Georeferencing locality descriptions and computing associated uncertainty using a probabilistic approach

  • Authors:

  • Q. Guo;Y. Liu;J. Wieczorek

  • Affiliations:

  • School of Engineering, University of California Merced, Merced, USA;School of Engineering, University of California Merced, Merced, USA,Institute of Remote Sensing and Geographic Information Systems, Peking University, Beijing 100871, PR China;Museum of Vertebrate Zoology, 3101 Valley Life Sciences Building, University of California, Berkeley, USA

  • Venue:
  • International Journal of Geographical Information Science - Digital Gazetteer Research
  • Year:
  • 2008

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Abstract

Locality information for specimens of geological, biological, and cultural objects is traditionally stored as textual descriptions. With an increasing demand for natural and cultural information, the lack of spatially explicit descriptions has become a major barrier to the management and analysis of these data using geographic information systems. In this paper, we propose a method to georeference descriptive data, using an uncertainty field model to represent the distribution of a locality based on two types of uncertainties: uncertainty of reference objects, and the uncertainty of spatial relationships. We propose probability distributions for each known form of these two types of uncertainties and present a probabilistic method to georeference localities based on the integration of different uncertainty sources.