A framework for spatial regionalization composed of novel clustering‐based algorithms under spatial contiguity constraints

Traditionally, the geospatial regionalization task consists of aggregating into regions, geographically connected areas that share similar characteristics. Although various spatial optimization approaches have been proposed for finding exact regionalization solutions, these approaches are not practi...

Full description

Saved in:
Bibliographic Details
Published in:Transactions in GIS Vol. 26; no. 4; pp. 1775 - 1800
Main Authors: Miranda, Leandro, Viterbo, José, Bernardini, Flávia
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.06.2022
Subjects:
Aggregation
Algorithms
Clustering
Figure of merit
Frameworks
Optimization
ISSN:1361-1682, 1467-9671
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Traditionally, the geospatial regionalization task consists of aggregating into regions, geographically connected areas that share similar characteristics. Although various spatial optimization approaches have been proposed for finding exact regionalization solutions, these approaches are not practical when applied to a large number of areas or problems for online aggregation, due to the long execution times using hardware with low resources. In this article, we present a framework for executing spatial regionalization tasks. The pre‐condition for using our framework is the definition of a map describing the neighborhood relations—as spatial contiguity constraints—among the areas (or objects) to be regionalized. In our framework we implemented three clustering algorithms with spatial contiguity constraints: RegK‐Means and Agglomerative Hierarchical Regionalization (AHR), our adaptation of k‐means partition‐based clustering and hierarchical‐based clustering algorithms, respectively; and the Automatic Zoning Procedure (AZP), a traditional algorithm for regionalization that also has the premise of simplifying the neighborhood relation representation. We conducted an exploratory analysis composed of two different experiments. Our results showed that our framework leads to a faster way of executing regionalization tasks in the experimental analysis, allowing us to observe a significant gain of AHR and RegK‐Means over AZP in execution time, while showing better or similar results in other metrics, such as figure of merit.
AbstractList Traditionally, the geospatial regionalization task consists of aggregating into regions, geographically connected areas that share similar characteristics. Although various spatial optimization approaches have been proposed for finding exact regionalization solutions, these approaches are not practical when applied to a large number of areas or problems for online aggregation, due to the long execution times using hardware with low resources. In this article, we present a framework for executing spatial regionalization tasks. The pre‐condition for using our framework is the definition of a map describing the neighborhood relations—as spatial contiguity constraints—among the areas (or objects) to be regionalized. In our framework we implemented three clustering algorithms with spatial contiguity constraints: RegK‐Means and Agglomerative Hierarchical Regionalization (AHR), our adaptation of k‐means partition‐based clustering and hierarchical‐based clustering algorithms, respectively; and the Automatic Zoning Procedure (AZP), a traditional algorithm for regionalization that also has the premise of simplifying the neighborhood relation representation. We conducted an exploratory analysis composed of two different experiments. Our results showed that our framework leads to a faster way of executing regionalization tasks in the experimental analysis, allowing us to observe a significant gain of AHR and RegK‐Means over AZP in execution time, while showing better or similar results in other metrics, such as figure of merit.
Traditionally, the geospatial regionalization task consists of aggregating into regions, geographically connected areas that share similar characteristics. Although various spatial optimization approaches have been proposed for finding exact regionalization solutions, these approaches are not practical when applied to a large number of areas or problems for online aggregation, due to the long execution times using hardware with low resources. In this article, we present a framework for executing spatial regionalization tasks. The pre‐condition for using our framework is the definition of a map describing the neighborhood relations—as spatial contiguity constraints—among the areas (or objects) to be regionalized. In our framework we implemented three clustering algorithms with spatial contiguity constraints: RegK‐Means and Agglomerative Hierarchical Regionalization (AHR), our adaptation of k ‐means partition‐based clustering and hierarchical‐based clustering algorithms, respectively; and the Automatic Zoning Procedure (AZP), a traditional algorithm for regionalization that also has the premise of simplifying the neighborhood relation representation. We conducted an exploratory analysis composed of two different experiments. Our results showed that our framework leads to a faster way of executing regionalization tasks in the experimental analysis, allowing us to observe a significant gain of AHR and RegK‐Means over AZP in execution time, while showing better or similar results in other metrics, such as figure of merit.
