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Book Chapter: Estimating Population Size Using Spatial Analysis Methods

TitleEstimating Population Size Using Spatial Analysis Methods
Authors
Keywordspopulation estimation
crisis
GIS
spatial interpolation
field survey
Issue Date2007
PublisherSpringer Berlin Heidelberg.
Citation
Estimating Population Size Using Spatial Analysis Methods. In Lai, PC and Mak, ASH (Eds.). GIS for Health and the Environment: Development in the Asia-Pacific Region, p. 271-287. New York, N.Y.: Springer Berlin Heidelberg, 2007 How to Cite?
AbstractIn population size is required within the first 24-72 hours to plan relief-related activities and target interventions. The estimation method should be easy to use by fieldworkers from various backgrounds, and minimize intrusion for the displaced population. Two methods have already been on trial: an adaptation of the Quadrat technique, and a newer T-Square technique. Here, we report the results of a field trial to test these alongside a newly adapted spatial interpolation approach. We compared the results with a population census of nine hamlets within the Tanowsri sub-district, Ratchaburi Province, Thailand. We mapped the study area to define the population for inclusion, as applications of this method would occur in closed settings. Before implementation, we simulated the spatial interpolation using geo-referenced positions of households in three hamlets. This procedure enabled us to establish some operational parameters to estimate population size, including the number of random points needed for the field test, the radius of the sample region and the dimension of the grid intersection on the interpolated surface. Each method was tested over the same area. The interpolation method seems to produce accurate results at 30m-grid spacing (at 104% of the census) with a worst-case estimate at 124%. These results are comparable to those of the Quadrat (92% to 108%) and T-Square (80%). The methods are proven feasible to apply, with a high acceptability among local workers we trained. The interpolation method seemed the easiest to conduct. The results were tested statistically where possible, though this was an experimental setting, and further trial is recommended.
Persistent Identifierhttp://hdl.handle.net/10722/117796
ISBN
ISSN
Series/Report no.Lecture Notes in Geoinformation and Cartography

 

DC FieldValueLanguage
dc.contributor.authorPinto, Aen_HK
dc.contributor.authorBrown, Ven_HK
dc.contributor.authorChan, KKWen_HK
dc.contributor.authorChavez, IFen_HK
dc.contributor.authorChupraphawan, Sen_HK
dc.contributor.authorGrais, RFen_HK
dc.contributor.authorLai, PCen_HK
dc.contributor.authorMak, ASHen_HK
dc.contributor.authorRigby, JEen_HK
dc.contributor.authorSinghasivanon, Pen_HK
dc.date.accessioned2010-09-26T07:34:19Z-
dc.date.available2010-09-26T07:34:19Z-
dc.date.issued2007en_HK
dc.identifier.citationEstimating Population Size Using Spatial Analysis Methods. In Lai, PC and Mak, ASH (Eds.). GIS for Health and the Environment: Development in the Asia-Pacific Region, p. 271-287. New York, N.Y.: Springer Berlin Heidelberg, 2007-
dc.identifier.isbn978-3-540-71317-3-
dc.identifier.issn1863-2246-
dc.identifier.urihttp://hdl.handle.net/10722/117796-
dc.description.abstractIn population size is required within the first 24-72 hours to plan relief-related activities and target interventions. The estimation method should be easy to use by fieldworkers from various backgrounds, and minimize intrusion for the displaced population. Two methods have already been on trial: an adaptation of the Quadrat technique, and a newer T-Square technique. Here, we report the results of a field trial to test these alongside a newly adapted spatial interpolation approach. We compared the results with a population census of nine hamlets within the Tanowsri sub-district, Ratchaburi Province, Thailand. We mapped the study area to define the population for inclusion, as applications of this method would occur in closed settings. Before implementation, we simulated the spatial interpolation using geo-referenced positions of households in three hamlets. This procedure enabled us to establish some operational parameters to estimate population size, including the number of random points needed for the field test, the radius of the sample region and the dimension of the grid intersection on the interpolated surface. Each method was tested over the same area. The interpolation method seems to produce accurate results at 30m-grid spacing (at 104% of the census) with a worst-case estimate at 124%. These results are comparable to those of the Quadrat (92% to 108%) and T-Square (80%). The methods are proven feasible to apply, with a high acceptability among local workers we trained. The interpolation method seemed the easiest to conduct. The results were tested statistically where possible, though this was an experimental setting, and further trial is recommended.-
dc.languageengen_HK
dc.publisherSpringer Berlin Heidelberg.en_HK
dc.relation.ispartofGIS for Health and the Environment: Development in the Asia-Pacific Region-
dc.relation.ispartofseriesLecture Notes in Geoinformation and Cartography-
dc.subjectpopulation estimation-
dc.subjectcrisis-
dc.subjectGIS-
dc.subjectspatial interpolation-
dc.subjectfield survey-
dc.titleEstimating Population Size Using Spatial Analysis Methodsen_HK
dc.typeBook_Chapteren_HK
dc.identifier.emailChan, KKW: kawinchankw@gmail.comen_HK
dc.identifier.emailLai, PC: pclai@hkucc.hku.hken_HK
dc.identifier.emailMak, ASH: a9491156@graduate.hku.hken_HK
dc.identifier.authorityLai, PC=rp00565en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-540-71318-0_20-
dc.identifier.hkuros126815en_HK
dc.identifier.spage271en_HK
dc.identifier.epage287en_HK

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