Academic literature on the topic 'Geographical data mining'
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Journal articles on the topic "Geographical data mining"
Estivill-Castro, Vladimir, and Ickjai Lee. "Clustering with obstacles for Geographical Data Mining." ISPRS Journal of Photogrammetry and Remote Sensing 59, no. 1-2 (August 2004): 21–34. http://dx.doi.org/10.1016/j.isprsjprs.2003.12.003.
Full textM. Khedr, Ahmed, Zaher AL Aghbari, and Ibrahim Kamel. "Privacy Preserving Decomposable Mining Association Rules on Distributed Data." International Journal of Engineering & Technology 7, no. 3.13 (July 27, 2018): 157. http://dx.doi.org/10.14419/ijet.v7i3.13.16343.
Full textLee, Sang-Moon, and Jeong-Min Seo. "A Spatial Data Mining and Geographical Customer Relationship Management System." Journal of the Korea Society of Computer and Information 15, no. 6 (June 30, 2010): 121–28. http://dx.doi.org/10.9708/jksci.2010.15.6.121.
Full textLu, Y. L., C. W. Liu, J. W. Li, and J. W. Jiang. "CONSTRUCTION METHOD OF “CELL-CUBE” SPATIO-TEMPORAL DATA MODEL FOR BIG DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 25–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-25-2020.
Full textBanerjee, Soumita. "The Role of Global Educational Database in Educational Data Mining." European Journal of Engineering and Technology Research 1, no. 6 (July 27, 2018): 16–26. http://dx.doi.org/10.24018/ejeng.2016.1.6.194.
Full textLeon, Florin, Gabriela Maria Atanasiu, and Dan Gâlea. "Using Data Mining Techniques for the Management of Seismic Vulnerability." Key Engineering Materials 326-328 (December 2006): 501–4. http://dx.doi.org/10.4028/www.scientific.net/kem.326-328.501.
Full textChakri, Sana, Said Raghay, and Salah El Hadaj. "Semantic Trajectory Knowledge Discovery: A Promising Way to Extract Meaningful Patterns from Spatiotemporal Data." International Journal of Software Engineering and Knowledge Engineering 27, no. 03 (April 2017): 399–421. http://dx.doi.org/10.1142/s0218194017500140.
Full textLiu, Zhewei, Xiaolin Zhou, Wenzhong Shi, and Anshu Zhang. "Towards Detecting Social Events by Mining Geographical Patterns with VGI Data." ISPRS International Journal of Geo-Information 7, no. 12 (December 17, 2018): 481. http://dx.doi.org/10.3390/ijgi7120481.
Full textGadekar, Dr Prof Amit R. "Location based Anomalies Detection on Geographical Map using Data Mining Techniques." International Journal for Research in Applied Science and Engineering Technology 8, no. 4 (April 30, 2020): 138–41. http://dx.doi.org/10.22214/ijraset.2020.4021.
Full textM. Almuttairi, Rafah, Mahdi S. Almhanna, Mohammed Q. Mohammed, and Saif Q Muhamed. "Promote Replica Management based on Data Mining Techniques." International Journal of Engineering & Technology 7, no. 4.19 (November 27, 2018): 838. http://dx.doi.org/10.14419/ijet.v7i4.19.28006.
Full textDissertations / Theses on the topic "Geographical data mining"
Demšar, Urška. "Exploring geographical metadata by automatic and visual data mining." Licentiate thesis, KTH, Infrastructure, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1779.
Full textMetadata are data about data. They describe characteristicsand content of an original piece of data. Geographical metadatadescribe geospatial data: maps, satellite images and othergeographically referenced material. Such metadata have twocharacteristics, high dimensionality and diversity of attributedata types, which present a problem for traditional data miningalgorithms.
Other problems that arise during the exploration ofgeographical metadata are linked to the expertise of the userperforming the analysis. The large amounts of metadata andhundreds of possible attributes limit the exploration for anon-expert user, which results in a potential loss ofinformation that is hidden in metadata.
In order to solve some of these problems, this thesispresents an approach for exploration of geographical metadataby a combination of automatic and visual data mining.
Visual data mining is a principle that involves the human inthe data exploration by presenting the data in some visualform, allowing the human to get insight into the data and torecognise patterns. The main advantages of visual dataexploration over automatic data mining are that the visualexploration allows a direct interaction with the user, that itis intuitive and does not require complex understanding ofmathematical or statistical algorithms. As a result the userhas a higher confidence in the resulting patterns than if theywere produced by computer only.
