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Journal articles on the topic 'Spatial data mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Wang, Shuliang, and Hanning Yuan. "Spatial Data Mining." International Journal of Data Warehousing and Mining 10, no. 4 (2014): 50–70. http://dx.doi.org/10.4018/ijdwm.2014100103.

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Big data brings the opportunities and challenges into spatial data mining. In this paper, spatial big data mining is presented under the characteristics of geomatics and big data. First, spatial big data attracts much attention from the academic community, business industry, and administrative governments, for it is playing a primary role in addressing social, economic, and environmental issues of pressing importance. Second, humanity is submerged by spatial big data, such as much garbage, heavy pollution and its difficulties in utilization. Third, the value in spatial big data is dissected. A

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2

Rastogi, Mohit. "Spatial data mining features between general data mining." South Asian Journal of Marketing & Management Research 11, no. 11 (2021): 96–101. http://dx.doi.org/10.5958/2249-877x.2021.00116.8.

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3

Wang, Ting. "Adaptive Tessellation Mapping (ATM) for Spatial Data Mining." International Journal of Machine Learning and Computing 4, no. 6 (2015): 478–82. http://dx.doi.org/10.7763/ijmlc.2014.v6.458.

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4

Midoun, Mohammed, and Hafida Belbachir. "A new process for mining spatial databases: combining spatial data mining and visual data mining." International Journal of Business Information Systems 39, no. 1 (2022): 17. http://dx.doi.org/10.1504/ijbis.2022.120366.

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Belbachir, Hafida, and Mohammed Midoun. "A new process for mining spatial databases: combining spatial data mining and visual data mining." International Journal of Business Information Systems 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijbis.2020.10024978.

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6

K, Sivakumar. "Spatial Data Mining: Recent Trends in the Era of Big Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 912–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202182.

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Bist, Asmita, and Mainaz Faridi. "A Survey:On Spatial Data Mining." International Journal of Engineering Trends and Technology 46, no. 6 (2017): 327–33. http://dx.doi.org/10.14445/22315381/ijett-v46p257.

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8

Fu, Chun Chang, and Nan Zhang. "The Application of Data Mining in GIS." Advanced Materials Research 267 (June 2011): 658–61. http://dx.doi.org/10.4028/www.scientific.net/amr.267.658.

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The spatial data mining is an important branch of data mining, this paper introduced the technology of spatial data mining based on GIS, the spatial data mining and the GIS integration of the steps and main mode are described. Research oriented GIS spatial data mining framework structure and basic flow, points out the data mining technology in GIS application of unsolved problems and development direction.

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Giovanni, Daian Rottoli, and Merlino Hernan. "Spatial association discovery process using frequent subgraph mining." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 1884–91. https://doi.org/10.12928/TELKOMNIKA.v18i4.13858.

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Spatial associations are one of the most relevant kinds of patterns used by business intelligence regarding spatial data. Due to the characteristics of this particular type of information, different approaches have been proposed for spatial association mining. This wide variety of methods has entailed the need for a process to integrate the activities for association discovery, one that is easy to implement and flexible enough to be adapted to any particular situation, particularly for small and medium-size projects to guide the useful pattern discovery process. Thus, this work proposes an ada

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Santhosh Kumar, Ch N. "Spatial Data Mining using Cluster Analysis." International Journal of Computer Science and Information Technology 4, no. 4 (2012): 71–77. http://dx.doi.org/10.5121/ijcsit.2012.4407.

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Wang, Shuliang, Deren Li, Wenzhong Shi, Deyi Li, and Xinzhou Wang. "Cloud Model-Based Spatial Data Mining." Annals of GIS 9, no. 1-2 (2003): 60–70. http://dx.doi.org/10.1080/10824000309480589.

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12

Alkathiri, Mazin, Jhummarwala Abdul, and M. B. Potdar. "Geo-spatial Big Data Mining Techniques." International Journal of Computer Applications 135, no. 11 (2016): 28–36. http://dx.doi.org/10.5120/ijca2016908542.

