Aplicación del análisis de umbral a los delitos patrimoniales en los barrios y distritos de Barcelona

The shortage of human and technical resources in the police forces complicates the study of crime patterns. To assist in this task, Chamberlayne (2008) introduced automation to the statistical technique of threshold analysis, which calculates the expected number of crimes in a region by using the weighted moving average. The volume of crime in an area can thus be estimated based on previous years in relation to the expected volume in the current year. It therefore facilitates the detection of areas with an increase or decrease in crime beyond expectations. Due to the problem of property crime in Barcelona, the objective of this paper is to apply the technique of threshold analysis to detect unusual volumes of this type of offence. We analysed 164’788 street offences against the property in districts of Barcelona between 2015 and 2017. The results suggest that greater attention should be paid to the districts of Ciutat Vella and San Martí, as well as to the neighbourhoods of Barceloneta, el Raval and El Parc i la Llacuna del Poblenou, due to the higher crime levels than expected in these zones. Significant reductions are also noted in other areas. Threshold analysis is therefore a useful and easy-to-use crime analysis technique in police management. Furthermore, it allows the identification of areas where the variation in crime requires further detailed study and analysis.La escasez de recursos humanos y técnicos de la policía dificulta el estudio de patrones delictivos. Para apoyar esta tarea, Chamberlayne (2008) introdujo la automatización a la técnica estadística del análisis de umbral. Esta técnica calcula el número previsto de delitos en una región concreta utilizando la media móvil ponderada, de forma que permite estimar, a partir de años anteriores, el volumen de delitos para la zona respecto del volumen esperado. Por tanto, facilita detectar aquellas zonas donde existe un aumento o descenso de delitos fuera de lo esperado. Debido al problema de la delincuencia patrimonial en Barcelona, el objetivo de este artículo es aplicar la técnica del análisis de umbral para detectar volúmenes inusuales de esta tipología. Para este estudio, se analizaron un total de 164.788 delitos contra la propiedad en vía urbana de los distritos de Barcelona entre los años 2015 y 2017. Los resultados obtenidos sugieren prestar mayor atención al distrito de Ciutat Vella y Sant Martí, así como a los barrios de la Barceloneta el Raval y El Parc i la Llacuna del Poblenou, debido a unos niveles de delincuencia muy superiores a los esperados. También se destacan reducciones significativas en otras zonas. Por consiguiente, el análisis de umbral es una técnica de análisis de la delincuencia útil y fácil de utilizar en la gestión policial. Además, permite identificar aquellas zonas en las que la variación de la delincuencia requiere un estudio y un análisis detallado. &nbsp

A Quantitative Approach to Evaluate and Develop Theories on (Fear of) Crime in Urban Environments

Well established work in criminological, architectural and urban studies suggests that there is a strong correlation between crime, perceived safety, the fear of crime, and the presence of different demographics, the people dynamics, in an urban environment. These studies have been conducted primarily using qualitative evaluation methods, and are typically limited in terms of the geographical area they cover, the number of respondents they reach out to, and the temporal frequency with which they can be repeated. As cities are rapidly growing and evolving complex entities, complementary approaches that afford social and urban scientists the ability to evaluate urban crime and fear of crime theories at scale are required. In this thesis, I propose a combination of methodologies following a data mining and crowdsourcing approach to quantitatively validate these theories at scale, and to support the exploration of new ones. To relate people dynamics to crime quantitatively, I first analyse footfall counts as recorded by telecommunication data, and extract metrics that act as proxies of urban crime theories. Using correlation and regression analysis between such proxies and crime activity derived from open crime data records, the method can help to understand to what extent different theories of urban crime hold, and where. To relate people dynamics to fear of crime quantitatively, I then built two image– based online crowdsourcing platforms to investigate to what extent online crowdsourcing can be used to gather safety perceptions about urban places, defined by the combination of built environment and the people inhabiting it. As existing theories suggest that knowing who the respondents are is crucial for understanding safety perceptions, I also gathered their demographic background information to discuss their perceptions accordingly. I applied analysis of variance (ANOVA) and covariance (ANCOVA) to these data. The method can help to understand what visual properties based on peopl