We are a modelling research group focusing on understanding urban land and transport development processes and policy mechanisms through applied urban modelling and data analytics.
The research group is led by Dr. Li WAN.
Dr. Li Wan
Emily Tianyuan Wang
Causality between the built environment and subjective wellbeing has thus far been segmentally explored and partially quantified. We identify two unresolved challenges in the literature. Firstly, a reliance on cross-sectional data produces associative findings. Secondly, a reductive approach to regress aggregate subjective wellbeing on limited and disparate built environment measurements risks significant confounding effects. We address the research gaps by leveraging residential relocation as a natural experiment to investigate the causality between built environment change and subjective wellbeing. Two causal inference methods (difference-in-differences and synthetic control) are applied and compared. Our research design incorporates novel extensions to the canonical forms of both causal inference methods – staggered difference-in-differences and generalised synthetic control methods – to accommodate individual-level data with multiple relocation timepoints.
This research switches the policy assessment from the single city and one policy analysis into a multi-policy city-regional scale assessment by considering the city network, different hierarchical city levels, and policy scenario comparison. The changing preferences for city-center and suburban amenities and the popularity of remote work in the post-pandemic are two new challenges to existing SCGE models. Those two new challenges on existing SCGE models have been considered in this research to model the public location choice and urban spatial structure.
This study builds on the activity-based approach in transport modelling and expands it as a unified analytical framework for quantifying the effects of the land-use and transport planning on long-term city-scale energy intensity in fast-growing city regions. The new modelling framework features a consistent activity-based choice model linking the building and transport sector, subject to explicit time and budget constraints and spatial equilibrium condition in the housing market. The study demonstrates the policy use of the model through an empirical model application for the Greater Beijing in China and estimates the carbon/energy intensity elasticities with respect to planning policy variables with the base-year model after calibration. The calibrated model will then be used to estimate the emission/energy outlook based on a series of urban spatial development scenarios and to test the magnitude and rate of technological and behavioural change required for achieving local sustainability goals.
The study aims to quantitatively assess the spatial and economic impact of Ethnic Integration Policy (EIP) on the development of housing market and urban spatial structure in Singapore. Specifically, two main research questions are proposed. (1) Whether EIP would have an impact on the spatial pattern and time-series changes of housing price in Singapore? If so, how would EIP affect the development of urban spatial structure in Singapore through its impact on the housing market? (2) How to mitigate/enhance the negative/positive impact of EIP in relation to the 2030 planning goals in Singapore? The research design includes three main work packages, population synthesis, modelling residential location choice under housing market equilibrium and policy scenario analysis.
Inter-city development disparity is a salient issue for both developed and developing countries. A causal determination of whether and to what extent variations in planning policies lead to different outcomes remains an onerous analytical challenge. Quantitative modelling of such causality requires the identification of a bundle of planning strategies from text-based planning documents in a holistic and temporally consistent manner such that the evolution of development outcomes could be investigated in relation to planning policy changes over time. Enabled by recent progress in natural language processing (NLP), this paper presents a novel NLP application for identifying key planning strategies from a large amount of text-based government documents through a case study of 117 prefecture-level cities in China. Based on official, city-level government reports from 2011 to 2019, the evolving policy strategies are identified and linked with the change of development outcome. Policy implications and directions for future research are discussed.
Night-time light (NTL) data provide a novel and accessible source for monitoring the Spatio-temporal dynamics of urban expansion. The static thresholds ignore the path-dependent nature of urban development. Using NTL data for 2012-2018, this study proposes a new method using dynamic threshold (DT) for extracting UBA using NTL data for 600+ Chinese cities. The dynamic thresholds explicitly address the temporal continuity of urban physical development and further consider intra-city heterogeneity in terms of NTL brightness change pattern. Through a comparison with official statistics in China and UN-Habitat Sentinel-2 Human Settlement data, it is demonstrated that the overall accuracy of the DT method exceeds 85%, and the Kappa index exceeds 0.45.
