Work Package WP1: Urban Data Analytics
Aim
To provide the empirical basis of how the clustering of urban functions – such as work, shopping, and leisure – can foster spatial interactions while reducing the demand for resources such as land and transportation (‘urban intensification’).
Fig. 1: Singapore - Mobility flows to central locations in Singapore derived from mobile phone data. Based on M. Schläpfer et al., Nature, 2021. (visualization by G. Du, MIT)
Proposed Approach
- Inference of city-wide mobility flow networks from anonymized mobile phone data (Fig. 1).
- Network theory and machine learning to relate mobility flows and resulting spatial interactions to (i) the clustering of urban functions and (ii) their resource demands.
Expected Results
- Classification of locations according to (i) their urban functions, (ii) the rate of spatial interactions, and (iii) the resource demand for these spatial interactions (travel distances of visitors and potential for sharedtransport; built-up volume).
- Systematic quantification of how the clustering of urban functions affects the resource efficiency of locations (rate of spatial interactions versus resource demand).
- Set of visualizations showing the resource efficiency of locations across urban space.
Work Package WP2: Urban Economic Modeling
Aim
To develop a quantitative theory of urban design, based on the mobility of people, information on the transport infrastructure, as well as the spatial distribution of residences and points of attraction (work, administration, leisure, commerce, shopping) in Singapore.
Proposed Approach
- Augment the quantifiable urban-spatial equilibrium model with many spatial units Ahlfeldt, Redding, Sturm, and Wolf, 2015; Dingel and Tintelnot, 2023), to capture a household’s consumption of goods (shopping), of services and leisure facilities, and of administrative services, paying attention to the distances traveled.
- Calibrate the model using the spatial locations of residences/households, work places, as well as of administrative and leisure points of interest.
- Simulate the model for alternative designs of the relative spatial locations of residences and points of attraction under alternative objective functions, incorporating different utility functions with weights on non-monetary factors such as social interactions.
Expected Results
- Quantitative spatial-urban model suited to simulate alternative spatial configurations of residences and points of attraction for Singapore.
- Data collection on five location types: (1) home/residence; (2) work/jobs; (3) commerce/shopping; (4) administration; and (5) leisure.
- Model simulations for alternative utility functions/government objectives with different relative importance for social interactions.
Work Package WP3: Urban Policy and Planning
Aim
To study places of social gathering in Singapore and Zürich to understand where and how people gather socially in the city, and derive the key planning and design attributes of vibrant public/semi-public social gathering spaces.
Proposed Approach
- Develop a conceptual study framework informed by theories on urban design of the social life of public spaces.
- Identify 2–3 sites through anecdotal and social media content analysis of photos taken of uses and users in public/semi-public spaces.
- Conduct qualitative study through observation and short interviews about use and user of public/semipublic space.
- Organize two-part Design-Thinking workshop where major stakeholders will be invited to discuss the solutions to various problems.
Expected Results and Implications
- Refined understanding of what we know about the important planning and design elements that enable vibrant social interaction in public/semi-public spaces through 2 in-depth city case studies with different population size, density, and culture.
- Empirically-informed design principles to enable vibrant and resource-efficient spaces by stakeholders.
References
Ahlfeldt, G.M., S.J. Redding, D. Sturm, and N. Wolf, 2015. The economicsof density: Evidence from the Berlin Wall. Econometrica 83(6), 2127–2189.
Dingel, J., and F. Tintelnot, 2023. Spatial economics for granular settings. Paper under revision at Econometrica.
Integration and Strategies / [EFF] Resource-Efficient Urban Intensification
Principal Investigators: Prof. Dr Peter Egger, Assoc. Prof. Dr Siew Ann Cheong
Co- Investigators: Prof. Eva Castro, Asst Prof. Dr Felicity Chan, Assoc. Prof. Dr Lock Yue Chew, Asst Prof. Dr Cheng Long, Asst. Prof. (adj.) Dr Markus Schlaepfer, PD Dr. Joris van Wezemael
Researchers: Dr Tianyu Dong
