SimUrb – software for identifying similar municipalities by comparing Urban indices using a graph algorithm

The paper describes the SimUrb tool, which was used to calculate the similarity between municipalities (or other territorial units) according to several characteristics (attributes). The SimUrb tool allows similar groups between ordered sequences to be found. The tool was designed to work with hundr...

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Published inEarth science informatics Vol. 12; no. 4; pp. 699 - 714
Main Authors Doležalová, Jitka, Burian, Jaroslav, Popelka, Stanislav
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019
Springer Nature B.V
Subjects
Online AccessGet full text
ISSN1865-0473
1865-0481
DOI10.1007/s12145-019-00399-8

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Abstract The paper describes the SimUrb tool, which was used to calculate the similarity between municipalities (or other territorial units) according to several characteristics (attributes). The SimUrb tool allows similar groups between ordered sequences to be found. The tool was designed to work with hundreds of records. In order to find similar groups, the tool employs graph theory, in which similar groups are represented as cliques on a simple graph. The Bron-Kerbosch algorithm was used to search for them. The degree of similarity was determined from a metric based on the Euclidean distance between strings. Finding all non-trivial cliques would have been difficult or impossible for such data to be feasible in this study. Therefore, the SimUrb tool was used to find similar groups as the largest disjoint cliques in the respective graph. A case study is introduced in the second part of the paper to illustrate SimUrb’s functionality. The results of standard grouping methods (two methods offered by ArcGIS software) and groups defined under official planning documents were compared to the results from the SimUrb software. We concluded that SimUrb can be used in many applications where the user needs to define groups of objects with the same degree of similarity.
AbstractList The paper describes the SimUrb tool, which was used to calculate the similarity between municipalities (or other territorial units) according to several characteristics (attributes). The SimUrb tool allows similar groups between ordered sequences to be found. The tool was designed to work with hundreds of records. In order to find similar groups, the tool employs graph theory, in which similar groups are represented as cliques on a simple graph. The Bron-Kerbosch algorithm was used to search for them. The degree of similarity was determined from a metric based on the Euclidean distance between strings. Finding all non-trivial cliques would have been difficult or impossible for such data to be feasible in this study. Therefore, the SimUrb tool was used to find similar groups as the largest disjoint cliques in the respective graph. A case study is introduced in the second part of the paper to illustrate SimUrb’s functionality. The results of standard grouping methods (two methods offered by ArcGIS software) and groups defined under official planning documents were compared to the results from the SimUrb software. We concluded that SimUrb can be used in many applications where the user needs to define groups of objects with the same degree of similarity.
Author Doležalová, Jitka
Burian, Jaroslav
Popelka, Stanislav
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ContentType Journal Article
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Earth Science Informatics is a copyright of Springer, (2019). All Rights Reserved.
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Similarity
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Snippet The paper describes the SimUrb tool, which was used to calculate the similarity between municipalities (or other territorial units) according to several...
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SubjectTerms Algorithms
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Euclidean geometry
Feasibility studies
Graph theory
Graphical representations
Information Systems Applications (incl.Internet)
Municipalities
Ontology
Similarity
Simulation and Modeling
Software
Software Article
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
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Title SimUrb – software for identifying similar municipalities by comparing Urban indices using a graph algorithm
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