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13

Meena, Kanak, and Nikita Jain. "A Brief on Spatial Data Mining." International Journal on Computer Science and Engineering 10, no. 3 (2018): 71–76. http://dx.doi.org/10.21817/ijcse/2018/v10i3/181003010.

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14

Maiti, Sandipan, and R. B. V. Subramanyam. "Mining behavioural patterns from spatial data." Engineering Science and Technology, an International Journal 22, no. 2 (2019): 618–28. http://dx.doi.org/10.1016/j.jestch.2018.10.007.

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15

Mihai, Dana. "New approaches to processing GIS Data using Artificial Neural Networks models." Annals of the University of Craiova - Mathematics and Computer Science Series 48, no. 1 (2021): 358–73. http://dx.doi.org/10.52846/ami.v48i1.1551.

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Spatial data mining is a special type of data mining. The main difference between data mining and spatial data mining is that in spatial data mining tasks we use not only non-spatial attributes but also spatial attributes. Spatial data mining techniques have strong relationship with GIS (Geographical Information System) and are widely used in GIS for inferring association among spatial attributes, clustering and classifying information with respect to spatial attributes. In this paper we use the statistical package Weka on two models, which consist of two parcels plans from the Olt area of Rom

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16

Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data." International Journal of Organizational and Collective Intelligence 3, no. 1 (2012): 52–71. http://dx.doi.org/10.4018/joci.2012010104.

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The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors fi

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Beaubouef, Theresa, Roy Ladner, and Frederick Petry. "Rough set spatial data modeling for data mining." International Journal of Intelligent Systems 19, no. 7 (2004): 567–84. http://dx.doi.org/10.1002/int.20019.

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18

Yu, Yong Ling, Tao Guan, and Jin Fa Shi. "Spatial Data Mining Based on Campus GIS." Advanced Materials Research 282-283 (July 2011): 641–45. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.641.

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With the rapid development of spatial database technology, spatial databases have been widely used in many engineering fields and rapidly increase in data capacity. Thus, to mine useful information from large spatial databases turns into a difficult but important task. In this paper, we apply the traditional data mining into spatial database and give a mining model for spatial data based on Campus GIS. Moreover, based on campus GIS, we implement a spatial data mining prototype system that is able to discovery the useful spatial features and patterns in spatial databases. The application in the

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Lakshmanan, Valliappa, and Travis Smith. "Data Mining Storm Attributes from Spatial Grids." Journal of Atmospheric and Oceanic Technology 26, no. 11 (2009): 2353–65. http://dx.doi.org/10.1175/2009jtecha1257.1.

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Abstract A technique to identify storms and capture scalar features within the geographic and temporal extent of the identified storms is described. The identification technique relies on clustering grid points in an observation field to find self-similar and spatially coherent clusters that meet the traditional understanding of what storms are. From these storms, geometric, spatial, and temporal features can be extracted. These scalar features can then be data mined to answer many types of research questions in an objective, data-driven manner. This is illustrated by using the technique to an

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20

Zhou, C., W. D. Xiao, and D. Q. Tang. "MINING CO-LOCATION PATTERNS FROM SPATIAL DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 85–90. http://dx.doi.org/10.5194/isprsannals-iii-2-85-2016.

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Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of sp

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Zhou, C., W. D. Xiao, and D. Q. Tang. "MINING CO-LOCATION PATTERNS FROM SPATIAL DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 85–90. http://dx.doi.org/10.5194/isprs-annals-iii-2-85-2016.

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Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of sp

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22

Li, Jinhong, Lizhen Wang, Hongmei Chen, and Zhengbao Sun. "Mining spatial high-average utility co-location patterns from spatial data sets." Intelligent Data Analysis 26, no. 4 (2022): 911–31. http://dx.doi.org/10.3233/ida-215848.

