Systems approach to data-driven integrated urban and infrastructure planning (SAPID)

What is the goal?

The project goals are

  • To develop a prototype of a Discovery Model (DM) that will provide input information for integrated urban planning. The DM model will be used in two ways
    • To provide inputs for Infrastructure Modelling Toolkit (IMT) defined as physical system and machine-learning (ML) tools designed to assess the impact of development on the environment, ecology, and human health. We will test this link using the water infrastructure as a case study, for which we already have developed physical system (development -> water quality) and ML (water quality à ecology) IMT.
    • To inform data-driven indicators that can help us better understand the opportunities and constraints when developing local plans (e.g., water efficiency index).
    • To develop a prototype of a Suitability Model (SM) that will integrate information obtained from DM and IMT to assess what is the optimal allocation of proposed development and the retrofit that would need to be included in the design. The development and retrofit targets and options will be developed by IMT and combined with the opportunities and constraint information produced by DM. We envisage that an AI-based approach will be developed to support SM allocation optimisation process.
    • To understand how the proposed methodology could be extended to other infrastructure systems (e.g., energy, transport), and to also understand impact (air quality, urban microclimate) and damages (human health). This will be done through a participatory workshop where the framework will be presented and input from relevant experts will be captured and integrated.

Fig. 1: Conceptual framework for systems approach to data-driven integrated urban and infrastructure planning

What are we working on?
We have recently started work on the project and we are carrying out the initial literature review.

Expected Outcomes
The project aims to provide an integrative and holistic vision of the urban system. Findings from the project will be published in a short Research Digest. The resulting material will be used for the development of a larger project proposal (e.g., EPSRC program grant). This project will have some real impact for different types of stakeholders (e.g., urban developers, Local Planning Authorities [LPAs], policymakers, etc.), and industry sector related to urban planning (e.g., water or energy companies).

Project Team
Professor Jennifer Whyte, Dr Stanislava Boskovic, Dr Ana Mijic, Pepe Puchol-Salort, Eduardo Rico.

How to engage with us?
For more information on the project contact:

Funding information
This research project is funded under the Data-driven engineering design under uncertainty challenge area which is part of the Alan Turing Institute’s Data-Centric Engineering research program.