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Journal articles on the topic 'Spatial data mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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1

Wang, Shuliang, and Hanning Yuan. "Spatial Data Mining." International Journal of Data Warehousing and Mining 10, no. 4 (October 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. As one of the fundamental resources, it may help people to recognize the world with population instead of sample, along with the potential effectiveness. Finally, knowledge discovery from spatial big data refers to the basic technologies to realize the value of big data, and relocate data assets. And the uncovered knowledge may be further transformed into data intelligences.

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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 (July 25, 2020): 912–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202182.

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7

Bist, Asmita, and Mainaz Faridi. "A Survey:On Spatial Data Mining." International Journal of Engineering Trends and Technology 46, no. 6 (April 25, 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 (July 14, 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 adaptable knowledge discovery process that uses graph theory to model different spatial relationships from multiple scenarios, and frequent subgraph mining to discover spatial associations. A proof of concept is presented using real data.

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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 (August 31, 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 (December 2003): 60–70. http://dx.doi.org/10.1080/10824000309480589.

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Alkathiri, Mazin, Jhummarwala Abdul, and M. B. Potdar. "Geo-spatial Big Data Mining Techniques." International Journal of Computer Applications 135, no. 11 (February 17, 2016): 28–36. http://dx.doi.org/10.5120/ijca2016908542.

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Meena, Kanak, and Nikita Jain. "A Brief on Spatial Data Mining." International Journal on Computer Science and Engineering 10, no. 3 (March 15, 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 (April 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 (June 30, 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 Romania. In our experimentation, we compare the results of the vector models depending on the values of the training datasets. Using these models with GIS data from the domain of Cadaster we analyze the performance of the Artificial Neural Networks in context of spatial data mining.

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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 (January 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 first provide overviews of uncertainty representations based on fuzzy, intuitionistic, and rough sets theory and data mining techniques. To illustrate the issues they focus on the application of the discovery of association rules in approaches for vague spatial data. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets are described. Finally an example of rule extraction for both fuzzy and rough set types of uncertainty representations is given

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17

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 campus GIS of a university has shown the feasibility and validity of the system.

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19

Lakshmanan, Valliappa, and Travis Smith. "Data Mining Storm Attributes from Spatial Grids." Journal of Atmospheric and Oceanic Technology 26, no. 11 (November 1, 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 answer questions of forecaster skill and lightning predictability.

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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 spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

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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 spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

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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 (July 11, 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, the evaluation criterion of the high utility co-location mining is unfair to the short patterns. In order to solve this problem, this paper first considers the utility and length of the co-location pattern comprehensively, and proposes a more reasonable High-Average Utility Co-location Pattern (HAUCP). Then, we propose a basic algorithm based on the extended average utility ratio of co-location patterns to mining all HAUCPs, which solves the problem that the average utility ratio of patterns does not satisfy the downward closure property. Next, an improved algorithm based on the local extended average utility ratio is developed which effectively reduces the search space of the basic algorithm and improves the mining efficiency. Finally, the practicability and robustness of the proposed method are verified based on real and synthetic data sets. Experimental results show that the proposed algorithm can effectively and efficiently find the HAUCPs from spatial data sets.

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23

Rajesh, D. "Application of Spatial Data mining for Agriculture." International Journal of Computer Applications 15, no. 2 (February 28, 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 (April 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 (June 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 (March 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 trends, methodologies, and future research directions. Practical implications and applications across domains like urban planning, environmental monitoring, and epidemiology are explored. As a comprehensive resource, this review facilitates understanding and utilization of machine learning approaches for extracting insights from spatial data, benefiting researchers, practitioners, and policymakers alike.

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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 (April 14, 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 (June 30, 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 (June 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, M. A. Naser, A. S. Mohamed, CD Fuller, L. van Dijk, and G. E. Marai. "DASS Good: Explainable Data Mining of Spatial Cohort Data." Computer Graphics Forum 42, no. 3 (June 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 can solve the disadvantage that fuzzy clustering cant process large data set, but also can give similarity measurement between objects.

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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 (November 19, 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 three stages. preprocessing, process of data mining, and post processing.For processing data and checking model, we built software application of Spatial Data Mining using SAR-Kriging method. An application is used to predict education quality at unsample locations at some cities at DIY Province. The result shows that SAR-Kriging method for some cities at DIY for elementary school has an average percentage error 6.43%. We can conclude that for elementary school, SAR-Kriging method can be used as a fitted model. Keywords— Expansion SAR, SAR-Kriging, quality education

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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 (April 18, 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 (December 31, 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 (December 16, 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 (September 2002): 533–49. http://dx.doi.org/10.1080/13658810210138715.