Author Viterbo, José
Bernardini, Flávia
Miranda, Leandro
Author_xml – sequence: 1
  givenname: Leandro
  orcidid: 0000-0002-1479-429X
  surname: Miranda
  fullname: Miranda, Leandro
  email: leandromiranda@id.uff.br
  organization: Fluminense Federal University – UFF
– sequence: 2
  givenname: José
  surname: Viterbo
  fullname: Viterbo, José
  organization: Fluminense Federal University – UFF
– sequence: 3
  givenname: Flávia
  surname: Bernardini
  fullname: Bernardini, Flávia
  organization: Fluminense Federal University – UFF
BookMark eNp9kNFKwzAUhoNMcJve-AQB74TOnKYk7eUYOgcDL5zXJUvTmtklM0kdEy98BJ_RJ7HdBEHE3JyTk-8_5P8HqGesUQidAxlBe65Cpf0I4ozQI9SHhPEoYxx6bU8ZRMDS-AQNvF8RQpIk4330NsalE2u1te4Jl9ZhvxFBixo7VWlrRK1f27s1WNr1xnpVYFtiY19UjWXd-KCcNtXn-8dSdG-irqzT4XHtcWMK9bNNWhN01eiw61ofnNAm-FN0XIraq7PvOkQPN9eLyW00v5vOJuN5JCkBGgkB2bLgPOMyi1OZKEpjDilhNClYAUk7VLykikGqoChFWmZptmz9SRYny4LSIbo47N04-9woH_KVbVxrzucxS4ElKQXeUuRASWe9d6rMpQ5789136xxI3mWcdxnn-4xbyeUvycbptXC7v2E4wFtdq90_ZL6Yzu4Pmi8Dp5Mq
CitedBy_id crossref_primary_10_1080_24694452_2024_2322477
Cites_doi 10.1007/978-3-319-59539-9_4
10.1068/a270425
10.1016/j.patcog.2018.02.015
10.14569/ijacsa.2016.070256
10.1007/s10723‐019‐09504‐z
10.1080/13658816.2015.1089442
10.1080/13658816.2015.1031671
10.1017/CBO9780511921803
10.1080/713811750
10.1111/j.1467‐9671.2012.01354.x
10.1145/2395116.2395117
10.1371/journal.pbio.2001573
10.1007/s10666‐007‐9084‐8
10.1080/13658810701674970
10.1111/2041‐210X.12208
10.7551/mitpress/9780262033589.001.0001
10.1093/bioinformatics/17.4.309
10.1002/widm.53
10.1111/tgis.12180
10.1007/978-3-319-14142-8
10.1080/09720502.2017.1386476
10.1080/13658810600665111
10.1111/tgis.12217
10.1007/978-3-662-03499-6_5
10.1093/bioinformatics/bti517
10.1109/BRACIS.2017.70
10.1111/tgis.12557
10.1016/j.compenvurbsys.2012.04.005
10.1007/BF02293706
10.1007/978-3-642-03647-7_11
10.1111/j.1538‐4632.1969.tb00615.x
10.1016/S0031‐3203(02)00060‐2
10.1016/j.ifacol.2015.06.441
10.1007/s00357‐005‐0012‐9
10.1145/3085228.3085294
10.1145/1656274.1656278
10.1177/0160017607301605
10.1016/j.knosys.2018.01.031
10.1145/3085228.3085288
10.1007/s40745‐015‐0040‐1
ContentType Journal Article
Copyright 2022 John Wiley & Sons Ltd
Copyright_xml – notice: 2022 John Wiley & Sons Ltd
DBID AAYXX
CITATION
7SC
8FD
F1W
FR3
H96
JQ2
KR7
L.G
L7M
L~C
L~D
DOI 10.1111/tgis.12903
DatabaseName CrossRef
Computer and Information Systems Abstracts
Technology Research Database
ASFA: Aquatic Sciences and Fisheries Abstracts
Engineering Research Database
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
ProQuest Computer Science Collection
Civil Engineering Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) Professional
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle CrossRef
Civil Engineering Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) Professional
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
Technology Research Database
Computer and Information Systems Abstracts – Academic
ASFA: Aquatic Sciences and Fisheries Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Engineering Research Database
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts Professional
DatabaseTitleList Civil Engineering Abstracts
CrossRef
DeliveryMethod fulltext_linktorsrc
Discipline Geography
EISSN 1467-9671
EndPage 1800
ExternalDocumentID 10_1111_tgis_12903
TGIS12903
Genre article