The Charging Infrastructure (CI) enables large uptakes of Electric Vehicle (EV) for transport decarbonisation. However, the large adoption of EVs may cause congestions. For sustainable mobility, it is timely important to incorporate congestion control when promoting EVs. Smart placement and operation of CIs should be applied to tackle congestion issues by proactively intervening traffic and driving behaviours for the more EV transport system. CI planning should be considered with the public transport while car dependency can be influenced by economic factors such as charging prices. The research question is how adaptive and inter-sectoral EV charging policies may help mitigate car dependence and complement public transport by spatial and technical configuration of CIs and the associated pricing mechanism. Parameters of traffic, parking, demography and public transport will be incorporated with spatial analysis, while the feasibility of employing dynamic and adaptive charging pricing to influence travel behaviour will be explored.
The growing penetration of ICT has redefined recreational activities and changed the demand, choice, and location of urban recreational services. As the Chinese proverb goes, “fragrant wine would not be restricted by deep alleys,” ICT has alleviated many restrictions imposed by conventional locations on people’s spatial preference for leisure activities, enabling new forms and levels of functional integration. Under the influence of ICT, urban recreational service space has been spreading to the urban suburbs and further penetrating the urban two-dimensional and three-dimensional spaces such as residential and office areas. In this context, urban mixed-use development evolves from block scale to the building scale and from ground level to ground above level, resulting in the need for new policies and regulations. Therefore, this study aims to measure the penetration degree of recreational service, identify the characteristics of the built environment elements, and explore urban planning and real estate policy by referring to international case studies. The findings could enhance the theory of mixed-use development and compact city in the digital era, and contribute to policymaking for managing and re-purposing urban spaces, especially for high-density cities.
Digital Twins for Smart Cities: Conceptualisation, challenges and practices，2023
Digital Twins for Smart Cities: Conceptualisation, challenges and practices，2023
Published on ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences，2022
Digital Twins in the Built Environment: Fundamentals, principles and applications，2022
Presentation for the Data for Policy 2021 Conference, 2021
Published on Journal of Urban Technology, 2020
Published on ISPRS International Journal of Geo-Information, 2020
Financial Times article, 2020
Published on International Conference on Smart Infrastructure and Construction, 2019
Journal of Urban Management, 2023
Published online by Cambridge University Press, 2023
Published on Cities, 2022
Published on Landscape Architecture Frontiers, 2020
Published on Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, 2019
Published on International Journal of Sustainable Transportation, 2023
Published on Travel Behaviour and Society, 2021
Published on Transport Policy, 2021
Published on Computers, Environment and Urban Systems, 2017
Lincoln Institute Working Paper, 2022
For the UK2070 Commission, 2020
Published on Environment and Planning B: Urban Analytics and City Science, 2019
Published on Transportation Research Part D: Transport and Environment, 2017
Published on Cambridge Journal of Regions, Economy and Society, 2022
Published on Sustainability, 2019
Published on Journal of Urban Planning and Development, ASCE, 2017
The lack of information on trip purpose and alternative mode in micromobility service usage data remains a major analytical challenge. Conventional survey method is subject to significant sampling and stated preference biases. To overcome this challenge, this paper presents a new inference method through a case study of rental e-scooters in London. The inference method features a rule-based algorithm for matching observed rental e-scooter trips with filtered trip samples in the English National Travel Survey (NTS) series. Probability distribution of trip purposes and alternative modes are then retrieved from NTS. Inference results are validated using official data. Discrepancies, sources of biases and correction measures are investigated. Based on the inferred mode substitution pattern, we estimate greenhouse gas emissions reduction of selected rental e-scooter trips in London (36-103g CO2e per mile). It is expected that the proposed method is applicable to a wide range of micromobility studies using service usage data.Open PDF in Browser
