Ítem
Acceso Abierto

Crimen y factores económicos en Medellín: un estudio de predicción con Machine Learning

Mostrar el registro sencillo de la publicación
dc.contributor.advisorGarcía Suaza, Andrés Felipe
dc.creatorArdila Chávarro, María Camila
dc.creator.degreeMagíster en Economía
dc.creator.degreeLevelMaestría
dc.creator.degreetypeFull time
dc.date.accessioned2023-12-13T17:10:28Z
dc.date.available2023-12-13T17:10:28Z
dc.date.created2023-12-12
dc.descriptionEl objetivo de este trabajo es estudiar los patrones espaciales de delitos a través de la implementación de técnicas de machine learning, para predecir la probabilidad de ocurrencia de diversos tipos de crímenes a nivel anual con diferencias espaciales en Medellín, Colombia, a partir de datos históricos y sociodemográficos.
dc.description.abstractCriminal activity negatively affects people's quality of life and economic progress. Given the advance in economic research, which leverages machine learning to detect patterns and analyze trends in specific fields, these techniques are being used in various contexts, including crime prevention. The objective of this work is to study the spatial patterns of crimes through the implementation of machine learning techniques, to predict the probability of occurrence of various types of crimes at an annual level with spatial differences in Medellín, Colombia, based on historical data. and sociodemographic. To carry out this objective, the Ordinary Least Squares, Random Forest and Extreme Gradient Boosting models were used, which obtained acceptable levels of performance, given their high precision. A relevant result is that the socioeconomic variables related to the proportion of men, people between 16 and 30 years of age, proportion of unemployed people, people who belong to SISBEN, proportion of people with multidimensional poverty, proportion of people with quantitative deficit of housing and who are part of socioeconomic stratum 1 or 2, both at the neighborhood and grid level had a high predictive power. For the purpose of this research, this will be used in decision-making and the formulation of public policies aimed at reducing crime.
dc.format.extent40 pp
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_41887
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/41887
dc.language.isospa
dc.publisherUniversidad del Rosariospa
dc.publisher.departmentFacultad de Economíaspa
dc.publisher.programMaestría en Economíaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)
dc.rights.licenciaEL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma. PARGRAFO: En caso de presentarse cualquier reclamación o acción por parte de un tercero en cuanto a los derechos de autor sobre la obra en cuestión, EL AUTOR, asumirá toda la responsabilidad, y saldrá en defensa de los derechos aquí autorizados; para todos los efectos la universidad actúa como un tercero de buena fe. EL AUTOR, autoriza a LA UNIVERSIDAD DEL ROSARIO, para que en los términos establecidos en la Ley 23 de 1982, Ley 44 de 1993, Decisión andina 351 de 1993, Decreto 460 de 1995 y demás normas generales sobre la materia, utilice y use la obra objeto de la presente autorización. -------------------------------------- POLITICA DE TRATAMIENTO DE DATOS PERSONALES. Declaro que autorizo previa y de forma informada el tratamiento de mis datos personales por parte de LA UNIVERSIDAD DEL ROSARIO para fines académicos y en aplicación de convenios con terceros o servicios conexos con actividades propias de la academia, con estricto cumplimiento de los principios de ley. Para el correcto ejercicio de mi derecho de habeas data cuento con la cuenta de correo habeasdata@urosario.edu.co, donde previa identificación podré solicitar la consulta, corrección y supresión de mis datos.spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.source.bibliographicCitationDetotto, Claudio; Otranto, Edoardo (2010) Does crime affect economic growth?. En: Kyklos. Vol. 63; No. 3; pp. 330-345 : Wiley Online Library;
dc.source.bibliographicCitationLiang, Weichao; Wang, Youquan; Tao, Haicheng; Cao, Jie (2022) Towards hour-level crime prediction: A neural attentive framework with. En: Neurocomputing. Vol. 486; pp. 286-297 : Elsevier;
dc.source.bibliographicCitationSun, Yuting; Chen, Tong; Yin, Hongzhi (2023) Spatial-temporal meta-path guided explainable crime prediction. En: World Wide Web. pp. 1-27 : Springer;
dc.source.bibliographicCitationBogomolov, Andrey; Lepri, Bruno; Staiano, Jacopo; Oliver, Nuria; Pianesi, Fabio; Pentland, Alex (2014) Once upon a crime: towards crime prediction from demographics and mobile. pp. 427-434
dc.source.bibliographicCitationKshatri, Sapna Singh; Narain, Bhawana (2020) Analytical study of some selected classification algorithms and crime. En: International Journal of Engineering and Advanced Technology. Vol. 9; No. 6; pp. 241-247
dc.source.bibliographicCitationZaidi, Nur Ain Syahira; Mustapha, Aida; Mostafa, Salama A; Razali, Muhammad Nazim (2020) A classification approach for crime prediction. pp. 68-78
dc.source.bibliographicCitationMejía, Daniel; Ortega, Daniel; Ortiz, Karen (2014) Un análisis de la criminalidad urbana en Colombia. En: Technical report, CAF.
dc.source.bibliographicCitationBecker, Gary S (1968) Crime and punishment: An economic approach. En: Journal of Political Economy. Vol. 76; No. 2; pp. 169-217 : The University of Chicago Press;
dc.source.bibliographicCitationMerton, Robert K (1938) Social structure and anomie. En: American sociological review. Vol. 3; No. 5; pp. 672-682 : JSTOR;
dc.source.bibliographicCitationCantor, David; Land, Kenneth C (1985) Unemployment and crime rates in the post-World War II United States: A. En: American sociological review. pp. 317-332 : JSTOR;
dc.source.bibliographicCitationShaw, Clifford R; McKay, Henry D (1931) Report on the Causes of Crime. En: Government Printing Office.
