(Looking) Back to the Future: using space-time patterns to better predict the location of street crime

Crime analysts attempt to identify regularities in police recorded crime data with a central view of disrupting the patterns found. One common method for doing so is hotspot mapping, focusing attention on spatial clustering as a route to crime reduction (Chainey & Ratcliffe, 2005; Clarke & Eck, 2003). Despite the widespread use of this analytical technique, evaluation tools to assess its ability to accurately predict spatial patterns have only recently become available to practitioners (Chainey, Tompson, & Uhlig, 2008). Crucially, none has examined this issue from a spatio-temporal standpoint. Given that the organisational nature of policing agencies is shift based, it is common-sensical to understand crime problems at this temporal sensitivity, so there is an opportunity for resources to be deployed swiftly in a manner that optimises prevention and detection. This paper tests whether hotspot forecasts can be enhanced when time-of-day information is incorporated into the analysis. Using street crime data, and employing an evaluative tool called the Predictive Accuracy Index (PAI), we found that the predictive accuracy can be enhanced for particular temporal shifts, and this is primarily influenced by the degree of spatial clustering present. Interestingly, when hotspots shrank (in comparison with the all-day hotspots), they became more concentrated, and subsequently more predictable. This is meaningful in practice; for if crime is more predictable during specific timeframes, then response resources can be used intelligently to reduce victimisation

Open source environment to define constraints in route planning for GIS-T

Route planning for transportation systems is strongly related to shortest path algorithms, an optimization problem extensively studied in the literature. To find the shortest path in a network one usually assigns weights to each branch to represent the difficulty of taking such branch. The weights construct a linear preference function ordering the variety of alternatives from the most to the least attractive.Postprint (published version

Immersive and non immersive 3D virtual city: decision support tool for urban sustainability

Sustainable urban planning decisions must not only consider the physical structure of the urban development but the economic, social and environmental factors. Due to the prolonged times scales of major urban development projects the current and future impacts of any decision made must be fully understood. Many key project decisions are made early in the decision making process with decision makers later seeking agreement for proposals once the key decisions have already been made, leaving many stakeholders, especially the general public, feeling marginalised by the process. Many decision support tools have been developed to aid in the decision making process, however many of these are expert orientated, fail to fully address spatial and temporal issues and do not reflect the interconnectivity of the separate domains and their indicators. This paper outlines a platform that combines computer game techniques, modelling of economic, social and environmental indicators to provide an interface that presents a 3D interactive virtual city with sustainability information overlain. Creating a virtual 3D urban area using the latest video game techniques ensures: real-time rendering of the 3D graphics; exploitation of novel techniques of how complex multivariate data is presented to the user; immersion in the 3D urban development, via first person navigation, exploration and manipulation of the environment with consequences updated in real-time. The use of visualisation techniques begins to remove sustainability assessment’s reliance on the existing expert systems which are largely inaccessible to many of the stakeholder groups, especially the general public

The spatial-temporal prediction of various crime types in Houston, TX based on hot-spot techniques

A series of hotspot mapping theories and methods have been proposed to predict where and when a crime will happen. Each method has its strengths and weaknesses. In addition, the predictive accuracy of each hotspot method varies depending on the study area, crime type, parameter settings of each method, etc. The predictive accuracy of hotspot methods can be quantified by three measures, which include the hit rate, the predictive accuracy index (PAI), and the recapture rate index (RRI). This thesis research applied eight hotspot mapping techniques from the crime analysis field to predict crime hotspot patterns. In addition, these hotspot methods were compared and evaluated in order to possibly find a single best method that outperforms all other methods based on the three predictive accuracy measures. Identifying the single best method is carried out for all Part1 Crimes combined and individually, for five of the nine Part 1 Crime. In addition to the spatial analysis, a spatial–temporal analysis of the same crime dataset was conducted to investigate the distribution of crime clusters from both the space and time dimensions. The reported crime data analyzed in this study are from the city of Houston, TX, from January 2011 to December 2012. The results show that the predictive accuracy is affected by both the hotspot mapping method and the crime type, although the crime type has a more moderate effect. Considering the use of the three predictive accuracy measures, the kernel density estimation could be identified as the method which could most accurately predict the overall Part1 Crimes for the city of Houston. The nearest neighbor hierarchical clustering and kernel density estimation could be identified as the methods which are best at predicting each of the five crime types examined based on PAI and RRI, respectively. Also, spatial-temporal analysis indicates that more crimes occurred during September to December, 2011 around the center and in the southwestern part of the city of Houston, TX

Recent Computer Technologies for an Innovative Cartographic Language: Espon Cartographic Language, Interim Report 1

Review of the state of the art in recent computer technologies and related cartographic software in support of ensuring an innovative cartographic language. The service provider is asked to review the state of the art in recent computer technologies and related cartographic software development in support of ensuring an innovative cartographic language. The service provider shall, based on this review, present options for modernising the ESPON Cartographic Language. The fulfilment of this task should not be limited only to more “traditional” cartography, but explore new options for adding new cartographic concepts, types of illustrations and computer animated presentations, that could support the presentation of the geography of policy orientations and forward-looking territorial evidence to the European territorial policy arena. The review shall lead to recommendations of cartographic technologies and techniques to consider in a modernised ESPON Cartographic Language. It shall be used as input for recommendations on new cartographic elements to consider in a modernised ESPON Cartographic Language under task 4 and 5. Three dimensions for an Innovative cartographic language will be explored in three directions:- Former Semiotic language combined with new technologies- Usability of the produced representations - Focus on added dimensions like interactivity, animation, multimedia, 3D, etc