GrantInformation_xml – fundername: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
  funderid: Process n.1420465
GroupedDBID -~X
.3N
.GA
.Y3
05W
0R~
10A
123
1OB
1OC
29Q
31~
33P
4.4
50Y
50Z
51W
51Y
52M
52O
52Q
52S
52T
52U
52W
5HH
5LA
5VS
66C
702
7PT
8-0
8-1
8-3
8-4
8-5
8UM
8V8
930
A04
AABNI
AAESR
AAHHS
AAHQN
AAMNL
AANHP
AAONW
AAOUF
AASGY
AAXRX
AAYCA
AAZKR
ABCQN
ABCUV
ABDBF
ABEML
ABJNI
ABPVW
ABSOO
ABTAH
ACAHQ
ACBKW
ACBWZ
ACCFJ
ACCZN
ACGFS
ACHQT
ACIWK
ACPOU
ACRPL
ACSCC
ACUHS
ACXQS
ACYXJ
ADBBV
ADEMA
ADEOM
ADIZJ
ADKYN
ADMGS
ADNMO
ADXAS
ADZMN
AEEZP
AEIGN
AEIMD
AEMOZ
AEQDE
AEUQT
AEUYR
AFBPY
AFEBI
AFFPM
AFGKR
AFKFF
AFPWT
AFRAH
AFWVQ
AFYRF
AFZJQ
AHBTC
AHQJS
AIFKG
AIURR
AIWBW
AJBDE
AKVCP
ALAGY
ALMA_UNASSIGNED_HOLDINGS
ALUQN
ALVPJ
AMBMR
AMYDB
ASPBG
ASTYK
AVWKF
AZBYB
AZFZN
AZVAB
BAFTC
BDRZF
BFHJK
BMXJE
BNVMJ
BQESF
BROTX
BRXPI
BY8
CAG
COF
CS3
D-C
D-D
DCZOG
DPXWK
DR2
DRFUL
DRSSH
DU5
EAD
EAP
EAYBP
EBA
EBO
EBR
EBS
EBU
EDH
EJD
EMK
ESX
F00
F01
FEDTE
G-S
G.N
G50
GODZA
HGLYW
HVGLF
HZI
HZ~
IHE
IX1
J0M
K1G
K48
LATKE
LC2
LC4
LEEKS
LH4
LITHE
LOXES
LP6
LP7
LUTES
LW6
LYRES
MEWTI
MK4
MM-
MRFUL
MRSSH
MSFUL
MSSSH
MXFUL
MXSSH
N04
N06
N9A
NF~
O66
O9-
OIG
P2W
P2Y
P4C
PALCI
PQQKQ
Q.N
Q11
QB0
R.K
RIWAO
RJQFR
ROL
RX1
SAMSI
SUPJJ
TH9
UB1
W8V
W99
WBKPD
WIH
WII
WMRSR
WOHZO
WQZ
WRC
WSUWO
WXSBR
XG1
ZY4
ZZTAW
~IA
~WP
AAMMB
AAYXX
AEFGJ
AEYWJ
AGHNM
AGQPQ
AGXDD
AIDQK
AIDYY
AIQQE
BANNL
CITATION
O8X
7SC
8FD
F1W
FR3
H96
JQ2
KR7
L.G
L7M
L~C
L~D
ID FETCH-LOGICAL-c3013-aa19bd7797c928c4e3327180634d6d14928e7f3e618e1dfa8f989b449c624bd33
IEDL.DBID DRFUL
ISICitedReferencesCount 2
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000761630800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1361-1682
IngestDate Fri Jul 25 22:40:18 EDT 2025
Sat Nov 29 05:20:07 EST 2025
Tue Nov 18 22:27:35 EST 2025
Wed Jan 22 16:22:03 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 4
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c3013-aa19bd7797c928c4e3327180634d6d14928e7f3e618e1dfa8f989b449c624bd33
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-1479-429X
PQID 2681648317
PQPubID 45950
PageCount 26
ParticipantIDs proquest_journals_2681648317
crossref_citationtrail_10_1111_tgis_12903
crossref_primary_10_1111_tgis_12903
wiley_primary_10_1111_tgis_12903_TGIS12903
PublicationCentury 2000
PublicationDate June 2022
2022-06-00
20220601
PublicationDateYYYYMMDD 2022-06-01
PublicationDate_xml – month: 06
  year: 2022
  text: June 2022
PublicationDecade 2020
PublicationPlace Oxford
PublicationPlace_xml – name: Oxford
PublicationTitle Transactions in GIS
PublicationYear 2022
Publisher Blackwell Publishing Ltd
Publisher_xml – name: Blackwell Publishing Ltd
References 2019; 8
2015; 2
2015; 1
2017; 20
2011
2010
2017; 21
2018; 145
2006; 7
2008
2003; 36
1997
2016; 30
2007
2008; 13
2006
2005; 21
2005
2003; 17
2007; 30
2005; 22
2020; 18
2009; 11
2015; 48
1982; 47
2016; 7
2014; 5
2012; 2
2013; 59
2013; 37
2006; 20
2013; 17
2012; 1
2017; 15
1969; 1
2001
1995; 27
2019; 23
2016; 20
2018
2017
2015
2008; 22
2001; 17
2013
1990; 8
2018; 79
1988
e_1_2_13_24_1
e_1_2_13_49_1
e_1_2_13_26_1
e_1_2_13_45_1
Müller E. (e_1_2_13_39_1) 2011
e_1_2_13_22_1
e_1_2_13_8_1
e_1_2_13_41_1
e_1_2_13_6_1
Jain A. K. (e_1_2_13_28_1) 1988
Fotheringham A. S. (e_1_2_13_20_1) 2008
e_1_2_13_17_1
e_1_2_13_19_1
e_1_2_13_13_1
e_1_2_13_15_1
e_1_2_13_38_1
Arthur D. (e_1_2_13_7_1) 2007
e_1_2_13_32_1
e_1_2_13_55_1
e_1_2_13_34_1
e_1_2_13_53_1
e_1_2_13_51_1
e_1_2_13_30_1
e_1_2_13_2_1