dc.source.bibliographicCitationSánchez-Torres, Fabio José; Núñez-Méndez, Jairo Augusto (2001) Determinantes del crimen violento en un país altamente violento: el caso. : Universidad de los Andes, Facultad de Economía, CEDE;
dc.source.bibliographicCitationDuarte-Velásquez, Yeizon Andrés; Cadavid-Carmona, Jahiler Alfredo (2020) Análisis de umbral: técnica diferencial en la interpretación de los. En: Revista Criminalidad. Vol. 62; No. 2; pp. 9-144 : Policía Nacional de Colombia;
dc.source.bibliographicCitationTita, George E; Petras, Tricia L; Greenbaum, Robert T (2006) Crime and residential choice: a neighborhood level analysis of the impact. En: Journal of Quantitative Criminology. Vol. 22; No. 4; pp. 299-317 : Springer;
dc.source.bibliographicCitationCollazos, Daniela; García, Eduardo; Mejía, Daniel; Ortega, Daniel; Tobón, Santiago (2021) Hot spots policing in a high-crime environment: An experimental evaluation. En: Journal of Experimental Criminology. Vol. 17; No. 3; pp. 473-506 : Springer;
dc.source.bibliographicCitationBea, David Colomer (2016) Transport engineering and reduction in crime: the Medellín case. En: Transportation research procedia. Vol. 18; pp. 88-92 : Elsevier;
dc.source.bibliographicCitationBlattman, Christopher; Duncan, Gustavo; Lessing, Benjamin; Tobón, Santiago; Mesa-Mejía, Juan Pablo (2020) Gobierno criminal en Medellín: panorama general del fenómeno y evidencia.
dc.source.bibliographicCitationBaumgartner, Kelli; Ferrari, Silvia; Palermo, George (2008) Constructing Bayesian networks for criminal profiling from limited data. En: Knowledge-Based Systems. Vol. 21; No. 7; pp. 563-572 : Elsevier;
dc.source.bibliographicCitationHinestroza Ramírez, Denniye (2018) El Machine Learning a través de los tiempos, y los aportes a la humanidad. : Universidad Libre Seccional Pereira;
dc.source.bibliographicCitationSpeiser, Jaime Lynn; Miller, Michael E; Tooze, Janet; Ip, Edward (2019) A comparison of Random Forest variable selection methods for. En: Expert Systems with Applications. Vol. 134; pp. 93-101 0957-4174; Disponible en: https://www.sciencedirect.com/science/article/pii/S0957417419303574; http://dx.doi.org/10.1016/j.eswa.2019.05.028. Disponible en: 10.1016/j.eswa.2019.05.028.
dc.source.bibliographicCitationYu, Chung-Hsien; Ward, Max W; Morabito, Melissa; Ding, Wei (2011) Crime forecasting using data mining techniques. pp. 779-786
dc.source.bibliographicCitationJoh, Elizabeth E (2019) Policing the smart city. En: International Journal of Law in Context. Vol. 15; No. 2; pp. 177-182 : Cambridge University Press;
dc.source.bibliographicCitationGahalot, Akanksha; Dhiman, Suraina; Chouhan, Lokesh; Others, (2020) Crime prediction and analysis. pp. 1-6
dc.source.bibliographicCitationHand, David J (2007) Principles of data mining. En: Drug safety. Vol. 30; pp. 621-622 : Springer;
dc.source.bibliographicCitationWitten, Ian H; Frank, Eibe; Mark, A (2011) Hall. 2011. Data Mining: Practical machine learning tools and techniques. En: Morgan Kaufmann Publishers Inc. , San Francisco, CA. Vol. 10; pp. 1972514
dc.source.bibliographicCitationAbhishek, Kumar; Kumar, Abhay; Ranjan, Rajeev; Kumar, Sarthak (2012) A rainfall prediction model using artificial Neural Network. pp. 82-87
dc.source.bibliographicCitationShaikh, Lubna; Sawlani, Kirti (2017) A rainfall prediction model using artificial Neural Network. En: International Journal of Technical Research and Applications. Vol. 5; No. 2; pp. 45-48
dc.source.bibliographicCitationAcosta-Portocarrero, Antony Christian; Ruiz-Vargas, Paul (2022) Machine learning para precedir el rendimiento académico en la IE. : Universidad César Vallejo;
dc.source.bibliographicCitationBorrero-Tigreros, Diego; Bedoya-Leiva, Oscar Fernando (2020) Predicción de riesgo crediticio en Colombia usando técnicas de. En: Revista UIS Ingenierías. Vol. 19; No. 4; pp. 37-52
dc.source.bibliographicCitationChiok, Cesar Higinio Menacho (2017) Predicción del rendimiento académico aplicando técnicas de minería de. Vol. 78; No. 1; pp. 26-33
dc.source.bibliographicCitationGiménez, María Hernández (2019) Inteligencia artificial y derecho penal. En: Actualidad jurídica iberoamericana. No. 10; pp. 792-843 : Instituto de derecho iberoamericano;
dc.source.bibliographicCitationHsiang, Solomon M; Burke, Marshall; Miguel, Edward (2013) Quantifying the influence of climate on human conflict. En: Science. Vol. 341; No. 6151; pp. 1235367 : American Association for the Advancement of Science;
dc.source.bibliographicCitationKrieger, Nancy; Chen, Jarvis T; Waterman, Pamela D; Soobader, Mah-Jabeen; Subramanian, S V; Carson, Rosa (2002) Geocoding and monitoring of US socioeconomic inequalities in mortality and. En: American journal of epidemiology. Vol. 156; No. 5; pp. 471-482 : Oxford University Press;
dc.source.bibliographicCitationMatijosaitiene, Irina; McDowald, Anthony; Juneja, Vishal (2019) Predicting safe parking spaces: A machine learning approach to geospatial. En: Sustainability. Vol. 11; No. 10; pp. 2848 : MDPI;
