O uso da informação dos cidadãos na investigação criminal através de um processo colaborativo de inovação tecnológica para combater o roubo de pessoas em Bogotá
DOI:
https://doi.org/10.47741/17943108.522Palavras-chave:
Investigacao criminal, Desenho, denuncia de crimes pelos cidadaos, roubos, inovacao policial e tecnologicaResumo
O roubo de pessoas e um dos crimes de maior impacto nas questoes de seguranca de Bogota, com uma participacao nacional de aproximadamente 38 %. Este crime levado ao conhecimento das autoridades e denominado pelos academicos como crime registrado ou denunciado e e utilizado pela instituicao policial para diversos fins, principalmente para investigacao criminal, mas com resultados ineficientes na identificacao dos autores. Portanto, o tipo de pesquisa e qualitativo e visa vincular os cidadaos por meio de um processo colaborativo de inovacao tecnologica, com o objetivo de coletar, tratar e analizar informacoes denunciadas e nao denunciadas (crimes ocultos) de maneira oportuna, anonima e eficiente, priorizando tecnologias disruptivas. para o projeto. A metodologia utilizada comeca com a fase de descoberta, identificando os principais atores e construindo historias de usuarios. Depois, na fase de compreensao, e proposta a proposta de valor atraves de uma hipotese que e validada num processo de experimentacao e, finalmente, na fase de construcao, e realizada uma analise de vigilancia tecnologica e e proposta a proposta de um sistema colaborativo entre cidadaos. e a policia com foco tecnologico. Os resultados baseiam-se na identificacao e priorizacao de cinco tecnologias, dois atores, tres variaveis e aplicacao de seis prototipos de baixa e media fidelidade, bem como na aceitacao dos cidadaos na recolha e partilha de informacao atempada em 87 %, esta informacao centra-se em videos, audios, fotos e localizacao com 55 %. Por outro lado, com a entrada em funcionamento do sistema colaborativo, os investigadores indicam que otimizaria a investigacao em 50 % atraves da identificacao atempada dos autores. Quanto a conclusao, a informacao analisada e obtida a partir dos resultados permite-nos chegar numa primeira fase a validacao da hipotese estabelecida, mas ao mesmo tempo, a importancia de incluir
metodologias como a Dinamica de Sistemas que permite a analise sistemica da informacao estabelecida por outros atores e seu impacto no sistema colaborativo proposto.
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Referências
Arenas, R. H. (2013). Satellite technology innovation for crime prevention in the State of Mexico. Journal del Instituto de Ciencias Jurídicas de Puebla, 7(31), 7-27.
Aunger, R. (2010). Types of technology. Technological Forecasting and Social Change, 77(5), 762-782. https://doi.org/10.1016/j.techfore.2010.01.008
Azhar, A. (2021). Exponential, 1. Order and Chaos in an Age of Accelerating Technology: Random House. https://shre.ink/rNx1
Becker, G. S. (1968). Crime and Punishment: An economic approach. Journal of Political Economy, 76(2), 169.- 217. https://doi.org/10.1086/259394
Bland, D. J., & Osterwalder, A. (2019). Testing Business Ideas. A Field Guide for Rapid Experimentation (A. Smith, & T. Papadakos, eds.). Wiley.
Boyd, D., & Goldenberg, J. (2014). Inside the box: A proven system of creativity for breakthrough results.
Caceres, O. R. (2017). El sistema de informacion e inteligencia Plataforma Mexico. URVIO - Revista Latinoamericana de Estudios de Seguridad, 21, 175. https://doi.org/10.17141/urvio.21.2017.2916
Caminha, C., Furtado, V., Pequeno, T. H. C., Ponte, C., Melo, H. P. M., Oliveira, E. A., & Andrade, J. S. (2017). Human mobility in large cities as a proxy for crime. PLOS ONE, 12(2), e0171609. https://doi.org/10.1371/journal.pone.0171609
Ceballos-Espinoza, F. (2021). From classical criminology to modern criminology: Multifactorial criminal investigation in the digital age. Formación y Desarrollo Policial, 3(1), 59-85.