In the thesis we present the Visual data mining tool (VDMtool), which was developed for exploration of geographicalmetadata for site planning. The tool provides five differentvisualisations: a histogram, a table, a pie chart, a parallelcoordinates visualisation and a clustering visualisation. Thevisualisations are connected using the interactive selectionprinciple called brushing and linking.
In the VDM tool the visual data mining concept is integratedwith an automatic data mining method, clustering, which finds ahierarchical structure in the metadata, based on similarity ofmetadata items. In the thesis we present a visualisation of thehierarchical structure in the form of a snowflake graph.
Keywords:visualisation, data mining, clustering, treedrawing, geographical metadata.
Sandell, Anna. "GIS, data mining and wild land fire data within Räddningstjänsten." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-543.
Full textGeographical information systems (GIS), data mining and wild land fire would theoretically be suitable to use together. However, would data mining in reality bring out any useful information from wild land fire data stored within a GIS? In this report an investigation is done if GIS and data mining are used within Räddningstjänsten today in some municipalities of the former Skaraborg. The investigation shows that neither data mining nor GIS are used within the investigated municipalities. However, there is an interest in using GIS within the organisations in the future but also some kind of analysis tool, for example data mining. To show how GIS and data mining could be used in the future within Räddningstjänsten some examples on this were constructed.
Dong, Zheng. "Automated Extraction and Retrieval of Metadata by Data Mining : a Case Study of Mining Engine for National Land Survey Sweden." Thesis, University of Gävle, Department of Technology and Built Environment, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-6811.
Full textMetadata is the important information describing geographical data resources and their key elements. It is used to guarantee the availability and accessibility of the data. ISO 19115 is a metadata standard for geographical information, making the geographical metadata shareable, retrievable, and understandable at the global level. In order to cope with the massive, high-dimensional and high-diversity nature of geographical data, data mining is an applicable method to discover the metadata.
This thesis develops and evaluates an automated mining method for extracting metadata from the data environment on the Local Area Network at the National Land Survey of Sweden (NLS). These metadata are prepared and provided across Europe according to the metadata implementing rules for the Infrastructure for Spatial Information in Europe (INSPIRE). The metadata elements are defined according to the numerical formats of four different data entities: document data, time-series data, webpage data, and spatial data. For evaluating the method for further improvement, a few attributes and corresponding metadata of geographical data files are extracted automatically as metadata record in testing, and arranged in database. Based on the extracted metadata schema, a retrieving functionality is used to find the file containing the keyword of metadata user input. In general, the average success rate of metadata extraction and retrieval is 90.0%.
The mining engine is developed in C# programming language on top of the database using SQL Server 2005. Lucene.net is also integrated with Visual Studio 2005 to build an indexing framework for extracting and accessing metadata in database.
Brindley, Paul. "Generating vague geographic information through data mining of passive web data." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33722/.
Full textAdu-Prah, Samuel. "GEOGRAPHIC DATA MINING AND GEOVISUALIZATION FOR UNDERSTANDING ENVIRONMENTAL AND PUBLIC HEALTH DATA." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/657.
Full textBogorny, Vania. "Enhancing spatial association rule mining in geographic databases." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2006. http://hdl.handle.net/10183/7841.
Full textThe association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.
Demšar, Urška. "Data mining of geospatial data: combining visual and automatic methods." Doctoral thesis, KTH, School of Architecture and the Built Environment (ABE), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3892.
Full textMost of the largest databases currently available have a strong geospatial component and contain potentially useful information which might be of value. The discipline concerned with extracting this information and knowledge is data mining. Knowledge discovery is performed by applying automatic algorithms which recognise patterns in the data.
Classical data mining algorithms assume that data are independently generated and identically distributed. Geospatial data are multidimensional, spatially autocorrelated and heterogeneous. These properties make classical data mining algorithms inappropriate for geospatial data, as their basic assumptions cease to be valid. Extracting knowledge from geospatial data therefore requires special approaches. One way to do that is to use visual data mining, where the data is presented in visual form for a human to perform the pattern recognition. When visual mining is applied to geospatial data, it is part of the discipline called exploratory geovisualisation.