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The spatial co-location pattern refers to a subset of non-empty spatial features whose instances are frequently located together in a spatial neighborhood. Traditional spatial co-location pattern mining is mainly based on the frequency of the pattern, and there is no difference in the importance or value of each spatial feature within the pattern. Although the spatial high utility co-location pattern mining solves this problem, it does not consider the effect of pattern length on the utility. Generally, the utility of the pattern also increases as the length of the pattern increases. Therefore

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23

Rajesh, D. "Application of Spatial Data mining for Agriculture." International Journal of Computer Applications 15, no. 2 (2011): 7–9. http://dx.doi.org/10.5120/1922-2566.

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24

Malerba, Donato. "A relational perspective on spatial data mining." International Journal of Data Mining, Modelling and Management 1, no. 1 (2008): 103. http://dx.doi.org/10.1504/ijdmmm.2008.022540.

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25

Ghosh, Suddhasheel, and Bharat Lohani. "Mining lidar data with spatial clustering algorithms." International Journal of Remote Sensing 34, no. 14 (2013): 5119–35. http://dx.doi.org/10.1080/01431161.2013.787499.

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26

Bailey-Kellogg, Chris, Naren Ramakrishnan, and Madhav V. Marathe. "Spatial data mining to support pandemic preparedness." ACM SIGKDD Explorations Newsletter 8, no. 1 (2006): 80–82. http://dx.doi.org/10.1145/1147234.1147246.

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27

Bajaj, Shalini Bhaskar, Ashima Narang, and Priyanka Vashisth. "Machine Learning Approaches in Spatial Data Mining." International Journal of Innovative Research in Computer Science and Technology 12, no. 2 (2024): 140–48. http://dx.doi.org/10.55524/ijircst.2024.12.2.25.

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This review paper surveys the integration of machine learning techniques in spatial data mining, a crucial intersection of geographic information systems and data mining. It examines the application of various machine learning algorithms such as classification, regression, clustering, and deep learning in spatial data analysis. The paper discusses challenges like data preprocessing, feature selection, and model interpretability, alongside recent advancements including spatial-temporal analysis and heterogeneous data integration. Through critical analysis of existing literature, it identifies t

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28

Li, Deren, Shuliang Wang, Hanning Yuan, and Deyi Li. "Software and applications of spatial data mining." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6, no. 3 (2016): 84–114. http://dx.doi.org/10.1002/widm.1180.

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29

Brito, Paula, and Monique Noirhomme-Fraiture. "Symbolic and spatial data analysis: Mining complex data structures." Intelligent Data Analysis 10, no. 4 (2006): 297–300. http://dx.doi.org/10.3233/ida-2006-10401.

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30

Keim, Daniel A., Christian Panse, Mike Sips, and Stephen C. North. "Pixel based visual data mining of geo-spatial data." Computers & Graphics 28, no. 3 (2004): 327–44. http://dx.doi.org/10.1016/j.cag.2004.03.022.

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31

Wentzel, A., C. Floricel, G. Canahuate, et al. "DASS Good: Explainable Data Mining of Spatial Cohort Data." Computer Graphics Forum 42, no. 3 (2023): 283–95. http://dx.doi.org/10.1111/cgf.14830.

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32

Zhao, Ting Ting. "Study on Fuzzy Clustering Algorithm of Spatial Data Mining." Applied Mechanics and Materials 416-417 (September 2013): 1244–50. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1244.

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With rapid development of space information crawl technology, different types of spatial database and data size of spatial database increases continuously. How to extract valuable information from complicated spatial data has become an urgent issue. Spatial data mining provides a new thought for solving the problem. The paper introduces fuzzy clustering into spatial data clustering field, studies the method that fuzzy set theory is applied to spatial data mining, proposes spatial clustering algorithm based on fuzzy similar matrix, fuzzy similarity clustering algorithm. The algorithm not only c

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33

Setiawan, Atje, and Rudi Rosadi. "SPASIAL DATA MINING MENGGUNAKAN MODEL SAR-KRIGING." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 5, no. 3 (2011): 52. http://dx.doi.org/10.22146/ijccs.5213.

