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Relevant bibliographies by topics / Spatial data mining

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Academic literature on the topic 'Spatial data mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Spatial data mining"

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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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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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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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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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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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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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Dissertations / Theses on the topic "Spatial data mining"

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Zhang, Xin Iris, and 張欣. "Fast mining of spatial co-location patterns." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30462708.

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Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.

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Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Bi

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Al-Naymat, Ghazi. "NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA." Thesis, The University of Sydney, 2009. http://hdl.handle.net/2123/5295.

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Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the

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Al-Naymat, Ghazi. "NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA." University of Sydney, 2009. http://hdl.handle.net/2123/5295.

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Doctor of Philosophy (PhD)<br>Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule pr

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Koperski, Krzysztof. "A progressive refinement approach to spatial data mining." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0024/NQ51882.pdf.

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Yang, Hui. "A general framework for mining spatial and spatio-temporal object association patterns in scientific data." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155319799.

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Yu, Ping. "FP-tree Based Spatial Co-location Pattern Mining." Thesis, University of North Texas, 2005. https://digital.library.unt.edu/ark:/67531/metadc4724/.

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A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in co-location pattern mining very difficult. This thesis investi

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SHENCOTTAH, K. N. KALYANKUMAR. "FINDING CLUSTERS IN SPATIAL DATA." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337.

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Lin, Zhungshan. "Optimal Candidate Generation in Spatial Co-Location Mining." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/377.

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Existing spatial co-location algorithms based on levels suffer from generating extra, nonclique candidate instances. Thus, they require cliqueness checking at every level. In this thesis, a novel, spatial co-location mining algorithm that automatically generates co-located spatial features without generating any nonclique candidates at any level is proposed. Subsequently, this algorithm generates fewer candidates than other existing level-wise, co-location algorithms without losing any pertinent information. The benefits of this algorithm have been clearly observed at early stages in the mini

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Pech, Palacio Manuel Alfredo. "Spatial data modeling and mining using a graph-based representation." Lyon, INSA, 2005. http://theses.insa-lyon.fr/publication/2005ISAL0118/these.pdf.

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Est proposé un unique modèle basé sur des graphes pour représenter des données spatiales, les données non-spatiales et les relations entre les objets spatiaux. Ainsi un graphe est généré à partir de ces trois éléments. On considère que l'outil de fouille de données basé sur les graphes peut découvrir des patterns incluant ces trois éléments, selon trois types de relation spatiale (topologique, cardinale et de distance). Dans notre modèle, les données spatiales, non-spatiales (attributs non-spatiaux), et les relations spatiales représentent une collections d'un ou plusieurs graphes orientés. Le

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Books on the topic "Spatial data mining"

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Li, Deren, Shuliang Wang, and Deyi Li. Spatial Data Mining. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5.

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Roddick, John F., and Kathleen Hornsby, eds. Temporal, Spatial, and Spatio-Temporal Data Mining. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45244-3.

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Alfred, Stein, Shi Wenzhong, and Bijker Wietske 1965-, eds. Quality aspects in spatial data mining. Chapman & Hall/CRC, 2008.

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Alfred, Stein, Shi Whenzhong, and Bijker Wietske 1965-, eds. Quality aspects in spatial data mining. Chapman & Hall/CRC, 2008.

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Pourghasemi, Hamid Reza, and Mauro Rossi, eds. Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-73383-8.

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International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining (2009 Wuhan, China). International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining: 13-14 October 2009, Wuhan, China. Edited by Liu Yaolin 1960-, Tang Xinming, Wuhan da xue. School of Resource and Environmental Science, China Jiao yu bu, and SPIE (Society). SPIE, 2009.

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Boris, Kovalerchuk, and Schwing James, eds. Visual and spatial analysis: Advances in data mining reasoning, and problem solving. Springer, 2004.

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Boris, Kovalerchuk, and Schwing James, eds. Visual and spatial analysis: Advances in data mining reasoning, and problem solving. Springer, 2004.