Cheng, T., & Williams, D. (2012). Space-time analysis of crime patterns in central London. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, 47-52. https://doi.org/10.5194/isprsarchives-XXXIX-B2-47-2012
Chun-Young, C., Lin-Chien, C., Yo-Hao, C., En-Chun, K., & Yuh-Shyan, H. (2019). A smart public security strategy: The new Taipei city technology defense plan. Procedia Computer Science, 159, 1715-1719. https://doi.org/10.1016/j.procs.2019.09.342
De Guzman, J. B., De Guzman, R. C. C., & Ado, R. G. (2014). Mobile Emergency response application using geolocation for command centers. International Journal of Computer and Communication Engineering, 3(4), 235-238. https://doi.org/10.7763/ijcce.2014.v3.327
Demestichas, K., Alexakis, T., Peppes, N., Remoundou, K., Loumiotis, I., Muller, W., & Avgerinakis, K. (2020). Prediction and visual intelligence platform for detection of irregularities and abnormal behaviour. Detection Machine Learning for Trend and Weak Signal Detection in Social Networks and Social Media, 2606, 25-30.
Diamandis, P. H., & Kotler, S. (2020). The future is faster than you think: How converging technologies are transforming business, industries, and our lives. Simon & Schuster. https://shre.ink/2vV4
Elnas, J. B., Duran, E. C., Mayol, J. L. E., & Lavilles, R. Q. (2015). Location-based reporting and mapping of crimes using Google Maps. Lecture Notes on Software Engineering, 3(1), 44-48. https://doi.org/10.7763/lnse.2015.v3.163
Feng, M., Zheng, J., Ren, J., Hussain, A., Li, X., Xi, Y., & Liu, Q. (2019). Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE Access, 7, 106111.-106123. https://doi.org/10.1109/ACCESS.2019.2930410
Gaver, B., Dunne, T., & Pacenti, E. (1999). Design: Cultural probes. Interactions, 6(1), 21-29. https://doi.org/10.1145/291224.291235
Crime Information Group (2022). Thefts from persons in the form of robbery and arrests by the National Police, years 2007 to 2022. 2022.
Kamthan, P., & Shahmir, N. (2017). Effective user stories are affective. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10586 LNCS, 605-611. https://doi.org/10.1007/978-3-319-67585-5_59
Keatley, D. A., Arntfield, M., Gill, P., Clare, J., Oatley, G., Bouhana, N., & Clarke, D. D. (2021). Behaviour tracking: Using geospatial and behaviour sequence analysis to map crime. Security Journal, 34(1), 184-201. https://doi.org/10.1057/s41284-019-00216-3
Kiran, C., Suresh, S., & Suresh, S. (2022). Intelligent vehicle license plate recognition by deploying deep learning model for smart cities. International Journal of Mechanical Engineering, 7(1), 6739-6744.
Lin, Y. L., Yen, M. F., & Yu, L. C. (2018). Grid-based crime prediction using geographical features. International Journal of Geo-Information, 7(8), 1-16. https://doi.org/10.3390/ijgi7080298
Lira, A. L., & Fuentes, C. (2021). Artificial Intelligence models for crime prediction in urban spaces. Machine Learning and Applications: An International Journal, 8(1), 1-13. https://doi.org/10.5121/mlaij.2021.8101
Lopez, J., & Carmina, L. (2020). Citizen security and technology: Use, planning and regulation of video surveillance in Latin America. Revista de Investigación en Derecho, Criminología y Consultoría Jurídica, 27, 1-15.
Martin, B., & Hanington, B. (2012). Universal methods of design: 100 ways to research complex problems, develop innovative ideas, and design effective solutions. Rockport Publishers.
Mondal, S., Singh, D., & Kumar, R. (2022). Crime hotspot detection using statistical and geospatial methods: A case study of Pune City, Maharashtra, India. GeoJournal, 1-18. https://doi.org/10.1007/s10708-022-10573-z
Muller, W., Pallmer, D., Muhlenberg, D., Loumiotis, I., Remoundou, K., Kosmides, P., & Demestichas, K. (2020). Machine learning for discovery analytics to support criminal investigations. SPIE-Intl Soc Optical Eng. https://doi.org/10.1117/12.2557541
O’Connor, G. C., Leifer, R., Paulson, A. S., & Peters, L. S. (2008). Grabbing lightning: Building a capability for breakthrough innovation (vol. 1.). Jossey-Bass.
Olaya, C. (2013). More engineering and less science please. In C. Olaya (ed.), XI Latin American Congress of System Dynamics Instituto Tecnológico y de Estudios Superiores (pp. 1-6). Universidad de los Andes.