dc.source.bibliographicCitationSanthi, P; Bhaskaran, V Murali; Others, (2010) Performance of clustering algorithms in healthcare database. En: International Journal for Advances in Computer Science. Vol. 2; No. 1; pp. 26-31
dc.source.bibliographicCitationBrown, Donald E; Oxford, Rosemary B (2001) Data mining time series with applications to crime analysis. Vol. 3; pp. 1453-1458
dc.source.bibliographicCitationMalathi, A; Baboo, Dr S Santhosh (2011) Algorithmic crime prediction model based on the analysis of crime clusters. En: Global Journal of Computer Science and Technology. Vol. 11; No. 11; pp. 47-51
dc.source.bibliographicCitationMalathi, A; Babboo, S S; Anbarasi, A (2011) An intelligent analysis of a city crime data using data mining. Vol. 6; pp. 130-134
dc.source.bibliographicCitationZubi, Zakaria Suliman; Mahmmud, Ayman Altaher (2013) Using data mining techniques to analyze crime patterns in the libyan. En: Proceedings of the 1st WSEAS Interna-tional Conference on Image Processing. Vol. 8; pp. 79-85
dc.source.bibliographicCitationBlair, Robert A; Blattman, Christopher; Hartman, Alexandra (2017) Predicting local violence: Evidence from a panel survey in Liberia. En: Journal of Peace Research. Vol. 54; No. 2; pp. 298-312 : SAGE Publications Sage UK: London, England;
dc.source.bibliographicCitationChauhan, Chhaya; Sehgal, Smriti (2017) A review: crime analysis using data mining techniques and algorithms. pp. 21-25
dc.source.bibliographicCitationMichalos, Alex C; Zumbo, Bruno D (2000) Criminal victimization and the quality of life. En: Social Indicators Research. Vol. 50; pp. 245-295 : Springer;
dc.source.bibliographicCitationGerber, Matthew S (2014) Predicting crime using Twitter and kernel density estimation. En: Decision Support Systems. Vol. 61; pp. 115-125 : Elsevier;
dc.source.bibliographicCitationPantazis, Christina (2000) ’Fear of crime’, vulnerability and poverty. En: British journal of criminology. Vol. 40; No. 3; pp. 414-436 : Oxford University Press;
dc.source.bibliographicCitationZhang, Xu; Liu, Lin; Xiao, Luzi; Ji, Jiakai (2020) Comparison of machine learning algorithms for predicting crime hotspots. En: IEEE access. Vol. 8; pp. 181302-181310 : IEEE;
dc.source.bibliographicCitationMemon, Qurban A; Mehboob, Shuja (2003) Crime investigation and analysis using neural nets. pp. 346-350
dc.source.bibliographicCitationKianmehr, Keivan; Alhajj, Reda (2008) Effectiveness of support vector machine for crime hot-spots prediction. En: Applied Artificial Intelligence. Vol. 22; No. 5; pp. 433-458 : Taylor & Francis;
dc.source.bibliographicCitationNasridinov, Aziz; Ihm, Sun-Young; Park, Young-Ho (2013) A decision tree-based classification model for crime prediction. pp. 531-538
dc.source.bibliographicCitationIqbal, Rizwan; Murad, Masrah Azrifah Azmi; Mustapha, Aida; Panahy, Payam Hassany Shariat; Khanahmadliravi, Nasim (2013) An experimental study of classification algorithms for crime prediction. En: Indian Journal of Science and Technology. Vol. 6; No. 3; pp. 4219-4225 : Indian Society for Education and Environment, 23(new) Neelkamal Apt, 3 d …;
dc.source.bibliographicCitationTayal, Devendra Kumar; Jain, Arti; Arora, Surbhi; Agarwal, Surbhi; Gupta, Tushar; Tyagi, Nikhil (2015) Crime detection and criminal identification in India using data mining. En: AI & society. Vol. 30; pp. 117-127 : Springer;
dc.source.bibliographicCitationSivaranjani, S; Sivakumari, S; Aasha, M (2016) Crime prediction and forecasting in Tamilnadu using clustering approaches. pp. 1-6
dc.source.bibliographicCitationAhishakiye, Emmanuel; Taremwa, Danison; Omulo, Elisha Opiyo; Niyonzima, Ivan (2017) Crime prediction using Decision Tree (J48) classification algorithm. En: International Journal of Computer and Information Technology. Vol. 6; No. 3; pp. 188-195
dc.source.bibliographicCitationAlves, Luiz G A; Ribeiro, Haroldo V; Rodrigues, Francisco A (2018) Crime prediction through urban metrics and statistical learning. En: Physica A: Statistical Mechanics and its Applications. Vol. 505; pp. 435-443 : Elsevier;
dc.source.bibliographicCitationKajita, Mami; Kajita, Seiji (2020) Crime prediction by data-driven Green’s function method. En: International Journal of Forecasting. Vol. 36; No. 2; pp. 480-488 : Elsevier;
dc.source.bibliographicCitationGoin, Dana E; Rudolph, Kara E; Ahern, Jennifer (2018) Predictors of firearm violence in urban communities: a machine-learning. En: Health & Place. Vol. 51; pp. 61-67 : Elsevier;
dc.source.bibliographicCitationPinto, Marcus; Wei, Hsinrong; Konate, Kiyatou; Touray, Ida (2020) Delving into factors influencing New York crime data with the tools of. En: Journal of Computing Sciences in Colleges. Vol. 36; No. 2; pp. 61-70 : Consortium for Computing Sciences in Colleges;
