La ciudad no es un árbol estático: comprender las áreas urbanas a través de la óptica de los datos de comportamiento en tiempo real

Cities are the main ground on which our society and culture develop today and will develop in the future. Against the traditional understanding of cities as physical spaces mostly around our neighborhoods, recent use of large-scale mobility datasets has enabled the study of our behavior at unprecedented spatial and temporal scales, much beyond our static residential spaces. Here we show how it is possible to use these datasets to investigate the role that human behavior plays in traditional urban problems like segregation, public health, or epidemics. Apart from measuring or monitoring such problems in a more comprehensive way, the analysis of those large datasets using modern machine learning techniques or causality detection permits to unveil of the behavioral roots behind them. As a result, only by incorporating real-time behavioral data can we design more efficient policies or interventions to improve such critical societal issues in our urban areas.Las ciudades son el principal terreno sobre el que se desarrollan —y se desarrollarán— nuestra sociedad y cultura. Frente a la concepción tradicional de las ciudades como espacio físico, en torno a nuestros barrios, el uso reciente de grandes conjuntos de datos de movilidad ha permitido estudiar el comportamiento humano a escalas espaciales y temporales sin precedentes, más allá de nuestros espacios residenciales. Este artículo muestra cómo es posible utilizar estos conjuntos de datos para investigar el papel que desempeña el comportamiento humano en problemas urbanos tradicionales como la segregación, la salud pública o las epidemias. Además de medir o monitorizar estos problemas de forma exhaustiva, el análisis de estos grandes conjuntos de datos mediante técnicas de aprendizaje automático o detección de causalidad permite desvelar raíces conductuales detrás de esos problemas. Como resultado, solo incorporando datos de comportamiento en tiempo real podemos diseñar políticas o intervenciones más eficientes que contribuyan a mejorar estos problemas sociales críticos en nuestras áreas urbanas

Effects of Population Co-location Reduction on Cross-county Transmission Risk of COVID-19 in the United States

The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing and travel reduction are recognized as essential non-pharmacologic approaches to control the spread of COVID-19. Prior studies demonstrated that human movement and mobility drove the spatiotemporal distribution of COVID-19 in China. Little is known, however, about the patterns and effects of co-location reduction on cross-county transmission risk of COVID-19. This study utilizes Facebook co-location data for all counties in the United States from March to early May 2020. The analysis examines the synchronicity and time lag between travel reduction and pandemic growth trajectory to evaluate the efficacy of social distancing in ceasing the population co-location probabilities, and subsequently the growth in weekly new cases. The results show that the mitigation effects of co-location reduction appear in the growth of weekly new cases with one week of delay. Furthermore, significant segregation is found among different county groups which are categorized based on numbers of cases. The results suggest that within-group co-location probabilities remain stable, and social distancing policies primarily resulted in reduced cross-group co-location probabilities (due to travel reduction from counties with large number of cases to counties with low numbers of cases). These findings could have important practical implications for local governments to inform their intervention measures for monitoring and reducing the spread of COVID-19, as well as for adoption in future pandemics. Public policy, economic forecasting, and epidemic modeling need to account for population co-location patterns in evaluating transmission risk of COVID-19 across counties.Comment: 12 pages, 7 figure

Amenity complexity and urban locations of socio-economic mixing

Cities host diverse people and their mixing is the engine of prosperity. In turn, segregation and inequalities are common features of most cities and locations that enable the meeting of people with different socio-economic status are key for urban inclusion. In this study, we adopt the concept of economic complexity to quantify the ability of locations -- on the level of neighborhoods and amenities -- to attract diverse visitors from various socio-economic backgrounds across the city. We construct the measures of neighborhood complexity and amenity complexity based on the local portfolio of diverse and non-ubiquitous amenities in Budapest, Hungary. Socio-economic mixing at visited third places is investigated by tracing the daily mobility of individuals and by characterizing their status by the real-estate price of their home locations. Results suggest that measures of ubiquity and diversity of amenities do not, but neighborhood complexity and amenity complexity are correlated with the urban centrality of locations. Urban centrality is a strong predictor of socio-economic mixing, but both neighborhood complexity and amenity complexity add further explanatory power to our models. Our work combines urban mobility data with economic complexity thinking to show that the diversity of non-ubiquitous amenities, central locations, and the potentials for socio-economic mixing are interrelated

Social integration as a determinant of inequalities in green space usage: insights from a theoretical agent-based model

Visiting urban green spaces (UGS) benefits physical and mental health. However, socio-economic and geographical inequalities in visits persist and their causes are under-explored. Perceptions of, and attitudes to, other UGS users have been theorised as a determinant of visiting. In the absence of data on these factors, we created a spatial agent-based model (ABM) of four cities in Scotland to investigate intra- and inter-city inequalities in UGS visiting. The ABM focused on the plausibility of a ‘social integration hypothesis' whereby the primary factor in decisions to visit UGS is an assessment of who else is likely to be using the space. The model identified the conditions under which this mechanism was sufficient to reproduce the observed inequalities. The addition of environmental factors, such as neighbourhood walkability and green space quality, increased the ability of the model to reproduce observed phenomena. The model identified the potential for unanticipated adverse effects on both overall visit numbers and inequalities of interventions targeting those in lower socio-economic groups

Network models of spatial interactions, human mobility and navigation ability

Networks provide a useful mathematical model for systems that are composed of agents, also called nodes, that interact in a pairwise fashion. Inside a spatial network, nodes are embedded in space and they typically interact much more strongly within their close surrounding, resulting in a special pattern of connections. Geographers, physicists and applied mathematicians have a set of tools at their disposal to understand these systems, and there is a close relationship between spatial networks and population-level models that were originally meant to represent human mobility, but that have been generalised to model more abstract spatial flows. Over the last decade, there has been an effort to adapt network tools for community detection — whose aim is to describe a system in terms of its mesoscale organisation into groups or communities — to spatial networks. So far, the methods that have been proposed are based on the modularity function, a heuristic function that compares network partitions against a suitable null model. However, a mesoscale description that is gaining traction relies instead on the stochastic block model (SBM), a generative model that can be fitted to an observation using statistical inference. We propose a methodology to leverage an SBM for the case of spatial networks. We are guided in this by an application to study a network linking customers to the stores they have shopped in, built from anonymised shopping records belonging to a large UK retailer. We also characterise customer shopping behaviours, specially from the lens of their mobility. Lastly, we study a dataset that was collected to assess spatial navigation, and we propose new metrics that are used in comparing the trajectories of healthy individuals, at-genetic-risk patients and patients diagnosed with dementia. Our hope is that this early proposal can be refined in the future by medical practitioners and used to detect early-onset Alzheimer’s Disease

Segregated interactions in urban and online space

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications

Segregated interactions in urban and online space

Abstract Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with significant socio-political and economic implications