scholarly journals Improvements to Information Entropy for Raster Spatial Data: A Thermodynamic-based Evaluation

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Hong Zhang ◽  
Peichao Gao ◽  
Zhilin Li
Keyword(s):  
Information Theory ◽  
Information Content ◽  
Information Entropy ◽  
Spatial Data ◽  
Spatial Information ◽  
Comprehensive Evaluation ◽  
Statistical Information ◽  
Late 20Th Century ◽  
Spatial Datasets ◽  
Spatial Dataset

<p><strong>Abstract.</strong> Spatial information is fundamentally important to our daily life. It has been estimated by many scholars that almost 80 percent or more of all information in this world are spatially referenced and can be regarded as spatial information. Given such importance, a discipline called spatial information theory has been formed since the late 20th century. In addition, international conferences on spatial information have been frequently held. For example, COSIT (Conference on Spatial Information Theory) was established in 1993 and are held every two years all over the world.</p><p>In spatial information theory, one fundamental question is how to measure the amount of information (i.e., information content) of a spatial dataset. A widely used method is to employ entropy, which is proposed by the American mathematician Claude Shannon in 1948 and usually referred to as Shannon entropy or information entropy. This information entropy was originally designed to measure the statistical information content of a telegraph message. However, a spatial dataset such as a map or a remote sensing image contains not only statistical information but also spatial information, which cannot be measured by using the information entropy.</p><p>As a consequence, considerable efforts have been made to improve the information entropy for spatial datasets in either a vector format of a raster format. There are two basic lines of thought. The first is to improve the information entropy by defining how to calculate its probability parameters, and the other is to introduce new parameters into the formula of the information entropy. The former results in a number of improved information entropies, while the latter leads to a series of variants of the information entropy. Both seem to be capable of distinguishing different spatial datasets, but there is a lack of comprehensive evaluation of their performance in measuring spatial information.</p><p>This study first presents a state-of-the-art review of the improvements to the information entropy for the information content of spatial datasets in a raster format (i.e., raster spatial data, such as a grey image and a digital elevation model). Then, it presents a comprehensive evaluation of the resultant measures (either improved information entropies or variants of the information entropy) according to the Second Law of Thermodynamics. A set of evaluation criteria were proposed, as well as corresponding measures. All resultant measures were ranked accordingly.</p><p>The results reported in this study should be useful for entropic spatial data analysis. For example, in image fusion, a crucial question is how to evaluate the performance of a fusion algorithm. This evaluation is usually achieved by using the information entropy to measure the increase in the information content during the fusion. It can now be performed by the best-improved information entropy reported in this study.</p>

scholarly journals Boltzmann Entropy for the Spatial Information of Raster Data

2019 ◽  
Vol 1 ◽  
pp. 1-1 ◽  
Author(s):  
Peichao Gao ◽  
Hong Zhang ◽  
Zhilin Li
Keyword(s):  
Boltzmann Equation ◽  
Information Content ◽  
Shannon Entropy ◽  
Spatial Data ◽  
Spatial Information ◽  
Statistical Information ◽  
Boltzmann Entropy ◽  
Raster Data ◽  
Data Set ◽  
The Boltzmann Equation

<p><strong>Abstract.</strong> Entropy is an important concept that originated in thermodynamics. It is the subject of the famous Second Law of Thermodynamics, which states that “the entropy of a closed system increases continuously and irrevocably toward a maximum” (Huettner 1976, 102) or “the disorder in the universe always increases” (Framer and Cook 2013, 21). Accordingly, it has been widely regarded as an ideal measure of disorder. Its computation can be theoretically performed according to the Boltzmann equation, which was proposed by the Austrian physicist Ludwig Boltzmann in 1872. In practice, however, the Boltzmann equation involves two problems that are difficult to solve, that is the definition of the macrostate of a system and the determination of the number of possible microstates in the microstate. As noted by the American sociologist Kenneth Bailey, “when the notion of entropy is extended beyond physics, researchers may not be certain how to specify and measure the macrostate/microstate relations” (Bailey 2009, 151). As a result, this entropy (also referred to as Boltzmann entropy and thermodynamic entropy) has remained largely at a conceptual level.</p><p> In practice, the widely used entropy is actually proposed by the American mathematician, electrical engineer, and cryptographer Claude Elwood Shannon in 1948, hence the term Shannon entropy. Shannon entropy was proposed to quantify the statistical disorder of telegraph messages in the area of communications. The quantification result was interpreted as the information content of a telegraph message, hence also the term information entropy. This entropy has served as the cornerstone of information theory and was introduced to various fields including chemistry, biology, and geography. It has been widely utilized to quantify the information content of geographic data (or spatial data) in either a vector format (i.e., vector data) or a raster format (i.e., raster data). However, only the statistical information of spatial data can be quantified by using Shannon entropy. The spatial information is ignored by Shannon entropy; for example, a grey image and its corresponding error image share the same Shannon entropy.</p><p> Therefore, considerable efforts have been made to improve the suitability of Shannon entropy for spatial data, and a number of improved Shannon entropies have been put forward. Rather than further improving Shannon entropy, this study introduces a novel strategy, namely shifting back from Shannon entropy to Boltzmann entropy. There are two advantages of employing Boltzmann entropy. First, as previously mentioned, Boltzmann entropy is the ideal, standard measure of disorder or information. It is theoretically capable of quantifying not only the statistical information but also the spatial information of a data set. Second, Boltzmann entropy can serve as the bridge between spatial patterns and thermodynamic interpretations. In this sense, the Boltzmann entropy of spatial data may have wider applications. In this study, Boltzmann entropy is employed to quantify the spatial information of raster data, such as images, raster maps, digital elevation models, landscape mosaics, and landscape gradients. To this end, the macrostate of raster data is defined, and the number of all possible microstates in the macrostate is determined. To demonstrate the usefulness of Boltzmann entropy, it is applied to satellite remote sensing image processing, and a comparison is made between its performance and that of Shannon entropy.</p>

