The objective of the thesis is to provide a better understanding of the safety conditions inurban environments, particularly related to those found in transport nodes, in this case,underground stations, and surrounding areas1. First, the study starts with an analysis of theoverall city, identifying concentrations of crime in the urban fabric and then focusing on thecriminogenic conditions at and around underground stations. The analysis combines the useof Geographical Information Systems (GIS), statistical techniques and data of different typesand sources. Regression models were used to assess the importance of the environmentalattributes of underground stations on crime rates. Findings show that violent and propertycrimes show different hotspots at different times. Crime patterns tend to follow people’sscheduled patterns of routine activity. The socio-economic composition of the surroundingenvironment of the stations has a significant impact on crime at these transport nodes, butmore important were attributes of the physical and social environment at the stations. Forinstance, low guardianship and poor visibility at the stations together with mixed land-usesin the surrounding areas induced crime rates at the stations. It is therefore suggested thatintervention to improve safety conditions at the stations should focus on a holistic approach,taking into account the station and surrounding areas, but also being aware of crimevariation on specific places at specific times. / <p>QC 20130207</p> / Safety in transport nodes: the influence of environmental attributes on crime and perceived safety

Diverse teorie si concentrano sui legami tra criminalità e caratteristiche specifiche di luoghi e comunità. Tuttavia, solo pochi studi applicati sostengono esplicitamente che i fattori contestuali possono combinarsi nel determinare il rischio di criminalità e che le loro influenze criminogene possono operare su scala diversa. Questo studio si propone di indagare come alcune caratteristiche del paesaggio urbano (microlivello) interagiscono tra loro, nonché con le caratteristiche demografiche, economiche e sociali dell'ambiente dei quartieri circostanti (livello meso), per determinare la vulnerabilità spaziale alla criminalità e, in definitiva, la probabilità di un evento criminale. Questo studio conduce una valutazione del rischio di criminalità spaziale per rapine e crimini violenti in due grandi aree urbane: Milano, Italia e Iztapalapa, Messico. I casi di studio sono focalizzati su due paesi molto diversi, il che consente sia la valutazione dell'influenza di effetti contestuali più ampi (livello macro) sia la verifica di alcuni presupposti teorici al di fuori dell'ambiente anglosassone. L'analisi si fonda sull'approccio del Risk Terrain Modeling. Tuttavia, contrariamente alle applicazioni precedenti, l'analisi in questo studio si basa su un modello di regressione multilivello che include termini di interazione. Lo studio propone inoltre metodi innovativi attraverso i quali esporre e comunicare i propri risultati. Nel complesso, i risultati dimostrano che fattori contestuali misurati a diverse scale geografiche interagiscono in modo significativo tra loro per determinare il rischio di criminalità. Questa scoperta suggerisce di combinare input provenienti da diverse teorie al fine di comprendere le dinamiche alla base del verificarsi del crimine. Inoltre, il metodo proposto generalmente consente di prevedere meglio i crimini futuri e consente la generazione di narrazioni di rischio più precise per informare politiche e interventi. / Several theories focus on the links between crime and specific characteristics of places and communities. However, only a few applied studies explicitly purport that contextual factors may combine in determining crime risk and that their criminogenic influences may operate at different geographical scales. This study aims to investigate how certain features of the urban landscape (micro-level) interact with each other, as well as with demographic, economic and social characteristics of the surrounding neighbourhoods (meso-level), to determine spatial vulnerability to crime and, ultimately, the likelihood of a criminal event. This study conducts a spatial crime risk assessment for robberies and violent crimes in two large urban areas: Milan, Italy and Iztapalapa, Mexico. The case studies are focused on two very different countries, which allows for both the assessment of the influence of broader contextual effects (macro-level) and to test certain theoretical assumptions outside the Anglo-Saxon environment. The analysis is grounded in the Risk Terrain Modeling approach. However, in contrast to previous applications, the analysis in this study relies on a multi-level regression model including interaction terms. The study also proposes innovative methods through which to display and communicate its findings. Overall, the results demonstrate that contextual factors measured at different geographical scales interact significantly among them to determine crime risk. This finding suggests combining inputs from different theories in order to understand the dynamics behind crime occurrence. Furthermore, the proposed method generally allows us to better predict the locations of future crimes and enables the generation of more precise risk narratives to inform policies and interventions.