dc.source.bibliographicCitationSaraiva, Miguel; Matijošaitienė, Irina; Mishra, Saloni; Amante, Ana (2022) Crime prediction and monitoring in Porto, Portugal, using machine. En: ISPRS International Journal of Geo-Information. Vol. 11; No. 7; pp. 400 : MDPI;
dc.source.bibliographicCitationCorcoran, Jonathan J; Wilson, Ian D; Ware, J Andrew (2003) Predicting the geo-temporal variations of crime and disorder. En: International Journal of Forecasting. Vol. 19; No. 4; pp. 623-634 : Elsevier;
dc.source.bibliographicCitationGelvez-Ferreira, Juan-David; Nieto-Rodríguez, María-Paula; Rocha-Ruiz, Carlos-Andrés (2022) Prediciendo el crimen en ciudades intermedias: un modelo de “machine. En: URVIO Revista Latinoamericana de Estudios de Seguridad. No. 34; pp. 82-98 : Flacso-Ecuador;
dc.source.bibliographicCitationLin, Ying-Lung; Yen, Meng-Feng; Yu, Liang-Chih (2018) Grid-based crime prediction using geographical features. En: ISPRS International Journal of Geo-Information. Vol. 7; No. 8; pp. 298 : MDPI;
dc.source.bibliographicCitationCarroll, John S; Payne, John W (1977) Crime seriousness, recidivism risk, and causal attributions in judgments. En: Journal of Applied Psychology. Vol. 62; No. 5; pp. 595 : American Psychological Association;
dc.source.bibliographicCitationMittal, Mamta; Goyal, Lalit Mohan; Sethi, Jasleen Kaur; Hemanth, D Jude (2019) Monitoring the impact of economic crisis on crime in India using machine. En: Computational Economics. Vol. 53; pp. 1467-1485 : Springer;
dc.source.bibliographicCitationDe Blasio, Guido; D'Ignazio, Alessio; Letta, Marco (2022) Gotham city. Predicting ‘corrupted’ municipalities with machine learning. En: Technological Forecasting and Social Change. Vol. 184; pp. 122016 : Elsevier;
dc.source.bibliographicCitationIngilevich, Varvara; Ivanov, Sergey (2018) Crime rate prediction in the urban environment using social factors. En: Procedia Computer Science. Vol. 136; pp. 472-478 : Elsevier;
dc.source.bibliographicCitationStalidis, Panagiotis; Semertzidis, Theodoros; Daras, Petros (2021) Examining deep learning architectures for crime classification and. En: Forecasting. Vol. 3; No. 4; pp. 741-762 : MDPI;
dc.source.bibliographicCitationReier-Forradellas, Ricardo Francisco; Náñez Alonso, Sergio Luis; Jorge-Vazquez, Javier; Rodriguez, Marcela Laura (2020) Applied machine learning in social sciences: Neural Networks and crime. En: Social Sciences. Vol. 10; No. 1; pp. 4 : MDPI;
dc.source.bibliographicCitationCaridade, Sónia; Magalhães, Mariana; Azevedo, Vanessa; Dinis, Maria Alzira Pimenta; Maia, Rui Leandro; Estrada, Rui; Sani, Ana Isabel; Nunes, Laura M (2022) Predicting frequent and feared crime typologies: individual and. En: Social Sciences. Vol. 11; No. 3; pp. 126 : MDPI;
dc.source.bibliographicCitationSathyadevan, Shiju; Devan, M S; Gangadharan, S Surya (2014) Crime analysis and prediction using data mining. pp. 406-412
dc.source.bibliographicCitationKang, Hyeon-Woo; Kang, Hang-Bong (2017) Prediction of crime occurrence from multi-modal data using deep learning. En: PloS one. Vol. 12; No. 4; pp. 1-19 : Public Library of Science San Francisco, CA USA;
dc.source.bibliographicCitationTong, Xiangzhi; Ni, Pin; Li, Qingge; Yuan, Qiao; Liu, Junru; Lu, Hanzhe; Li, Gangmin (2021) Urban Crime Trends Analysis and Occurrence Possibility Prediction based on. pp. 98-103
dc.source.bibliographicCitationKim, Sunjae; Lee, Sugie (2023) Nonlinear relationships and interaction effects of an urban environment on. En: Sustainable Cities and Society. Vol. 91; pp. 104419 : Elsevier;
dc.source.bibliographicCitationBrantingham, Paul J; Brantingham, Patricia L (1998) Environmental criminology: From theory to urban planning practice. En: Studies on crime and crime prevention. Vol. 7; No. 1; pp. 31-60
dc.source.bibliographicCitationSaltos, Ginger; Cocea, Mihaela (2017) An exploration of crime prediction using data mining on open data. En: International Journal of Information Technology & Decision Making. Vol. 16; No. 05; pp. 1155-1181 : World Scientific;
dc.source.bibliographicCitationChainey, Spencer; Tompson, Lisa; Uhlig, Sebastian (2008) The utility of hotspot mapping for predicting spatial patterns of crime. En: Security journal. Vol. 21; pp. 4-28 : Springer;
dc.source.bibliographicCitationDavid, H; Suruliandi, A (2017) SURVEY ON CRIME ANALYSIS AND PREDICTION USING DATA MINING TECHNIQUES. En: ICTACT journal on soft computing. Vol. 7; No. 3;
dc.source.bibliographicCitationWright, John Paul; Beaver, Kevin M (2013) Parenting and crime. En: The Oxford handbook of criminological theory. pp. 40-65 : Oxford University Press New York, NY;
dc.source.bibliographicCitationMohler, George O; Short, Martin B; Malinowski, Sean; Johnson, Mark; Tita, George E; Bertozzi, Andrea L; Brantingham, P Jeffrey (2015) Randomized controlled field trials of predictive policing. En: Journal of the American statistical association. Vol. 110; No. 512; pp. 1399-1411 : Taylor & Francis;