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The region of Indonesia is very sparse and it has a variation condition in social, economic and culture, so the problem in education quality at many locations is an interesting topic to be studied. Database used in this research is Base Survey of National Education 2003, while a spatial data is presented by district coordinate as a least analysis unit. The aim of this research is to study and to apply spatial data mining to predict education quality at elementary and junior high schools using SAR-Kriging method which combines an expansion SAR and Kriging method. Spatial data mining process has

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34

Padmapriya, A., and N. Subitha. "Clustering Algorithm for Spatial Data Mining: An Overview." International Journal of Computer Applications 68, no. 10 (2013): 28–33. http://dx.doi.org/10.5120/11617-7014.

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35

Syed, M. A., R. Decoupes, E. Arsevska, M. Roche, and M. Teisseire. "Spatial Opinion Mining from COVID-19 Twitter Data." International Journal of Infectious Diseases 116 (March 2022): S27. http://dx.doi.org/10.1016/j.ijid.2021.12.065.

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36

Manjula, Aakunuri, and Dr G. Narsimha. "SPATIAL TEMPORAL DATA MINING FOR CROP YIELD PREDICTION." International Journal of Advanced Research 4, no. 12 (2016): 848–59. http://dx.doi.org/10.21474/ijar01/2466.

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37

Klösgen, Willi, Michael May, and Jim Petch. "Mining census data for spatial effects on mortality." Intelligent Data Analysis 7, no. 6 (2003): 521–40. http://dx.doi.org/10.3233/ida-2003-7603.

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38

Moran, Christopher J., and Elisabeth N. Bui. "Spatial data mining for enhanced soil map modelling." International Journal of Geographical Information Science 16, no. 6 (2002): 533–49. http://dx.doi.org/10.1080/13658810210138715.

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39

Oh, Byoung-Woo. "Parallel Algorithm for Spatial Data Mining Using CUDA." JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 9, no. 2 (2019): 89–97. http://dx.doi.org/10.14801/jaitc.2019.9.2.89.

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40

Wang, Shuliang, Juebo Wu, Feng Cheng, Hong Jin, and Shi Zeng. "Behavior mining of spatial objects with data field." Geo-spatial Information Science 12, no. 3 (2009): 202–11. http://dx.doi.org/10.1007/s11806-009-0076-5.

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41

FAKIR, Youssef, Rachid ELAYACHI, and Btissam MAHI. "Clustering objects for spatial data mining: a comparative study." Journal of Big Data Research 1, no. 3 (2023): 1–11. http://dx.doi.org/10.14302/issn.2768-0207.jbr-23-4478.

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Spatial data mining (SDM) is searching important relationships and characteristics that can clearly exist in spatial databases. This content aims to compare object clustering algorithms for spatial data mining, before identifying the most efficient algorithm. To this end, this paper compare k-means, Partionning Around Medoids (PAM) and Clustering Large Applications based on RANdomized Search (CLARANS) algorithms based on computing time. Experimental results indicate that, CLARANS is very efficient and effective.

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42

Wei, Qian. "Product Shape Design Scheme Evaluation Method Based on Spatial Data Mining." Mathematical Problems in Engineering 2022 (July 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/3231357.

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The stage of product modeling design implies a lot of complex tacit knowledge, which is the embodiment of the design concept centered on product modeling design and is also the hot spot and difficulty of modern design theory and method research. Aiming at the evaluation and decision of product modeling design scheme, a decision-making method of approaching ideal solution ranking based on grey relational analysis was proposed, which realized the convergence of tacit knowledge. The empty association rule is an important knowledge content of spatial data mining. A fuzzy genetic algorithm can solv

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43

Zhang, Cheng, Yiwen Wang, Haozhe Cheng, and Wanfeng Dou. "Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method." ISPRS International Journal of Geo-Information 14, no. 3 (2025): 113. https://doi.org/10.3390/ijgi14030113.

