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A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety

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A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety

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Salcedo-González, ML.; Suarez-Paez, JE.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE. (2020). A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety. ISPRS International Journal of Geo-Information. 9(3):1-17. https://doi.org/10.3390/ijgi9030160

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/166659

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Título: A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety
Autor: Salcedo-González, Mayra Liliana Suarez-Paez, Julio Ernesto Esteve Domingo, Manuel Gomez, J.A. Palau Salvador, Carlos Enrique
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Fecha difusión:
Resumen:
[EN] This article shows a novel geo-visualization method of dynamic spatiotemporal data that allows mobility and concentration of criminal activity to be study. The method was developed using, only and significantly, real ...[+]
Palabras clave: Smart city , Safe city , Command and Control Systems (C2S) , Command and Control Information System (C2IS) , Dynamic data geo-visualization , Crime mobility , Situational awareness , Situation understanding , Decision making improvement , Agility and efficiency improvement
Derechos de uso: Reconocimiento (by)
Fuente:
ISPRS International Journal of Geo-Information. (eissn: 2220-9964 )
DOI: 10.3390/ijgi9030160
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/ijgi9030160
Código del Proyecto:
info:eu-repo/grantAgreement/EC/H2020/740754/EU/Video analysis for Investigation of Criminal and TerrORIst Activities/
Agradecimientos:
This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-thrtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors, and the Commission cannot ...[+]
Tipo: Artículo

References

Lacinák, M., & Ristvej, J. (2017). Smart City, Safety and Security. Procedia Engineering, 192, 522-527. doi:10.1016/j.proeng.2017.06.090

Neumann, M., & Elsenbroich, C. (2016). Introduction: the societal dimensions of organized crime. Trends in Organized Crime, 20(1-2), 1-15. doi:10.1007/s12117-016-9294-z

Phillips, P., & Lee, I. (2012). Mining co-distribution patterns for large crime datasets. Expert Systems with Applications, 39(14), 11556-11563. doi:10.1016/j.eswa.2012.03.071 [+]
Lacinák, M., & Ristvej, J. (2017). Smart City, Safety and Security. Procedia Engineering, 192, 522-527. doi:10.1016/j.proeng.2017.06.090

Neumann, M., & Elsenbroich, C. (2016). Introduction: the societal dimensions of organized crime. Trends in Organized Crime, 20(1-2), 1-15. doi:10.1007/s12117-016-9294-z

Phillips, P., & Lee, I. (2012). Mining co-distribution patterns for large crime datasets. Expert Systems with Applications, 39(14), 11556-11563. doi:10.1016/j.eswa.2012.03.071

Linning, S. J. (2015). Crime seasonality and the micro-spatial patterns of property crime in Vancouver, BC and Ottawa, ON. Journal of Criminal Justice, 43(6), 544-555. doi:10.1016/j.jcrimjus.2015.05.007

Spicer, V., & Song, J. (2017). The impact of transit growth on the perception of crime. Journal of Environmental Psychology, 54, 151-159. doi:10.1016/j.jenvp.2017.09.002

Beland, L.-P., & Brent, D. A. (2018). Traffic and crime. Journal of Public Economics, 160, 96-116. doi:10.1016/j.jpubeco.2018.03.002

Newspaper of National Circulation in Colombia, E.T. Robos en Trancones en El Tintal—Bogotá—.ELTIEMPO.COM https://www.eltiempo.com/bogota/robos-en-trancones-en-el-tintal-168226

Nueva Modalidad de Atraco a Conductores en Los Trancones de Bogotá|ELESPECTADOR.COM http://www.elespectador.com/noticias/bogota/nueva-modalidad-de-atraco-conductores-en-los-trancones-de-bogota-articulo-697716

Carrillo, P. E., Lopez-Luzuriaga, A., & Malik, A. S. (2018). Pollution or crime: The effect of driving restrictions on criminal activity. Journal of Public Economics, 164, 50-69. doi:10.1016/j.jpubeco.2018.05.007

Twinam, T. (2017). Danger zone: Land use and the geography of neighborhood crime. Journal of Urban Economics, 100, 104-119. doi:10.1016/j.jue.2017.05.006

Sadler, R. C., Pizarro, J., Turchan, B., Gasteyer, S. P., & McGarrell, E. F. (2017). Exploring the spatial-temporal relationships between a community greening program and neighborhood rates of crime. Applied Geography, 83, 13-26. doi:10.1016/j.apgeog.2017.03.017

Roth, R. E., Ross, K. S., Finch, B. G., Luo, W., & MacEachren, A. M. (2013). Spatiotemporal crime analysis in U.S. law enforcement agencies: Current practices and unmet needs. Government Information Quarterly, 30(3), 226-240. doi:10.1016/j.giq.2013.02.001

Sustainable Development Goals|UNDP https://www.undp.org/content/undp/en/home/sustainable-development-goals.html

Giménez-Santana, A., Caplan, J. M., & Drawve, G. (2018). Risk Terrain Modeling and Socio-Economic Stratification: Identifying Risky Places for Violent Crime Victimization in Bogotá, Colombia. European Journal on Criminal Policy and Research, 24(4), 417-431. doi:10.1007/s10610-018-9374-5