Patil, G. P. (2010). Digital governance, hotspot geoinformatics, and sustainable development: A preface. Environmental and Ecological Statistics, 17(2), 133-147. https://doi.org/10.1007/s10651-010-0144-x
Perez, F. J., Garrido, V. J., Garcia, A., Zambrano, M., Kozik, R., Choraś, M., Muhlenberg, D., Pallmer, D., & Muller, W. (2021). Multimedia analysis platform for crime prevention and investigation: Results of MAGNETO project. Multimedia Tools and Applications, 80(15), 23681-23700. https://doi.org/10.1007/s11042-020-10206-y
Pokharel, P., & Vaidya, P. (2020). “A Study of Userb Story in Practice,” 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain, 2020, pp. 1-5, https://doi.org/10.1109/ICDABI51230.2020.9325670
National Police (2023). Crime statistics. https://www.policia.gov.co/grupo-informacion-criminalidad/estadistica-delictiva
Pramanik, M. I., Lau, R. Y. K., Yue, W. T., Ye, Y., & Li, C. (2017). Big data analytics for security and criminal investigations. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(4), 1-19. https://doi.org/10.1002/widm.1208
Rahmani, A. M., Azhir, E., Ali, S., Mohammadi, M., Ahmed, O. H., Ghafour, M. Y., Ahmed, S.H., & Hosseinzadeh, M. (2021). Artificial intelligence approaches and mechanisms for big data analytics: A systematic study. PeerJ Computer Science, 7, 1-28. https://doi.org/10.7717/peerj-cs.488
Rawashdeh, M., Al Zamil, M. G. H., Samarah, S. M., Obaidat, M., & Masud, M. (2021). IoT- based service migration for connected communities. Computers and Electrical Engineering, 96. https://doi.org/10.1016/j.compeleceng.2021.107530
Razzouk, R., & Shute, V. (2012). What is design thinking and why is it important? Review of Educational Research, 82(3), 330.-348. https://doi.org/10.3102/0034654312457429
Restrepo, J. (2008). Fifty years of criminality recorded by the National Police. Revista Criminalidad, 50(1), 27-36.
Rodríguez, J. D., Mejía, D., Caro, L. del P., Romero, M., & Campos, F. (2018). Implications of the process of integration of administrative crime records between the SPOA of the Attorney General’s Office and the SIEDCO of the National Police of Colombia, and the implementation of the “!ADenunciar!” application on crime figures. Revista Criminalidad, 60(3), 9-27. https://revistacriminalidad.policia.gov.co:8000/index.php/revcriminalidad/article/view/28/23
Schilling, M. A. (2020). Strategic management of technological innovation (6th ed., vol. 1). McGraw-Hill Education.
Tundis, A., Kaleem, H., & Muhlhauser, M. (2020). Detecting and tracking criminals in the real world through an IoT-based system. Sensors, 20(13), 1-27. https://doi.org/10.3390/s20133795
Tundis, A., Uzair, M., & Muhlhauser, M. (2021). An IoTbased context-aware model for danger situations detection. Computers and Electrical Engineering, 96. https://doi.org/10.1016/j.compeleceng.2021.107571
Valderrama-Cumbe, J. M., Arboleda-Cardenas, Y. C., Criollo-Rey, P. A., & Ospina-Baena, J.H. (2021). Recurrence as a tool in the judge’s assessment when imposing a preventive detention measure in a prison in Colombia. Revista Criminalidad, 63(2), 53-66. https://doi.org/10.47741/17943108.316
Van der Meij, M. G., Kupper, F., Beers, P. J., & Broerse, J. E. W. (2016). Hybrid e-learning tool TransLearning: Video storytelling to foster vicarious learning within multi-stakeholder collaboration networks. International Journal of Lifelong Education, 35(4), 413-429. https://doi.org/10.1080/02601370.2016.1197331
Villalobos, H. (2020). Technological development in policing: A recipe for success in crime prevention. Journal of International Relations, Strategy and Security, 15(1), 79-97. https://doi.org/10.18359/ries.4243
Wang, D., Ding, W., Lo, H., Stepinski, T., Salazar, J., & Morabito, M. (2013). Crime hotspot mapping using the crime related factors - A spatial data mining approach. Applied Intelligence, 39(4), 772-781. https://doi.org/10.1007/s10489-012-0400-x
Yin, H. (2022). Public security video image detection system construction platform in cloud computing environment. Computational Intelligence and Neuroscience, 2022, 1- 13. https://doi.org/10.1155/2022/4113803
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