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Open AccessArticle

A Head/Tail Breaks-Based Method for Efficiently Estimating the Absolute Boltzmann Entropy of Numerical Raster Data

Faculty of Geosciences & Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
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ISPRS Int. J. Geo-Inf. 2020, 9(2), 103; https://doi.org/10.3390/ijgi9020103
Received: 10 December 2019 / Revised: 28 January 2020 / Accepted: 6 February 2020 / Published: 7 February 2020
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
Shannon entropy is the most popular method for quantifying information in a system. However, Shannon entropy is considered incapable of quantifying spatial data, such as raster data, hence it has not been applied to such datasets. Recently, a method for calculating the Boltzmann entropy of numerical raster data was proposed, but it is not efficient as it involves a series of numerical processes. We aimed to improve the computational efficiency of this method by borrowing the idea of head and tail breaks. This paper relaxed the condition of head and tail breaks and classified data with a heavy-tailed distribution. The average of the data values in a given class was regarded as its representative value, and this was substituted into a linear function to obtain the full expression of the relationship between classification level and Boltzmann entropy. The function was used to estimate the absolute Boltzmann entropy of the data. Our experimental results show that the proposed method is both practical and efficient; computation time was reduced to about 1% of the original method when dealing with eight 600×600 pixel digital elevation models.
Keywords: head/tail breaks; numerical raster data; absolute Boltzmann entropy; computational efficiency head/tail breaks; numerical raster data; absolute Boltzmann entropy; computational efficiency
MDPI and ACS Style

Zhang, H.; Wu, Z. A Head/Tail Breaks-Based Method for Efficiently Estimating the Absolute Boltzmann Entropy of Numerical Raster Data. ISPRS Int. J. Geo-Inf. 2020, 9, 103.

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