ELEVATE explores how urban environments shape physical activity in daily life. It combines smartphone sensing, GPS tracking, and artificial intelligence to capture how people interact with streetscapes across the city. By analyzing large-scale street-view imagery alongside GPS-based movement data, ELEVATE identifies which environmental features encourage or hinder active lifestyles. The project emphasizes co-creation, working closely with citizens, planners, and other stakeholders to ensure inclusive, practical solutions. ELEVATE will also deliver an AI-powered tool that provides evidence-based recommendations for designing healthier, more active urban environments. By linking cutting-edge science with real-world application, the project aims to reduce health inequalities and support more liveable cities.
Cities around the world are densifying. This puts pressure on urban public spaces, which are crucial for healthy cities as they contribute to peoples’ health and well-being. Urban planning practice strives to provide citizens with positive experiences in public spaces. As a design and decision-support tool for these authorities, urban-scale digital twins have emerged to test future urban-design scenarios. However, the status quo of urban-scale digital twins is full of 3D buildings, but lack humans. That is, they do not incorporate virtual citizens, i.e. software agents, that can intrinsically experience the digital environment and react to it, as real citizens would do. Incorporating agents in digital twins, would allow for testing how these citizens respond to possible urban design scenarios and how this affects their health and well-being.
Therefore, our aim is two-fold: (i) to devise an agent architecture of intelligent and emotional citizens who can experience the city and articulate their sense of wellbeing in a digital twin, (ii) to form an interdisciplinary research community to tackle this.
This publication resulted from the project.
Multiple sustainable development goals (SDGs) identified by the United Nations relate directly to healthy and safe urban environments. To realize the designed targets, the appearance of streetscapes plays a vital role. Urban environments perceived as safe, pleasant, and walkable stimulate sustainable and healthy human behavior in terms of mobility, outdoor activities, access to environmental resources etc. Data on people’s environmental perception are, however, difficult to collect typically relying on a few in-situ neighborhood audits which are impossible to scale-up while being time-consuming, costly, and labor-intensive. A solution to this challenge may arise through urban big data analytics based on artificial intelligence (AI).
We propose an innovative AI-based approach to map human perceptions of streetscapes (e.g., safety, pleasantness, walkability) through a state-of-the-art deep learning computer vision-based model trained using crowdsourced stated preferences of street view data. Based on a coordinated effort exploiting synergies between three disciplines within the Faculty of Geosciences, the project will deliver robust evidence on the perceived qualities of streetscapes putting the FAIR principles into practice. We envision these data will be helpful in formulating actionable progress by making suggestions for expanding and refining SDG indicators on sustainable communities.
Blootstelling aan natuurlijke buitenomgevingen, waaronder groen, is van vitaal belang voor de gezondheid van mensen. Remote sensing via satellieten wordt het meest gebruikt om groenmaatregelen te genereren, maar dit geeft niet het perspectief op straatniveau weer dat mensen ervaren. Bezoeken ter plaatse zijn bevooroordeeld door subjectieve beoordelingen en het is arbeidsintensief, tijdrovend en inefficiënt op grote schaal. We hebben street view data gebruikt om een objectieve maat te creëren voor het meten van groenheid.
Cities in the global South are rapidly growing in size, but many marginalised and vulnerable residents (such as lower-income households, older adults, women and people with disabilities) do not have affordable, safe and accessible public transport, which reduces their ability to have decent work, healthcare and social life. Transport planning largely ignores access inequalities but prioritises efficiency and economic benefits. This project will go beyond traditional engineering approaches by taking a novel, user-centred intersectional approach that recognises how multiple forms of discrimination (e.g. classism, sexism, ageism and ableism) intersect to produce urban mobility inequalities for marginalised groups. The central objective is to develop evidence-based insights for affordable, safe and accessible urban mobility. More specifically, we aim to: 1) explore how physical and social barriers to urban transport are widened by the existing systems and the social and economic implications of such barriers (SDG-11&9), 2) develop and contextualise measures to improve access to work (SDG-8), healthcare (SDG-3) and social life (SDG-10) through improvements in the public transport system, and 3) co-design an inclusive urban mobility evaluative framework that can provide guidelines for inclusive cities. We will apply an innovative multi-sited mixed-methods approach combining visual surveys, GPS-led-geo-narratives and multi-stakeholder hackathons. Inequalities of urban mobility will be studied in Delhi, Bengaluru and Dhaka, as these cities are experiencing major infrastructural changes and have populations with multiple access disparities. Inclusive cities with affordable, safe and accessible low-carbon public transport lead to a reduction of emissions and improvements in public health and wellbeing.
Depression is a serious health concern and people affected by depression have a significantly higher suicide risk. Although the World Health Organization attributes modifiable environmental factors including urban environments to the health outcomes, they are largely disregarded as either stressors or buffers in scientific debates on depression and suicide.
Current studies are often limited to urban environmental characteristics of the neighborhoods in which people live. This may result in incorrect conclusions about health-influencing factors and incorrect policies. Human life ultimately unfolds over space-time; People are exposed to more urban environments, not only in daily life, but also during their lives.
The NEEDS project aims at interactions between urban environments, depression and suicide in the Netherlands. A multidisciplinary approach combining health, geographic information science and urban geography will be used, which will be based on smartphone-based human tracking, health record data and spatial temporal modeling. Knowledge about dynamic urban exposures is the key to revealing disease ethics, promoting health prevention and formulas that promote a healthier urban life.