Causality between the built environment and subjective wellbeing has thus far been segmentally explored and partially quantified. We identify two unresolved challenges in the literature. Firstly, a reliance on cross-sectional data produces associative findings. Secondly, a reductive approach to regress aggregate subjective wellbeing on limited and disparate built environment measurements risks significant confounding effects. We address the research gaps by leveraging residential relocation as a natural experiment to investigate the causality between built environment change and subjective wellbeing (measured with composite score of negatively phrased General Health Questionnaire-12 items). Two causal inference methods (difference-in-differences and synthetic control) are applied and compared. The use of the ‘Understanding Society’ dataset (The UK Household Longitudinal Study, 2009-2019), combined with holistic locational attributes (Area Classification at the Lower Super Output Area level as per the UK Census) for exploring such causality is novel in literature. Specifically, to estimate the wellbeing effects of residential relocation, we compare movers (treatment n=773) to non-movers (control n=4,619). To estimate the effects of built environment change, we compare movers with a change of built environment type (n=506) to those moving into the same built environment type (n=267). Our research design incorporates novel extensions to the canonical forms of both causal inference methods – staggered difference-in-differences and generalised synthetic control methods – to accommodate individual-level data with multiple relocation timepoints.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430978
Mental health in the UK had deteriorated compared with pre-pandemic trends. The impact of COVID-19 on the subjective wellbeing of working populations with distinct lifestyles is not yet studied. Methods: Combining time use surveys collected pre- and during COVID-19, latent class analysis was used to identify distinct lifestyles based on aggregated daily activity patterns and reported working modes. We provide qualitative pen portraits alongside pre-versus-during pandemic comparisons of intraday time use and wellbeing patterns. Lifestyle heterogeneity in wellbeing was quantified in relation to aggregated activity types. Results: COVID-19 impact on wellbeing varied significantly between usual working hours (6am-6pm) and rest of the day. The decline in wellbeing outside of usual working hours was significant and consistent across lifestyles. During usual working hours, the direction of impact varied in line with working modes: wellbeing of homeworkers decreased, remained relatively stable for commuters, and increased for certain hybrid workers. Magnitude of impact correlates strongly with lifestyle: those working long and dispersed hours are more sensitive, whereas non-work dominated lifestyles are more resilient. Conclusion: The direction and magnitude of impact from COVID-19 were not uniformly manifested across activity types, time of day, and latent lifestyles. Blurring work-life boundaries and general anxiety about the pandemic may be key determinants of the decline outside of usual working hours. During usual working hours, strong yet complex correlations between wellbeing and time-use changes suggested that policies aiming to enhance wellbeing of workers need to consider not only spatial flexibility but also provide wider support for temporal flexibility.https://www.medrxiv.org/content/10.1101/2022.04.27.22273297v1
Our PhD Candidate Jerry Chen joined the 40th International Conference on Machine Learning (ICML) from 23-29 July at Hawaii Convention Center. He provided poster presentation highlighting his recent work titled "Counterfactuals for subjective wellbeing panel data: Integrated application of statistical ensemble and causal forest methods".
Five members of our group, Qiancheng Wang (PhD Student), Emily Tianyuan Wang (PhD Student), Enjia Zhang (Visitng Student), Dr. Donggyun Ku (Visting Academic Researcher), and Shan Yu (MPhil Student), recently joined the 18th Computational Urban Planning and Urban Management (CUPUM) Conference at McGill Uni from 20-22 June 2023. They delivered five presentations in total for each of their recent research work.
Dr. Li Wan has recently got this new book published: Originated from manufacturing and aerospace engineering, the concept of ‘digital twin’ has been celebrated as the next-generation smart city technology, with a number of high-profile applications emerging across the globe. By employing a socio-technical framework, this book critically examines the limitations and potential risks of deploying ‘digital twins’ as a generic technology to cities, and calls for a socio-technical perspective for conceptualising, developing, evaluating and governing city digital twins. The book provides both conceptual clarity and practical guidance for supporting the development of city digital twins...  [Read more]
PhD student Emily Tianyuan Wang recently participated in the 7th Smart Data Smart City (SDSC) Conference, hosted by University of New South Wales (UNSW) in Australia. She presented her recent research work titled "A novel use of latent class modelling to understand the heterogeneity of urban land use efficiency"...  [Read more]
Earlier this month, the UK Government unveiled an overdue yet ambitious 'levelling-up’ plan that aims to spread opportunity and prosperity to all parts of the UK. A quick word search through the Executive Summary reveals that the word ‘planning’ appears only twice, one referring to the protection of Green Belts and another alluding to the seemingly stalled planning reform...  [Read more]
A new method for identifying built-up areas using night-time light data – A case study of 600+ Chinese cities: Night-time light (NTL) data provide a novel and accessible source for monitoring the spatio-temporal dynamics of urban expansion. Existing methods tend to use national/regional and temporally static thresholds for separating urban built-up areas (UBA) and non-UBA...  [Read more]