INFORMATION THEORY OF CARTOGRAPHY: A FRAMEWORK FOR ENTROPY-BASED CARTOGRAPHIC COMMUNICATION THEORY

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 11-16
Author(s):  
Z. Li
Keyword(s):  
Information Theory ◽  
Communication Theory ◽  
Spatial Information ◽  
Digital Communication ◽  
Spatial Configuration ◽  
Statistical Information ◽  
Fundamental Theory ◽  
Boltzmann Entropy ◽  
Spatial Objects ◽  
Thematic Information

Abstract. Map is an effective communication means. It carries and transmits spatial information about spatial objects and phenomena, from map makers to map users. Therefore, cartography can be regarded as a communication system. Efforts has been made on the application of Shannon Information theory developed in digital communication to cartography to establish an information theory of cartography, or simply cartographic information theory (or map information theory). There was a boom during the period from later 1960s to early 1980s. Since later 1980s, researcher have almost given up the dream of establishing the information theory of cartography because they met a bottleneck problem. That is, Shannon entropy is only able to characterize the statistical information of map symbols but not capable of characterizing the spatial configuration (patterns) of map symbols. Fortunately, break-through has been made, i.e. the building of entropy models for metric and thematic information as well as a feasible computational model for Boltzmann entropy. This paper will review the evolutional processes, examine the bottleneck problems and the solutions, and finally propose a framework for the information theory of cartography. It is expected that such a theory will become the most fundamental theory of cartography in the big data era.

scholarly journals The Challenge of Spatial Information Accessibility for Agricultural Policies: Case of Pakistan

2021 ◽  
Author(s):  
Asmat Ali ◽  
Muhammad Imran
Keyword(s):  
Food Security ◽  
Spatial Data ◽  
Policy Making ◽  
Socioeconomic Development ◽  
Spatial Information ◽  
Agricultural Policies ◽  
Quality Health Care ◽  
Agriculture Sector ◽  
Spatial Datasets ◽  
Unrestricted Access

Agriculture is directly linked to the socioeconomic development of every region. Agriculture impacts us all, whether we are seeking food security, better health or striving to conserve natural resources. Goal 2 of Sustainable Development Goals (SDGs) underlines the significance of agriculture as a means to achieve food security. United Nations in its recently published report titled "World Economic Situation and Prospects 2020"(UN, 2020) has declared agriculture as one of the global priorities for achieving high-quality health care and formal employment opportunities. Agriculture is a spatial subject. Policy makers demand unrestricted access to spatial data of various kinds in order to address agricultural issues and for evidence-based policy-making. Therefore, what types of spatial datasets are required for agricultural policy-making is a relevant question which is the objective of this paper. This paper also explores agriculture in Pakistan, main challenges faced by the agriculture sector of the country, and how many as well as what kind of spatial datasets are required to address these policy challenges.

