Query: crime forecasting

title: A systematic review on spatial crime forecasting
abstract: Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools. The goal of this paper is to provide an overview and evaluation of the state of the art in spatial crime forecasting focusing on study design and technical aspects.  We follow the PRISMA guidelines for reporting this systematic literature review and we analyse 32 papers from 2000 to 2018 that were selected from 786 papers that entered the screening phase and a total of 193 papers that went through the eligibility phase. The eligibility phase included several criteria that were grouped into: (a) the publication type, (b) relevance to research scope, and (c) study characteristics.  The most predominant type of forecasting inference is the hotspots (i.e. binary classification) method. Traditional machine learning methods were mostly used, but also kernel density estimation based approaches, and less frequently point process and deep learning approaches. The top measures of evaluation performance are the Prediction Accuracy, followed by the Prediction Accuracy Index, and the F1 Score. Finally, the most common validation approach was the train test split while other approaches include the cross validation, the leave one out, and the rolling horizon.  Current studies often lack a clear reporting of study experiments, feature engineering procedures, and are using inconsistent terminology to address similar problems.  There is a remarkable growth in spatial crime forecasting studies as a result of interdisciplinary technical work done by scholars of various backgrounds. These studies address the societal need to understand and combat crime as well as the law enforcement interest in almost real time prediction.  Although we identified several opportunities and strengths there are also some weaknesses and threats for which we provide suggestions. Future studies should not neglect the juxtaposition of (existing) algorithms, of which the number is constantly increasing (we enlisted 66) To allow comparison and reproducibility of studies we outline the need for a protocol or standardization of spatial forecasting approaches and suggest the reporting of a study's key data items.

title: CSAN: A neural network benchmark model for crime forecasting in spatio temporal scale
abstract: Understanding the evolving discipline of crime situations is a long standing but significant problem. Former methods prefer the stochastic modeling of the crime phenomenon in physics or statistical equations, which are elegant in theoretical explanations but less efficient in real applications. Recently, some data driven models, especially neural network models, are illustrating promising performance in capturing dynamics of the complex phenomenon, and available massive dataset enables the task beneficial information utilization. However, there exist several difficulties in regional crime situation awareness, including the high dimensionality, the intractable correlations as well as information redundancies in spatio temporal dataset. To achieve efficient information processing and disentangle relationships from a recent crime dataset of fifteen years, we construct the crime situation awareness network (CSAN) as a new benchmark forecasting model via integrating structures of variational auto encoders and context based sequence generative neural network. Final experiments demonstrate that CSAN mostly outperforms other commonly used spatio temporal forecasting algorithms, such as Conv LSTM, in regional multi type crime frequency prediction.

title: Deep Learning for Real Time Crime Forecasting and its Ternarization
abstract: Real time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this work, we first present a proper representation of crime data. We then adapt the spatial temporal residual network on the well represented data to predict the distribution of crime in Los Angeles at the scale of hours in neighborhood sized parcels. These experiments as well as comparisons with several existing approaches to prediction demonstrate the superiority of the proposed model in terms of accuracy. Finally, we present a ternarization technique to address the resource consumption issue for its deployment in real world. This work is an extension of our short conference proceeding paper [Wang et al,

title: Artificial Intelligence Techniques and External Factors used in Crime Forecasting in Violence and Property: A Review
abstract: Crime forecasting is beneficial in providing useful information to authorities in planning effective crime prevention measures. The two types of analysis used in crime forecasting are univariate and multivariate. Comparatively, multivariate analysis provides better forecasting accuracy because of its ability to discover crime patterns not previously seen. Crime is strongly influenced by several external factors, including economic, social and demographic. Hence, an analysis is needed to identify and select relevant factors that influence crime and can later be used to improve forecasting accuracy. Neighborhood Component Analysis (NCA) is a reliable form of analysis for identifying significant relationships between factors and crime data. Several model types have been introduced in crime forecasting, including statistical and artificial intelligence models. Recently, the artificial intelligence model has come into favour because of its ability to handle nonlinearity patterns in crime data well. Within the artificial intelligence model, Gradient Tree Boosting (GTB) shows good performance as it produces a robust and reliable forecast result. GTB uses least square function as a loss function for error fitting during training. Findings show that, in addition to using least square function, implementing other standard mathematical functions that fit to the crime data increases forecasting accuracy. In other cases, both NCA and GTB are sensitive to parameters input. Dragonfly Algorithm (DA) is a promising, nature inspired metaheuristic algorithm that is capable of solving such problems.