Metz J. (e_1_2_13_36_1) 2006
PySal Developers (e_1_2_13_43_1) 2010
e_1_2_13_29_1
Wagstaff K. (e_1_2_13_50_1) 2001
e_1_2_13_48_1
e_1_2_13_27_1
e_1_2_13_46_1
e_1_2_13_21_1
e_1_2_13_44_1
e_1_2_13_23_1
e_1_2_13_42_1
e_1_2_13_9_1
e_1_2_13_40_1
Han J. (e_1_2_13_25_1) 2005
Aggarwal C. C. (e_1_2_13_4_1) 2015
e_1_2_13_18_1
Demšar J. (e_1_2_13_16_1) 2006; 7
e_1_2_13_35_1
Afonso R. A. (e_1_2_13_3_1) 2015
e_1_2_13_37_1
e_1_2_13_10_1
e_1_2_13_31_1
e_1_2_13_12_1
e_1_2_13_54_1
e_1_2_13_52_1
Chaudhari B. (e_1_2_13_14_1) 2012; 1
Sreedhar Kumar S. (e_1_2_13_47_1) 2019; 8
e_1_2_13_5_1
Browdy M. H. (e_1_2_13_11_1) 1990; 8
Lichman M. (e_1_2_13_33_1) 2013
References_xml – year: 2011
– start-page: 577
  year: 2001
  end-page: 584
– volume: 20
  start-page: 755
  issue: 5
  year: 2016
  end-page: 774
  article-title: Spawnn: A toolkit for spatial analysis with self‐organizing neural networks
  publication-title: Transactions in GIS
– start-page: 83
  year: 1997
  end-page: 100
– year: 2005
– volume: 8
  start-page: 29
  issue: 1
  year: 2019
  end-page: 37
  article-title: A brief survey of unsupervised agglomerative hierarchical clustering schemes
  publication-title: International Journal of Engineering Technology
– start-page: 1027
  year: 2007
  end-page: 1035
– volume: 15
  issue: 4
  year: 2017
  article-title: Molecular regionalization of the developing amphioxus neural tube challenges major partitions of the vertebrate brain
  publication-title: PLoS Biology
– start-page: 452
  year: 2017
  end-page: 460
– volume: 22
  start-page: 151
  issue: 2
  year: 2005
  end-page: 183
  article-title: Hierarchical clustering via joint between–within distances: Extending Ward's minimum variance method
  publication-title: Journal of Classification
– volume: 17
  start-page: 41
  issue: 1
  year: 2013
  end-page: 61
  article-title: Support vector machine for spatial variation
  publication-title: Transactions in GIS
– volume: 8
  start-page: 163
  issue: 1
  year: 1990
  end-page: 179
  article-title: Simulated annealing: An improved computer model for political redistricting
  publication-title: Yale Law & Policy Review
– volume: 1
  start-page: 699
  year: 2015
  end-page: 702
– volume: 2
  start-page: 86
  issue: 1
  year: 2012
  end-page: 97
  article-title: Algorithms for hierarchical clustering: An overview
  publication-title: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
– volume: 30
  start-page: 390
  issue: 2
  year: 2016
  end-page: 404
  article-title: Spatially constrained clustering of ecological units to facilitate the design of integrated water monitoring networks in the St. Lawrence Basin
  publication-title: International Journal of Geographical Information Science
– volume: 20
  start-page: 797
  issue: 7
  year: 2006
  end-page: 811
  article-title: Efficient regionalization techniques for socio‐economic geographical units using minimum spanning trees
  publication-title: International Journal of Geographical Information Science
– volume: 13
  start-page: 93
  issue: 1
  year: 2008
  end-page: 113
  article-title: Optimal expansion strategy for a sewer system under uncertainty
  publication-title: Environmental Modeling and Assessment
– volume: 30
  start-page: 451
  issue: 3
  year: 2016
  end-page: 473
  article-title: Spatial optimization for regionalization problems with spatial interaction: A heuristic approach
