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      Prediction of crime occurrence from multi-modal data using deep learning

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      PLoS ONE

      Public Library of Science

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          Abstract

          In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusion method with environmental context based on a deep neural network (DNN). Our dataset consists of data collected from various online databases of crime statistics, demographic and meteorological data, and images in Chicago, Illinois. Prior to generating training data, we select crime-related data by conducting statistical analyses. Finally, we train our DNN, which consists of the following four kinds of layers: spatial, temporal, environmental context, and joint feature representation layers. Coupled with crucial data extracted from various domains, our fusion DNN is a product of an efficient decision-making process that statistically analyzes data redundancy. Experimental performance results show that our DNN model is more accurate in predicting crime occurrence than other prediction models.

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          Most cited references 32

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          Social Change and Crime Rate Trends: A Routine Activity Approach

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            Representation learning: a review and new perspectives.

            The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
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              Inequality and Crime

               Jana Kelly (2000)
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2017
                24 April 2017
                : 12
                : 4
                Affiliations
                Dept. of Digital Media, Catholic University of Korea, Bucheon, Gyonggi-Do, Korea
                University of Texas at San Antonio, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: HBK HWK.

                • Data curation: HWK HBK.

                • Formal analysis: HWK HBK.

                • Funding acquisition: HBK.

                • Investigation: HBK HWK.

                • Methodology: HBK HWK.

                • Project administration: HBK.

                • Resources: HBK.

                • Software: HWK.

                • Supervision: HBK.

                • Validation: HBK HWK.

                • Visualization: HBK HWK.

                • Writing – original draft: HBK HWK.

                • Writing – review & editing: HBK HWK.

                Article
                PONE-D-17-00370
                10.1371/journal.pone.0176244
                5402948
                28437486
                © 2017 Kang, Kang

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 8, Tables: 5, Pages: 19
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;
                Award ID: 2015R1A2A1A10056304
                Award Recipient :
                This research was supported by Basin Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (No. 2015R1A2A1A10056304).
                Categories
                Research Article
                Social Sciences
                Sociology
                Criminology
                Crime
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Forecasting
                Medicine and Health Sciences
                Public and Occupational Health
                Traumatic Injury Risk Factors
                Violent Crime
                Social Sciences
                Sociology
                Criminology
                Crime
                Violent Crime
                Research and Analysis Methods
                Research Design
                Survey Research
                Census
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Data
                Social Sciences
                Sociology
                Criminology
                Police
                People and Places
                Population Groupings
                Professions
                Police
                People and Places
                Demography
                Social Sciences
                Economics
                Human Capital
                Economics of Training and Education
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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