Special Issue "Spatial Information Theory"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 15 October 2019.

Special Issue Editors

Guest Editor
Prof. Dr. Christophe Claramunt Website E-Mail
Naval Academy Research Institute, Lanveoc-Poulmic, 29240 Brest Cedex 9, France
Interests: geographical information science; spatio-temporal models; web and wireless GIS; spatial databases; urban GIS; maritime GIS; environmental GIS
Guest Editor
Dr. Didier G Leibovici Website E-Mail
School of Mathematics & Statistics, University of Sheffield, UK
Interests: Spatial Analysis, Geo-computational Data Analytics, Scientific Workflow Modelling, Spatial Data quality, Statistical Machine Learning, Clustering

Special Issue Information

Dear Colleagues,

Over the past few years the development of theoretical aspects of space and time within the context of geographical information science has been a key research issue with many useful developments for many environmental and urban sciences. Within an interdisciplinary approach, the aim has been to embrace visions, theories and developments from a range of disciplines including computing science, mathematics, statistics, geography, ecology, linguistics, cognitive sciences, psychology and philosophy to name a few. In line with this framework several qualitative and quantitative conceptual approaches have led to increase the ability to perform spatio-temporal reasoning and spatio-temporal analysis in the context of geographical information. Among these, the concepts behind the notion of entropy have brought ways of describing and analysing spatial and spatio-temporal information.

For this special issue, and within the remits of the above contextual framework, we would encourage contributions that address the complexity of spatial or spatio-temporal information, the fuzziness and uncertainty attached along with the granularity and multiple scales that may be considered as key factors when representing spatio-temporal data. New theoretical developments or new insights, comparative studies, reviews and novel applications illustrating the handling of spatial or spatio-temporal information improving the data analytics, are welcome.

Prof. Dr. Christophe Claramunt
Dr. Didier G Leibovici
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Geographical information
  • Complex spatio-temporal systems
  • Spatial structuring
  • Spatio-temporal analysis
  • Spatio-temporal data uncertainty
  • Fuzzy spatio-temporal information
  • Geospatial Big Data
  • Spatial, spatio-temporal sampling
  • Neo-geography
  • Spatial, spatio-temporal scale and granularity
  • Quantitative geography
  • Spatio-temporal reasoning
  • Information theory
  • Spatio-temporal entropy
  • Innovative applications of entropy concept

Published Papers (3 papers)

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Research

Open AccessFeature PaperArticle
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
Entropy 2019, 21(2), 184; https://doi.org/10.3390/e21020184 - 15 Feb 2019
Cited by 3
Abstract
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. Recently, as deep learning models have become more common, RNNs have been used to forecast increasingly [...] Read more.
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. Recently, as deep learning models have become more common, RNNs have been used to forecast increasingly complicated systems. Dynamical spatio-temporal processes represent a class of complex systems that can potentially benefit from these types of models. Although the RNN literature is expansive and highly developed, uncertainty quantification is often ignored. Even when considered, the uncertainty is generally quantified without the use of a rigorous framework, such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a more formal framework while maintaining the forecast accuracy that makes these models appealing, by presenting a Bayesian RNN model for nonlinear spatio-temporal forecasting. Additionally, we make simple modifications to the basic RNN to help accommodate the unique nature of nonlinear spatio-temporal data. The proposed model is applied to a Lorenz simulation and two real-world nonlinear spatio-temporal forecasting applications. Full article
(This article belongs to the Special Issue Spatial Information Theory)
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Open AccessArticle
A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity
Entropy 2018, 20(7), 490; https://doi.org/10.3390/e20070490 - 23 Jun 2018
Abstract
The rapid proliferation of sensors and big data repositories offer many new opportunities for data science. Among many application domains, the analysis of large trajectory datasets generated from people’s movements at the city scale is one of the most promising research avenues still [...] Read more.
The rapid proliferation of sensors and big data repositories offer many new opportunities for data science. Among many application domains, the analysis of large trajectory datasets generated from people’s movements at the city scale is one of the most promising research avenues still to explore. Extracting trajectory patterns and outliers in urban environments is a direction still requiring exploration for many management and planning tasks. The research developed in this paper introduces a spatio-temporal framework, so-called STE-SD (Spatio-Temporal Entropy for Similarity Detection), based on the initial concept of entropy as introduced by Shannon in his seminal theory of information and as recently extended to the spatial and temporal dimensions. Our approach considers several complementary trajectory descriptors whose distribution in space and time are quantitatively evaluated. The trajectory primitives considered include curvatures, stop-points, self-intersections and velocities. These primitives are identified and then qualified using the notion of entropy as applied to the spatial and temporal dimensions. The whole approach is experimented and applied to urban trajectories derived from the Geolife dataset, a reference data benchmark available in the city of Beijing. Full article
(This article belongs to the Special Issue Spatial Information Theory)
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Open AccessArticle
Spatial Heterogeneity Analysis: Introducing a New Form of Spatial Entropy
Entropy 2018, 20(6), 398; https://doi.org/10.3390/e20060398 - 23 May 2018
Cited by 2
Abstract
Distinguishing and characterizing different landscape patterns have long been the primary concerns of quantitative landscape ecology. Information theory and entropy-related metrics have provided the deepest insights in complex system analysis, and have high relevance in landscape ecology. However, ideal methods to compare different [...] Read more.
Distinguishing and characterizing different landscape patterns have long been the primary concerns of quantitative landscape ecology. Information theory and entropy-related metrics have provided the deepest insights in complex system analysis, and have high relevance in landscape ecology. However, ideal methods to compare different landscape patterns from an entropy view are still lacking. The overall aim of this research is to propose a new form of spatial entropy (Hs) in order to distinguish and characterize different landscape patterns. Hs is an entropy-related index based on information theory, and integrates proximity as a key spatial component into the measurement of spatial diversity. Proximity contains two aspects, i.e., total edge length and distance, and by including both aspects gives richer information about spatial pattern than metrics that only consider one aspect. Thus, Hs provides a novel way to study the spatial structures of landscape patterns where both the edge length and distance relationships are relevant. We compare the performances of Hs and other similar approaches through both simulated and real-life landscape patterns. Results show that Hs is more flexible and objective in distinguishing and characterizing different landscape patterns. We believe that this metric will facilitate the exploration of relationships between landscape patterns and ecological processes. Full article
(This article belongs to the Special Issue Spatial Information Theory)
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