  publication-title: International Journal of Geographical Information Science
– volume: 5
  start-page: 771
  issue: 8
  year: 2014
  end-page: 779
  article-title: Spatially constrained clustering of ecological networks
  publication-title: Methods in Ecology and Evolution
– volume: 18
  start-page: 263
  issue: 2
  year: 2020
  end-page: 273
  article-title: Research on parallel adaptive Canopy‐K‐means clustering algorithm for big data mining based on cloud platform
  publication-title: Journal of Grid Computing
– volume: 23
  start-page: 1048
  issue: 5
  year: 2019
  end-page: 1077
  article-title: Spatially constrained regionalization with multilayer perceptron
  publication-title: Transactions in GIS
– volume: 48
  start-page: 2362
  issue: 3
  year: 2015
  end-page: 2367
  article-title: Optimal allocation of public service centres in the central places of functional regions
  publication-title: IFAC‐PapersOnLine
– volume: 59
  start-page: 28:1
  issue: 6
  year: 2013
  end-page: 28:22
  article-title: The effectiveness of Lloyd‐type methods for the k‐means problem
  publication-title: Journal of the ACM
– volume: 1
  start-page: 154
  issue: 2
  year: 2012
  end-page: 158
  article-title: A comparative study of clustering algorithms using Weka tools
  publication-title: International Journal of Application or Innovation in Engineering & Management
– year: 2008
– volume: 7
  start-page: 1
  year: 2006
  end-page: 30
  article-title: Statistical comparisons of classifiers over multiple data sets
  publication-title: Journal of Machine Learning Research
– volume: 79
  start-page: 402
  year: 2018
  end-page: 413
  article-title: I‐k‐means−+: An iterative clustering algorithm based on an enhanced version of the ‐means
  publication-title: Pattern Recognition
– year: 2015
– volume: 21
  start-page: 647
  issue: 4
  year: 2017
  end-page: 660
  article-title: Using spatial analysis to understand the spatial heterogeneity of disability employment in China
  publication-title: Transactions in GIS
– start-page: 347
  year: 2011
  end-page: 366
– volume: 27
  start-page: 425
  issue: 3
  year: 1995
  end-page: 446
  article-title: Algorithms for reengineering 1991 census geography
  publication-title: Environment and Planning A
– start-page: 31
  year: 2017
  end-page: 36
– volume: 17
  start-page: 181
  issue: 2
  year: 2003
  end-page: 196
  article-title: Extending the automated zoning procedure to reconcile incompatible zoning systems
  publication-title: International Journal of Geographical Information Science
– start-page: 139
  year: 2017
  end-page: 148
– volume: 2
  start-page: 165
  issue: 2
  year: 2015
  end-page: 193
  article-title: A comprehensive survey of clustering algorithms
  publication-title: Annals of Data Science
– volume: 1
  start-page: 196
  issue: 2
  year: 1969
  end-page: 212
  article-title: Regionalization: Theory and alternative algorithms
  publication-title: Geographical Analysis
– volume: 22
  start-page: 801
  issue: 7
  year: 2008
  end-page: 823
  article-title: Regionalization with dynamically constrained agglomerative clustering and partitioning (Redcap)
  publication-title: International Journal of Geographical Information Science
– year: 2010
– volume: 47
  start-page: 413
  issue: 4
  year: 1982
  end-page: 426
  article-title: Clustering with relational constraint
  publication-title: Psychometrika
