Relevant bibliographies by topics / GIS Data Mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'GIS Data Mining'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "GIS Data Mining"

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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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Fu, Chun Chang. "The Research of WebGIS-Based Data Mining." Advanced Materials Research 267 (June 2011): 774–77. http://dx.doi.org/10.4028/www.scientific.net/amr.267.774.

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Data mining provide us a technical found potential knowledge technical from mass data , the data mining technology is applied to GIS can promote GIS analysis of the data, so as to improve the ability of decision support level. WebGIS is traditional GIS space outspread, current network analysis and decision making support capability are low, the network-based integration of data mining and WebGIS can strengthen the analytical capacity.
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Yu, Yong Ling, Tao Guan, and Jin Fa Shi. "Spatial Data Mining Based on Campus GIS." Advanced Materials Research 282-283 (July 2011): 641–45. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.641.

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With the rapid development of spatial database technology, spatial databases have been widely used in many engineering fields and rapidly increase in data capacity. Thus, to mine useful information from large spatial databases turns into a difficult but important task. In this paper, we apply the traditional data mining into spatial database and give a mining model for spatial data based on Campus GIS. Moreover, based on campus GIS, we implement a spatial data mining prototype system that is able to discovery the useful spatial features and patterns in spatial databases. The application in the
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Mihai, Dana. "New approaches to processing GIS Data using Artificial Neural Networks models." Annals of the University of Craiova - Mathematics and Computer Science Series 48, no. 1 (2021): 358–73. http://dx.doi.org/10.52846/ami.v48i1.1551.

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Spatial data mining is a special type of data mining. The main difference between data mining and spatial data mining is that in spatial data mining tasks we use not only non-spatial attributes but also spatial attributes. Spatial data mining techniques have strong relationship with GIS (Geographical Information System) and are widely used in GIS for inferring association among spatial attributes, clustering and classifying information with respect to spatial attributes. In this paper we use the statistical package Weka on two models, which consist of two parcels plans from the Olt area of Rom
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Karimipour, F. "Water Quality Management Using GIS Data Mining." Journal of Environmental Informatics 5, no. 2 (2005): 61–71. http://dx.doi.org/10.3808/jei.200500047.

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Spielman, Seth E., and Jean-Claude Thill. "Social area analysis, data mining, and GIS." Computers, Environment and Urban Systems 32, no. 2 (2008): 110–22. http://dx.doi.org/10.1016/j.compenvurbsys.2007.11.004.

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Chen, Junyu. "Research on Intelligent Data Mining Technology Based on Geographic Information System." Journal of Computer Science and Artificial Intelligence 2, no. 2 (2025): 12–16. https://doi.org/10.54097/1skxpp12.

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Geographic Information Systems (GIS), as a key means of managing, storing, and analyzing spatial information, have attracted much attention in the field of GIS due to the rapid development of big data and intelligent technology. This article elaborates on the intelligent data mining process based on information collection, data storage, data processing, and data analysis, supplemented by practical application cases, including urban traffic flow research, intelligent transportation system design, and commercial network layout optimization, to demonstrate its broad prospects in practical applica
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Foody, Giles M. "Uncertainty, knowledge discovery and data mining in GIS." Progress in Physical Geography: Earth and Environment 27, no. 1 (2003): 113–21. http://dx.doi.org/10.1191/0309133303pp345pr.

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Korting, Thales, Leila Fonseca, and Gilberto Camara. "GeoDMA—Geographic Data Mining Analyst." Computers & Geosciences 57, August 2013 (2013): 133–45. https://doi.org/10.1016/j.cageo.2013.02.007.

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Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in 1 year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques. Data mining methods are being extensively used for statistical a
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Sui, Jia Yin, Ying Nan Wang, and Zhe Ren. "The Distribution Network Reconfiguration Based on Spatial Data Mining." Applied Mechanics and Materials 427-429 (September 2013): 2748–51. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2748.

