Relevant bibliographies by topics / Spatial data mining

Academic literature on the topic 'Spatial data mining'

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Published: 4 June 2021
Last updated: 4 March 2023

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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 (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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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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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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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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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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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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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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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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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 Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.
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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 association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002.
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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)
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 association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002.
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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 investigates a projection based co-location pattern mining paradigm. In particular, a FP-tree based co-location mining framework and an algorithm called FP-CM, for FP-tree based co-location miner, are proposed. It is proved that FP-CM is complete, correct, and only requires a small constant number of database scans. The experimental results show that FP-CM outperforms candidate generation-and-test based co-location miner by an order of magnitude.
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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 mining process.
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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. Les sommets représentent soit les objets spatiaux, soit les relations spatiales entre deux objets spatiaux, ou les attributs non-spatiaux. De plus, un sommet peut représenter soit un attribut, soit le nom d'une relation spatiale. Les noms des attributs peuvent référencer des objets spatiaux ou non-spatiaux. Les arcs orientés sont utilisés pour représenter des informations directionnelles sur les relations entre les éléments, et pour décrire les attributs des objets. On a adopté SUBDUE comme un outil de fouille de graphes. Une caractéristique particulière dite de recouvrement joue un rôle important dans la découverte de patterns. Cependant, elle peut-être implémentée pour recouvrir la totalité du graphe, ou bien ne considérer aucun sommet. En conséquence, nous proposons une troisième piste nommée recouvrement limité, laquelle donne à l'utilisateur la capacité de choisir le recouvrement. On analyse directement trois caractéristiques de l'algorithme proposé, la réduction de l'espace de recherche, la réduction du temps de calcul, et la découverte de patterns grâce à ce type de recouvrement
We propose a unique graph-based model to represent spatial data, non-spatial data and the spatial relations among spatial objects. We will generate datasets composed of graphs with a set of these three elements. We consider that by mining a dataset with these characteristics a graph-based mining tool can search patterns involving all these elements at the same time improving the results of the spatial analysis task. A significant characteristic of spatial data is that the attributes of the neighbors of an object may have an influence on the object itself. So, we propose to include in the model three relationship types (topological, orientation, and distance relations). In the model the spatial data (i. E. Spatial objects), non-spatial data (i. E. Non-spatial attributes), and spatial relations are represented as a collection of one or more directed graphs. A directed graph contains a collection of vertices and edges representing all these elements. Vertices represent either spatial objects, spatial relations between two spatial objects (binary relation), or non-spatial attributes describing the spatial objects. Edges represent a link between two vertices of any type. According to the type of vertices that an edge joins, it can represent either an attribute name or a spatial relation name. The attribute name can refer to a spatial object or a non-spatial entity. We use directed edges to represent directional information of relations among elements (i. E. Object x touches object y) and to describe attributes about objects (i. E. Object x has attribute z). We propose to adopt the Subdue system, a general graph-based data mining system developed at the University of Texas at Arlington, as our mining tool. A special feature named overlap has a primary role in the substructures discovery process and consequently a direct impact over the generated results. However, it is currently implemented in an orthodox way: all or nothing. Therefore, we propose a third approach: limited overlap, which gives the user the capability to set over which vertices the overlap will be allowed. We visualize directly three motivations issues to propose the implementation of the new algorithm: search space reduction, processing time reduction, and specialized overlapping pattern oriented search
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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. Berlin, Heidelberg: 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. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45244-3.

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

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Alfred, Stein, Shi Wenzhong, and Bijker Wietske 1965-, eds. Quality aspects in spatial data mining. Boca Raton, FL: 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. Cham: 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). Bellingham, Wash: SPIE, 2009.

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Bu ting xie de si suo: Li Deren yuan shi wen ji. Wuchang: Wuhan da xue chu ban she, 2009.

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1974-, Wang Shuliang, and Li Deyi 1944-, eds. Kong jian shu ju wa jue li lun yu ying yong. Beijing: Ke xue chu ban she, 2006.