– volume: 36
  start-page: 451
  issue: 2
  year: 2003
  end-page: 461
  article-title: The global k‐means clustering algorithm
  publication-title: Pattern Recognition
– volume: 11
  start-page: 10
  issue: 1
  year: 2009
  end-page: 18
  article-title: The WEKA data mining software: An update
  publication-title: SIGKDD Explorations
– volume: 7
  start-page: 410
  issue: 2
  year: 2016
  end-page: 419
  article-title: A variant of genetic algorithm based categorical data clustering for compact clusters and an experimental study on soybean data for local and global optimal solutions
  publication-title: International Journal of Advanced Computer Science and Applications
– volume: 37
  start-page: 59
  year: 2013
  end-page: 69
  article-title: Housi: Heuristic for delimitation of housing submarkets and price homogeneous areas
  publication-title: Computers, Environment and Urban Systems
– volume: 21
  start-page: 3201
  issue: 15
  year: 2005
  end-page: 3212
  article-title: Computational cluster validation in post‐genomic data analysis
  publication-title: Bioinformatics
– year: 1988
– year: 2006
– volume: 30
  start-page: 195
  issue: 3
  year: 2007
  end-page: 220
  article-title: Supervised regionalization methods: A survey
  publication-title: International Regional Science Review
– volume: 17
  start-page: 309
  issue: 4
  year: 2001
  end-page: 318
  article-title: Validating clustering for gene expression data
  publication-title: Bioinformatics
– start-page: 37
  year: 2018
  end-page: 54
– volume: 145
  start-page: 289
  year: 2018
  end-page: 297
  article-title: Improved K‐means algorithm based on density Canopy
  publication-title: Knowledge‐Based Systems
– start-page: 175
  year: 2010
  end-page: 193
– year: 2013
– volume: 20
  start-page: 1489
  issue: 6–7
  year: 2017
  end-page: 1492
  article-title: User clustering based on Canopy+ K‐means algorithm in cloud computing
  publication-title: Journal of Interdisciplinary Mathematics
– ident: e_1_2_13_15_1
  doi: 10.1007/978-3-319-59539-9_4
– ident: e_1_2_13_41_1
  doi: 10.1068/a270425
– ident: e_1_2_13_27_1
  doi: 10.1016/j.patcog.2018.02.015
– ident: e_1_2_13_46_1
  doi: 10.14569/ijacsa.2016.070256
– volume: 1
  start-page: 154
  issue: 2
  year: 2012
  ident: e_1_2_13_14_1
  article-title: A comparative study of clustering algorithms using Weka tools
  publication-title: International Journal of Application or Innovation in Engineering & Management
– ident: e_1_2_13_52_1
  doi: 10.1007/s10723‐019‐09504‐z
– ident: e_1_2_13_2_1
  doi: 10.1080/13658816.2015.1089442
– ident: e_1_2_13_30_1
  doi: 10.1080/13658816.2015.1031671
– ident: e_1_2_13_29_1
  doi: 10.1017/CBO9780511921803
– ident: e_1_2_13_35_1
  doi: 10.1080/713811750
– ident: e_1_2_13_6_1
  doi: 10.1111/j.1467‐9671.2012.01354.x
– ident: e_1_2_13_42_1
  doi: 10.1145/2395116.2395117
– volume-title: UCI machine learning repository
  year: 2013
  ident: e_1_2_13_33_1
– ident: e_1_2_13_5_1
  doi: 10.1371/journal.pbio.2001573
– ident: e_1_2_13_12_1
  doi: 10.1007/s10666‐007‐9084‐8
– ident: e_1_2_13_22_1
  doi: 10.1080/13658810701674970
– start-page: 699
  volume-title: Proceedings of the 2015 Annual Conference on Brazilian Symposium on Information Systems
  year: 2015
  ident: e_1_2_13_3_1
– volume-title: Data mining: Concepts and techniques
  year: 2005
  ident: e_1_2_13_25_1
– ident: e_1_2_13_37_1
  doi: 10.1111/2041‐210X.12208