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With the rapid development of geographic information technology, the expression of topographical spatial semantic relationships has become a research hotspot in the field of intelligent geographic information systems. Geographical spatial semantic relationships refer to the spatial relationships and inherent meanings between geographical entities, including topological relationships, metric relationships, etc. This study proposes a novel method of viewshed analysis, which solves the limitation of treating the viewshed as a unified unit in traditional viewshed analysis by decomposing the viewsh

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44

An, De Zhi, Guang Li Wu, and Jun Lu. "The Key Technology Research of Spatial Data Crawl Based on Web." Applied Mechanics and Materials 651-653 (September 2014): 1988–91. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1988.

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With the rapid development of Web technology, provides abundant information for people, at the same time bring a lot of information redundancy. How to quickly locate the user requirements, is currently one of the common problems in the network retrieval, especially in the field of space information. Spatial data mining has caught more and more scholar's attention. With the rapid development of computer network technology, how to carry on the spatial data mining on the Internet or Intranet, namely how to make Web based spatial data mining is a new research field of SDM, is also one of related s

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45

Dr., Sivasankar.S*1 Raghav M. S2 &. V.Kuppurathinam3. "DATA MININGTECHNIQUES FROM A GEOSPATIAL PERSPECTIVE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 6 (2019): 103–7. https://doi.org/10.5281/zenodo.3249478.

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Geospatial technology involves a combination of data capturing and analyzing devices that tend to generate myriad amounts of data day in and day out in a systematic manner if instructed to do so which puts in front of us a serious issue of vetting out the irrelevant and culling out objects of interest from a maze of collections that can really aid in decision making to solve issues of concern. Data mining, in general addresses this predicament by providing apt statistical tools that carry out an  in depth search for obscured correlations among data variables which is the same in case of g

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46

D’Aubigny, Gérard. "A Statistical Toolbox For Mining And Modeling Spatial Data." Comparative Economic Research. Central and Eastern Europe 19, no. 5 (2017): 5–24. http://dx.doi.org/10.1515/cer-2016-0035.

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Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in

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47

Reyes, Gary. "Trajectory analysis using data mining techniques." Journal of Computer Science and Technology 25, no. 1 (2025): e06. https://doi.org/10.24215/16666038.25.e06.

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This study presents an innovative method for identifying variability in vehicular flow, designed for dynamic urban environments with fluctuating traffic conditions. The proposed approach integrates real-time data flowprocessing with a two-level clustering strategy to detect and analyze vehicular density patterns. The first levelperforms dynamic clustering of GPS locations, forming microclusters that represent spatially homogeneoustraffic zones. Each microcluster is continuously updated based on similarity criteria and a forgetting mechanismthat ensures data relevance. Periodic snapshots captur

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Samson, Grace L., Joan Lu, and Aminat A. Showole. "Mining Complex Spatial Patterns: Issues and Techniques." Journal of Information & Knowledge Management 13, no. 02 (2014): 1450019. http://dx.doi.org/10.1142/s0219649214500191.

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Spatial data mining is the quantitative study of phenomena that are located in space. This paper investigates methods of mining patterns of a complex spatial data set (which generally describes any kind of data where the location in space of object holds importance). We based this research on the analysis of some spatial characteristics of certain objects. We began with describing the spatial pattern of events or objects with respect to their attributes; we looked at how to describe the spatial nature/characteristics of entities in an environment with respect to their spatial and non-spatial a

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Wang, Xianmin, and Ruiqing Niu. "Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining." Sensors 9, no. 3 (2009): 2035–61. http://dx.doi.org/10.3390/s90302035.

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50

Zhang, Jun, Xin Sui, and Xiong He. "Research on the Simulation Application of Data Mining in Urban Spatial Structure." Journal of Advanced Transportation 2020 (August 3, 2020): 1–9. http://dx.doi.org/10.1155/2020/8863363.

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Data mining and simulation of the Internet of things (IOT) have been applied more and more widely in the rapidly developing urban research discipline. Urban spatial structure is an important field that needs to be explored in the sustainable urban development, while data mining is relatively rare in the research of urban spatial structure. In this study, 705,747 POI (Point of Interest) were used to conduct simulation analysis of western cities in China by mining the data of online maps. Through kernel density analysis and spatial correlation index, the distribution and aggregation characterist

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