Spatio-temporal stratified associations between urban human activities and crime patterns: a case study in San Francisco around the COVID-19 stay-at-home mandate

Crime changes have been reported as a result of human routine activity shifting due to containment policies, such as stay-at-home (SAH) mandates during the COVID-19 pandemic. However, the way in which the manifestation of crime in both space and time is affected by dynamic human activities has not been explored in depth in empirical studies. Here, we aim to quantitatively measure the spatio-temporal stratified associations between crime patterns and human activities in the context of an unstable period of the ever-changing socio-demographic backcloth. We propose an analytical framework to detect the stratified associations between dynamic human activities and crimes in urban areas. In a case study of San Francisco, United States, we first identify human activity zones (HAZs) based on the similarity of daily footfall signatures on census block groups (CBGs). Then, we examine the spatial associations between crime spatial distributions at the CBG-level and the HAZs using spatial stratified heterogeneity statistical measurements. Thirdly, we use different temporal observation scales around the effective date of the SAH mandate during the COVID-19 pandemic to investigate the dynamic nature of the associations. The results reveal that the spatial patterns of most crime types are statistically significantly associated with that of human activities zones. Property crime exhibits a higher stratified association than violent crime across all temporal scales. Further, the strongest association is obtained with the eight-week time span centred around the SAH order. These findings not only enhance our understanding of the relationships between urban crime and human activities, but also offer insights into that tailored crime intervention strategies need to consider human activity variables

Structure and Cooptition in Urban Networks

Over the past decades, demographic changes, advances in transportation and communication technology, and the growth of the services sector have had a significant impact on the spatial structure of regions. Monocentric cities are disappearing and developing into polycentric metropolitan areas, while at the same time, social economic processes are taking place at an ever larger geographical scale, beyond that of the city, in which historically separate metropolitan areas are becoming increasingly functionally connected to form polycentric urban regions. Such urban networks are characterised by the lack of an urban hierarchy, a significant degree of spatial integration between different cities and, complementary relationships between centres, in that cities and towns have different economic specialisations. The growing literature on changing urban systems coincides with the increasing popularity of the urban network concept in contemporary spatial planning and policy, in which urban networks are often seen as a panacea for regional economic development problems. Polycentricity and spatial integration have become catchphrases, where polycentric development policies have been introduced to support territorial cohesion as well as higher levels of territorial competitiveness. Despite the enthusiasm for the ideas of a polycentric and networked spatial organisation, the assessment of the network concept leaves much to be desired. To what extent are regions becoming more polycentric and spatially integrated? Are relationships between cities in polycentric, spatially integrated regions complementary rather than competitive? And are polycentric, spatially integrated regions more economically efficient than their monocentric, non-integrated counterparts? In this study, these questions will be addressed

Investigating the Relationship Between Micro and Macro Levels of Efficacy and Their Effects on Crime

The concepts of self-efficacy and collective efficacy have both been used by scholars to explain involvement in individual-level crime. Scholars have found that both types of efficacy are related to crime at the individual level. However, little research has examined the relationship between self-efficacy and collective efficacy and its influence on youths' involvement in crime. Using the Project on Human Development in Chicago Neighborhoods (PHDCN) data, this study focuses on the independent influences of self-efficacy and collective efficacy on involvement in crime among youths ages 9 to 19, and examines the potential moderating effect of collective efficacy on the relationship between self-efficacy and crime. The relationship between self-efficacy, collective efficacy, and crime is addressed by asking three questions. First, does a general measure of youth's self-efficacy influence their involvement in crime? Second, does a macro level measure of collective efficacy influence youths' involvement in crime? Third, does collective efficacy moderate the relationship between self-efficacy and crime? To control for the contexts in which youths live and individual-level factors that can influence involvement in crime, and may influence efficacy, neighborhood context, family context, and individual-level demographic variables are also examined. Using Hierarchical Linear Modeling, the analyses indicate mixed support for a relationship between efficacy and individual-level involvement in crime. First, a significant negative relationship exists between self-efficacy and crime. Second, no significant effects emerge between collective efficacy and crime. Third, collective efficacy completely moderates the relationship between self-efficacy and crime, but not in the expected direction. After controlling for collective efficacy, the significant negative relationship between self-efficacy and crime is nullified. The conclusion then is that a general measure of self-efficacy influences a youth's involvement in crime, while a macro level measure of collective efficacy does not. Areas of future research and implications for theory and policy are discussed

