Spatial Information Theory A Theoretical Basis for GIS

1993 ◽  
Keyword(s):  
Information Theory ◽  
Theoretical Basis ◽  
Spatial Information

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

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 11-16
Author(s):  
Z. Li
Keyword(s):  
Information Theory ◽  
Communication Theory ◽  
Spatial Information ◽  
Digital Communication ◽  
Spatial Configuration ◽  
Statistical Information ◽  
Fundamental Theory ◽  
Boltzmann Entropy ◽  
Spatial Objects ◽  
Thematic Information

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.

scholarly journals An Information Theory based Approach to Multisource Clustering

2018 ◽  
Author(s):  
Pierre-Alexandre Murena ◽  
Jérémie Sublime ◽  
Basarab Matei ◽  
Antoine Cornuéjols
Keyword(s):  
Information Theory ◽  
Ensemble Learning ◽  
Theoretical Basis ◽  
Clustering Algorithm ◽  
Real Life ◽  
Theoretical Framework ◽  
Data Sets ◽  
Artificial Data ◽  
Collaborative Clustering ◽  
Theory Of Information

Clustering is a compression task which consists in grouping similar objects into clusters. In real-life applications, the system may have access to several views of the same data and each view may be processed by a specific clustering algorithm: this framework is called multi-view clustering and can benefit from algorithms capable of exchanging information between the different views. In this paper, we consider this type of unsupervised ensemble learning as a compression problem and develop a theoretical framework based on algorithmic theory of information suitable for multi-view clustering and collaborative clustering applications. Using this approach, we propose a new algorithm based on solid theoretical basis, and test it on several real and artificial data sets.

Sign in / Sign up

Export Citation Format

Share Document