Entropy and Diversity Indices for Spatial and Temporal Data
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 4547
Special Issue Editors
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
Interests: statistics; spatio-temporal statistics; stochastic processes; Bayesian inference; INLA; entropy; spatial entropy and entropy estimation; environmental data; capture–recapture data
Special Issue Information
Dear Colleagues,
Entropy, as a concept and a tool to measure heterogeneity in distributional properties, has been widely used in many disciplines, and is still of significant importance in geography, ecology and other fields dealing with discrete spatio-temporal data. The roots of entropy come from information theory, and it belongs to the set of diversity indices, together with other popular examples such as Simpson’s index and the general Hill’s number. They are known to evaluate alpha, beta and gamma diversity in biodiversity and ecological studies; however, synthetic measures of heterogeneity are of interest over a wide variety of natural phenomena, such as earthquakes, wildfires, polluting agent, meteorological events and epidemiological data. Other fields of application, though not traditionally open to entropy and diversity measures, may benefit from the flexibility and interpretability of such indices.
In the aforementioned studies, the spatio-temporal support of data constitutes a stimulating challenge. The definition of spatial and spatio-temporal entropy for categorical data in the literature has evolved from Shannon’s entropy to a more complex description taking into account unequal grid sizes, area partitions, co-occurrences of observations for contiguous areas or based on a certain distance range of interest; they are applied to both areal and point data, and ascribe to various approaches. When the goal expands from data description to inference, indices need to be complicated accordingly in order to deal with data dependence on available covariates and on temporal/spatial effects.
This Special Issue intends to explore some of these aspects. Papers are welcome which cover new challenges in entropy and other diversity indices, either from a pertinent data analysis perspective (e.g., compelling examples and best use) or from a more methodological focus (e.g., critical thinking, new approaches and novel concepts).
Dr. Didier G Leibovici
Dr. Linda Altieri
Guest Editors
Manuscript Submission Information
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Keywords
- entropy
- diversity indices
- mutual information
- conditional entropy
- spatial data
- spatio-temporal data
- co-occurrences
- observations
- proximity of observations
- information theory
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