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Oh, Byoung-Woo. "Parallel Algorithm for Spatial Data Mining Using CUDA." JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 9, no. 2 (December 31, 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 (January 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 (March 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 solve the characteristics of random and nonlinear problems and solve the data mining problems of spatial association rules. The fuzzy genetic algorithm of discrete crossover probability and mutation probability is applied to data mining of spatial association rules in a spatial database, the coding method of the fuzzy genetic algorithm and the construction of fitness function are discussed, and the process of mining spatial association rules is given. The results show that the method of mining s association rules with the fuzzy genetic algorithm is feasible and has higher mining efficiency. This paper discusses the construction method of designing a decision support database based on linear regression and neural network and then proposes a decision method combining TOPSIS and grey relational analysis, which comprehensively considers the position and shape of the scheme data curve.

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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 (March 4, 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 viewshed into multiple viewsheds and quantifying their spatial semantic relationships. The method uses a DBSCAN clustering algorithm with terrain adaptability to divide a viewshed into spatially different viewsheds and characterizes these viewsheds through a systematic measurement framework, including azimuth, area, and sparsity. The method was applied to a case study of Purple Mountain in Nanjing. The experiment used 12.5 m accuracy topographic data from Purple Mountain, and two observation points were selected. For the first observation point near the mountain park, during the DBSCAN clustering partition of the viewshed, the number of clusters and the number of noise points were compared with determine the neighborhood radius of 18 m and the minimum sample point number of 4. Five viewsheds were successfully generated, with the largest viewshed having 468 visible points and the smallest only 16, located in different locations from the observer, reflecting the spatial variability of terrain features. All viewsheds are basically distributed to the north of the observer, two of which also share the northeast 87° direction with the observer in a straight line distribution but at different distances. In three-dimensional space, the distance between the two viewsheds is 317.298 m. Azimuth angle verification showed significant aggregation in the northeast direction. The second point is near the ridgeline, where one viewshed accounts for 87.52% of the total viewshed, showing significant visual effects. One viewshed is 3121.113 m away from the observer, with only 113 visible points, and is not located at a low altitude, so it is suitable for a long-distance fixed-point intermittent observation. The experimental results of the two observation points reveal the directional dominance and distance stratification of viewshed spatial relationships. This paper proposes a model to express topographical viewshed spatial relationships. The model analyzes and describes the spatial features of the viewshed through quantitative and qualitative methods. These metric features provide a basis for constructing spatial topological relationships between observation points and viewsheds, helping optimize viewpoint selection and enhance landscape planning. Compared with traditional methods, the proposed method significantly improves the resolution of spatial semantic relationship expression and has practical application value in fields such as archaeology, tourism planning, and urban design.

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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 scholars increasingly focus on research topics. This paper mainly studies and summarizes the application prospect of the technology of spatial data crawl based web.

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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 (June 18, 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 geospatial data mining with the exception of giving specific emphasis on locational identities that can be either descriptive or quantitative in nature.

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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 (March 30, 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 their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free) software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP), valuable in exploratory spatial data analysis.

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Reyes, Gary. "Trajectory analysis using data mining techniques." Journal of Computer Science and Technology 25, no. 1 (April 30, 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 capture the temporal evolution of the traffic distribution, whichserves as input for the second level of clustering. The second level aggregates microclusters based on proximity,taking advantage of historical density data to classify traffic variability. By comparing current and baselinedensities, the method identifies congestion-prone areas and dynamically adjusts cluster formations. This twolevelapproach improves traffic management and provides a robust framework for detecting congestion trends.Through validation in three urban case studies, San Francisco, Rome and Guayaquil, the methodologysuccessfully captured the spatial and temporal variability of traffic, identifying congestion hotspots anduncovering patterns of flow evolution over time.

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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 (March 18, 2009): 2035–61. http://dx.doi.org/10.3390/s90302035.

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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 (June 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 attributes. We also looked at modelling (predictive modelling/knowledge management of complex spatial systems), querying and implementing a complex spatial database (using data structure and algorithms). Critically speaking, the presence of spatial auto-correlation and the fact that continuous data types are always present in spatial data makes it important to create methods, tools and algorithms to mine spatial patterns in a complex spatial data set. This work is particularly useful to researchers in the field of data mining as it contributes a whole lot of knowledge to different application areas of data mining especially spatial data mining. It can also be useful in teaching and likewise for other study purposes.

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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 characteristics of different types of POI data in urban space were analyzed and the spatial analysis and correlation characteristics among different functional centers of the city were obtained. The spatial structure of the city is characterized by “multicenters and multigroups”, and the distribution of multicenters is also shown in cities with different functional types. The development degree of different urban centers varies significantly, but most of them are still in their infancy. Data mining of Internet of things (IOT) has good adaptability in city simulation and will play an important role in urban research in the future.

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