ANALYZING DETERMINANTS OF URBAN VIBRANCY – A BIG DATA APPROACH ON CONNECTING BUILT ENVIRONMENT, SOCIAL ACTIVITY, AND IMAGES OF PLACES

This dissertation includes three self-contained and interrelated papers on built environment, urban vibrancy and vibrant places. Paper 1: Built environment and social activity: Exploring the relationship between place pattern and tweeting in Chicago Understanding the relationships between human activities and the built environment are central to urban planning. The increase in readily available, location-based social media data offers scholars important new data for understanding this relationship. This study examines the relationship between the spatial distribution of geotagged tweets and key characteristics of the built environment at the census block group level in Chicago. First, we performed a hotspot analysis to ascertain the distribution of tweets in the study area. Then, we employed a count regression model with Twitter message counts by census block group as the dependent variable to test the significance and magnitude of the associations between the built environment and tweeting. Then, we standardized the coefficients to compare the variables’ effects on tweeting. The analysis found that the built environment significantly influenced tweeting and provides empirical statistical evidence to guide urban planners’ placemaking decisions. Paper 2: Built environment and social activity: Exploring the relationship between place pattern and tweeting in the U.S. This study focuses on the relationship between the spatial distribution of geotagged tweets and the key characteristics of built environments as well as the pattern of the features at the census block group (CBG) level within the contiguous U.S. First, we employed a count regression model, setting the Twitter message density by CBG as the dependent variable to test the significance and magnitude of the associations between the built environment and tweeting behavior. Then, we utilized a combined research framework based on cluster analysis, hotspot analysis, and standardized score to explore the built environment pattern of the most vibrant tweeting areas. Results revealed that the built environment significantly influenced tweeting behavior. We further discovered four different built environment pattern types that provides empirical statistical evidence to guide urban planners’ placemaking decisions. Paper3: Built environment and vibrancy perception: Applications of deep learning and computer vision techniques in streetview. This study shows that street view image data from digital platforms such as Google Maps can further improve our understanding of the built environment patterns in cities. Compared with traditional human environment audit methods, combining deep learning and computer vision techniques efficiently provides finer resolution information with greater environmental detail. This will allow for many larger-scale street view studies to be conducted in the future. Using online survey data on vibrancy perception, the findings indicate that factors such as street parking have a strong positive influence on vibrancy perception while the proportion of sky has a strongly negative association. The study identifies six different patterns of street view landscape and shows that the complex relationship between the built environment and vibrancy perception is better analyzed using pattern discovery methods rather than global regression. A further regression analysis based on each street view pattern confirms that the context is crucial in determining both the significance and magnitude of the built environment factors on urban vibrancy perception.Doctor of Philosoph

Características de los vecindarios y la distribución espacial de problemas sociales en la ciudad de Valencia

The aim of this doctoral thesis is to explore the influence of neighborhood-level variables on the spatial and spatio-temporal distribution of different social problems in the city of Valencia. In Study 1, we present data on the development and validation of an observational instrument to assess neighborhood disorder. Results supported a three-factor model (physical disorder, social disorder and physical deterioration), and they showed good reliability and validity evidences. In Study 2, we assess the psychometric properties of a neighborhood disorder scale using Google Street View. Results supported a bifactorial solution with a general factor (general neighborhood disorder) and two specific factors (physical disorder and physical decay), and also showed good indicators of reliability and validity. In Study 3, we analyze the spatial distribution of drug-related police interventions and the neighborhood characteristics influencing these spatial patterns. Results indicated that high physical decay, low socioeconomic status, and high immigrant concentration were associated with high levels of drug-related police interventions. In Study 4, we analyze the spatio-temporal distribution of alcohol outlet density and its relationship with neighborhood characteristics. Results showed that off-premise density was higher in areas with lower economic status, higher immigrant concentration, and lower residential instability; restaurant and cafe density was higher in areas with higher spatially-lagged economic status, and bar density was higher in areas with higher economic status and higher spatially-lagged economic status. Furthermore, restaurant and cafe density was negatively associated with alcohol-related police calls-for-service, while bar density was positively associated with alcohol-related calls-for-service. In Study 5, we analyze the spatio-temporal distribution of suicide-related emergency calls. Results showed the importance of using a spatio-temporal modeling that also includes a seasonality effect. In Study 6, we analyze the relationship of suicide-related calls with neighborhood-level variables. Results showed that neighborhoods with lower levels of education level and population density, and higher levels of residential instability, percentage of one-person households and aging population had higher levels of suicide-related calls for service. Finally, in Study 7, we analyze the influence of university campuses on intimate partner violence against women risk. Results showed that the distance to the university campuses was associated with an increased risk of intimate partner violence against women, once controlled for other types of neighborhood-level variables. This doctoral thesis contributes to the understanding of the neighborhood-level characteristics associated with different social problems. These results are useful when planning and implementing community-level prevention and intervention strategies

