Groups and frequent visitors shaping the space dynamics

Our research is about a dynamic symbolic space model that is fed with data from the environment by a set of processing modules that receive raw data from sensor networks. For the conducted experiments we have been using data from a WiFi network as it is a widely available infrastructure in our campus. Here we propose two processing modules which will provide more information about the spaces described in the model. The first one tries to implement our human perception of the usual visitors of a place using two measures, the long term and the short term tenant level. The second one detects where groups of users emerge, how many there are and what are their dimensions. Based on this new perspective of the campus we intend to realize how the presence of people shapes the dynamics of a space.Fundação para a Ciência e a Tecnologia (FCT

A Centrality Measure for Urban Networks Based on the Eigenvector Centrality Concept.

A massive amount of information as geo-referenced data is now emerging from the digitization of contemporary cities. Urban streets networks are characterized by a fairly uniform degree distribution and a low degree range. Therefore, the analysis of the graph constructed from the topology of the urban layout does not provide significant information when studying topology–based centrality. On the other hand, we have collected geo-located data about the use of various buildings and facilities within the city. This does provide a rich source of information about the importance of various areas. Despite this, we still need to consider the influence of topology, as this determines the interaction between different areas. In this paper, we propose a new model of centrality for urban networks based on the concept of Eigenvector Centrality for urban street networks which incorporates information from both topology and data residing on the nodes. So, the centrality proposed is able to measure the influence of two factors, the topology of the network and the geo-referenced data extracted from the network and associated to the nodes. We detail how to compute the centrality measure and provide the rational behind it. Some numerical examples with small networks are performed to analyse the characteristics of the model. Finally, a detailed example of a real urban street network is discussed, taking a real set of data obtained from a fieldwork, regarding the commercial activity developed in the city

Evidence for a Conserved Quantity in Human Mobility

Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations. A concurrent study has emphasized the explorative nature of human behaviour, showing that the number of visited places grows steadily over time. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyse high-resolution multi-year traces of ~40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ~25. We use this finding to improve state-of-the-art modelling of human mobility. Furthermore, shifting the attention from aggregated quantities to individual behaviour, we show that the size of an individual’s set of preferred locations correlates with their number of social interactions. This result suggests a connection between the conserved quantity we identify, which as we show cannot be understood purely on the basis of time constraints, and the ‘Dunbar number’ describing a cognitive upper limit to an individual’s number of social relations. We anticipate that our work will spark further research linking the study of human mobility and the cognitive and behavioural sciences

Human Movement Is Both Diffusive and Directed

Understanding the influence of the built environment on human movement requires quantifying spatial structure in a general sense. Because of the difficulty of this task, studies of movement dynamics often ignore spatial heterogeneity and treat movement through journey lengths or distances alone. This study analyses public bicycle data from central London to reveal that, although journey distances, directions, and frequencies of occurrence are spatially variable, their relative spatial patterns remain largely constant, suggesting the influence of a fixed spatial template. A method is presented to describe this underlying space in terms of the relative orientation of movements toward, away from, and around locations of geographical or cultural significance. This produces two fields: one of convergence and one of divergence, which are able to accurately reconstruct the observed spatial variations in movement. These two fields also reveal categorical distinctions between shorter journeys merely serving diffusion away from significant locations, and longer journeys intentionally serving transport between spatially distinct centres of collective importance. Collective patterns of human movement are thus revealed to arise from a combination of both diffusive and directed movement, with aggregate statistics such as mean travel distances primarily determined by relative numbers of these two kinds of journeys

Hot and Bothered. Exploring the Effect of Heat on Pedestrian Behavior and Accessibility

