DOI: 10.5285/cdfea06f-d47c-4967-99d4-cc71bddea45d
title: Exposure datasets representing a synthetic future urban context, including socio-demographic, building, and land-use data from 10 cities, for natural hazard risk modelling

description: 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).
description: 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.