dc.source.bibliographicCitationRidgeway, Greg (2018) Policing in the era of big data. En: Annual review of criminology. Vol. 1; pp. 401-419 : Annual Reviews;
dc.source.bibliographicCitationGélvez-Ferreira, Juan David (2019) ¿Cuáles determinantes se relacionan con la percepción de inseguridad? Un. En: Revista Criminalidad. Vol. 61; No. 1; pp. 69-84 : Policía Nacional de Colombia;
dc.source.bibliographicCitationWang, Senzhang; Cao, Jiannong; Philip, S Yu (2020) Deep learning for spatio-temporal data mining: A survey. En: IEEE Transactions on Knowledge and Data Engineering. Vol. 34; No. 8; pp. 3681-3700 : IEEE;
dc.source.bibliographicCitationCohen, Jacqueline; Gorr, Wilpen L; Olligschlaeger, Andreas M (2007) Leading indicators and spatial interactions: A crime-forecasting model for. En: Geographical Analysis. Vol. 39; No. 1; pp. 105-127 : Wiley Online Library;
dc.source.bibliographicCitationRummens, Anneleen; Hardyns, Wim; Pauwels, Lieven (2017) The use of predictive analysis in spatiotemporal crime forecasting:. En: Applied geography. Vol. 86; pp. 255-261 : Elsevier;
dc.source.bibliographicCitationQuick, Matthew; Li, Guangquan; Brunton-Smith, Ian (2018) Crime-general and crime-specific spatial patterns: A multivariate spatial. En: Journal of Criminal Justice. Vol. 58; pp. 22-32 : Elsevier;
dc.source.bibliographicCitationGiménez-Santana, Alejandro; Caplan, Joel M; Drawve, Grant (2018) Risk terrain modeling and socio-economic stratification: Identifying risky. En: European Journal on Criminal Policy and Research. Vol. 24; pp. 417-431 : Springer;
dc.source.bibliographicCitationRosser, Gabriel; Davies, Toby; Bowers, Kate J; Johnson, Shane D; Cheng, Tao (2017) Predictive crime mapping: Arbitrary grids or street networks?. En: Journal of Quantitative Criminology. Vol. 33; pp. 569-594 : Springer;
dc.source.bibliographicCitationLiu, Hua; Brown, Donald E (2003) Criminal incident prediction using a point-pattern-based density model. En: International journal of forecasting. Vol. 19; No. 4; pp. 603-622 : Elsevier;
dc.source.bibliographicCitationNguyen, Trung T; Hatua, Amartya; Sung, Andrew H; Others, (2017) Building a learning machine classifier with inadequate data for crime. En: Journal of Advances in Information Technology Vol. Vol. 8; No. 2;
dc.source.bibliographicCitationCadena Urzúa, Pablo; Letelier Saavedra, Leonardo (2018) Factores determinantes de los Delitos de Mayor Connotación Social en la. En: Política criminal. Vol. 13; No. 26; pp. 1170-1189 : SciELO Chile;
dc.source.bibliographicCitationCatlett, Charlie; Cesario, Eugenio; Talia, Domenico; Vinci, Andrea (2018) A data-driven approach for spatio-temporal crime predictions in smart. pp. 17-24
dc.source.bibliographicCitationOrong, Markdy Y; Sison, Ariel M; Hernandez, Alexander A (2018) Mitigating vulnerabilities through forecasting and crime trend analysis. pp. 57-62
dc.source.bibliographicCitationFeng, Mingchen; Zheng, Jiangbin; Han, Yukang; Ren, Jinchang; Liu, Qiaoyuan (2018) Big data analytics and mining for crime data analysis, visualization and. pp. 605-614
dc.source.bibliographicCitationZhang, Yang; Cheng, Tao (2020) Graph deep learning model for network-based predictive hotspot mapping of. En: Computers, Environment and Urban Systems. Vol. 79; pp. 101403 : Elsevier;
dc.source.bibliographicCitationFalade, Adesola; Azeta, Ambrose; Oni, Aderonke; Odun-ayo, Isaac (2019) Systematic Literature Review of Crime Prediction and Data Mining. En: Review of Computer Engineering Studies. Vol. 6; No. 3;
dc.source.bibliographicCitationKounadi, Ourania; Ristea, Alina; Araujo, Adelson; Leitner, Michael (2020) A systematic review on spatial crime forecasting. En: Crime Science. Vol. 9; pp. 1-22 : Springer;
dc.source.bibliographicCitationThuraisingham, Bhavani (2004) Data mining for counter-terrorism. En: Data Mining: Next Generation Challenges and Future Directions. pp. 157-183 : Citeseer;
dc.source.bibliographicCitationOkonkwo, Raphael Obi; Enem, Francis O (2011) Combating crime and terrorism using data mining techniques.
dc.source.bibliographicCitationAlmuhanna, Abrar A; Alrehili, Marwa M; Alsubhi, Samah H; Syed, Liyakathunisa (2021) Prediction of crime in neighbourhoods of New York City using spatial data. En: 2021 1st International conference on artificial intelligence and data. pp. 23-30
dc.source.bibliographicCitationKaikhah, Khosrow; Doddameti, Sandesh (2006) Discovering trends in large datasets using neural networks. En: Applied Intelligence. Vol. 24; pp. 51-60 : Springer;
dc.source.bibliographicCitationChen, Hsinchun; Chung, Wingyan; Xu, Jennifer Jie; Wang, Gang; Qin, Yi; Chau, Michael (2004) Crime data mining: a general framework and some examples. En: The Comp. Vol. 37; No. 4; pp. 50-56 : IEEE;
dc.source.bibliographicCitationWang, Bao; Zhang, Duo; Zhang, Duanhao; Brantingham, P Jeffery; Bertozzi, Andrea L (2017) Deep learning for real time crime forecasting.
