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On small area composite indicators and classifications for urban planning: theory-driven and data-driven approaches

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  • Alessia Calafiore
Alessia Calafiore
university of edinburgh
DOI: https://doi.org/10.36253/contest-15767

Published 2026-03-02

Keywords

  • evidence based ,
  • urban planning,
  • multidimensional indicators,
  • GeoAI

How to Cite

Calafiore, A. (2026). On small area composite indicators and classifications for urban planning: theory-driven and data-driven approaches. Contesti. Città, Territori, Progetti. https://doi.org/10.36253/contest-15767

Copyright (c) 2026 Alessia Calafiore

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

The collection of data by public and private organisations has increased rapidly and continuously in the last decades. While some have called for “the end of theory” in a society dominated by data, this paper argues that theory and data driven approaches are both fundamental to make informed evidence-based decisions.

Taking the case of geographic data products aimed at supporting urban planning, this paper critically examines the approaches that underpin their development. Through illustrative examples, it describes two types of research data products — small-area multidimensional theory-driven indicators and data-driven classifications — and outlines their respective advantages, limitations, and applications.

While it is recognised that geographic data products can be a valuable asset to support urban planning, challenges remain in translating research outputs into practice. To avoid technocratic and decontextualised applications of such data, it is suggested to prioritise reflexivity, situate knowledge, acknowledge uncertainty, and embrace openness throughout the data production and use process.

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