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Built-up areas in Paris and the metropolis: a reading of the urban fabric

These two datasets cover the entire territory of the Greater Paris Metropolis (MGP), with specific coverage for Paris and another for communes outside Paris. They make it possible to precisely locate the floor area occupied by buildings, whether isolated or interconnected, and to qualify certain characteristics, in particular the period of construction and height/floor area, and for Paris the typology of roofs.
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Built-up areas in Paris and the Greater Paris region © Apur

A built-up area is the footprint of one or more buildings associated by function or ownership. This type of data forms an essential basis for morphological analysis of territories, for reading the urban fabric and for implementing development projects.

These rights-of-way can be used for a variety of purposes: spatial location, identification of densely built-up or under-utilized sectors, evaluation of the effects of verticality on the urban landscape, monitoring of construction dynamics and study of interactions between buildings and the environment (heat islands, soil sealing, vegetation potential, etc.).

The existence of two distinct data sets is explained by the heterogeneity and richness of the associated data: those relating to Paris include a greater number of variables.
These data provide a precise picture of the distribution, shape and characteristics of buildings throughout Paris and the Greater Paris Metropolis. They constitute a reference resource for analyzing urban structure, monitoring its evolution and informing development or urban transition policies.

Technical details

Data sets on built-up areas include different variables for Paris and the rest of the Greater Paris Metropolis. These indicators can be used to classify buildings according to their morphology, height, use or certain energy or architectural characteristics.

Main variables for Paris built-up areas (surface representation)

  • Period of construction/rehabilitation: year and period of construction (before 1800, 1801-1850, ..., 2008 and later), year of rehabilitation.
  • Heights: minimum, maximum, average, median.
  • Floor area: calculated on the basis of equipment types, median heights and number of storeys.
  • Roofs: surface area (in m²) by roof type (tile, green, mineral, zinc, etc.), shape (flat roof, gable roof, typical house/villa shapes)
  • Sunshine: average annual sunshine, classified by energy received (in kWh/m²/year).
  • Building morphology: single-family homes, apartment buildings, residential, office or business complexes, towers and high-rise buildings (≥ 37 m).
  • Type of urban fabric: regular, composite, remarkable buildings, etc. (based on field surveys).

Main variables for built-up areas outside Paris (surface representation)

  • Construction period: year and period of construction (before 1800, 1801-1850, ..., 2008 and later).  Currently being consolidated
  • Heights: minimum, maximum, average, median.
  • Floor area: estimate based on median height, equipment types and number of storeys.
  • Building morphology: same categories as for Paris (single-family, multi-family, high-rise, etc.).

For further technical information, please consult the detailed Geocatalog data sheet.

Sources
Data on built-up areas is based on a combination of several sources:

  • Topographic cadastral base (DGFiP), consistent with the plot of land;
  • Apur's BD Projets (planned buildings) developed to monitor development projects;
  • Successive orthophotoplans (2008, 2012, 2013, 2015, 2021, 2024);
  • For construction dates: historical building plans, DGFiP, BD Projets, terrain;
  • For heights / floor area: MNT, MNS, Apur's equipment base.

License and terms of use

Before any use, you are invited to read and accept the terms of the ODbL license and the limits of use specified here: https://www.apur.org/open_data/resume_licence_ODbl.pdf

Data reuse

If you have any questions or suggestions, please don't hesitate to contact us at data@apur.org
Your feedback and contributions are most welcome to enrich these datasets.