dc.source.bibliographicCitationWheeler, Andrew P; Steenbeek, Wouter (2021) Mapping the risk terrain for crime using machine learning. En: Journal of Quantitative Criminology. Vol. 37; pp. 445-480 : Springer;
dc.source.bibliographicCitationBabakura, Abba; Sulaiman, Md Nasir; Yusuf, Mahmud A (2014) Improved method of classification algorithms for crime prediction. pp. 250-255
dc.source.bibliographicCitationAgarwal, Jyoti; Nagpal, Renuka; Sehgal, Rajni (2013) Crime analysis using K-means clustering. En: International Journal of Computer Applications. Vol. 83; No. 4; Foundation of Computer Science;
dc.source.bibliographicCitationKiani, Rasoul; Mahdavi, Siamak; Keshavarzi, Amin (2015) Analysis and prediction of crimes by clustering and classification. En: International Journal of Advanced Research in Artificial Intelligence. Vol. 4; No. 8; pp. 11-17
dc.source.bibliographicCitationKim, Suhong; Joshi, Param; Kalsi, Parminder Singh; Taheri, Pooya (2018) Crime analysis through machine learning. pp. 415-420
dc.source.bibliographicCitationBappee, Fateha Khanam; Soares Júnior, Amílcar; Matwin, Stan (2018) Predicting crime using spatial features. pp. 367-373
dc.source.bibliographicCitationBogahawatte, Kaumalee; Adikari, Shalinda (2013) Intelligent criminal identification system. pp. 633-638
dc.source.bibliographicCitationHino, Kimihiro; Amemiya, Mamoru (2019) Spatiotemporal analysis of burglary in multifamily housing in Fukuoka. En: Cities. Vol. 90; pp. 15-23 : Elsevier;
dc.source.bibliographicCitationGarcía, Héctor Iván; Giraldo, Carlos Alberto; López, María Victoria; Pastor, María del Pilar; Cardona, Marleny; Tapias, Clara Eugenia; Cuartas, Deiman; Gómez, Vanessa; Vera, Claudia Yaneth (2012) Treinta años de homicidios en Medellín, Colombia, 1979-2008. En: Cadernos de Saude Pública. Vol. 28; pp. 1699-1712 : SciELO Public Health;
dc.source.bibliographicCitationMojica-Muñoz, Kevin Steven (2021) Inteligencia Artificial para Detectar Corrupción en la Administración. En: Documentos CEDE. No. 31;
dc.source.bibliographicCitationGallego, Jorge; Prem, Mounu; Vargas, Juan F (2022) Predicting politicians’ misconduct: Evidence from Colombia. En: Data & Policy. Vol. 4; pp. e41 : Cambridge University Press;
dc.source.bibliographicCitationFerro-Briceño, Paula Vanessa; Others, (2021) Uso de redes neuronales para determinar la influencia del estado del. : Universidad de los Andes;
dc.source.bibliographicCitationRojas-Guerrero, Mateo; Grautoff Laverde, Manfred; Others, (2022) Predicción de las masacres en Colombia empleando inteligencia artificial. : Universidad de los Andes;
dc.source.bibliographicCitationAlegría, Santiago Andrés Giraldo; Palacios, Luis Eduardo Ordoñez; Guerrero, Víctor Bucheli; Erazo, Hugo Ordoñez (2020) Modelo de redes neuronales para predecir la tendencia de víctimas de. En: Investigación e Innovación en Ingenierías. Vol. 8; No. 3; pp. 38-49
dc.source.bibliographicCitationOrdoñez-Eraso, Hugo-Armando; Pardo-Calvache, César-Jesús; Cobos-Lozada, Carlos-Alberto (2020) Detection of Homicide Trends in Colombia Using Machine Learning. En: Learning. Vol. 29; No. 54; pp. e11740
dc.source.bibliographicCitationBazzi, Samuel; Blair, Robert A; Blattman, Christopher; Dube, Oeindrila; Gudgeon, Matthew; Peck, Richard (2022) The promise and pitfalls of conflict prediction: evidence from Colombia. En: Review of Economics and Statistics. Vol. 104; No. 4; pp. 764-779 : MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info …;
dc.source.bibliographicCitationKhanna, Gaurav; Medina, Carlos; Nyshadham, Anant; Ramos, Daniel; Tamayo, Jorge; Tiew, Audrey (2022) Spatial Mobility, Economic Opportunity, and Crime.
dc.source.bibliographicCitationVarian, Hal R (2014) Big data: New tricks for econometrics. En: Journal of Economic Perspectives. Vol. 28; No. 2; pp. 3-28 : American Economic Association 2014 Broadway, Suite 305, Nashville, TN;
dc.source.bibliographicCitationLima, Marcio Salles Melo; Delen, Dursun (2020) Predicting and explaining corruption across countries: A machine learning. En: Government Information Quarterly. Vol. 37; No. 1; pp. 101407 : Elsevier;
dc.source.bibliographicCitationAndini, Monica; Ciani, Emanuele; de Blasio, Guido; D'Ignazio, Alessio; Salvestrini, Viola (2018) Targeting with machine learning: An application to a tax rebate program in. En: Journal of Economic Behavior & Organization. Vol. 156; pp. 86-102 : Elsevier;
dc.source.bibliographicCitationOrdóñez, Hugo; Cobos, Carlos; Bucheli, Víctor (2020) Modelo de machine learning para la predicción de las tendencias de hurto. En: Revista Ibérica de Sistemas e Tecnologias de Informação. No. E29; pp. 494-506 : Associação Ibérica de Sistemas e Tecnologias de Informacao;
dc.source.bibliographicCitationMuñoz, Victor; Vallejo, Monica; Aedo, Jose Edinson (2021) Exploratory analysis of crime behavior in the city of Medellín. pp. 1-5 : 2021 2nd Sustainable Cities Latin America Conference (SCLA);
dc.source.bibliographicCitationKanstrén, T (2020) A look at precision, recall, and f1-score: Exploring the relations between.
