Exposure datasets representing a synthetic future urban context, including socio-demographic, building, and land-use data from 10 cities, for natural hazard risk modelling
https://doi.org/10.5285/cdfea06f-d47c-4967-99d4-cc71bddea45d
Cite this dataset as:
Menteşe, E.Y.; Özer, E.; Rawal, P.; Comelli, T.; Cetin, N.İ.; Dabeek, J.; Kyesii, A.; Chengo, V.; Zisan, B.; Cubillo, P. (2025). Exposure datasets representing a synthetic future urban context, including socio-demographic, building, and land-use data from 10 cities, for natural hazard risk modelling. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/cdfea06f-d47c-4967-99d4-cc71bddea45d
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PLEASE NOTE:
By accessing or using this dataset, you agree to the terms of the relevant licence agreement(s). You will ensure that this dataset is cited in any publication that describes research in which the data have been used.
This dataset is available under the terms of the Open Government Licence 
This dataset includes synthetically produced data from 10 different cities (Istanbul, Nablus, Chattogram, Cox’s Bazaar, Nairobi, Nakuru, Quito, Kokhana, Rapti and Darussalam) for a future urban context. The data includes physical elements in a city such as buildings, roads, and power networks, as well as social elements such as households and individuals. The dataset contains a maximum of 9 different data types, described below. For some cities power and road network data were not considered due to context specific priorities.
landuse: The land use plan data depicting how the land will be zoned and used in the next fifty years within the area or interest. The attributes include the land use type, areal coverage in hectares, maximum population density and existing population.
building: Data representing the building footprints that will emerge as a result of the future exposure generation procedure. It includes the attributes of the building such as its identifier number, construction type, number of floors, footprint area, occupation type and construction code level.
road nodes: Data representing the points where road segments (edges) are connected to each other, including the identifier number for each node.
road edges: Data representing the road segments, including the ID numbers of the starting and ending point (node).
power nodes: Data representing the points where power lines (edges) are connected to each other, including the identifier number for each node.
power edges: Data representing the power segments, including the including the ID numbers of the starting and ending point (node).
household: Data that contains social attributes of a household living in a building. The attributes include number of individuals, income level and commonly used facility ID (such as hospital).
individual: Data that contains the attributes of the individuals that are a part of a household. The attributes are age, gender, school ID (if relevant), workplace ID (if relevant) and last attained education level.
Distribution table: The future projections for each city that identifies the socio-demographic changes and expected physical development in the next 50 years.
The data can be used in geospatial platforms. The nomenclature for the data is as follows:
“CitynameFutureExposureDataset/Cityname_CommunityCode_DataType”. This dataset was created as case studies for the Tomorrows Cities: Tomorrowville virtual testbed. It is supported by NERC as part of the GCRF Urban Disaster Risk Hub (NE/S009000/1).
landuse: The land use plan data depicting how the land will be zoned and used in the next fifty years within the area or interest. The attributes include the land use type, areal coverage in hectares, maximum population density and existing population.
building: Data representing the building footprints that will emerge as a result of the future exposure generation procedure. It includes the attributes of the building such as its identifier number, construction type, number of floors, footprint area, occupation type and construction code level.
road nodes: Data representing the points where road segments (edges) are connected to each other, including the identifier number for each node.
road edges: Data representing the road segments, including the ID numbers of the starting and ending point (node).
power nodes: Data representing the points where power lines (edges) are connected to each other, including the identifier number for each node.
power edges: Data representing the power segments, including the including the ID numbers of the starting and ending point (node).
household: Data that contains social attributes of a household living in a building. The attributes include number of individuals, income level and commonly used facility ID (such as hospital).
individual: Data that contains the attributes of the individuals that are a part of a household. The attributes are age, gender, school ID (if relevant), workplace ID (if relevant) and last attained education level.
Distribution table: The future projections for each city that identifies the socio-demographic changes and expected physical development in the next 50 years.
The data can be used in geospatial platforms. The nomenclature for the data is as follows:
“CitynameFutureExposureDataset/Cityname_CommunityCode_DataType”. This dataset was created as case studies for the Tomorrows Cities: Tomorrowville virtual testbed. It is supported by NERC as part of the GCRF Urban Disaster Risk Hub (NE/S009000/1).
Publication date: 2025-08-20
View numbers valid from 20 August 2025 Download numbers valid from 20 August 2025 (information prior to this was not collected)
Formats
Comma-separated values (CSV), json, geojson, qmd
Spatial information
Study area
Spatial representation types
Tabular (text)
Vector
Vector
Spatial reference system
WGS 84 / Pseudo-Mercator
Provenance & quality
For generating the future exposure dataset for each city, a specific computational algorithm that takes into account the land use plan and user defined future projections is used. The algorithm is explained in detail by Mentese et al., 2023.
The production of the land use plan relies on a community based process, that includes different community groups’ involvement and participatory engagement. In these engagements, in each city, specific groups are identified with the guidance of local experts as part of the Tomorrow’s Cities Project. After the identification, experts from Tomorrow’s Cities Project, local experts and community representatives from the community groups gather for participatory spatial planning for their future urban contexts.
Then, the produced land use plan is integrated (using the software algorithm) with the future socio-demographic projections and expected physical development defined by the local experts and existing literature on the context. As a result of this procedures, building data, socio-demographic datasets and network related data (where relevant) is produced.
The production of the land use plan relies on a community based process, that includes different community groups’ involvement and participatory engagement. In these engagements, in each city, specific groups are identified with the guidance of local experts as part of the Tomorrow’s Cities Project. After the identification, experts from Tomorrow’s Cities Project, local experts and community representatives from the community groups gather for participatory spatial planning for their future urban contexts.
Then, the produced land use plan is integrated (using the software algorithm) with the future socio-demographic projections and expected physical development defined by the local experts and existing literature on the context. As a result of this procedures, building data, socio-demographic datasets and network related data (where relevant) is produced.
Licensing and constraints
This dataset is available under the terms of the Open Government Licence
Cite this dataset as:
Menteşe, E.Y.; Özer, E.; Rawal, P.; Comelli, T.; Cetin, N.İ.; Dabeek, J.; Kyesii, A.; Chengo, V.; Zisan, B.; Cubillo, P. (2025). Exposure datasets representing a synthetic future urban context, including socio-demographic, building, and land-use data from 10 cities, for natural hazard risk modelling. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/cdfea06f-d47c-4967-99d4-cc71bddea45d
Supplemental information
Menteşe, E. Y., Cremen, G., Gentile, R., Galasso, C., Filippi, M. E., & McCloskey, J. (2023). Future exposure modelling for risk-informed decision making in urban planning. International Journal of Disaster Risk Reduction, 90, 103651.
Correspondence/contact details
Menteşe, E.Y.
Anofa Engineering, Planning and Informatics Ltd.
Environment Centre Wales, Deiniol Road
Bangor
Gwynedd
LL57 2UW
UNITED KINGDOM
emin.mentese@anofa.co
Bangor
Gwynedd
LL57 2UW
UNITED KINGDOM
Authors
Menteşe, E.Y.
Anofa Engineering, Planning and Informatics Ltd.
https://orcid.org/0000-0002-7187-4384 Other contacts
Rights holder
University of Edinburgh
Custodian
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
Publisher
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk