Adapting Existing Spatial Data Sets to New Uses: An Example from Energy Modeling

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Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, and economic projections. These data are available at various spatial and temporal scales, which may be different from those needed by the energy modeling community. If the translation from the original format to the format required by the energy researcher is incorrect, then resulting models can produce misleading conclusions. This is of increasing importance, because of the fine resolution data required by models for new alternative energy sources such as wind and distributed generation. This paper addresses the matter ... continued below

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Johanesson, G; Stewart, J S; Barr, C; Sabeff, L B; George, R; Heimiller, D et al. June 23, 2006.

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Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, and economic projections. These data are available at various spatial and temporal scales, which may be different from those needed by the energy modeling community. If the translation from the original format to the format required by the energy researcher is incorrect, then resulting models can produce misleading conclusions. This is of increasing importance, because of the fine resolution data required by models for new alternative energy sources such as wind and distributed generation. This paper addresses the matter by applying spatial statistical techniques which improve the usefulness of spatial data sets (maps) that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) imputing missing data and (3) merging spatial data sets.

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PDF-file: 22 pages; size: 0.7 Mbytes

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  • Journal Name: Journal of Map & Geography Libraries, vol. 4, no. 2, July 1, 2008, pp. 285-295; Journal Volume: 4; Journal Issue: 2

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  • Report No.: UCRL-JRNL-222460
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 936674
  • Archival Resource Key: ark:/67531/metadc900740

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  • June 23, 2006

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  • Sept. 27, 2016, 1:39 a.m.

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  • Dec. 6, 2016, 6:46 p.m.

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Johanesson, G; Stewart, J S; Barr, C; Sabeff, L B; George, R; Heimiller, D et al. Adapting Existing Spatial Data Sets to New Uses: An Example from Energy Modeling, article, June 23, 2006; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc900740/: accessed October 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.