Shaping socio-functional urban identity
When morphometrics meet social media data to reveal neighbourhood structure and cores
Bibliographic Data
| ID | 22414542 |
|---|---|
| Authors | Almudena Nolasco-Cirugeda (0000-0002-3653-1650, University of Alicante, corresponding author), Jesús López-Baeza (University of Alicante), Sergio García-Pérez (0000-0002-1672-4965, Universidad de Zaragoza), Clara Garcia-Mayor (0000-0002-7714-3363, University of Alicante) |
| Year | 2026 |
| Volume | 178 |
| Pages | 107373 |
| Publication date | 2026-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cities (JOURNAL) |
| Journal identifiers | ISSN: 0264-2751 • E-ISSN: 1873-6084 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.cities.2026.107373 |
| OpenAlex | W7168133822 |
| Language | EN |
| References cited | 41 |
Understanding how urban form and activity interact to shape neighbourhood structure remains a central challenge in urban analysis. While morphological and activity-based approaches have been widely developed, their integration remains methodologically uneven, which can limit their ability to capture the socio-functional organisation of urban space. This paper proposes a reproducible computational workflow that integrates morphometric descriptors with location-based social media data to address two questions: to what extent does combining form and activity signals reveal socio-functional differentiation that morphology-only approaches cannot capture at comparable spatial resolution, and do the resulting units expose identifiable neighbourhood cores structured around concentrated activity? Using MomePy-based tessellations enriched with Foursquare-derived activity indexes within a unified clustering framework, the method is applied to 29 Southern European cities. The results show that this integration reveals consistent and recurrent patterns across cities: socio-functional differentiation within morphologically homogeneous fabrics, functional convergence across morphologically distinct areas, and the emergence of neighbourhood cores as centres of gravity within the resulting units. These patterns remain less visible when relying on morphology alone. The consistency of these findings across diverse urban contexts supports the robustness of the approach and its transferability as a basis for fine-grained urban diagnostics using open data, with direct relevance for neighbourhood-scale analysis and planning.
Morphometrics · Neighbourhood (mathematics) · Social media · Social network analysis · Human Mobility and Location-Based Analysis · Urban Design and Spatial Analysis · Urban, Neighborhood, and Segregation Studies
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