Crime Level Prediction using Stacked Maps with Deep Convolutional Autoencoder

Esquivel, Nicolas; Peralta, Billy; Nicolis, Orietta

doi:10.1109/chilecon47746.2019.8988082

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The results (in terms of MAE) showed that crimes decrease for smaller areas. A different approach using autoencoder architecture with convolutions for predicting the number of crimes in the town of Chicago has been proposed by [36]. The results provided a very good performance (around 97% of R 2 ) when using a small dataset (less or equal to a year).…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

et al. 2020

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“…This maximizes the likelihood of the provided forecasts. Therefore, efforts that might be useful in terms of the stated needs include: the geo-visualization of forecasts based on spatial clustering to reflect the characteristics of adjacent terrains [32][33][34][35][36]; forecast geo-visualization for sparse data [37][38][39][40]; the geo-visualization of the forecasting of criminal activities using ma-chine learning and deep learning techniques [34][35][36][39][40][41][42][43]; event forecasting using classical, improved classical, machine learning, and deep learning techniques for multivariate time series [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60]; and, finally, multivariate time series forecasting with sparse data [61][62][63][64][65].…”

Section: Work Related To the Concept Of Spatiotemporal Predictive Geo...mentioning

confidence: 99%

Spatiotemporal Predictive Geo-Visualization of Criminal Activity for Application to Real-Time Systems for Crime Deterrence, Prevention and Control

Salcedo-Gonzalez

Suarez-Paez

Esteve

et al. 2023

IJGI

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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 and continuous time horizons, according to the real events that are happening: that is, for those geographical areas, time slots, and dates that are of interest to users, with the ability to consider individual events or groups of events. This work used real data collected by the Colombian National Police (PONAL); it constitutes a tool that is especially effective when applied to Real-Time Systems for crime deterrence, prevention, and control. For its creation, the spatial and temporal correlation of the events is carried out and the following deep learning techniques are employed: CNN-1D (Convolutional Neural Network-1D), MLP (multilayer perceptron), LSTM (long short-term memory), and the classical technique of VAR (vector autoregression), due to its appropriate performance in the multi-step and multi-parallel forecasting of multivariate time series with sparse data. This tool was developed with Open-Source Software (OSS) as it is implemented in the Python programming language with the corresponding machine learning libraries. It can be implemented with any geographic information system (GIS) and used in relation to other types of activities, such as natural disasters or terrorist activities.

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