Although many cities are incentivizing non-auto modes of transportation in response to the climate crisis, their sustainable mobility transition efforts are being challenged by the rising intensity and frequency of heatwaves. Pedestrians are exposed to high levels of heat stress on hot days, which may reduce their willingness to walk. It is important to understand how heat affects pedestrian behavior and accessibility, so that climate mitigation strategies can be better targeted to support walking as a mode but also as a first-/last-mile connection to public transit. In this study, we used a dataset of pedestrian trips undertaken during the summer of 2014 in Boston, MA. Along with several route attributes (such as length, turns, sidewalk width, amenities, NDVI, and SVF), we also included a measure of heat stress (UTCI) to explain pedestrian route choice. Using path-size logit models, we established that heat stress has a considerable and statistically significant effect on the perceived walking distance. We also found that the effect was non-linear and possibly exponential. Additionally, we illustrated the extent to which heat stress can reduce pedestrian accessibility to important destinations (such as public transit). This reduction was significant on a typical summer day, with an even sharper reduction on the hottest summer day. Non-White residents were observed to have lower accessibility levels compared to all pedestrians, likely because of disparities in urban heat exposure. Our findings highlight the importance of incorporating heat into transportation planning and urban design frameworks, especially with an equity lens to address unequal consequences

The amenity mix of urban neighborhoods

Advances in computational urbanism have stimulated the rise of generative and parametric approaches to urban design. Yet, most generative and parametric approaches focus on physical characteristics, such as a neighborhoods walkability, energy efficiency, and urban form. Here, we study the colocation patterns of more than one million amenities in 47 U.S. cities to model the amenity mix of neighborhoods, and to identify the amenities that are over- or under-supplied in a neighborhood. We build this model by combining a clustering algorithm, designed to identify amenity-dense neighborhoods, and a network, connecting amenities that are likely to collocate. Our findings extend generative and parametric urban design approaches to the amenity mix of neighborhoods, by leveraging the idea of relatedness from the economic geography literature, to evaluate and optimize a neighborhood's amenity mix

Packing Optimization for Digital Fabrication

We present a design-computation method of design-to-production automation and optimization in digital fabrication; an algorithmic process minimizing material use, reducing fabrication time and improving production costs of complex architectural form. Our system compacts structural elements of variable dimensions within fixed-size sheets of stock material, revisiting a classical challenge known as the two-dimensional bin-packing problem. We demonstrate improvements in performance using our heuristic metric, an approach with potential for a wider range of architectural and engineering design-built digital fabrication applications, and discuss the challenges of constructing free-form design efficiently using operational research methodologies

Urban Heat Risk Assessment: Exploring a Novel Pedestrian Network-Based Framework

Climate change and its associated increase in heat-related hazards pose a pressing threat to urban residents' health and well-being. This study introduces a novel framework to assess pedestrian heat-related exposure and risk in urban areas by integrating mean radiant temperature as a hazard, pedestrian flows as exposure, and vulnerability. We produce two key outcomes: (1) a heatmap of urban areas prone to higher risks, considering different origin-destination pairs, and (2) a street-level heat exposure index for each segment of the pedestrian network, combining mean radiant temperature and foot traffic. Our approach allows for a comprehensive heat risk assessment along walking routes, providing detailed insights into potential pedestrian heat risks. The findings offer valuable information to urban planners and policymakers, supporting evidence-based adaptation strategies and policy decisions essential for climate-proof planning. Identifying at-risk areas and evaluating heat exposure on sidewalks is a crucial foundation for developing effective strategies to mitigate the adverse effects of heat-related risks on urban populations. By implementing targeted interventions and urban design improvements, cities can enhance outdoor comfort and create heat-resilient, pedestrian-friendly environments, prioritizing the health and wellbeing of vulnerable groups like children and the elderly. Our pedestrian network-based approach enables a more precise assessment of outdoor spaces and pedestrians’ well-being, empowering policymakers and planners to identify priority areas for interventions and allocate resources more effectively during extreme heat events. This research contributes to the growing knowledge of robust risk assessment methodologies for climate-proof planning, specifically addressing outdoor heat-related risks during extreme heat events in cities