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Distribution network reconfiguration is one of the essential functions of the DMS system; it can be attributed to a number of constraints of large-scale nonlinear combinatorial optimization problem in mathematics. The characteristic of load change of time and space will affect the results of load forecasting. Distribution network reconfiguration relies on the load forecasting results. This paper proposes the more realistic distribution reconfiguration scheme based on the GIS system with space information through spatial data mining. For the real-time and efficiency of effective assurance data,
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Dissertations / Theses on the topic "GIS Data Mining"

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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.

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<p>Geographical 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
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Zhang, Bo. "Data Mining, Gis And Remote Sensing: Application In Wetland Hydrological Investigation." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1220021657.

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Kudowor, Andrew Yao Tete. "Subsurface data management and volume estimation : techniques, procedures, and concepts." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285787.

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Griffin, Terry W. "GPS CaPPture: a System for GPS Trajectory Collection, Processing, and Destination Prediction." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc115089/.

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In the United States, smartphone ownership surpassed 69.5 million in February 2011 with a large portion of those users (20%) downloading applications (apps) that enhance the usability of a device by adding additional functionality. a large percentage of apps are written specifically to utilize the geographical position of a mobile device. One of the prime factors in developing location prediction models is the use of historical data to train such a model. with larger sets of training data, prediction algorithms become more accurate; however, the use of historical data can quickly become a down
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Salap, Seda. "Development Of A Gis-based Monitoring And Management System For Underground Mining Safety." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609815/index.pdf.

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Mine safety is of paramount concern to the mining industry. The generation of a Geographic Information Systems (GIS) which can administrate relevant spatial data and metadata of underground mining safety efficiently is a very vital issue in this sense. In an effort to achieve a balance of safety and productivity, GIS can contribute to the creation of a safe working environment in underground (U/G) mining. Such a system should serve to a continuous risk analysis and be designed for applications in case of emergency. Concept for safety should require three fundamental components, namely (i)
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Shcherbaniuk, Mykola Vasylyovych. "Modeling land use and cover change : predicting re-enrollment in the Conservation Reserve Program using GIS and data mining procedures /." Available to subscribers only, 2006. http://proquest.umi.com/pqdweb?did=1208146881&sid=6&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Zhou, Guoqing. "Co-Location Decision Tree for Enhancing Decision-Making of Pavement Maintenance and Rehabilitation." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/26059.

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A pavement management system (PMS) is a valuable tool and one of the critical elements of the highway transportation infrastructure. Since a vast amount of pavement data is frequently and continuously being collected, updated, and exchanged due to rapidly deteriorating road conditions, increased traffic loads, and shrinking funds, resulting in the rapid accumulation of a large pavement database, knowledge-based expert systems (KBESs) have therefore been developed to solve various transportation problems. This dissertation presents the development of theory and algorithm for a new decision tree
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Abugessaisa, Imad. "Analytical tools and information-sharing methods supporting road safety organizations." Doctoral thesis, Linköpings universitet, GIS - Geografiska informationssystem, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11596.

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A prerequisite for improving road safety are reliable and consistent sources of information about traffic and accidents, which will help assess the prevailing situation and give a good indication of their severity. In many countries there is under-reporting of road accidents, deaths and injuries, no collection of data at all, or low quality of information. Potential knowledge is hidden, due to the large accumulation of traffic and accident data. This limits the investigative tasks of road safety experts and thus decreases the utilization of databases. All these factors can have serious effects
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Gonçalves, Erick Sobreiro. "Análise de padrões operacionais da frota de ônibus de transporte público no municí­pio de São Paulo e a influência de fatores climáticos em sua dinâmica." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3138/tde-12032018-134651/.

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Atualmente existem muitas fontes de dados oriundos de movimentação de pessoas e veículos. Deduzir o comportamento do tráfego a partir desses dados pode ser de grande utilidade para a gestão do tráfego. Entretanto, os métodos existentes podem não abordar todas as dimensões referentes ao comportamento do tráfego, em especial sob efeito de adversidades, como por exemplo - eventos climáticos. O objetivo dessa dissertação é apresentar uma combinação de métodos estatísticos e de análise espacial, para uso na compreensão dos padrões temporais e espaciais de variabilidade das velocidades, em condições
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Naji, Adel Ali. "Data Mining for Accurately Estimating Residential Natural Gas Energy Consumption and Savings Using a Random Forest Approach." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1557422487896673.

