Improving benefit transfer demand functions: a GIS approach.
The writers outline how they developed a model to anticipate the number of visitors to a recreational woodland in eastern England, Thetford Forest's Lynford Stag, by integrating data from several sources within a geographical information system (GIS). They undertook such a task because of the rarit...
| Published in: | Journal of Environmental Management Vol. 51; pp. 373 - 390 |
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| Main Authors: | , , |
| Format: | Article |
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Academic Press Inc.
December 1997
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=506067124&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 506067124 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: December 1997 vid: 51 pid: 735 pub: Academic Press Inc. artinfo: ui: 506067124 10.1006/jema.1997.0150 ppf: 373 ppct: 17 formats: tig: atl: Improving benefit transfer demand functions: a GIS approach. aug: au: Lovett, Andrew A. Brainard, Julii S. Bateman, Ian J. su: Geographic information systems Valuation of forests Contingent valuation Travel costs Economic demand Outdoor recreation Economics Forests & forestry United Kingdom sug: subj: United Kingdom Geographic information systems Valuation of forests Contingent valuation Travel costs Economic demand Outdoor recreation Economics Forests & forestry ab: The writers outline how they developed a model to anticipate the number of visitors to a recreational woodland in eastern England, Thetford Forest's Lynford Stag, by integrating data from several sources within a geographical information system (GIS). They undertook such a task because of the rarity of previous efforts to include in a single arrivals model visitor demand functions that incorporate travel time, demographic, and substitute factors, on the development of which successful benefit transfer must necessarily rely. They classify variables into discrete groups that were gathered into comparatively homogeneous zones from which to determine visit rates. In addition, in order to evaluate the impact of each determinant, they apply Poisson regression techniques in a stepwise procedure. They point out that their investigation highlighted both substantial promise and some caveats in employing GIS for future benefit transfer work. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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