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Kennelly, Patrick. Development of an Internet mapping applications to allow spatial queries and data extration from the MBMG abandoned--interactive mine database. Montana University System, Water Resources Center, 2002.

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Egenhofer, Max. Spatial Information Theory: 10th International Conference, COSIT 2011, Belfast, ME, USA, September 12-16, 2011. Proceedings. Springer-Verlag Berlin Heidelberg, 2011.

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Book chapters on the topic "Spatial data mining"

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Aggarwal, Charu C. "Mining Spatial Data." In Data Mining. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_16.

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Li, Deren, Shuliang Wang, and Deyi Li. "GIS Data Mining." In Spatial Data Mining. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5_8.

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Li, Deren, Shuliang Wang, and Deyi Li. "Data Field." In Spatial Data Mining. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5_6.

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Shekhar, Shashi, Zhe Jiang, James Kang, and Vijay Gandhi. "Spatial Data Mining." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_357-2.

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Shekhar, Shashi, and Hui Xiong. "Spatial Data Mining." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1257.

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Shekhar, Shashi, James Kang, and Vijay Gandhi. "Spatial Data Mining." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_357.

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Wang, Shuliang, and Tisinee Surapunt. "Spatial Data Mining." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_66-1.

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Shekhar, Shashi, Pusheng Zhang, and Yan Huang. "Spatial Data Mining." In Data Mining and Knowledge Discovery Handbook. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_43.

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Wang, Shuliang, and Tisinee Surapunt. "Spatial Data Mining." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_66.

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Shekhar, Shashi, Zhe Jiang, James Kang, and Vijay Gandhi. "Spatial Data Mining." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_357.

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Conference papers on the topic "Spatial data mining"

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Bogorny, Vania, and Shashi Shekhar. "Spatial and Spatio-temporal Data Mining." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.166.

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Mei, Kun, Yangge Tian, and Fulin Bian. "Uncertainty in spatial data mining." In Second International Conference on Spatial Information Technology, edited by Cheng Wang, Shan Zhong, and Jiaolong Wei. SPIE, 2007. http://dx.doi.org/10.1117/12.775281.

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Yang, Tie-li, Ping-Bai, and Yu-Sheng Gong. "Spatial Data Mining Features between General Data Mining." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.167.

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Shuliang, Wang, Ding Gangyi, and Zhong Ming. "Big spatial data mining." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691764.

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Binzani, Kanika, and Jin Soung Yoo. "Spark-based Spatial Association Mining." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622419.

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Liu, Dianfeng, Yaolin Liu, Yin Xia, Xiaofeng Hong, and Zhongjun Zhao. "Indicator mining model for spatial multi-scale degraded land evaluation." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838297.

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Wei, M., A. F. Sung, and M. Cather. "Mining Spatially Abnormal Data in Spatial Databases." In Canadian International Petroleum Conference. Petroleum Society of Canada, 2004. http://dx.doi.org/10.2118/2004-142.

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Su, Hongjun, Yehua Sheng, and Yongning Wen. "Data mining based on spectral and spatial features for hyperspectral classification." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837304.

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Zhang, Jie-lin. "Multisource geological data mining and its utilization of uranium resources exploration." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837428.

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Niu, Jiqiang, Yaolin Liu, Feng Xu, and Yang Zhang. "Data mining of synergetic coupling for land use based on extenics." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837518.

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Reports on the topic "Spatial data mining"

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Haeckel, Matthias, and Peter Linke. RV SONNE Fahrtbericht/Cruise Report SO268 - Assessing the Impacts of Nodule Mining on the Deep-sea Environment: NoduleMonitoring, Manzanillo (Mexico) – Vancouver (Canada), 17.02. – 27.05.2019. GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, 2021. http://dx.doi.org/10.3289/geomar_rep_ns_59_20.