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Books on the topic "GIS Data Mining"

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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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Lee, M. T. Development and applications of GIS data bases for surface mining hydrology. s.n, 1987.

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Antoinette, Smith M., Ferderer David A, and United States. Bureau of Mines, eds. Data dictionary and discussion for the Midnite Mine GIS database. U.S. Dept. of the Interior, Bureau of Mines, 1996.

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Bernhardt, Uwe. GIS-Technologien in der new economy: Markttransparenz durch Geoinformationssysteme. H. Wichmann, 2002.

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Bienen, Derk. Die politische Oekonomie von Arbeitsmarktreformen in Argentinien. Peter Lang International Academic Publishers, 2005.

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Technology, Society for Underwater, and Institution of Electrical Engineers, eds. Subsea control and data acquisition--for oil and gas production systems: Proceedings of an international conference (Subsea Control and Data Acquision--for Oil and Gas Production Systems). Graham & Trotman, 1986.

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Sidorova, I. P. Metodicheskie rekomendat͡sii po obrabotke iskhodnykh geologo-geofizicheskikh dannykh dli͡a prognoza vybrosoopasnosti ugolʹnykh plastov. In-t gornogo dela im. A.A. Skochinskogo, 1989.

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Rossi, Mauro, and Hamid Reza Pourghasemi. Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Springer, 2018.

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Lu, Chang-Tien. Quest'09: 1st SIGSPATIAL ACM GIS 2009 International Workshop on Querying and Mining Uncertain Spatio-Temporal Data. Association for Computing Machinery, 2009.

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Johnson, Eric O. Working as a Data Librarian. ABC-CLIO, LLC, 2018. http://dx.doi.org/10.5040/9798216038719.

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Many librarians’ job responsibilities increasingly require them to understand and handle data. Learn how to be an effective data librarian—even if you never expected to need data skills. The field of data librarianship is rapidly growing, and some librarians may feel that their training and experience does not cover data questions asked by patrons seeking advice. With this gentle guide for librarians moving—sometimes unexpectedly—into the world of data librarianship, all you need is a willingness to learn the skills required for the rapidly growing number of jobs requiring data librarianship.
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Book chapters on the topic "GIS Data Mining"

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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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Shekhar, Shashi, and Hui Xiong. "Geographic Data Mining." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_459.

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Shekhar, Shashi, and Hui Xiong. "Biological Data Mining." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_101.

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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, and Hui Xiong. "Stream Data Mining." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1357.

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Shekhar, Shashi, and Hui Xiong. "Visual Data Mining." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1452.

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Shekhar, Shashi, and Hui Xiong. "Active Data Mining." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_20.

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Marsolo, Keith, Michael Twa, Mark A. Bullimore, and Srinivasan Parthasarathy. "Biomedical Data Mining, Spatial." In Encyclopedia of GIS. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_102-2.

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Marsolo, Keith, Michael Twa, Mark A. Bullimore, and Srinivasan Parthasarathy. "Biomedical Data Mining, Spatial." In Encyclopedia of GIS. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_102.

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Marsolo, Keith, Michael Twa, Mark A. Bullimore, and Srinivasan Parthasarathy. "Biomedical Data Mining, Spatial." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_102.

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Conference papers on the topic "GIS Data Mining"

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Chen, Yao. "Optimizing GIS Spatial Analysis Method by Using Big Data Mining Technology." In 2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI). IEEE, 2024. https://doi.org/10.1109/icdacai65086.2024.00188.

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Xiao, Kaidi, Fengxia Gu, and He Zhu. "A GIS-Based Data Mining of Landuse Changes in the Yellow River Basin, China." In The International Conference on Data Mining, E-Learning, and Information Systems. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012877100004536.

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Chen Hongfei and Wang Xiaoyan. "Research on GIS-based spatial data mining." In 2011 International Conference on Business Management and Electronic Information (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/icbmei.2011.5916946.

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Deng, Shijun, Lingkui Meng, Xinzhong Zhou, Chenhan Wu, and Lin Xu. "Spatial data integration services for grid GIS." 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/10.1117/12.651123.

