Plan de acción de lucha contra los delitos de odio

En el año 2012 se puso en marcha el proyecto "formación para la identificación y registro de incidentes racistas" (FIRIR). Con este programa se publicó en el año 2013 el Manual de apoyo a la formación de fuerzas y cuerpos de seguridad en la identificación y registro de incidentes racistas o xenófobos como herramienta específica dirigida a los Cuerpos y Fuerzas de Seguridad nacionales, autonómicos y locales; para dotarles de los conocimientos precisos que les permitan llevar a cabo una eficaz detección y registro de incidentes racistas y xenófobos. Desde el año 2013 se viene elaborando un informe anual sobre la evolución de los incidentes relacionados con los delitos de odio en España. Dentro de la web del Ministerio del Interior hay un apartado específico dedicado a los delitos de odio. Durante los meses de marzo del 2015 al mes diciembre 2017 se realizó la "Encuesta sobre experiencias con incidentes relacionados con los delitos de odio" para mejorar la atención que reciben las víctimas de delitos de odio. Con la Orden General 2285 del 12 de febrero de 2018 se creó la Oficina Nacional de Lucha contra los Delitos de Odio. Está Integrada en el Gabinete de Coordinación y Estudios de la SES (Área del Sistema Estadístico y Atención a la Víctima) y formada por componentes de las Fuerzas y Cuerpos de Seguridad del Estado. Tiene los objetivos de formar, investigar, establecer relaciones entre instituciones y el Tercer Sector y centralizar los datos relevantes acabados por las FFCCSE. Con la Instrucción 17/2017 se da vía a la elaboración del Protocolo de actuación de las Fuerzas y Cuerpos de Seguridad para los para los delitos de odio y conductas que vulneran las normas legales sobre discriminación. Más recientemente, este mis mo año, se ha presentado por parte del Ministerio de Interior un ambicioso Plan de Acción de lucha contra los Delitos de Odio, que describe la estrategia de los próximos años para la lucha contra estos delitos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Detecting and Monitoring Hate Speech in Twitter

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge

Applications of data science in policing: VeriPol as an investigation support tool

Data Science is an interdisciplinary field involving the development of processes and systems to extract knowledge and understanding from data in different formats and from different sources. Considering the large amount of data generated and managed by public safety agencies, Data Science applications in the police sector are numerous. More important are the advantages that the different applications of Data Science could provide the police on issues such as the optimization of resources, the increase of efficiency and effectiveness, the modernization and its exemplariness when compared with other institutions. In this paper we present different potential applications fields of Data Science for the police. In addition, we focus on the case of VeriPol, a tool for automatic detection of false violent robbery reports, currently under development by the Spanish National Police. In particular, we illustrate a detailed analysis of the results of a recent pilot study aimed at assessing the effectiveness of the tool

Applying automatic text-based detection of deceptive language to police reports: Extracting behavioral patterns from a multi-step classification model to understand how we lie to the police

Filing a false police report is a crime that has dire consequences on both the individual and the system. In fact, it may be charged as a misdemeanor or a felony. For the society, a false report results in the loss of police resources and contamination of police databases used to carry out investigations and assessing the risk of crime in a territory. In this research, we present VeriPol, a model for the detection of false robbery reports based solely on their text. This tool, developed in collaboration with the Spanish National Police, combines Natural Language Processing and Machine Learning methods in a decision support system that provides police officers the probability that a given report is false. VeriPol has been tested on more than 1000 reports from 2015 provided by the Spanish National Police. Empirical results show that it is extremely effective in discriminating between false and true reports with a success rate of more than 91%, improving by more than 15% the accuracy of expert police officers on the same dataset. The underlying classification model can be analysed to extract patterns and insights showing how people lie to the police (as well as how to get away with false reporting). In general, the more details provided in the report, the more likely it is to be honest. Finally, a pilot study carried out in June 2017 has demonstrated the usefulness of VeriPol on the field

Towards social fairness in smart policing: leveraging territorial, racial, and workload fairness in the police districting problem

Recent events (e.g., George Floyd protests) have shown the impact that inequality in policing can have on society. Thus, police operations should be planned and designed taking into account the interests of three main groups of directly affected stakeholders (i.e., general population, minorities, and police agents) to pursue fairness. Most models presented so far in the literature failed at this, optimizing cost efficiency or operational effectiveness instead while disregarding other social goals. In this paper, a Smart Policing model that produces operational patrolling districts and includes territorial, racial, and workload fairness criteria is proposed. The patrolling configurations are designed according to the territorial distribution of crime risk and population subgroups, while equalizing the total risk exposure across the districts, according to the preferences of a decision-maker. The model is formulated as a multi-objective mixed-integer program. Computational experiments on randomly generated data are used to empirically draw insights into the relationship between the fairness criteria considered. Finally, the model is tested and validated on a real-world dataset about the Central District of Madrid (Spain). Experiments show that the model identifies solutions that dominate the current patrolling configuration used

Applications of data science in policing: VeriPol as an investigation support tool

