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Household Forecasts for the Planning of Long-Term Domestic Water Demand: Application to London and the Thames Valley - Dataset

Rees, Philip and Clark, Stephen and Nawaz, Rizwan (2020) Household Forecasts for the Planning of Long-Term Domestic Water Demand: Application to London and the Thames Valley - Dataset. University of Leeds. [Dataset] https://doi.org/10.5518/729

This item is part of the Long-Term Population and Property Forecasts for Thames Water collection.

Dataset description

The dataset, Household & Water Demand Forecasts.xlsx, provides the results of a forecast of household populations by household type (property type, size and ethnicity of household representative person) for six water resource zones supplied with domestic water by Thames Water Utilities Ltd. The file Metadata-PSP-18-0065.docx describes the contents of the Excel workbook, step by step. The workbook follows the structure set out in Figure 1 in the associated paper published in the journal Population, Space and Place in 2019. The paper describes the household forecasting models, results, and adds forecast water consumption to produce forecast total domestic water consumption. For details of the water consumption models see Rizwan Nawaz, Philip Rees, Stephen Clark, Gordon Mitchell, Adrian McDonald, Michelle Kalamandeen, Chris Lambert, and Ross Henderson (2019) Long-Term Projections of Domestic Water Demand: A Case Study of London and the Thames Valley. Journal of Water Resource Planning & Management, 2019, 145(11): 05019017. DOI: 10.1061/(ASCE)WR.1943-5452.0001088

Subjects: L000 - Social studies > L700 - Human & social geography
F000 - Physical sciences > F800 - Physical geographical sciences > F810 - Environmental geography
Divisions: Faculty of Environment > School of Geography
Related resources:
LocationType
https://doi.org/10.1002/psp.2288Publication
http://eprints.whiterose.ac.uk/151315/Publication
https://doi.org/10.5518/376Dataset
http://www.ethpop.orgDataset
https://doi.org/10.5255/UKDA-SN-852508Dataset
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited: 07 Feb 2020 13:09
URI: https://archive.researchdata.leeds.ac.uk/id/eprint/639

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