Both automatic and visual data mining have their respective advantages. Computers can treat large amounts of data much faster than humans, while humans are able to recognise objects and visually explore data much more effectively than computers. A combination of visual and automatic data mining draws together human cognitive skills and computer efficiency and permits faster and more efficient knowledge discovery.
This thesis investigates if a combination of visual and automatic data mining is useful for exploration of geospatial data. Three case studies illustrate three different combinations of methods. Hierarchical clustering is combined with visual data mining for exploration of geographical metadata in the first case study. The second case study presents an attempt to explore an environmental dataset by a combination of visual mining and a Self-Organising Map. Spatial pre-processing and visual data mining methods were used in the third case study for emergency response data.
Contemporary system design methods involve user participation at all stages. These methods originated in the field of Human-Computer Interaction, but have been adapted for the geovisualisation issues related to spatial problem solving. Attention to user-centred design was present in all three case studies, but the principles were fully followed only for the third case study, where a usability assessment was performed using a combination of a formal evaluation and exploratory usability.
Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.
Full textKINSEY, MICHAEL LOY. "PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448.
Full textSengstock, Christian [Verfasser], and Michael [Akademischer Betreuer] Gertz. "Geographic Feature Mining: Framework and Fundamental Tasks for Geographic Knowledge Discovery from User-generated Data / Christian Sengstock ; Betreuer: Michael Gertz." Heidelberg : Universitätsbibliothek Heidelberg, 2015. http://d-nb.info/1180395662/34.
Full textBooks on the topic "Geographical data mining"
Sui, Daniel. Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice. Dordrecht: Springer Netherlands, 2013.
Find full textLakshmanan, Valliappa. Automating the Analysis of Spatial Grids: A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications. Dordrecht: Springer Netherlands, 2012.
Find full textCaluwe, Rita. Spatio-Temporal Databases: Flexible Querying and Reasoning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
Find full textJ, Miller Harvey, and Han Jiawei, eds. Geographic data mining and knowledge discovery. London: Taylor & Francis, 2001.
Find full textMiller, Harvey J., and Jiawei Han. Geographic Data Mining and Knowledge Discovery. Abingdon, UK: Taylor & Francis, 2001. http://dx.doi.org/10.4324/9780203468029.
Full textBienen, Derk. Die politische Oekonomie von Arbeitsmarktreformen in Argentinien. Bern: Peter Lang International Academic Publishers, 2005.
Find full textInternational Conference on Geospatial Semantics (3rd 2009 Mexico City, Mexico). GeoSpatial semantics: Third international conference, GeoS 2009, Mexico City, Mexico, December 3-4, 2009 : proceedings. Berlin: Springer, 2009.
Find full textBlanuca, Viktor, Leonid Bezrukov, Egor Sherin, and Anatoliy Yakobson. Public geography: Digital priorities of the XXI century. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1863096.
Full textP, Sheth Amit, and SpringerLink (Online service), eds. Geospatial Semantics and the Semantic Web: Foundations, Algorithms, and Applications. Boston, MA: Springer Science+Business Media, LLC, 2011.
Find full textKevin, Shaw, and Abdelguerfi Mahdi, eds. Mining spatio-temporal information systems. Boston: Kluwer Academic Publishers, 2002.
Find full textBook chapters on the topic "Geographical data mining"
Rinzivillo, S., F. Turini, V. Bogorny, C. Körner, B. Kuijpers, and M. May. "Knowledge Discovery from Geographical Data." In Mobility, Data Mining and Privacy, 243–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75177-9_10.
Full textNissi, Eugenia, Annalina Sarra, Sergio Palermi, and Gaetano De Luca. "The Application of M-Function Analysis to the Geographical Distribution of Earthquake Sequence." In Classification and Data Mining, 271–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28894-4_32.
Full textDoan, Thanh-Nam, and Ee-Peng Lim. "Modeling Check-In Behavior with Geographical Neighborhood Influence of Venues." In Advanced Data Mining and Applications, 429–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69179-4_30.
Full textWang, Fengjiao, Chun-Ta Lu, Yongzhi Qu, and Philip S. Yu. "Collective Geographical Embedding for Geolocating Social Network Users." In Advances in Knowledge Discovery and Data Mining, 599–611. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_47.
Full textHe, Yuan, Cheng Wang, and Changjun Jiang. "Multi-perspective Hierarchical Dirichlet Process for Geographical Topic Modeling." In Advances in Knowledge Discovery and Data Mining, 811–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_63.
