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Department of Land Economy

Environment, Law & Economics

Our Research

Our research focuses on several key areas employing spatial analysis methodologies and metrics, as well as  dynamic simulation models. Both ‘off-the-shelve’ commercial software and software developed by us are used.

Examples of research at our LISA Lab include

-Complexity analysis and dynamic simulation

-Creative cities/firms/industries simulation models

-Energy efficient cities, urban form and decision making

-Land  Use Change and Scenarios for City and Regional Planning

-Spatial analysis and urban spatial metrics (in particular metrics for urban growth and shrinkage)

-Integrated Land Use and Transport Models

- Big Data, data mining, data validation and model calibration


In the sections that follow, you will find some examples of our research:


Projects, models & metrics 


19.eMOTIONAL Cities - Mapping the cities through the senses of those who make them

Funded by the European Commission’s Horizon 2020 Framework Programme, eMOTIONAL Cities is a 48-month project, with a total budget of nearly 5 million Euros, that is designed to fully characterise the intensity and complexity of urban health challenges and inequalities.

As the world is becoming more urbanized and cities of the future need to be people-centred, robust evidence-based knowledge on the underlying biological and psychological processes, by which Urban Planning & Design influence brain circuits and human behaviour, will be critical for policy making on urban health. Emotions are key drivers of our decisions; similarly, our choices are the conduit for our well-being and health.

The eMOTIONAL Cities research focusing on the signals triggered in our neurobiological architecture, responsible for emotions and decisions, while humans interact with the urban environment, will shed light on how to improve population health, physical and/or mental.

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Work developed by Elisabete A. Silva, Haifeng Niu, Ana Paula Seraphim and Xinmeng Tu

In order to know more about our work on this project please see the following website:

18.Sensing Urban Dynamics through Crowdsourced Data with the Support of Machine Learning Techniques

Crowdsourced data such as social media data, points of interest and geotagged images has attracted the attention of urban researchers, as it provides first-hand information regarding human activities, perception and the interaction with the built environment, helping researchers illuminate a richer sense of what cities are all about from data itself. Despite the appealing potential of crowdsourced data in answering urban-related questions, there are challenges inherent to the whole process of transforming immense data into accurate and actionable insights in the urban domain, which overshadow the benefits of this type of data in well-designed cases of use. This research focuses on leveraging crowdsourced data in sensing urban dynamics by implementing advanced machine learning techniques to deal with hitherto unsolved challenges. The main aim is to contribute to the current knowledge on crowdsourced data-driven studies for better management and planning of cities. This thesis then demonstrates its application by carrying out three empirical studies of the Greater London – sensing urban function from points of interest data, sensing activity pattern from location-based social media data, and sensing public opinion from social media data.


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Work developed by Haifeng Niu and Elisabete A. Silva

In order to know more about our work on this subject please see following papers:

Niu, H.*, & Silva, E. A., 2021. Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London. Computers, Environment and Urban Systems, 88, 101651.


Niu, H.*, Silva, E.A., 2020. Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods. Journal of Urban Planning and Development, 146, 04020007.

Chen, Y., Niu, H.*, & Silva, E. A. (forthcoming). The Road to Recovery: sensing public opinion towards reopening measures with social media data in post-lockdown cities.

17. Towards smart city and smart transport in English metropolitan areas: enhancing the understanding and governance through multi-sourced data analysis

Researching data-driven smart city governance through applying advanced data analytics such as natural language processing and machine learning to multi-sourced urban big data, including official and crowdsourced data. Specifically studying activity-travel patterns of citizens and public attitudes towards governance issues. Also exploring how data-driven evidence can support smart governance. Current work includes using unsupervised and supervised algorithms to identify patterns and determinants.