dc.source.bibliographicCitationAgarwal, Shivam (2013) Data mining: Data mining concepts and techniques. pp. 203-207
dc.source.bibliographicCitationRumi, Shakila Khan; Deng, Ke; Salim, Flora Dilys (2018) Crime event prediction with dynamic features. En: EPJ Data Science. Vol. 7; No. 1; pp. 43 : Springer Berlin Heidelberg;
dc.source.bibliographicCitationTolan, Ghada M; Soliman, Omar S (2015) An experimental study of classification algorithms for terrorism. En: International Journal of Knowledge Engineering-IACSIT. Vol. 1; No. 2; pp. 107-112 : EJournal Publishing;
dc.source.bibliographicCitationKhan, Muzammil; Ali, Azmat; Alharbi, Yasser; Others, (2022) Predicting and preventing crime: a crime prediction model using San. En: Complexity. Vol. 2022; Hindawi;
dc.source.bibliographicCitationKshatri, Sapna Singh; Singh, Deepak; Narain, Bhavana; Bhatia, Surbhi; Quasim, Mohammad Tabrez; Sinha, Ganesh Ram (2021) An empirical analysis of machine learning algorithms for crime prediction. En: IEEE Access. Vol. 9; pp. 67488-67500 : IEEE;
dc.source.bibliographicCitationAziz, Rabia Musheer; Sharma, Prajwal; Hussain, Aftab (2022) Machine learning algorithms for crime prediction under Indian Penal Code. En: Annals of Data Science. Vol. 6; pp. 1-32 : Springer;
dc.source.bibliographicCitationKumar, Akash; Verma, Aniket; Shinde, Gandhali; Sukhdeve, Yash; Lal, Nidhi (2020) Crime prediction using K-nearest neighboring algorithm. pp. 1-4
dc.source.bibliographicCitationDas, Priyanka; Das, Asit Kumar (2019) Application of classification techniques for prediction and analysis of. pp. 191-201
dc.source.bibliographicCitationShohan, Faisal Tareque; Akash, Abu Ubaida; Ibrahim, Muhammad; Alam, Mohammad Shafiul (2022) Crime Prediction using Machine Learning with a Novel Crime Dataset.
dc.source.bibliographicCitationRaza, Dewan Mamun; Victor, Debasish Bhattacharjee (2021) Data mining and region prediction based on crime using Random Forest. pp. 980-987
dc.source.bibliographicCitationLi, Guang; Wang, Yadong; Others, (2012) A Privacy-Preserving Classification Method Based on Singular Value. En: Int. Arab J. Inf. Technol. Vol. 9; No. 6; pp. 529-534
dc.source.bibliographicCitationHan, Jiawei; Kamber, Micheline; Mining, Data (2006) Concepts and techniques. En: Morgan kaufmann. Vol. 340; pp. 94104-93205
dc.source.bibliographicCitationYao, Shuyu; Wei, Ming; Yan, Lingyu; Wang, Chunzhi; Dong, Xinhua; Liu, Fangrui; Xiong, Ying (2020) Prediction of crime hotspots based on spatial factors of Random forest. pp. 811-815
dc.source.bibliographicCitationLu, R; Li, L Y (2019) Crime prediction model based on Random Forest. En: Journal of China Interpol Academy. Vol. 5; No. 3; pp. 108-112
dc.source.bibliographicCitationLim, Noha (2007) Classification by ensembles from random partitions using logistic. En: State University of New York at Stony Brook.
dc.source.bibliographicCitationLevine, Ned (2008) The “Hottest” part of a hotspot: comments on “The utility of hotspot. En: Security Journal. Vol. 21; No. 4; pp. 295-302 : Springer;
dc.source.bibliographicCitationHart, Timothy C; Miethe, Terance D (2014) Street robbery and public bus stops: A case study of activity nodes and. En: Security Journal. Vol. 27; pp. 180-193 : Springer;
dc.source.bibliographicCitationMustard-David, B (2010) How Do Labor Markets Affect Crime? New Evidence on an Old Puzzle. En: Handbook On The Economics Of Crime. Elsevier.
dc.source.bibliographicCitationRaphael, Steven; Winter-Ebmer, Rudolf (2001) Identifying the effect of unemployment on crime. En: The journal of law and economics. Vol. 44; No. 1; pp. 259-283 : The University of Chicago Press;
dc.source.bibliographicCitationFarrington, David P; Gallagher, Bernard; Morley, Lynda; Ledger, Raymond J St; West, Donald J (1986) Unemployment, school leaving, and crime. pp. 101-122 : Routledge;
dc.source.bibliographicCitationKhan, Nabeela; Ahmed, Junaid; Nawaz, Muhammad; Zaman, Khalid (2015) The socio-economic determinants of crime in Pakistan: New evidence on an. En: Arab Economic and Business Journal. Vol. 10; No. 2; pp. 73-81 : Elsevier;
dc.source.bibliographicCitationSánchez-Jabba, Andrés (2013) La reinvención de Medellín. En: Lecturas de Economía. No. 78; pp. 185-227 : Universidad de Antioquia;
dc.source.bibliographicCitationBreiman, Leo (2001) Random forests. En: Machine learning. Vol. 45; pp. 5-32 : Springer;
dc.source.bibliographicCitationBlair, Lesli; Wilcox, Pamela; Eck, John (2017) Facilities, opportunity, and crime: An exploratory analysis of places in. En: Crime prevention and community safety. Vol. 19; pp. 61-81 : Springer;
dc.source.bibliographicCitationHe, Li; Páez, Antonio; Liu, Desheng (2017) Persistence of crime hot spots: an ordered probit analysis. En: Geographical analysis. Vol. 49; No. 1; pp. 3-22 : Wiley Online Library;
dc.source.bibliographicCitationTobler, Waldo R (1979) Cellular geography. En: Springer. pp. 379-386
dc.source.bibliographicCitationWeisburd, David; Green, Lorraine (1995) Policing drug hot spots: The Jersey City drug market analysis experiment. En: Justice Quarterly. Vol. 12; No. 4; pp. 711-735 : Taylor & Francis;