Full textMarjoribanks, Roger. "GIS Geographical Information Systems and exploration data basesExploration Databases." In Geological Methods in Mineral Exploration and Mining, 165–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-74375-0_10.
Full textHan, Yuqiang, Yao Wan, Liang Chen, Guandong Xu, and Jian Wu. "Exploiting Geographical Location for Team Formation in Social Coding Sites." In Advances in Knowledge Discovery and Data Mining, 499–510. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_39.
Full textMalerba, Donato, Annalisa Appice, and Michelangelo Ceci. "A Data Mining Query Language for Knowledge Discovery in a Geographical Information System." In Database Support for Data Mining Applications, 95–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44497-8_5.
Full textMazzola, Luca, Pedro Chahuara, Aris Tsois, and Mauro Pedone. "Resolution of Geographical String Name through Spatio-Temporal Information." In Machine Learning and Data Mining in Pattern Recognition, 498–512. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08979-9_38.
Full textLee, Ickjai, and Vladimir Estivill-Castro. "Polygonization of Point Clusters through Cluster Boundary Extraction for Geographical Data Mining." In Advances in Spatial Data Handling, 27–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56094-1_3.
Full textConference papers on the topic "Geographical data mining"
Shu, Hong, and Cuihong Qi. "Temporal uncertainty of geographical information." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.651233.
Full textZhou, Fang, Q. Claire, and Ross D. King. "Predicting the Geographical Origin of Music." In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.73.
Full textLi, Zonghua, Mingjun Peng, and Wei Fan. "A SOA-based approach to geographical data sharing." 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/10.1117/12.838415.
Full textGong, Jian, Yaolin Liu, Zhi Zhang, and Jianfeng Li. "Urban land space evolution based on geographical simulation systems." 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/10.1117/12.838662.
Full textCui, Wei, and Deren Li. "The geographical ontology, LDAP, and the space information semantic grid." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.650271.
Full textAhuja, Aman, Wei Wei, Wei Lu, Kathleen M. Carley, and Chandan K. Reddy. "A Probabilistic Geographical Aspect-Opinion Model for Geo-Tagged Microblogs." In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.82.
Full textde Vries, Gerben K. D., Willem Robert van Hage, and Maarten van Someren. "Comparing Vessel Trajectories Using Geographical Domain Knowledge and Alignments." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.123.
Full textHu, Shunguang, Zengxiang Zhang, Xianhu Wei, and Fang Liu. "A research on natural geographical factors based on Chinese urban expansion." 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/10.1117/12.837745.
Full textCheng, Gang, and Qingyun Du. "Construction of geographical names knowledge base with ontology and production rule." 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/10.1117/12.838292.
Full textMo, Fan, and Hayato Yamana. "Point of Interest Recommendation by Exploiting Geographical Weighted Center and Categorical Preference." In 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00021.
Full textReports on the topic "Geographical data mining"
Kindt, Roeland, Ian K Dawson, Jens-Peter B Lillesø, Alice Muchugi, Fabio Pedercini, and James M Roshetko. The one hundred tree species prioritized for planting in the tropics and subtropics as indicated by database mining. World Agroforestry, 2021. http://dx.doi.org/10.5716/wp21001.pdf.
Full textMatenga, Chrispin, and Munguzwe Hichaambwa. A Multi-Phase Assessment of the Effects of COVID-19 on Food Systems and Rural Livelihoods in Zambia. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/apra.2021.039.
Full textHenderson, Tim, Mincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Chihuahuan Desert Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285306.
Full textHenderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Northern Colorado Plateau Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285337.
Full textHenderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Klamath Inventory & Monitoring Network. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286915.
Full textHenderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Mojave Desert Inventory & Monitoring Network. National Park Service, December 2021. http://dx.doi.org/10.36967/nrr-2289952.
Full textHenderson, Tim, Vincet Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: North Coast and Cascades Inventory & Monitoring Network. National Park Service, March 2022. http://dx.doi.org/10.36967/nrr-2293013.
Full textHenderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Central Alaska Inventory & Monitoring Network. National Park Service, May 2022. http://dx.doi.org/10.36967/nrr-2293381.
Full textHenderson, Tim, Vincent Santucciq, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: San Francisco Bay Area Inventory & Monitoring Network. National Park Service, May 2022. http://dx.doi.org/10.36967/nrr-2293533.
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