Recipes for Spatial Statistics with Global Datasets: A Martian Case Study

  • Authors:

  • Suniti Karunatillake;Steven W. Squyres;Olivier Gasnault;John M. Keller;Daniel M. Janes;William V. Boynton;Michael J. Finch

  • Affiliations:

  • Research Foundation, 255 ESS BLDG, Geosciences DEPT, University of Stony Brook, Stony Brook, USA 11794-2100 and Department of Astronomy, Cornell University, Ithaca, USA;Department of Astronomy, Cornell University, Ithaca, USA;Centre d'Etude Spatiale des Rayonnements, Centre National de la Recherche Scientifique, Université Paul Sabatier Toulouse, Toulouse, France;Physics Department, California Polytechnic State University, San Luis Obispo, USA;Lunar and Planetary Laboratory, University of Arizona, Tucson, USA;Lunar and Planetary Laboratory, University of Arizona, Tucson, USA;Lunar and Planetary Laboratory, University of Arizona, Tucson, USA

  • Venue:
  • Journal of Scientific Computing
  • Year:
  • 2011

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Abstract

The Mars Odyssey Gamma Ray Spectrometer has yielded planetary data of global extent. Such remote-sensing missions usually assign the value of a continuous-valued geospatial attribute to a uniform latitude-longitude grid of bins. Typical attributes include elemental-mass fraction, areal fraction of a mineral type, areal fraction of rocks, thermal inertia, etc. The fineness of the grid is chosen according to the spatial resolution of the orbiter and concomitant data processing. We describe methods to maximize the information extracted from both bin and regional data. Rigorous use of statistical parameters and related methods for inter- and intra- regional comparisons are also discussed. While we discuss results from the Mars Odyssey mission, the techniques we describe are applicable whenever continuous-valued attributes of a planet's surface are characterized with bins and regions. Our goal is to distill the simplest statistical methods for regional comparisons that would be intuitively accessible to planetary scientists.