The use of spatial information in entropy measures

19 April 2018
April 19, 2018
Doctoral School of Social Sciences
via Verdi 26, 38122 - Trento
+39 0461 283756 - 2290
+39 0461 282335

Skype: school.socialsciences

2 PM, Seminar Room, Department of Economics and Management, via Inama 5 - Trento



A very recent proposal of a set of entropy measures for spatial data, based on building pairs of realizations, allows to split the data heterogeneity, usually assessed via Shannon’s entropy, into two components: spatial mutual information, identifying the role of space, and spatial residual entropy, measuring heterogeneity due to other sources. A further decomposition into partial terms deeply investigates the role of space at speci?c distances: the new set of spatial entropies satis?es a list of desirable properties. We also show that the approach is more general, better performing and more interpretable than the most popular proposals in the literature, thanks to the property of additivity and a new way of computing the entropy that explicitly discards the order within sets. A novel procedure for building the necessary quantities for computations is also provided. A comparative study illustrates the superior performance of the new set of measures over representative spatial con?gurations. Practical questions are answered by means of a case study on land use data.

The paper is co-authored with Linda Altieri and Giulia Roli - Univeristy of Bologna


application/pdfPoster - Daniela Cocchi(PDF | 666 KB)