Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.7
Journals
Entropy
Special Issues
Spatial Information Theory
entropy-logo
Submit to Entropy Review for Entropy Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Spatial Information Theory

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

Deadline for manuscript submissions: closed (15 October 2019) | Viewed by 30190

Special Issue Editors

Prof. Dr. Christophe Claramunt

E-Mail Website
Guest Editor
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
Dr. Didier G Leibovici

E-Mail Website
Guest Editor
School of Mathematics and Statistics, University of Sheffield, Sheffield S10 2TN, UK
Interests: geographical information science; geocomputational statistics; spatial data science; scientific workflow; interoperability; data quality and uncertainty; spatio-temporal data structuring and analysis; descriptive analytics; data optimisation and simulation
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2600 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 (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 4537 KiB  
Article
Spatio-Temporal Evolution Analysis of Drought Based on Cloud Transformation Algorithm over Northern Anhui Province
by Xia Bai, Yimin Wang, Juliang Jin, Shaowei Ning, Yanfang Wang and Chengguo Wu
Entropy 2020, 22(1), 106; https://doi.org/10.3390/e22010106 - 16 Jan 2020
Cited by 4 | Viewed by 2248
Abstract
Drought is one of the most typical and serious natural disasters, which occurs frequently in most of mainland China, and it is crucial to explore the evolution characteristics of drought for developing effective schemes and strategies of drought disaster risk management. Based on [...] Read more.
Drought is one of the most typical and serious natural disasters, which occurs frequently in most of mainland China, and it is crucial to explore the evolution characteristics of drought for developing effective schemes and strategies of drought disaster risk management. Based on the application of Cloud theory in the drought evolution research field, the cloud transformation algorithm, and the conception zooming coupling model was proposed to re-fit the distribution pattern of SPI instead of the Pearson-III distribution. Then the spatio-temporal evolution features of drought were further summarized utilizing the cloud characteristics, average, entropy, and hyper-entropy. Lastly, the application results in Northern Anhui province revealed that the drought condition was the most serious during the period from 1957 to 1970 with the SPI12 index in 49 months being less than −0.5 and 12 months with an extreme drought level. The overall drought intensity varied with the highest certainty level but lowest stability level in winter, but this was opposite in the summer. Moreover, drought hazard would be more significantly intensified along the elevation of latitude in Northern Anhui province. The overall drought hazard in Suzhou and Huaibei were the most serious, which is followed by Bozhou, Bengbu, and Fuyang. Drought intensity in Huainan was the lightest. The exploration results of drought evolution analysis were reasonable and reliable, which would supply an effective decision-making basis for establishing drought risk management strategies. Full article
(This article belongs to the Special Issue Spatial Information Theory)
►▼ Show Figures

Figure 1

24 pages, 4045 KiB  
Article
On Integrating Size and Shape Distributions into a Spatio-Temporal Information Entropy Framework
by Didier G. Leibovici and Christophe Claramunt
Entropy 2019, 21(11), 1112; https://doi.org/10.3390/e21111112 - 13 Nov 2019
Cited by 6 | Viewed by 3349
Abstract
Understanding the structuration of spatio-temporal information is a common endeavour to many disciplines and application domains, e.g., geography, ecology, urban planning, epidemiology. Revealing the processes involved, in relation to one or more phenomena, is often the first step before elaborating spatial functioning theories [...] Read more.
Understanding the structuration of spatio-temporal information is a common endeavour to many disciplines and application domains, e.g., geography, ecology, urban planning, epidemiology. Revealing the processes involved, in relation to one or more phenomena, is often the first step before elaborating spatial functioning theories and specific planning actions, e.g., epidemiological modelling, urban planning. To do so, the spatio-temporal distributions of meaningful variables from a decision-making viewpoint, can be explored, analysed separately or jointly from an information viewpoint. Using metrics based on the measure of entropy has a long practice in these domains with the aim of quantification of how uniform the distributions are. However, the level of embedding of the spatio-temporal dimension in the metrics used is often minimal. This paper borrows from the landscape ecology concept of patch size distribution and the approach of permutation entropy used in biomedical signal processing to derive a spatio-temporal entropy analysis framework for categorical variables. The framework is based on a spatio-temporal structuration of the information allowing to use a decomposition of the Shannon entropy which can also embrace some existing spatial or temporal entropy indices to reinforce the spatio-temporal structuration. Multiway correspondence analysis is coupled to the decomposition entropy to propose further decomposition and entropy quantification of the spatio-temporal structuring information. The flexibility from these different choices, including geographic scales, allows for a range of domains to take into account domain specifics of the data; some of which are explored on a dataset linked to climate change and evolution of land cover types in Nordic areas. Full article
(This article belongs to the Special Issue Spatial Information Theory)
►▼ Show Figures

Figure 1

22 pages, 9953 KiB  
Article
Permutation Entropy-Based Analysis of Temperature Complexity Spatial-Temporal Variation and Its Driving Factors in China
by Ting Zhang, Changxiu Cheng and Peichao Gao
Entropy 2019, 21(10), 1001; https://doi.org/10.3390/e21101001 - 13 Oct 2019
Cited by 6 | Viewed by 3583
Abstract
Air temperature fluctuation complexity (TFC) describes the uncertainty of temperature changes. The analysis of its spatial and temporal variation is of great significance to evaluate prediction uncertainty of the regional temperature trends and the climate change. In this study, annual-TFC from 1979–2017 and [...] Read more.
Air temperature fluctuation complexity (TFC) describes the uncertainty of temperature changes. The analysis of its spatial and temporal variation is of great significance to evaluate prediction uncertainty of the regional temperature trends and the climate change. In this study, annual-TFC from 1979–2017 and seasonal-TFC from 1983–2017 in China were calculated by permutation entropy (PE). Their temporal trend is described by the Mann-Kendall method. Driving factors of their spatial variations are explored through GeoDetector. The results show that: (1). TFC shows a downward trend generally, with obvious time variation. (2). The spatial variation of TFC is mainly manifested in the differences among the five sub-regions in China. There is low uncertainty in the short-term temperature trends in the northwest and southeast. The northeastern and southwestern regions show high uncertainties. TFC in the central region is moderate. (3). The vegetation is the main factor of spatial variation, followed by the climate and altitude, and the latitude and terrain display the lowest impact. The interactions of vegetation-altitude, vegetation-climate and altitude-latitude can interpret more than 50% of the spatial variations. These results provide insights into causes and mechanisms of the complexity of the climate system. They can help to determine the influencing process of various factors. Full article
(This article belongs to the Special Issue Spatial Information Theory)
►▼ Show Figures

Figure 1

25 pages, 1379 KiB  
Article
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
by Patrick L. McDermott and Christopher K. Wikle
Entropy 2019, 21(2), 184; https://doi.org/10.3390/e21020184 - 15 Feb 2019
Cited by 62 | Viewed by 8242
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)
►▼ Show Figures

Figure 1

22 pages, 7013 KiB  
Article
A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity
by Amin Hosseinpoor Milaghardan, Rahim Ali Abbaspour and Christophe Claramunt
Entropy 2018, 20(7), 490; https://doi.org/10.3390/e20070490 - 23 Jun 2018
Cited by 6 | Viewed by 4808
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)
►▼ Show Figures

Figure 1

13 pages, 1980 KiB  
Article
Spatial Heterogeneity Analysis: Introducing a New Form of Spatial Entropy
by Chaojun Wang and Hongrui Zhao
Entropy 2018, 20(6), 398; https://doi.org/10.3390/e20060398 - 23 May 2018
Cited by 23 | Viewed by 6686
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)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop