A Point-Based Intelligent Approach to Areal Interpolation.
Areal interpolation is the data transfer from one zonal system to another. A survey of previous literature on this subject points out that the most effective methods for areal interpolation are the intelligent approaches, which often take two-dimensional (2-D) land use or one-dimensional (1-D) road...
| Published in: | Professional Geographer Vol. 63; no. 2; pp. 262 - 277 |
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| Main Authors: | , |
| Format: | Article |
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Taylor & Francis Ltd
May 2011
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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=511510475&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511510475 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00330124 PGG jtl: Professional Geographer issn: 00330124 maglogo: N pubinfo: dt: May 2011 vid: 63 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 511510475 ppf: 262 ppct: 15 formats: tig: atl: A Point-Based Intelligent Approach to Areal Interpolation. aug: au: Zhang, Caiyun Qiu, Fang su: Interpolation Geographic information systems Geography -- Methodology Geography -- Statistical methods sug: subj: Interpolation Geographic information systems Geography -- Methodology Geography -- Statistical methods ab: Areal interpolation is the data transfer from one zonal system to another. A survey of previous literature on this subject points out that the most effective methods for areal interpolation are the intelligent approaches, which often take two-dimensional (2-D) land use or one-dimensional (1-D) road network information as ancillary data to give insight on the underlying distribution of a variable. However, the 2-D or 1-D ancillary information is not always applicable for the variable of interest in a specific study area. This article introduces a point-based intelligent approach to the areal interpolation problem by using zero-dimensional (0-D) points as ancillary data that are locationally associated with the variable of interest. The connection between zonal variables and point locations can be modeled with a linear or a nonlinear exponential function, which incorporates the distribution of the variables in the transferring of the information from the source zone to the target zone. An experimental study interpolating the population data at a suburbanized area suggests that the proposed method is an attractive alternative to other areal interpolation solutions based on the evaluation of its resulting accuracy and efficiency. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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