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

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Computing with spatial trajectories. New York: Springer, c2011., 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, 531–55. Cham: 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, 203–56. Berlin, Heidelberg: 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, 175–85. Berlin, Heidelberg: 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, 1–8. New York, NY: 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, 1087. Boston, MA: 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, 2695–98. Boston, MA: 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, 1–10. Cham: 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, 837–54. Boston, MA: 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, 1546–55. Cham: 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, 3575–83. New York, NY: 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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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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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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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, November 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 the spatial and temporal spread of the produced sediment plume during the trials as well as a first assessment of the generated environmental impacts. However, during SO268 Leg 1 DEME-GSR informed us that the collector trials would not take place as scheduled due to unresolvable technical problems. Thus, we adjusted our work plan accordingly by implementing our backup plan. This involved conducting a small-scale sediment plume experiment with a small chain dredge to quantify the spatial and temporal dispersal of the suspended sediment particles, their concentration in the plume as well as the spatial footprint and thickness of the deposited sediment blanket on the seabed.
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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), January 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 values from wireline logs onto unstructured tetrahedral meshes to reflect, with the help of 3-D models obtained from lithostratigraphic and lithofacies drillhole logs, the complexity of the host-rock geological structure. The resulting stochastic model provides a more realistic representation of the heterogeneous spatial distribution of the electric resistivity values around the massive, stringer, and disseminated sulfide ore zones. Both models were combined into one seamless tetrahedral mesh of the resistivity field. To capture the complex resistivity distribution in the geophysical forward model, a finite-element code was developed. Comparative analyses of the forward models with MT data acquired at the Earth's surface show a reasonable agreement that explains the regional variations associated with the host rock geological structure and detects the local anomalies associated with the MT response of the ore zones.
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, 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 development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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Bowles, David, Michael Williams, Hope Dodd, Lloyd Morrison, Janice Hinsey, Tyler Cribbs, Gareth Rowell, Michael DeBacker, Jennifer Haack-Gaynor, and Jeffrey Williams. Protocol for monitoring aquatic invertebrates of small streams in the Heartland Inventory & Monitoring Network: Version 2.1. National Park Service, April 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 threatened by numerous stressors, most of which originate outside park boundaries. Stream condition and ecosystem health are dependent on processes occurring in the entire watershed as well as riparian and floodplain areas; therefore, they cannot be manipulated independently of this interrelationship. Land use activities—such as timber management, landfills, grazing, confined animal feeding operations, urbanization, stream channelization, removal of riparian vegetation and gravel, and mineral and metals mining—threaten stream quality. Accordingly, the framework for this aquatic monitoring is directed towards maintaining the ecological integrity of the streams in those parks. Invertebrates are an important tool for understanding and detecting changes in ecosystem integrity, and they can be used to reflect cumulative impacts that cannot otherwise be detected through traditional water quality monitoring. The broad diversity of invertebrate species occurring in aquatic systems similarly demonstrates a broad range of responses to different environmental stressors. Benthic invertebrates are sensitive to the wide variety of impacts that influence Ozark streams. Benthic invertebrate community structure can be quantified to reflect stream integrity in several ways, including the absence of pollution sensitive taxa, dominance by a particular taxon combined with low overall taxa richness, or appreciable shifts in community composition relative to reference condition. Furthermore, changes in the diversity and community structure of benthic invertebrates are relatively simple to communicate to resource managers and the public. To assess the natural and anthropo-genic processes influencing invertebrate communities, this protocol has been designed to incorporate the spatial relationship of benthic invertebrates with their local habitat including substrate size and embeddedness, and water quality parameters (temperature, dissolved oxygen, pH, specific conductance, and turbidity). Rigid quality control and quality assurance are used to ensure maximum data integrity. Detailed standard operating procedures (SOPs) and supporting information are associated with this protocol.
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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 and oxythermobarometric database for accessory minerals in the MIC represents the first examination of such parameters in an Archean magmatic complex in a world-class mineralized district. Mineral thermobarometry yields P-T constraints on accessory mineral crystallization consistent with the expected conditions of tonalite-trondhjemite-granite (TTG) magma genesis, well above peak metamorphic conditions in the DBL camp. Together with textural observations, and mineral trace element data, the P-T estimates reassert that the studied minerals are of magmatic origin and not a product of metamorphism. Oxygen fugacity constraints indicate that while the magmas are relatively oxidizing (as indicated by the presence of magmatic epidote, titanite, and anhydrite), zircon trace element systematics indicate that the magmas were not as oxidized as arc magmas in younger (post-Archean) porphyry environments. The data presented provides first constraints on the depth and other conditions of melt generation and crystallization of the MIC. The P-T estimates and qualitative fO2 constraints have significant implications for the overall model for formation (crystallization, emplacement) of the MIC and potentially related mineral deposits.
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7

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 (as electrum) from both deposits occurs relatively late in the systems as it is primarily observed along fractures in pyrite and gangue minerals. At Grand Duc gold appears to have formed syn- to post-crystallization relative to base metal sulphides (e.g. chalcopyrite, sphalerite, pyrrhotite), whereas base metal sulphides at Doyon are relatively rare. The accessory ore mineral assemblage at Doyon is relatively simple compared to Grand Duc, consisting of petzite (Ag3AuTe2), calaverite (AuTe2), and hessite (Ag2Te), while accessory ore minerals at Grand Duc are comprised of tellurobismuthite (Bi2Te3), volynskite (AgBiTe2), native Te, tsumoite (BiTe) or tetradymite (Bi2Te2S), altaite (PbTe), petzite, calaverite, and hessite. Pyrite trace element distribution maps from representative pyrite grains from Doyon and Grand Duc were collected and confirm petrographic observations that Au occurs relatively late. Pyrite from Doyon appears to have been initially trace-element poor, then became enriched in As, followed by the ore metal stage consisting of Au-Ag-Te-Bi-Pb-Cu enrichment and lastly a Co-Ni-Se(?) stage enrichment. Grand Duc pyrite is more complex with initial enrichments in Co-Se-As (Stage 1) followed by an increase in As-Co(?) concentrations (Stage 2). The ore metal stage (Stage 3) is indicated by another increase in As coupled with Au-Ag-Bi-Te-Sb-Pb-Ni-Cu-Zn-Sn-Cd-In enrichment. The final stage of pyrite growth (Stage 4) is represented by the same element assemblage as Stage 3 but at lower concentrations. Preliminary sulphur isotope data from Grand Duc indicates pyrite, pyrrhotite, and chalcopyrite all have similar delta-34S values (~1.5 � 1 permille) with no core-to-rim variations. Pyrite from Doyon has slightly higher delta-34S values (~2.5 � 1 permille) compared to Grand Duc but similarly does not show much core-to-rim variation. At Grand Duc, the occurrence of Au concentrating along the rim of pyrite grains and associated with an enrichment in As and other metals (Sb-Ag-Bi-Te) shares similarities with porphyry and epithermal deposits, and the overall metal association of Au with Te and Bi is a hallmark of other intrusion-related gold systems. The occurrence of the ore metal-rich rims on pyrite from Grand Duc could be related to fluid boiling which results in the destabilization of gold-bearing aqueous complexes. Pyrite from Doyon does not show this inferred boiling texture but shares characteristics of dissolution-reprecipitation processes, where metals in the pyrite lattice are dissolved and then reconcentrated into discrete mineral phases that commonly precipitate in voids and fractures created during pyrite dissolution.
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