International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 11–16, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-11-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 11–16, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-11-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  24 Aug 2020

24 Aug 2020

INFORMATION THEORY OF CARTOGRAPHY: A FRAMEWORK FOR ENTROPY-BASED CARTOGRAPHIC COMMUNICATION THEORY

Z. Li1,2 Z. Li
  • 1State-Province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University, Chengdu, China
  • 2Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR

Keywords: Information Theory of Cartography, Cartographic communication, Cartographic Information Theory, Boltzmann entropy, Generalized Shannon Entropy

Abstract. Map is an effective communication means. It carries and transmits spatial information about spatial objects and phenomena, from map makers to map users. Therefore, cartography can be regarded as a communication system. Efforts has been made on the application of Shannon Information theory developed in digital communication to cartography to establish an information theory of cartography, or simply cartographic information theory (or map information theory). There was a boom during the period from later 1960s to early 1980s. Since later 1980s, researcher have almost given up the dream of establishing the information theory of cartography because they met a bottleneck problem. That is, Shannon entropy is only able to characterize the statistical information of map symbols but not capable of characterizing the spatial configuration (patterns) of map symbols. Fortunately, break-through has been made, i.e. the building of entropy models for metric and thematic information as well as a feasible computational model for Boltzmann entropy. This paper will review the evolutional processes, examine the bottleneck problems and the solutions, and finally propose a framework for the information theory of cartography. It is expected that such a theory will become the most fundamental theory of cartography in the big data era.