Geographic Information System and Big Spatial Data

2020 ◽  
Vol 16 (4) ◽  
pp. 101-145
Author(s):  
Raja Sher Afgun Usmani ◽  
Ibrahim Abaker Targio Hashem ◽  
Thulasyammal Ramiah Pillai ◽  
Anum Saeed ◽  
Akibu Mahmoud Abdullahi
Keyword(s):  
Information System ◽  
Big Data ◽  
Geographic Information System ◽  
Spatial Data ◽  
Spatial Information ◽  
Geographic Information ◽  
Data Sources ◽  
Spatial Datasets ◽  
Big Data Technologies ◽  
The Impact

Geographic information system (GIS) is designed to generate maps, manage spatial datasets, perform sophisticated “what if” spatial analyses, visualize multiple spatial datasets simultaneously, and solve location-based queries. The impact of big data is in every industry, including the GIS. The location-based big data also known as big spatial data has significant implications as it forces the industry to contemplate how to acquire and leverage spatial information. In this study, a comprehensive taxonomy is created to provide a better understanding of the uses of GIS and big spatial data. The taxonomy is made up of big data technologies, GIS data sources, tools, analytics, and applications. The authors look into the importance of big spatial data and its implications, review the data sources, and GIS analytics, applications, and GIS tools. Furthermore, in order to guide researchers interested in GIS, the challenges that require substantial research efforts are taken into account. Lastly, open issues in GIS that require further observation are summarized.

DARCY RIBEIRO UNIVERSITY CAMPUS MAP – 2017- TECHNICAL NOTE

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Rafael Sanzio Araújo dos Anjos ◽  
Jose Leandro de Araujo Conceição ◽  
Jõao Emanuel ◽  
Matheus Nunes
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Technical Note ◽  
University Campus ◽  
Communication Tool ◽  
Sources Of Information ◽  
Data Manipulation ◽  
Geographic Space ◽  
The Many ◽  
New Generation

The spatial information regarding the use of territory is one of the many strategies used to answer and to inform about what happened, what is happening and what may happen in geographic space. Therefore, the mapping of land use as a communication tool for the spatial data made significant progress in improving sources of information, especially over the last few decades, with new generation remote sensing products for data manipulation.

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

scholarly journals Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

2021 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Ching-Yun Mu ◽  
Tien-Yin Chou ◽  
Thanh Van Hoang ◽  
Pin Kung ◽  
Yao-Min Fang ◽  
...  
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Retrieval ◽  
Spatial Data Mining ◽  
Map Matching ◽  
Matching Algorithm ◽  
Table Method ◽  
Look Up Table ◽  
The Look

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

scholarly journals SPATIAL DATA: FROM CARTOGRAPHY TO GEODATABASE

2017 ◽  
Vol 43 (4) ◽  
pp. 142-146 ◽  
Author(s):  
Ugo FALCHI
Keyword(s):  
Spatial Data ◽  
Common Good ◽  
Spatial Information ◽  
Geographic Information ◽  
The Public ◽  
Significant Acceleration ◽  
Management And Development ◽  
The Status ◽  
History Of ◽  
Essential Knowledge

The final goal of this paper was to fix a brief summary on the status of geographic information in Italy due to the technological steps and national regulations. The acquisition, processing and sharing of spatial data has experienced a significant acceleration thanks to the development of computer technology and the acknowledgment of the need for standardization and homogenization of information held by pub­lic authorities and individuals. The spatial data represents the essential knowledge in the management and development of a territory both in terms of planning for safety and environmental prevention. In Italy there is an enormous heritage of spatial information which is historically affected by a problem of consistency and uniformity, in order to make it often contradictory in its use by the public decision-maker and private par­ties. The recent history of geographic information is characterized by a significant effort aimed at optimiz­ing this decisive technical and cultural heritage allowing the use of it to all citizens in a logic of sharing and re-use and may finally represent a common good available to all.

Fuzzy Matter-Element Evaluation Method for Analysis of Debris Flow Risk Assessment

2012 ◽  
Vol 446-449 ◽  
pp. 3058-3061 ◽  
Author(s):  
Chun Tan ◽  
Jian Ping Chen ◽  
Yu Zhen Pan ◽  
Cen Cen Niu ◽  
Li Ming Xu
Keyword(s):  
Risk Assessment ◽  
Debris Flow ◽  
Information Entropy ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Practical Application ◽  
Element Analysis ◽  
Matter Element Analysis ◽  
Concept Of Information

Based on the principle of fuzzy matter-element analysis, the concept of information entropy is introduced to establish a fuzzy matter-element evaluation method. This method is utilized to comprehensively evaluate the degree of debris flow. The classifications of debris flow are regarded as the objects of matter-element and their indexes for evaluation as well as the corresponding fuzzy values are used to construct the composite fuzzy matter-elements. By calculating the relevancy the comprehensive evaluation of debris flow can be carried out. This model is applied to analyze the degree of debris flow in the practical application. The application shows that the model is effective and practical.

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