The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 209–213, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-209-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 209–213, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-209-2016

  22 Jun 2016

22 Jun 2016

EXTENDING LKN CLIMATE REGIONALIZATION WITH SPATIAL REGULARIZATION: AN APPLICATION TO EPIDEMIOLOGICAL RESEARCH

Alexander Liss1, Yulia R. Gel2, Alexandra Kulinkina1, and Elena N. Naumova1,3 Alexander Liss et al.
  • 1Department of Civil and Environmental Engineering, Tufts University, Medford, USA
  • 2University of Texas, Dallas, USA
  • 3Friedman School of Nutrition Science and Policy, Tufts University, Boston, USA

Keywords: Remote Sensing, Geographic Information Systems, Environmental Health, Climate Classification, Geospatial Statistics

Abstract. Regional climate is a critical factor in public health research, adaptation studies, climate change burden analysis, and decision support frameworks. Existing climate regionalization schemes are not well suited for these tasks as they rarely take population density into account. In this work, we are extending our recently developed method for automated climate regionalization (LKN-method) to incorporate the spatial features of target population. The LKN method consists of the data limiting step (L-step) to reduce dimensionality by applying principal component analysis, a classification step (K-step) to produce hierarchical candidate regions using k-means unsupervised classification algorithm, and a nomination step (N-step) to determine the number of candidate climate regions using cluster validity indexes. LKN method uses a comprehensive set of multiple satellite data streams, arranged as time series, and allows us to define homogeneous climate regions. The proposed approach extends the LKN method to include regularization terms reflecting the spatial distribution of target population. Such tailoring allows us to determine the optimal number and spatial distribution of climate regions and thus, to ensure more uniform population coverage across selected climate categories. We demonstrate how the extended LKN method produces climate regionalization can be better tailored to epidemiological research in the context of decision support framework.