Journal of Geography and Regional Planning
Subscribe to JGRP
Full Name*
Email Address*

Article Number - 2ABEAC544310

Vol.7(3), pp. 47-57 , May 2014
ISSN: 2070-1845

 Total Views: 0
 Downloaded: 0

Full Length Research Paper

A grid-based approach for refining population data in rural areas

Dulal Chandra Roy
  • Dulal Chandra Roy
  • Ministry of Planning, Government of Bangladesh, Sher-e-Bangla Nagar, Dhaka 1207, Bangladesh.
  • Google Scholar
Thomas Blaschke
  • Thomas Blaschke
  • Department of Geoinformatics - Z_GIS, University of Salzburg, Hellbrunnerstrasse 34, Salzburg 5020, Austria.
  • Google Scholar

 Received: 21 August 2013  Accepted: 15 April 2014  Published: 31 May 2014

Copyright © 2014 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0

Accurate and up-to-date population data is a prerequisite for many different applications, including risk and vulnerability management. There is, however, a shortage of data with a high spatial resolution, particularly in developing countries. Population densities are determined either by population census, typically conducted every ten years, or from global grid-based (raster) population datasets with relatively low resolution. Global population datasets are designed for global modeling studies, including climate change research, and their resolution is generally too low for local or community purposes. This paper presents a methodology for transforming population census data into grid-based (raster) population data with a relatively high resolution (100 m). Population census data, land cover, rural settlement data, and other geospatial datasets were utilized for a study area in the Khulna district of Bangladesh. Local experts validated the geographic information system (GIS)-derived population dataset as realistic and reasonably accurate. Our derived gridded population data was compared with the available LandScan global dataset. The overall difference between the population for 2010, which was projected from the 2001 census data, and our gridded population data was about 2.4% whereas the LandScan data overestimated the population in the study area by 49%.


Key words: Population density, spatial interpolation, geographic information system, bangladesh, Land Scan.