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Cruise SO268 is fully integrated into the second phase of the European collaborative JPI-Oceans project MiningImpact and is designed to assess the environmental impacts of deep-sea mining of polymetallic nodules in the Clarion-Clipperton Fracture Zone (CCZ). In particular, the cruise aimed at conducting an independent scientific monitoring of the first industrial test of a pre-protoype nodule collector by the Belgian company DEME-GSR. The work includes collecting the required baseline data in the designated trial and reference sites in the Belgian and German contract areas, a quantification of

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Gaffney, S., and P. Smyth. Final report: spatio-temporal data mining of scientific trajectory data. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/15005339.

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Ansari, S. M., E. M. Schetselaar, and J. A. Craven. Three-dimensional magnetotelluric modelling of the Lalor volcanogenic massive-sulfide deposit, Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328003.

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Unconstrained magnetotelluric inversion commonly produces insufficient inherent resolution to image ore-system fluid pathways that were structurally thinned during post-emplacement tectonic activity. To improve the resolution in these complex environments, we synthesized the 3-D magnetotelluric (MT) response for geologically realistic models using a finite-element-based forward-modelling tool with unstructured meshes and applied it to the Lalor volcanogenic massive-sulfide deposit in the Snow Lake mining camp, Manitoba. This new tool is based on mapping interpolated or simulated resistivity va

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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo

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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo

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Robert, Gillian. PR-420-153722-R01 Pipeline Right-of-Way Ground Movement Monitoring from InSAR. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0011463.

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Longwall mining induces large surface motion that may impact active pipelines. Typical remediation for longwall mining involves shutting down and exposing the pipeline. The use of InSAR has the potential to provide accurate measurements confirming the expected ground movement that will occur with the mining operations. Used correctly, with an appropriate survey design, InSAR can provide extremely high densities of ground movement over time. Exploiting the wide-area capabilities of InSAR could become an important part of integrity management for pipelines where longwall mining is a consideratio

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Bowles, David, Michael Williams, Hope Dodd, et al. Protocol for monitoring aquatic invertebrates of small streams in the Heartland Inventory & Monitoring Network: Version 2.1. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2284622.

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The Heartland Inventory and Monitoring Network (HTLN) is a component of the National Park Service’s (NPS) strategy to improve park management through greater reliance on scientific information. The purposes of this program are to design and implement long-term ecological monitoring and provide information for park managers to evaluate the integrity of park ecosystems and better understand ecosystem processes. Concerns over declining surface water quality have led to the development of various monitoring approaches to assess stream water quality. Freshwater streams in network parks are threaten

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Neyedley, K., J. J. Hanley, Z. Zajacz, and M. Fayek. Accessory mineral thermobarometry, trace element chemistry, and stable O isotope systematics, Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328986.

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The Mooshla Intrusive Complex (MIC) is an Archean polyphase magmatic body located in the Doyon-Bousquet-LaRonde (DBL) mining camp of the Abitibi greenstone belt, Québec, that is spatially associated with numerous gold (Au)-rich VMS, epizonal 'intrusion-related' Au-Cu vein systems, and shear zone-hosted (orogenic?) Au deposits. To elucidate the P-T conditions of crystallization, and oxidation state of the MIC magmas, accessory minerals (zircon, rutile, titanite) have been characterized using a variety of analytical techniques (e.g., trace element thermobarometry). The resulting trace element an

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Neyedley, K., J. J. Hanley, P. Mercier-Langevin, and M. Fayek. Ore mineralogy, pyrite chemistry, and S isotope systematics of magmatic-hydrothermal Au mineralization associated with the Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328985.

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The Mooshla Intrusive Complex (MIC) is an Archean polyphase magmatic body located in the Doyon-Bousquet-LaRonde (DBL) mining camp of the Abitibi greenstone belt, Québec. The MIC is spatially associated with numerous gold (Au)-rich VMS, epizonal 'intrusion-related' Au-Cu vein systems, and shear zone-hosted (orogenic?) Au deposits. To elucidate genetic links between deposits and the MIC, mineralized samples from two of the epizonal 'intrusion-related' Au-Cu vein systems (Doyon and Grand Duc Au-Cu) have been characterized using a variety of analytical techniques. Preliminary results indicate gold

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