Prediction of Suspect Location Based on Spatiotemporal Semantics

Duan, Lian; Ye, Xinyue; Hu, Tao; Zhu, Xinyan

doi:10.3390/ijgi6070185

Cited by 12 publications

(11 citation statements)

References 52 publications

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“…For example, while we may know on which street a rape or a drug overdose has occurred, it is far harder to understand that event in terms of the knowledge that can be transferred elsewhere. Advances to more traditional crime data analysis include both big data [ 10 , 30 ] and primary data solutions using new field methods. In this paper, we leverage aspects from both of these advances [ 74 , 75 ].…”

Section: Related Workmentioning

confidence: 99%

Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach

et al. 2021

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The complex interrelationship between the built environment and social problems is often described but frequently lacks the data and analytical framework to explore the potential of such a relationship in different applications. We address this gap using a machine learning (ML) approach to study whether street-level built environment visuals can be used to classify locations with high-crime and lower-crime activities. For training the ML model, spatialized expert narratives are used to label different locations. Semantic categories (e.g., road, sky, greenery, etc.) are extracted from Google Street View (GSV) images of those locations through a deep learning image segmentation algorithm. From these, local visual representatives are generated and used to train the classification model. The model is applied to two cities in the U.S. to predict the locations as being linked to high crime. Results show our model can predict high- and lower-crime areas with high accuracies (above 98% and 95% in first and second test cities, accordingly).

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Section: Related Workmentioning

confidence: 99%

Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach

et al. 2021

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“…The matrix

boldX = false({boldX}_{1}, {boldX}_{2}, ⋯, {boldX}_{m} false)

can be described as a mathematical representation of the MTS whose samples are usually denoted as

x_{i} false(t false) false(i = 1, 2, ⋯, n; t = 1, 2, ⋯, m false)

, where

n

denotes the number of variables and

m

denotes the total length of time. Making prediction on MTS plays a crucial role in the real world, especially the potential applications, such as traffic predictions, 2,3 crime predictions, 4,5 human action recognition, 6,7 and even COVID‐19 pandemics 8,9 . For example, forecasting traffic conditions can help decision‐makers better control traffic flow and thus reduce congestion 10 ; predicting stock index can help professional researchers and investors reap profits 11 …”

Section: Introductionmentioning

confidence: 99%

A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction

et al. 2021

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Multivariate time series (MTS) prediction aims at predicting future time series by extracting multiple forms of dependencies of past time series. Traditional prediction methods and deep learning‐based prediction methods focus on extracting the dynamic relationships of certain aspects of MTS, especially the temporal characteristics, often neglecting the spatial and temporal dynamic correlations of MTS. Inspired by convolution neural network (CNN) and attention mechanism, this paper proposes a convolution LSTM network model based on MTS prediction with two‐stage attention. Specifically, we first propose a new MTS preprocessing method to perform convolution operations better. Then convolution layer extracts spatial correlation of MTS and LSTM model extracts temporal correlation. It is worth mentioning that the combination of attention mechanism and LSTM can effectively solve the problem of insufficient time dependency in MTS prediction. In addition, dual‐stage attention mechanism can effectively eliminate irrelevant information, select the relevant exogenous sequence, give it higher weight, and increase the past value of the target sequence to further eliminate irrelevant information. Finally, the MTS spatio‐temporal correlation is extracted to improve the prediction accuracy, and the model is interpreted. Experimental results show that the model has broad application prospects. Experiments based on typical datasets of finance, environment, and energy determine the optimal window size and hidden size of the prediction, and demonstrate that the model achieves the state‐of‐the‐art effect compared to the other four deep learning models. On top of that, the model is not only suitable for single‐step prediction of MTS, but also suitable for multistep prediction of time step in a certain range.

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“…this study proposes combining time and space features along with new geographic features obtained from the Google Place API to improve predictive performance. In terms of algorithms, various machine-learning models, such as Naïve Bayes [15,16], Ensemble [17], or Deep Learning Structure [18] have been used for crime prediction, but Deep Neural Networks (DNN) provided better results in our previous experiments. This study uses DNN because it reflects representation learning and has been used in crosslingual transfer [19], speech recognition [20][21][22][23], image recognition [24][25][26][27], sentiment analysis [28][29][30][31][32], and biomedical [33].…”

Section: Data and Analysis Toolsmentioning

confidence: 89%

Grid-Based Crime Prediction Using Geographical Features

Lin

Yen

2018

IJGI

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Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design's baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

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Prediction of Suspect Location Based on Spatiotemporal Semantics

Cited by 12 publications

References 52 publications

Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach

Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach

A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction

Grid-Based Crime Prediction Using Geographical Features

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