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Estimating Hilly Areas Population Using a Dasymetric Mapping Approach: A Case of Sri Lanka’s Highest Mountain Range

1
Department of Geography, College of Social Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
2
Department of Geography, University of Colombo, 94 Kumaratunga Munidasa Mawatha, Colombo 00700, Sri Lanka
3
Institute for Korean Regional Studies, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 166; https://doi.org/10.3390/ijgi8040166
Received: 25 January 2019 / Revised: 26 March 2019 / Accepted: 27 March 2019 / Published: 2 April 2019
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Abstract

Since populations in the developing world have been rapidly increasing, accurately determining the population distribution is becoming more critical for many countries. One of the most widely used population density estimation methods is dasymetric mapping. This can be defined as a precise method for areal interpolation between different spatial units. In most applications of dasymetric mapping, land use and land cover data have been considered as ancillary data for the areal disaggregation process. This research presents an alternative dasymetric approach using area specific ancillary data for hilly area population mapping in a GIS environment. Specifically, we propose a Hilly Area Dasymetric Mapping (HDM) technique by combining topographic variables and land use to better disaggregate hilly area population distribution at fine-grain division of ancillary units. Empirical results for Sri Lanka’s highest mountain range show that the combined dasymetric approach estimates hilly area population most accurately because of the significant association that is found to exist between topographic variables and population distribution within this setting. This research is expected to have significant implications for national and regional planning by providing useful information about actual population distributions in environmentally hazardous and sparsely populated areas. View Full-Text
Keywords: Hilly area Dasymetric Mapping (HDM); population estimation; area specific ancillary data; topographic variables; GIS and cartographic application Hilly area Dasymetric Mapping (HDM); population estimation; area specific ancillary data; topographic variables; GIS and cartographic application
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Karunarathne, A.; Lee, G. Estimating Hilly Areas Population Using a Dasymetric Mapping Approach: A Case of Sri Lanka’s Highest Mountain Range. ISPRS Int. J. Geo-Inf. 2019, 8, 166.

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