The LandScan Global Population Distribution Project: Current State of the Art and Prospective Innovation

Advances in remote sensing, dasymetric mapping techniques, and the ever-increasing availability of spatial datasets have enhanced global human population distribution databases. These datasets demonstrate an enormous improvement over the conventional use of choropleth maps to represent population distribution and are vital for analysis and planning purposes including humanitarian response, disease mapping, risk analysis, and evacuation modeling. Dasymetric mapping techniques have been employed to address spatial mismatch, but also to develop finer resolution population distributions in areas of the world where subnational census data are coarse or non-existent. One such implementation is the LandScan Global model which provides a 30 arc-second global population distribution based on ancillary datasets such as land cover, slope, proximity to roads, and settlement locations. This work will review the current state of the LandScan model, future innovations aimed at increasing spatial and demographic resolution, and situate LandScan within the landscape of other global population distribution datasets.

[1]  Anil M. Cheriyadat,et al.  Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Pinki Mondal,et al.  Uncertainties in Measuring Populations Potentially Impacted by Sea Level Rise and Coastal Flooding , 2012, PloS one.

[3]  J. E. Dobson,et al.  LandScan: A Global Population Database for Estimating Populations at Risk , 2000 .

[4]  U. Deichmann,et al.  The Global Demography Project (95-6) , 1995 .

[5]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[6]  J. E. Dobson,et al.  LandScan: Locating People is What Matters , 2002 .

[7]  C. Lo,et al.  CHINESE URBAN POPULATION ESTIMATES , 1977 .

[8]  Ola Hall,et al.  From Census to Grids: Comparing the Gridded Population of the World with Swedish Census Records , 2012 .

[9]  A. Tatem,et al.  Population Distribution, Settlement Patterns and Accessibility across Africa in 2010 , 2012, PloS one.

[10]  Michael F. Goodchild,et al.  Areal interpolation: A variant of the traditional spatial problem , 1980 .

[11]  Jeremy Mennis,et al.  Intelligent Dasymetric Mapping and Its Application to Areal Interpolation , 2006 .

[12]  John K. Wright.  A Method of Mapping Densities of Population: With Cape Cod as an Example , 1936 .

[13]  N. Lam.  Spatial Interpolation Methods: A Review , 1983 .

[14]  Daniele Ehrlich,et al.  Settlement location and population density estimation in rugged terrain using information derived from Landsat ETM and SRTM data , 2008 .

[15]  Cynthia A. Brewer,et al.  Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation , 2001 .

[16]  J. E. Cohen,et al.  Hypsographic demography: the distribution of human population by altitude. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Robin Flowerdew,et al.  Developments in Areal Interpolation Methods and GIS , 1992 .

[18]  N. Clinton,et al.  Modeling population density using land cover data , 2005 .

[19]  I. Bracken,et al.  The Generation of Spatial Population Distributions from Census Centroid Data , 1989, Environment & planning A.

[20]  A. Tatem,et al.  The effects of spatial population dataset choice on estimates of population at risk of disease , 2011, Population health metrics.

[21]  P. Hofmann,et al.  Detecting informal settlements from QuickBird data in Rio de Janeiro using an object based approach , 2008 .

[22]  Budhendra L. Bhaduri,et al.  Mapping of settlements in high-resolution satellite imagery using high performance computing , 2007 .

[23]  A. Petrov,et al.  One Hundred Years of Dasymetric Mapping: Back to the Origin , 2012 .

[24]  Michael F. Goodchild,et al.  A Framework for the Areal Interpolation of Socioeconomic Data , 1993 .

[25]  R. Flowerdew,et al.  Using areal interpolation methods in geographic information systems , 1991 .

[26]  W. Tobler.  Smooth pycnophylactic interpolation for geographical regions. , 1979, Journal of the American Statistical Association.

[27]  Olufemi A. Omitaomu,et al.  Construction of Synthetic Populations with Key Attributes: Simulation Set-Up While Accommodating Multiple Approaches within a Flexible Simulation Platform , 2010, 2010 IEEE Second International Conference on Social Computing.

[28]  Peter F. Fisher,et al.  Modeling Sensitivity to Accuracy in Classified Imagery: A Study of Areal Interpolation by Dasymetric Mapping* , 1996 .

[29]  Peter A. Rogerson,et al.  GIS and Spatial Analytical Problems , 1993, Int. J. Geogr. Inf. Sci..

[30]  Budhendra L. Bhaduri,et al.  High resolution urban feature extraction for global population mapping using high performance computing , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.