– ident: e_1_2_13_13_1
  doi: 10.7551/mitpress/9780262033589.001.0001
– volume-title: Algorithms for clustering data
  year: 1988
  ident: e_1_2_13_28_1
– ident: e_1_2_13_54_1
  doi: 10.1093/bioinformatics/17.4.309
– ident: e_1_2_13_40_1
  doi: 10.1002/widm.53
– ident: e_1_2_13_23_1
  doi: 10.1111/tgis.12180
– volume-title: Data mining: The text book
  year: 2015
  ident: e_1_2_13_4_1
  doi: 10.1007/978-3-319-14142-8
– ident: e_1_2_13_49_1
  doi: 10.1080/09720502.2017.1386476
– ident: e_1_2_13_8_1
  doi: 10.1080/13658810600665111
– volume-title: Pysal: The Python Spatial Analysis Library for open source, cross platform geospatial data science
  year: 2010
  ident: e_1_2_13_43_1
– ident: e_1_2_13_32_1
  doi: 10.1111/tgis.12217
– ident: e_1_2_13_51_1
  doi: 10.1007/978-3-662-03499-6_5
– ident: e_1_2_13_26_1
  doi: 10.1093/bioinformatics/bti517
– ident: e_1_2_13_38_1
  doi: 10.1109/BRACIS.2017.70
– volume: 8
  start-page: 163
  issue: 1
  year: 1990
  ident: e_1_2_13_11_1
  article-title: Simulated annealing: An improved computer model for political redistricting
  publication-title: Yale Law & Policy Review
– start-page: 1027
  volume-title: Proceedings of the 18th Annual ACM–SIAM Symposium on Discrete Algorithms
  year: 2007
  ident: e_1_2_13_7_1
– ident: e_1_2_13_21_1
  doi: 10.1111/tgis.12557
– ident: e_1_2_13_45_1
  doi: 10.1016/j.compenvurbsys.2012.04.005
– volume: 8
  start-page: 29
  issue: 1
  year: 2019
  ident: e_1_2_13_47_1
  article-title: A brief survey of unsupervised agglomerative hierarchical clustering schemes
  publication-title: International Journal of Engineering Technology
– ident: e_1_2_13_19_1
  doi: 10.1007/BF02293706
– ident: e_1_2_13_44_1
  doi: 10.1007/978-3-642-03647-7_11
– volume-title: Generated clusters interpretation by hierarchical clustering algorithms
  year: 2006
  ident: e_1_2_13_36_1
– start-page: 347
  volume-title: Datenbanksysteme für Business, Technologie und Web
  year: 2011
  ident: e_1_2_13_39_1
– volume-title: The SAGE handbook of spatial analysis
  year: 2008
  ident: e_1_2_13_20_1
– ident: e_1_2_13_31_1
  doi: 10.1111/j.1538‐4632.1969.tb00615.x
– ident: e_1_2_13_34_1
  doi: 10.1016/S0031‐3203(02)00060‐2
– ident: e_1_2_13_17_1
  doi: 10.1016/j.ifacol.2015.06.441
– ident: e_1_2_13_48_1
  doi: 10.1007/s00357‐005‐0012‐9
– ident: e_1_2_13_9_1
  doi: 10.1145/3085228.3085294
– ident: e_1_2_13_24_1
  doi: 10.1145/1656274.1656278
– ident: e_1_2_13_18_1
  doi: 10.1177/0160017607301605
– ident: e_1_2_13_55_1
  doi: 10.1016/j.knosys.2018.01.031
– ident: e_1_2_13_10_1
  doi: 10.1145/3085228.3085288
– start-page: 577
  volume-title: Proceedings of the 18th International Conference on Machine Learning
  year: 2001
  ident: e_1_2_13_50_1
– volume: 7
  start-page: 1
  year: 2006
  ident: e_1_2_13_16_1
  article-title: Statistical comparisons of classifiers over multiple data sets
  publication-title: Journal of Machine Learning Research
– ident: e_1_2_13_53_1
  doi: 10.1007/s40745‐015‐0040‐1
SSID ssj0004497
Score 2.2998216
Snippet Traditionally, the geospatial regionalization task consists of aggregating into regions, geographically connected areas that share similar characteristics....
SourceID proquest
crossref
wiley
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 1775
SubjectTerms Aggregation
Algorithms
Clustering
Figure of merit
Frameworks
Optimization