title: Comparative analysis of time series model and machine testing systems for crime forecasting
abstract: Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

title: Flag and boost theories for hot spot forecasting: An application of NIJ's Real Time Crime forecasting algorithm using Colorado Springs crime data
abstract: By operationalizing two theoretical frameworks, we forecast crime hot spots in Colorado Springs. First, we use a population heterogeneity (flag) framework to find places where the hot spot forecasting is consistently successful over months. Second, we use a state dependence (boost) framework of the number of crimes in the periods prior to the forecasted month. This algorithm is implemented in Microsoft Excel(r) making it simple to apply and completely transparent. Results shows high accuracy and high efficiency in hot spot forecasting, even if the data set and the type of crime we used in this study were different from what the original algorithm was based on. Results imply that the underlying mechanisms of serious and non serious crime for forecasting are different from each other. We also find that the spatial patterns of forecasted hot spots are different between calls for service and crime event. Future research should consider both flag and boost theories in hot spot forecasting.

title: The data based methodology for crime forecasting
abstract: The project's main goal was to create an analytical service platform for forecasting crime, which can strengthen the ability to prevent and combat crime based on verifiable forecasts and optimize the use of available Police forces and resources. As in forecasting criminal events over time, future events are associated with a sequence of historical ones by time series of observational irregularly spaced data and other exogenous variables affecting crime, especially various factors related to the entire environment: natural, social, economic, legal, and political, to which the forecast is to affect crime level and structure. The development of sufficient crime threat data based prediction models may require an appropriate combination of criminal event history, determining the risk level, and geographic data characterizing the areas for which the threat is predicted. The article presents the data based methodology for crime forecasting system and exemplary operating results. The final evaluation was done to verify the forecasts obtained based on actual data for selected categories of crime, considering the optimization of the use of forces and resources and identify proposals for changes in the criminal policy.

title: Analysis of Crime Rates in Rizal Province using Crime Forecasting Models
abstract: Crime is one of the major problems of countries all over the world, and the Philippines is no exception. Crime prediction and prevention strategies are vital for police forces to face inevitable increases in the crime rate as a side effect of the growth of the urban population. This paper focuses on determining the significant predictors using Pearson Correlation and prediction of crime and crime rates in Rizal Province. Different time series forecasting models were compared and evaluated to determine the best forecasting model in the crime datasets. Datasets include the disposition of troops, station raw materials/resources and historical data of crime statistics from 2013 to 2017 which were extracted from fourteen (14) municipal police stations of Rizal Province. MAPE was used to determine the accuracy of each model. In testing the normality of the gathered data, the Shapiro test was used, if normal, t test will be employed otherwise the Wilcoxon test will be used in testing the statistical significance of the prediction. The prediction results can be useful for the police stations to identify problematic regions to patrol and it can be valuable information in decision making specifically in the disposition of patrol manpower and allocation of station resources for a given urban area to suppress crime.

title: Interpretable Hawkes Process Spatial Crime Forecasting with TV Regularization
abstract: Interpretable models for criminal justice forecasting are desirable due to the high stakes nature of the application. While interpretable models have been developed for individual level forecasts of recidivism, interpretable models are lacking for the application of space time crime hotspot forecasting. Here we introduce an interpretable Hawkes process model of crime that allows forecasts to capture near repeat effects and spatial heterogeneity while being consumable in the form of easy to read score cards. For this purpose we employ penalized likelihood estimation of the point process with a total variation regularization that enforces the triggering kernel to be piece wise constant. We derive an efficient expectation maximization algorithm coupled with forward backward splitting for the TV constraint to estimate the model. We apply our methodology to synthetic data and space time crime data from Indianapolis. The TV Hawkes process achieves similar accuracy to standard Hawkes process models of crime while increasing interpretability and transparency.

title: Adaptive Matrix Model for a Crime Forecasting Task
abstract: In the police activities of any country, there are two major directions: identifying signs of crime preparation and preventing its commission (prediction) as well as crime prevention by eliminating the conditions for its commission (prevention) At the same time, various theories are explaining that the place and time of the crime occur at random, and when certain conditions are met for its commission, which depend on the type of crime and various factors of the objective side of the crime (place, time, mechanism of commission) and object of encroachment. Currently, a huge number of criminal events has been accumulated in the databases of law enforcement agencies over a long time (more than 20 years) These events occurred at a specific place (geolocation) and at a specific date and time. Considering a certain residential area as a closed system with processes taking place according to some laws, it can be assumed that criminal events also occur according to some hidden patterns. In modern science, some technologies enable identifying such hidden patterns in large data arrays Data Mining. Identification of hidden patterns allows performing the function of predicting the commission of new criminal events in space and time. This article considers one of the technology approaches Adaptive Matrix Model. As a result, we obtained a fairly simple and small model for crime prediction that produced promising experimental results. Ways to further improve accuracy are suggested.