed approach's performance is attested to validate the identified patterns' quality. The result indicates that the approach can effectively identify functional patterns to characterize the data set for further analysis in achieving good clustering quality. Furthermore, we developed a generic framework named TensorAnalyzer, where the effectiveness and usefulness of the proposed methodology are tested by a set of experiments and a real-world case study showing the relationship between the crime events around schools and students performance and other variables involved in the analysis.

Scientists are looking for a way to predict crime using, you guessed it, artificial intelligence. There are loads of studies that show using AI to predict crime results in consistently racist outcomes. For instance, one AI crime prediction model that the Chicago Police Department tried out in 2016 tried to get rid of its racist biases but had the opposite effect. It used a model to predict who might be most at risk of being involved in a shooting, but 56% of 20-29 year old Black men in the city appeared on the list. Despite it all, scientists are still trying to use the tool to find out when, and where, crime might occur.

Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the beforehand decision-making of government to alleviate the increasing concern about the public safety. While many efforts have been devoted to proposing various spatial-temporal forecasting techniques to explore dependence across locations and time periods, most of them follow a supervised learning manner, which limits their spatial-temporal representation ability on sparse crime data. Inspired by the recent success in self-supervised learning, this work proposes a Spatial-Temporal Hypergraph Self-Supervised Learning framework (ST-HSL) to tackle the label scarcity issue in crime prediction. Specifically, we propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space. Furthermore, we design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation by augmenting region self-discrimination. We perform extensive experiments on two real-life crime datasets. Evaluation results show that our ST-HSL significantly outperforms state-of-the-art baselines. Further analysis provides insights into the superiority of our ST-HSL method in the representation of spatial-temporal crime patterns. The implementation code is available at https://github.com/LZH-YS1998/STHSL.

The crime forecasting is an important problem as it greatly contributes to urban safety. Typically, the goal of the problem is to predict different types of crimes for each geographical region (like a neighborhood or censor tract) in the near future. Since nearby regions usually have similar socioeconomic characteristics which indicate similar crime patterns, recent state-of-the-art solutions constructed a distance-based region graph and utilized Graph Neural Network (GNN) techniques for crime forecasting, because the GNN techniques could effectively exploit the latent relationships between neighboring region nodes in the graph. However, this distance-based pre-defined graph cannot fully capture crime correlation between regions that are far from each other but share similar crime patterns. Hence, to make an accurate crime prediction, the main challenge is to learn a better graph that reveals the dependencies between regions in crime occurrences and meanwhile captures the temporal patterns from historical crime records. To address these challenges, we propose an end-to-end graph convolutional recurrent network called HAGEN with several novel designs for crime prediction. Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution Gated Recurrent Unit (DCGRU). Based on the homophily assumption of GNN, we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution. It also incorporates crime embedding to model the interdependencies between regions and crime categories. Empirical experiments and comprehensive analysis on two real-world datasets showcase the effectiveness of HAGEN.

Cameras with artificial intelligence (AI) software that the South Korean government claims can detect the likelihood of crime will be installed in Seoul within the year. The Seocho District of South Korea's capital and Electronics and Telecommunications Research Institute (ERTI), a national research institute, said they will install 3,000 cameras at the district by July. The cameras will use AI software that processes the location, time, and behaviour patterns of passersby to measure the likelihood of a crime taking place. The cameras will automatically measure whether somebody is walking normally or tailing someone. It will also detect what passersby are wearing -- such as hats, masks, or glasses -- and what they are carrying with them such as bags or dangerous objects that have a strong possibility of being used to commit a crime.

The NYPD has a new digital pattern-spotting partner to help connect crimes across precincts and find their suspect quicker. The software, designed in-house over two years and dubbed Patternizr, automates traditional police leg-work through machine learning to find patterns in crimes. "The existing process was a little bit time intensive," Evan Levine, assistant commissioner of data analytics in the NYPD, said in a podcast, Resoundingly Human. "[Patternizr] gives the analyst a good head start in finding these patterns." Levine said crime patterns are a critical part of police work and this program hones investigators, saving time and money.

When a syringe-wielding drill thief tried sticking up a Home Depot near Yankee Stadium, police figured out quickly that it wasn't a one-off. A man had also used a syringe a few weeks earlier while stealing a drill at another Home Depot 7 miles (11 kilometers) south in Manhattan. The match, though, wasn't made by an officer looking through files. It was done by pattern-recognition computer software developed by the New York Police Department. The software, dubbed Patternizr, allows crime analysts stationed in each of the department's 77 precincts to compare robberies, larcenies and thefts to hundreds of thousands of crimes logged in the NYPD's database, transforming their hunt for crime patterns with the click of a button.