Title A framework for spatial regionalization composed of novel clustering‐based algorithms under spatial contiguity constraints
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Ftgis.12903
https://www.proquest.com/docview/2681648317
Volume 26
WOSCitedRecordID wos000761630800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVWIB
  databaseName: Wiley Online Library - Journals
  customDbUrl:
  eissn: 1467-9671
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0004497
  issn: 1361-1682
  databaseCode: DRFUL
  dateStart: 19970101
  isFulltext: true
  titleUrlDefault: https://onlinelibrary.wiley.com
  providerName: Wiley-Blackwell
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB50FfTiW1xfBPSiUHGbbpOAl0VdFUTEB3grbR7rwrqVbVcQPPgT_I3-EjNp6yqIIN5ySIaQyWS-CTPzAWwbYxKJWY2hBbNeEBrqcV9wT1nfxPdDYU3Ktcw_ZxcX_O5OXI7BQVULU_SH-PxwQ8tw7zUaeJxkX4w8t4H9Hv6i0HGYwKoqG3pNHF21b89HdZFBQa5CQyRD5n7ZnhQzeUarvzukEcr8ilWds2nP_m-bczBTgkzSKm7FPIzp_gJMlXzn98-L8NIipsrKIha2kgwTq-0S5Glw2LyoziSYcZ5mWpHUkH76pHtE9obYW8F6vPfXN_SBisS9Tjro5vcPGcGStJE0zIPvdoYW6OMwc3QUebYEt-3jm8NTr-Rh8CRF8oc4bohEMSaYFD6XgabUZ6hJGqhQ2RDL55oZqsMG1w1lYm4EF4k9fBn6QaIoXYZaP-3rFSAm0YI1hZXIVKBFU1jAkQRN2pQsSISJ67BTKSOSZZNy3FwvqoIVPM_InWcdtj7nPhatOX6ctV7pNCrNM4v8kNswkVvsVIddp71fJEQ3J2fXbrT6l8lrMO1jqYT7sVmHWj4Y6g2YlE95Nxtsllf1AzTp8QI
linkProvider Wiley-Blackwell
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1fSxtBEB9sFPRFbW0x1tqF9kXhSnO72T-Poo1K01BqBN-Ou_0TAzEnuYsg-OBH8DP2k7izdzEKIpS-7cPusuzs7Pxm-M0MwFfnXKaR1cg9mI0YdzSSsZKR8bZJfufKq1Qomd8VvZ48P1e_a24O5sJU9SEeA26oGeG_RgXHgPQTLS-9Z_8Nwyj0DSwyToVswOLhn85Zd54YyaruKpRjN2QZ1_VJkcozX_3cIs1h5lOwGqxNZ-0_z7kOqzXMJPvVu3gLC3b8DpbrjucXNxtwu0_cjJdFPHAlBVKr_RLs1BDQeZWfSZBznhfWkNyRcX5tR0SPplhdwdu8v3f3aAUNSUeDfDIsLy4Lgklp892QCT8cTD3Ux2ERGlKUxXs46_zoHxxHdSeGSFNs_5CmLZUZIZTQKpaaWUpjgbKkzHDjnaxYWuGo5S1pW8al0impMn_7mscsM5R-gMY4H9tNIC6zSrSV31EYZlVbeciRsTZta8Ey5dIm7M6kkei6TDkebpTM3BW8zyTcZxO-PM69qopzvDhreybUpFbQIom59I6i9OipCXtBfK_skPSPTk7DaOtfJn-G5eP-r27SPen9_AgrMSZOhPjNNjTKydR-giV9XQ6LyU79bh8Alq708g
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1dSxwxFL3UVaovVm3FrWsN6IuFke4kM0keF3Vb6bKIH-DbMJMPXVh3ZGdWEHzwJ_Q3-kvMzcy6CqVQfMtDJoTc3LnnhnPvAdi11mYKWY2xA7MBiy0NRChFoF1sEj9i6VzKt8zv8X5fXF7Kk5qbg7UwVX-Ilwc39Az_v0YHN7favvLy0mX2-_iMQudgnkUyYg2YPzztXvRmhZGsUlehMaohi7DuT4pUntnXbyPSDGa-Bqs-2nQ_vXOfK7Bcw0zSqe7FKnwwozVYrBXPr-8_w0OH2CkvizjgSgqkVrtPUKnBo_OqPpMg5zwvjCa5JaP8zgyJGk6wu4KLeU-PfzAKapIOr_LxoLy-KQgWpc1WQyb84GrioD4OCy9IURZf4KJ7dH7wK6iVGAJFUf4hTdsy05xLrmQoFDOUhhxtSZmOtUuyQmG4pSZuC9PWNhVWCpm501dxyDJN6To0RvnIbACxmZE8km5FrpmRkXSQI2MRjRRnmbRpE_am1khU3aYcNzdMpukKnmfiz7MJOy9zb6vmHH-d1ZoaNakdtEjCWLhEUTj01ITv3nz_WCE5_3l85kdf_2fyNnw8OewmveP-701YCrFuwj_ftKBRjidmCxbUXTkoxt_qa_sMNA70bQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+framework+for+spatial+regionalization+composed+of+novel+clustering%E2%80%90based+algorithms+under+spatial+contiguity+constraints&rft.jtitle=Transactions+in+GIS&rft.au=Miranda%2C+Leandro&rft.au=Viterbo%2C+Jos%C3%A9&rft.au=Bernardini%2C+Fl%C3%A1via&rft.date=2022-06-01&rft.issn=1361-1682&rft.eissn=1467-9671&rft.volume=26&rft.issue=4&rft.spage=1775&rft.epage=1800&rft_id=info:doi/10.1111%2Ftgis.12903&rft.externalDBID=10.1111%252Ftgis.12903&rft.externalDocID=TGIS12903
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1361-1682&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1361-1682&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1361-1682&client=summon