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

[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 data of Santiago de Cali (Colombia), collected by the Colombian National Police (PONAL). This method constitutes a tool that allows criminal influx to be analyzed by concentration, zone, time slot and date. In addition to the field experience of police commanders, it allows patterns of criminal activity to be detected, thereby enabling a better distribution and management of police resources allocated to crime deterrence, prevention and control. Additionally, it may be applied to the concepts of safe city and smart city of the PONAL within the architecture of Command and Control System (C2S) of Command and Control Centers for Public Safety. Furthermore, it contributes to a better situational awareness and improves the future projection, agility, efficiency and decision-making processes of police officers, which are all essential for fulfillment of police missions against crime. Finally, this was developed using an open source software, it can be adapted to any other city, be used with real-time data and be implemented, if necessary, with the geographic software of any other C2S.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 be held responsible for any use which may be made of the information contained therein. The authors would like to thank Colombian National Police and its Office of Telematics for their support on development of this project.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/ijgi9030160S11793Lacinák, M., & Ristvej, J. (2017). Smart City, Safety and Security. Procedia Engineering, 192, 522-527. doi:10.1016/j.proeng.2017.06.090Neumann, 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-zPhillips, 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.2012.03.071Linning, 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.2015.05.007Spicer, 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.2017.09.002Beland, L.-P., & Brent, D. A. (2018). Traffic and crime. Journal of Public Economics, 160, 96-116. doi:10.1016/j.jpubeco.2018.03.002Newspaper 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-168226Nueva 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-697716Carrillo, 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.2018.05.007Twinam, T. (2017). Danger zone: Land use and the geography of neighborhood crime. Journal of Urban Economics, 100, 104-119. doi:10.1016/j.jue.2017.05.006Sadler, 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.2017.03.017Roth, 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.2013.02.001Sustainable Development Goals|UNDP https://www.undp.org/content/undp/en/home/sustainable-development-goals.htmlGiménez-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/s10610-018-9374-5Kim, 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.2017.2666146Kounadi, 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.12125Khalid, 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-xLopez-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.2017.2741478Xue, 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.2004.06.007Nakaya, 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.xAnuar, 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_27Malik, 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.2014.2346926Arietta, 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.2014.2346446Hu, 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.2018.08.001Yang, 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/s11280-017-0515-4ToppiReddy, 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.2018.05.075Devia, N., & Weber, R. (2013). Generating crime data using agent-based simulation. Computers, Environment and Urban Systems, 42, 26-41. doi:10.1016/j.compenvurbsys.2013.09.001Kuo, 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.2013.04.006Camacho-Collados, M., & Liberatore, F. (2015). A Decision Support System for predictive police patrolling. Decision Support Systems, 75, 25-37. doi:10.1016/j.dss.2015.04.012Kagawa, 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-04-2018-0032Jentner, 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/s00371-018-1483-0Suarez-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.2018.25905186Suarez-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/info10120365Esteve, 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.2013.6470440Esteve, 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.2007.4284862OpenStreetMap http://www.openstreetmap.or

Spatio-Temporal Analysis of Crime Incidents for Forensic Investigation

Crime analysis and mapping has been routinely employed to gather intelligence which informs security efforts and forensic investigations. Traditionally, geographic information systems in the form of third-party mapping applications are used for analysis of crime data but are often expensive and lack flexibility, transparency, or efficiency in uncovering associations and relationships in crime. Each crime incident and article of evidence within that incident has an associated spatial and temporal component which may yield significant and relevant information to the case. Wide variations exist in the techniques that departments use and commonly spatial and temporal components of crime are evaluated independently, if at all. Thus, there is a critical need to develop and implement spatio-temporal investigative strategies so police agencies can gain a foundational understanding of crime occurrence within their jurisdiction, develop strategic action for disruption and resolution of crime, conduct more informed investigations, better utilize resources, and provide an overall more effective service. The purpose of this project was to provide foundational knowledge to the investigative and security communities and demonstrate the utility of empirical spatio-temporal methods for the assessment and interpretation of crime incidents. Two software packages were developed as an open source (R) solution to expand current techniques and provide an implementable spatio-temporal methodology for crime analysis. Additionally, an actionable method for near repeat analysis was developed. Firstly, the premise of the near repeat phenomenon was evaluated across crime types and cities to discern optimal parameters for spatial and temporal bandwidths. Using these parameters, a method for identifying near repeat series was developed which draws inter-incident linkages given the spatio-temporal clustering of the incidents. Resultant crime networks and maps provide insight regarding near repeat crime incidents within the landscape of their jurisdiction for targeted investigation. Finally, a new approach to the geographic profiling problem was developed which assesses and integrates the travel environment of road networks, beliefs and assumptions formed through the course of the investigation process about the perpetrator, and information derived from the analysis of evidence. Each piece of information is evaluated in conjunction with spatio-temporal routing functions and then used to update prior beliefs about the anchor point of the perpetrator. Adopting spatio-temporal methodologies for the investigation of crime offers a new framework for forensic operations in the investigation of crime. Systematic consideration about the value and implications of the relationship between space, time, and crime was shown to provide insight regarding crime. In a forward-looking sense this work shows that the interpretation of crime within a spatio-temporal context can provide insight into crime occurrence, linkage of crime incidents, and investigations of those incidents