Data Science is an interdisciplinary field involving the development of processes and systems to extract knowledge and understanding from data in different formats and from different sources. Considering the large amount of data generated and managed by public safety agencies, Data Science applications in the police sector are numerous. More important are the advantages that the different applications of Data Science could provide the police on issues such as the optimization of resources, the increase of efficiency and effectiveness, the modernization and its exemplariness when compared with other institutions. In this paper we present different potential applications fields of Data Science for the police. In addition, we focus on the case of VeriPol, a tool for automatic detection of false violent robbery reports, currently under development by the Spanish National Police. In particular, we illustrate a detailed analysis of the results of a recent pilot study aimed at assessing the effectiveness of the tool

Equity in the Police Districting Problem: balancing territorial and racial fairness in patrolling operations

Objectives The Police Districting Problem concerns the definition of patrol districts that distribute police resources in a territory in such a way that high-risk areas receive more patrolling time than low-risk areas, according to a principle of territorial fairness. This results in patrolling configurations that are efficient and effective at controlling crime but that, at the same time, might exacerbate racial disparity in police stops and arrests. In this paper, an Equitable Police Districting Problem that combines crime-reduction effectiveness with racial fairness is proposed. The capability of this model in designing patrolling configurations that find a balance between territorial and racial fairness is assessed. Also, the trade-off between these two criteria is analyzed. Methods The Equitable Police Districting Problem is defined as a mixed-integer program. The objective function is formulated using Compromise Programming and Goal Programming. The model is validated on a real-world case study on the Central District of Madrid, Spain, and its solutions are compared to standard patrolling configurations currently used by the police. Results A trade-off between racial fairness and crime control is detected. However, the experiments show that including the proposed racial criterion in the optimization of patrol districts greatly improves racial fairness with limited detriment to the policing effectiveness. Also, the model produces solutions that dominate the patrolling configurations currently in use by the police. Conclusions The results show that the model successfully provides a quantitative evaluation of the trade-off between the criteria and is capable of defining patrolling configurations that are efficient in terms of both racial and territorial fairness

Statistical analysis of spatio-temporal crime patterns: Optimization of patrolling strategies

In Spain, the security of towns is borne by the Spanish National Police Corps (SNPC), usually sharing territory with other local security forces. The SNPC is an armed Institute of civil nature, subordinated to the Ministry of Home Affairs. Among its duties are: keeping and restoring order and public safety and to prevent the commission of criminal acts. The SNPC is one of the country's most valued institutions and is located at the global forefront in the fight against crime, with the aim of constant innovation. To improve the effectiveness of patrolling operations and increase the efficiency in the use of resources, the SNPC has started to develop a Decision Support System (DSS) comprising tools and models to assist various public security tasks. One of the main objectives of the system is the implementation of a predictive patrolling policy to increase the presence of agents in the areas where they are most needed, to reduce the probability of occurrence of crime. To this end, the author, in collaboration with professionals from the SNPC, developed a Predictive Policing tool for crime risk forecasting based on the statistical analysis of spatio-tempooral crime patterns, and an optimization model for the definition of patrolling sectors configuration, tailored to suit the requirements of the SNPC..Tesis Univ. Granada. Programa Oficial de Doctorado en: Matemáticas y Estadístic

Statistical analysis of spatio-temporal crime patterns: Optimization of patrolling strategies

In Spain, the security of towns is borne by the Spanish National Police Corps (SNPC), usually sharing territory with other local security forces. The SNPC is an armed Institute of civil nature, subordinated to the Ministry of Home Affairs. Among its duties are: keeping and restoring order and public safety and to prevent the commission of criminal acts. The SNPC is one of the country's most valued institutions and is located at the global forefront in the fight against crime, with the aim of constant innovation. To improve the effectiveness of patrolling operations and increase the efficiency in the use of resources, the SNPC has started to develop a Decision Support System (DSS) comprising tools and models to assist various public security tasks. One of the main objectives of the system is the implementation of a predictive patrolling policy to increase the presence of agents in the areas where they are most needed, to reduce the probability of occurrence of crime. To this end, the author, in collaboration with professionals from the SNPC, developed a Predictive Policing tool for crime risk forecasting based on the statistical analysis of spatio-tempooral crime patterns, and an optimization model for the definition of patrolling sectors configuration, tailored to suit the requirements of the SNPC..Tesis Univ. Granada. Programa Oficial de Doctorado en: Matemáticas y Estadístic

A year in Madrid as described through the analysis of geotagged Twitter data

Gaining a complete picture of the activity in a city using vast data sources is challenging yet potentially very valuable. One such source of data is Twitter which generates millions of short spatio-temporally localized messages that, as a collection, have information on city regions and many forms of city activity. The quantity of data, however, necessitates summarization in a way that makes consumption by an observer efficient, accurate, and comprehensive. We present a two-step process for analyzing geotagged twitter data within a localized urban environment. The first step involves an efficient form of latent Dirichlet allocation, using an expectation maximization, for topic content summarization of the text information in the tweets. The second step involves spatial and temporal analysis of information within each topic using two complimentary metrics. These proposed metrics characterize the distributional properties of tweets in time and space for all topics. We integrate the second step into a graphical user interface that enables the user to adeptly navigate through the space of hundreds of topics. We present results of a case study of the city of Madrid, Spain, for the year 2011 in which both large-scale protests and elections occurred. Our data analysis methods identify these important events, as well as other classes of more mundane routine activity and their associated locations in Madrid