Kim, S., Jeong, S., Woo, I., Jang, Y., Maciejewski, R., & Ebert, D. S. (2018). Data Flow Analysis and Visualization for Spatiotemporal Statistical Data without Trajectory Information. IEEE Transactions on Visualization and Computer Graphics, 24(3), 1287-1300. doi:10.1109/tvcg.2017.2666146

Kounadi, O., & Leitner, M. (2014). Spatial Information Divergence: Using Global and Local Indices to Compare Geographical Masks Applied to Crime Data. Transactions in GIS, 19(5), 737-757. doi:10.1111/tgis.12125

Khalid, S., Shoaib, F., Qian, T., Rui, Y., Bari, A. I., Sajjad, M., … Wang, J. (2017). Network Constrained Spatio-Temporal Hotspot Mapping of Crimes in Faisalabad. Applied Spatial Analysis and Policy, 11(3), 599-622. doi:10.1007/s12061-017-9230-x

Lopez-Cuevas, A., Medina-Perez, M. A., Monroy, R., Ramirez-Marquez, J. E., & Trejo, L. A. (2018). FiToViz: A Visualisation Approach for Real-Time Risk Situation Awareness. IEEE Transactions on Affective Computing, 9(3), 372-382. doi:10.1109/taffc.2017.2741478

Xue, Y., & Brown, D. E. (2006). Spatial analysis with preference specification of latent decision makers for criminal event prediction. Decision Support Systems, 41(3), 560-573. doi:10.1016/j.dss.2004.06.007

Nakaya, T., & Yano, K. (2010). Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics. Transactions in GIS, 14(3), 223-239. doi:10.1111/j.1467-9671.2010.01194.x

Anuar, N. B., & Yap, B. W. (2018). Data Visualization of Violent Crime Hotspots in Malaysia. Soft Computing in Data Science, 350-363. doi:10.1007/978-981-13-3441-2_27

Malik, A., Maciejewski, R., Towers, S., McCullough, S., & Ebert, D. S. (2014). Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1863-1872. doi:10.1109/tvcg.2014.2346926

Arietta, S. M., Efros, A. A., Ramamoorthi, R., & Agrawala, M. (2014). City Forensics: Using Visual Elements to Predict Non-Visual City Attributes. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2624-2633. doi:10.1109/tvcg.2014.2346446

Hu, Y., Wang, F., Guin, C., & Zhu, H. (2018). A spatio-temporal kernel density estimation framework for predictive crime hotspot mapping and evaluation. Applied Geography, 99, 89-97. doi:10.1016/j.apgeog.2018.08.001

Yang, D., Heaney, T., Tonon, A., Wang, L., & Cudré-Mauroux, P. (2017). CrimeTelescope: crime hotspot prediction based on urban and social media data fusion. World Wide Web, 21(5), 1323-1347. doi:10.1007/s11280-017-0515-4

ToppiReddy, H. K. R., Saini, B., & Mahajan, G. (2018). Crime Prediction & Monitoring Framework Based on Spatial Analysis. Procedia Computer Science, 132, 696-705. doi:10.1016/j.procs.2018.05.075

Devia, N., & Weber, R. (2013). Generating crime data using agent-based simulation. Computers, Environment and Urban Systems, 42, 26-41. doi:10.1016/j.compenvurbsys.2013.09.001

Kuo, P.-F., Lord, D., & Walden, T. D. (2013). Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data. Journal of Transport Geography, 30, 138-148. doi:10.1016/j.jtrangeo.2013.04.006

Camacho-Collados, M., & Liberatore, F. (2015). A Decision Support System for predictive police patrolling. Decision Support Systems, 75, 25-37. doi:10.1016/j.dss.2015.04.012

Kagawa, T., Saiki, S., & Nakamura, M. (2019). Analyzing street crimes in Kobe city using PRISM. International Journal of Web Information Systems, 15(2), 183-200. doi:10.1108/ijwis-04-2018-0032

Jentner, W., Sacha, D., Stoffel, F., Ellis, G., Zhang, L., & Keim, D. A. (2018). Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool. The Visual Computer, 34(9), 1225-1241. doi:10.1007/s00371-018-1483-0

Suarez-Paez, J., Salcedo-Gonzalez, M., Esteve, M., Gómez, J. A., Palau, C., & Pérez-Llopis, I. (2018). Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia. International Journal of Computational Intelligence Systems, 12(1), 123. doi:10.2991/ijcis.2018.25905186

Suarez-Paez, J., Salcedo-Gonzalez, M., Climente, A., Esteve, M., Gómez, J. A., Palau, C. E., & Pérez-Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information, 10(12), 365. doi:10.3390/info10120365

Esteve, M., Perez-Llopis, I., & Palau, C. E. (2013). Friendly Force Tracking COTS solution. IEEE Aerospace and Electronic Systems Magazine, 28(1), 14-21. doi:10.1109/maes.2013.6470440

Esteve, M., Perez-Llopis, I., Hernandez-Blanco, L. E., Palau, C. E., & Carvajal, F. (2007). SIMACOP: Small Units Management C4ISR System. Multimedia and Expo, 2007 IEEE International Conference on. doi:10.1109/icme.2007.4284862

OpenStreetMap http://www.openstreetmap.org

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