Data in Brief paper

This month saw a new paper/open data product of the Urban Gramar sphere see the light of day. As part of the spin-off ITINERANT project, and in collaboration with GDSL colleagues Krasen Samardzhiev, Alessia Calafiore, and Francisco Rowe, we published an open data product and data descriptor that presents a new classification of signatures entirely based on function. Here are the coordinates where you can find everything:

Samardzhiev, K.; Fleischmann, M.; Arribas-Bel, D.; Calafiore, A.; Rowe, F. (2022). “Functional signatures in Great Britain: A dataset”. Data in Brief, 43. 10.1016/j.dib.2022.108335

            title = {
              Functional signatures in Great Britain: A dataset
            journal = {Data in Brief},
            volume = {43},
            pages = {108335},
            year = {2022},
            issn = {2352-3409},
            doi = {},
            url = {},
            author = {Krasen Samardzhiev and Martin Fleischmann and Daniel Arribas-Bel and Alessia Calafiore and Francisco Rowe},
            keywords = {
              Geographic data science, Urban analytics, 
              Functional areas, Spatial data, Land use
            abstract = {
              The spatial distribution of activities and agents within cities, conceptualised as an urban function, profoundly affects how different areas are perceived and lived. This dataset introduces the concept of functional signatures - contiguous areas of a similar urban function delineated based on enclosed tessellation cells (ETC) - and applies it to the area of Great Britain. ETCs are granular spatial units, which capture function based on interpolations from open data inputs stretching from remote sensing to land use, census and points of interest data. The spatial extent of each signature type is defined by grouping ETCs using cluster analysis, based on similarity between their functional profiles, inferred by the data linked to each cell. This approach results in a dataset that reflects urban function as a composite of aspects, rather than a singular use, and is built up from granular spatial units. Furthermore, the underlying data are sourced from available open data products, which together with a method and code fully available, yields a fully reproducible pipeline and makes our dataset and open data product. Both the final classification composed of 17 types of functional signatures and the underlying data collected on the level of enclosed tessellation cells are included in the release and described in this report.