dc.source.bibliographicCitationWeisburd, David; Maher, Lisa; Sherman, Lawrence; Buerger, Michael; Cohn, Ellen; Petrosino, Anthony (2023) Contrasting crime general and crime specific theory: The case of hot spots. En: Routledge. pp. 45-70
dc.source.bibliographicCitationBraga, Anthony A; Papachristos, Andrew V; Hureau, David M (2014) The effects of hot spots policing on crime: An updated systematic review. En: Justice Quarterly. Vol. 31; No. 4; pp. 633-663 : Taylor & Francis;
dc.source.bibliographicCitationZou, Kelly H; Tuncali, Kemal; Silverman, Stuart G (2003) Correlation and simple linear regression. En: Radiology. Vol. 227; No. 3; pp. 617-628 : Radiological Society of North America;
dc.source.bibliographicCitationGordon, Mirta B (2010) A random walk in the literature on criminality: A partial and critical. En: European Journal of Applied Mathematics. Vol. 21; No. 4-5; pp. 283-306 : Cambridge University Press;
dc.source.bibliographicCitationZhao, Xiangyu; Tang, Jiliang (2017) Modeling temporal-spatial correlations for crime prediction. pp. 497-506
dc.source.bibliographicCitationYi, Fei; Yu, Zhiwen; Zhuang, Fuzhen; Zhang, Xiao; Xiong, Hui (2018) An integrated model for crime prediction using temporal and spatial. pp. 1386-1391
dc.source.bibliographicCitationBrowning, Christopher R; Byron, Reginald A; Calder, Catherine A; Krivo, Lauren J; Kwan, Mei-Po; Lee, Jae-Yong; Peterson, Ruth D (2010) Commercial density, residential concentration, and crime: Land use. En: Journal of Research in Crime and Delinquency. Vol. 47; No. 3; pp. 329-357 : Sage Publications Sage CA: Los Angeles, CA;
dc.source.bibliographicCitationJames, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert; Others, (2013) An introduction to statistical learning. Vol. 112; Springer;
dc.source.bibliographicCitationHand, D J (2002) The elements of statistical learning: Data mining, inference, and. En: Biometrics. Vol. 58; No. 1; pp. 252 : International Biometric Society;
dc.source.bibliographicCitationAL Mansour, Hanan; Lundy, Michele (2019) Crime types prediction. En: Advances in Data Science, Cyber Security and IT Applications: First. pp. 260-274
dc.source.bibliographicCitationZhang, Xu; Liu, Lin; Lan, Minxuan; Song, Guangwen; Xiao, Luzi; Chen, Jianguo (2022) Interpretable machine learning models for crime prediction. En: Computers, Environment and Urban Systems. Vol. 94; pp. 101789 0198-9715; Disponible en: https://www.sciencedirect.com/science/article/pii/S0198971522000333; http://dx.doi.org/10.1016/j.compenvurbsys.2022.101789. Disponible en: 10.1016/j.compenvurbsys.2022.101789.
dc.source.bibliographicCitationDeng, Yue; He, Rixing; Liu, Yang (2023) Crime risk prediction incorporating geographical spatiotemporal dependency. En: Information Sciences. Vol. 646; pp. 119414 0020-0255; Disponible en: https://www.sciencedirect.com/science/article/pii/S0020025523009994; http://dx.doi.org/10.1016/j.ins.2023.119414. Disponible en: 10.1016/j.ins.2023.119414.
dc.source.bibliographicCitationMaloof, Marcus A (2003) Learning when data sets are imbalanced and when costs are unequal and. Vol. 2; pp. 2-1
dc.source.bibliographicCitationMartin, Gerard (2012) Medellín, tragedia y resurrección: mafias, ciudad y Estado, 1975-2013. : La Carrera Editores EU;
dc.source.bibliographicCitationRamírez, Jorge Giraldo (2008) La Conflicto armado urbano y violencia homicida. El caso de Medellín. En: URVIO: Revista Latinoamericana de Estudios de Seguridad. No. 5; pp. 99-113 : Facultad Latinoamericana de Ciencias Sociales (FLACSO), Sede Ecuador;
dc.source.bibliographicCitationCornish, Derek B; Clarke, Ronald V (2016) The rational choice perspective. pp. 48-80 : Routledge;
dc.source.bibliographicCitationCornish, Derek B; Clarke, Ronald V (1989) Crime specialisation, crime displacement and rational choice theory. pp. 103-117 : Springer;
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subjectMachine Learning
dc.subjectVariables socioeconómicas
dc.subjectPatrones espaciales
dc.subject.keywordMachine learning
dc.subject.keywordCrime patterns
dc.subject.keywordClassification models
dc.subject.keywordCrime prediction
dc.subject.keywordCrime analysis
dc.subject.keywordPublic politics
dc.subject.keywordSocioeconomic variables
dc.titleCrimen y factores económicos en Medellín: un estudio de predicción con Machine Learning
dc.title.TranslatedTitleCrime and Economic Factors in Medellín: A Prediction Study with Machine Learning
dc.typebachelorThesis
dc.type.documentTrabajo de grado
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
dc.type.spaTrabajo de grado
local.department.reportFacultad de Economía
Archivos
Bloque original
Mostrando1 - 2 de 2
Miniatura
Nombre:
Crimen_y_Factores_Economicos_en_Medellin_Un_Estudio_de_Prediccion_con_Machine_Learning.pdf
Tamaño:
3.28 MB
Formato:
Adobe Portable Document Format
Descripción:
Descargar
Miniatura
Nombre:
references.ris
Tamaño:
42.49 KB
Formato:
Descripción:
Descargar
Colecciones
Maestría en Economía