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

Autor:	Jeffrey Stewart, Christopher Barr, Donna Heimiller, R. George, Liz Brady Sabeff, Gardar Johannesson, Anelia Milbrandt
Rok vydání:	2008
Předmět:	Computer science business.industry Energy modeling Library and Information Sciences Missing data Translation (geometry) computer.software_genre Distributed generation Data mining Temporal scales business Focus (optics) Spatial analysis computer Energy (signal processing)
Zdroj:	Journal of Map & Geography Libraries. 4:285-295
ISSN:	1542-0361 1542-0353
DOI:	10.1080/15420350802142520
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::02f65ee4ae5afa5f5571efe62747f542 https://doi.org/10.1080/15420350802142520 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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