Crime and Greenspace: Extending the Analysis Across Cities

The role of greenspace in urban areas has become a focus of research as municipalities seek to increase the quality of life in cities. Multiple benefits are found to be associated with greenspace, but disservices such as crime are often overlooked. Studies investigating the link between crime and greenspace have revealed mixed results and been limited in geographic scope. This dissertation sought to examine the crime and greenspace relationship, extending the analysis to multiple cities in order to describe how the relationship may vary in different contexts. Additionally, one possible cause of crime, increased temperatures, was investigated to determine how greenspace may moderate the impact of hot weather on crime risk. As urban parks are an important type of greenspace, the relationship between proximity to parks and crime was examined in four case cities. Parks are typically green areas of cities but also encompass less green land uses. This broad analysis revealed a more comprehensive understanding of how crime and greenspace are related which can inform residents and decision-makers of the benefits and possible drawbacks from including greenspace in city and community development

Aplicació de models d'efectes aleatoris en l'epidemiologia quantitativa

[cat] L'objectiu d'aquesta tesi és analitzar i comparar els procediments de models amb efectes aleatoris a partir de dos exemples reals, un relacionat amb les lesions d'un esport de contacte, i l'altre amb la supervivència d'un estudi longitudinal en una població gitana. En el primer capítol es fa èmfasi a certes limitacions que ens podem trobar i d'aquesta manera presentar models més sofisticats davant dissenys més complexes. En el segon capítol s'introdueixen els models amb efectes aleatoris, es revisa i avalua la qualitat de la informació aportada respecte l'anàlisi dels Generalized Linear Mixed Model (GLMM) en articles de medicina clínica. En el tercer capítol es compara el rendiment de l'estimació dels paràmetres del GLMM a través de tres filosofies estadístiques (marginal likelihood, hierarchical likelihood, Bayesian analysis) via estudis de simulació. En aquest mateix capítol s’ajusta un model GLMM per conèixer les associacions amb factors de risc en lesions d'un esport de contacte per així iniciar programes de prevenció i control de lesions en aquest esport. En el quart capítol s'introdueixen els models de supervivència amb efectes aleatoris o frailty models i es centra en els models de supervivència semiparamètrics. Finalment, en el darrer capítol s’inclou el resum de la tesi i les principals conclusions de la tesi.[eng] The aim of this PhD is to analyze and compare approaches of mixed models from two real datasets, one about sport injuries on contact wrestling, and the other one about survival among the Roma population: a longitudinal cohort study. Chapter 1 highlights certain limitations that we can find and it shows sophisticated models when facing more complex designs. Chapter 2 introduces mixed models, reviews the application of Generalized Linear Mixed Model (GLMM) and evaluates the quality of reported information in original articles in the field of clinical medicine. Chapter 3 compares the performance of parameter estimation in GLMM of three different statistical principles (Marginal likelihood, Extended likelihood, Bayesian analysis) via simulation studies. In this chapter a GLMM model is fitted to know the risk factors in injuries of a contact sport in order to carry out prevention and control programs in this sport. Chapter 4 introduces random effect models for survival data or frailty models and it focuses on semiparametric survival models. Finally, the last chapter includes the abstract and the main conclusions of this work