Aubrecht C, Levy M, De Sherbinin A, Yetman G, Jaite M, Steinnocher K, Metzler S (2010a). Refinement of regionally modeled coastal zone population data enabling more accurate vulnerability and exposure assessments. Proceedings of International Disaster and Risk Conference IDRC 2010, Davos, Switzerland.
Aubrecht C, Yetman G, Balk D, Steinnocher K (2010b). What is to be expected from broad-scale population data? Showcase accessibility model validation using high-resolution census information. Proceedings of the 13th AGILE International Conference on Geographic Information Science, 2010, Guimaraes, Portugal.
Aubrecht C, Özceylan D, Steinnocher K, Freire S (2013). Multi-level geospatial modeling of human exposure patterns and vulnerability indicators. Natural Hazards 68(1):147–163.
Balk DL, Deichmann U, Yetman G, Pozzi F, Hay SI, Nelson A (2006). Determining Global Population Distribution: Methods, Applications and Data. Adv. Parasitol. 62:119-156.
BBS (2001). Bangladesh National Population Census 2001. Bangladesh Bureau of Statistics (BBS), Statistics and Informatics Division, Ministry of Planning, Government of Bangladesh, Dhaka.,%20Ministry%20of%20Planning,%20Government%20of%20Bangladesh,%20Dhaka
CIESIN (Center for International Earth Science Information Network) (2005). Gridded population of the world (GPW). V3 [online], New York, Columbia University, USA.
Deichmann U (1996). A review of spatial population database design and modeling. Technical Report 96-3, National Center for Geographic Information and Analysis, Santa Barbara, USA.
Deichmann U, Balk D, Yetman G (2001). Transforming population data for interdisciplinary usages: from census to grid.
Elvidge CD, Sutton PC, Ghosh T, Tuttle BT, Baugh KE, Bhaduri B, Bright E (2009). A global poverty map derived from satellite data. Comput. Geosci. 35(8):1652-1660.
Gaughan AE, Stevens FR, Linard C, Jia P, Tatem AJ (2013). High resolution population distribution maps for Southeast Asia in 2010 and 2015. PLoS ONE 8(2):e55882.
Gallego FJ, Batista F, Rocha C, Mubareka S (2011). Disaggregating population density of the European Union with CORINE land cover. Int. J. Geogr. Inform. Sci. 25(12):2051-2069.
Hall O, Duit A, Caballero L (2008). World poverty, environmental vulnerability and population at risk for natural hazards. J. Maps 151-160.
Hall O, Stroh E, Paya F (2012). From census to grids: comparing gridded population of the world with Swedish census records. The Open Geogr J. 5:1-5.
Kienberger S (2012). Spatial modelling of social and economic vulnerability to floods at the district level in Buzi, Mozambique. Natural Hazards 64(3):2011-2019.
Kienberger S, Lang S, Zeil P (2009). Spatial vulnerability units - experts based spatial modeling of socio-economic vulnerability in the Salzach catchment, Austria. Natural Hazards and Earth System Sciences, 9:767-778.
Land Scan (2010). Land Scan documentation. Oak Ridge National Laboratory (ORNL), USA,
Linard C, Gilbert M, Tatem AJ (2010). Assessing the use of global land cover data for guiding large area population distribution modeling. Geojournal, 76(5):525-538.
Linard C, Gilbert M, Snow RW, Noor AM, Tatem AJ (2012). Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS ONE 7(2):e31743.
Mirella S, Pozzi F, Ataman E, Huddlestone B, Bloise M (2005). Mapping global urban and rural population distributions. Environment and Natural Resources Series, 24, FAO, Rome.
MODMR (Ministry of Disaster Management and Relief) (2008). National Plan for Disaster Management 2008-2015. Ministry of Disaster Management and Relief, Dhaka, Bangladesh.
Mondal P, Tatem AJ (2012). Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding. PLoS ONE 7(10):e48191.
Rafiq L, Blaschke T (2012). Disaster risk and vulnerability in Pakistan at a district level. Geomatics, Natural Hazards Risk 3(4):324-341.
Roy DC, Blaschke T (in press). Spatial vulnerability assessment of floods in the coastal regions of Bangladesh. Geomatics, Natural Hazards and Risk.
Schneiderbauer S (2007). Risk and vulnerability to natural disasters – from broad view to focused perspective: theoretical background and applied methods for the identification of the most endangered populations in two case studies at different scales. PhD Dissertation, Free University of Berlin, Germany.
Scholz J, Andorfer M, Mittlböck M (2013). Spatial Accuracy Evaluation of Population Density Grid Disaggregations with Corine Land cover. Geographic Information Science at the Heart of Europe, Berlin, Heidelberg: Springer pp.267-283.
Sweitzer J, Langaas S (1995). Modelling population density in the Baltic Sea states using the digital chart of the world and other small scale datasets. In: Gudelis V, Povilanskas R, Roepstorff A (eds) Coastal conservation and management in the Baltic region: Proceedings of the EUCC-WWF conference, 2-8 May 1994, Riga-Klaipeda-Kaliningrad, pp.257-267.
Tatem AJ, Linard C (2011). Population mapping of poor countries. Nature 474:36.
Tatem AJ, Noor AM, Hagen CV, Gregorio AD, Hay SI (2007). High resolution population maps for low income nations: combining land cover and census in East Africa. PLoS ONE 2007(2):e1298.
Tobler W, Deichmann U, Gottsegen J, Maloy K (1997). World population in a grid of spherical quadrilaterals. Int. J. Popul.Geogr. 3:203-225.<203::AID-IJPG68>3.0.CO;2-C<203::AID-IJPG68>3.3.CO;2-3


APA Roy, D. C., & Blaschke, T. (2014). A grid-based approach for refining population data in rural areas. Journal of Geography and Regional Planning, 7(3), 47-57.
Chicago Dulal Chandra Roy and Thomas Blaschke. "A grid-based approach for refining population data in rural areas." Journal of Geography and Regional Planning 7, no. 3 (2014): 47-57.
MLA Dulal Chandra Roy and Thomas Blaschke. "A grid-based approach for refining population data in rural areas." Journal of Geography and Regional Planning 7.3 (2014): 47-57.

Subscription Form