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Vognimary, Marie Odette, Yanfang Liu, and Feng Xu. "Sustainable development strategy based on GIS." 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/10.1117/12.837524.

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Wang, Hongyan, and Xinming Tang. "Exploratory research on GIS software testing." 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/10.1117/12.839397.

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Wang, Jinlin, Xi Chen, Kefa Zhou, Haibo Zhang, and Wei Wang. "Research of GIS-based Spatial Data Mining Model." In 2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD). IEEE, 2009. http://dx.doi.org/10.1109/wkdd.2009.13.

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Ouchra, Hafsa, Abdessamad Belangour, and Allae Erraissi. "Spatial Data Mining technology for GIS: a review." In 2022 International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2022. http://dx.doi.org/10.1109/icdabi56818.2022.10041574.

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Wu, Chenhan, Lingkui Meng, and Shijun Deng. "A new grid algorithm for GIS visualization." 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/10.1117/12.650268.

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Jia, Wenjue, Jianja Gong, and Bin Li. "Dynamic selection mechanism of GIS service chain." 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/10.1117/12.651218.

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Reports on the topic "GIS Data Mining"

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James R. Weber. DATA MINING AT THE NEBRASKA OIL & GAS COMMISSION. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/825386.

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Moreno, Ángel Iván, and Teresa Caminero. Assessing the data challenges of climate-related disclosures in european banks. A text mining study. Banco de España, 2023. http://dx.doi.org/10.53479/33752.

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The Intergovernmental Panel on Climate Change (IPCC) estimates that global net-zero should be achieved by 2050. To this end, many private firms are pledging to reach net-zero emissions by 2050. The Climate Data Steering Committee (CDSC) is working on an initiative to create a global central digital repository of climate disclosures, which aims to address the current data challenges. This paper assesses the progress within European financial institutions towards overcoming the data challenges outlined by the CDSC. Using a text-mining approach, coupled with the application of commercial Large La
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Obermajer, M., K. Dewing, and M. G. Fowler. Geological and geochemical data from the Canadian Arctic Islands. Part VI: saturate fraction gas chromatograms of organic extracts from outcrop and mining core samples. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2007. http://dx.doi.org/10.4095/224040.

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Costa, Julio, Vincenzo Rago, Pedro Brito, et al. External and internal load during training sessions in elite women’s soccer: a systematic mini review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.12.0038.

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Review question / Objective: The present systematic mini review aim to provide an overview about external and internal load during training sessions in elite women’s soccer, with special focus on fatigue, training adaptions and injuries. Condition being studied: Continuous training load monitoring in the context of the regular team routine. Eligibility criteria: To investigate continuous monitoring, we include articles with a minimum of one week of monitoring, irrespective of gender and study focus (e.g. studies reporting descriptive data of training load without studying its effects will be i
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Schofield, Ian S., Paul L. Brown, Mark J. Logsdon, and Matthew P. Wickham. Waste Rock Dump Characterization Studies at the Bingham Canyon Mine. Utah Geological Survey, 2024. http://dx.doi.org/10.34191/mp-179.

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The Bingham Canyon Mine, located near Salt Lake City, Utah, is surrounded by more than 6 billion tons of waste rock developed over the open cut mining history from 1903 to present; the surface area of the waste rock is approximately 5000 acres. Waste rock dumps have a thickness of more than 1 200 feet from crest to toe. From 1930 to 2000, selected portions of the waste rock dumps were commercially leached using a ferric-sulfate-based lixiviant to extract copper, whereas other portions have only received meteoric leaching. From 2011 to present, Rio Tinto Kennecott has studied the evolution and
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L51661 Pipeline Response to Blasting in Rock. Pipeline Research Council International, Inc. (PRCI), 1991. http://dx.doi.org/10.55274/r0010293.

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Blasting near buried gas pipelines is a common occurrence, and the blast-induced stresses can be significant relative to normal operational stress limits on a pipeline. Use of explosives for trench blasting in construction of pipelines adjacent to older ones, for strip mining, for highway construction, for quarry blasting, for seismic exploration, for utility construction, etc., in the vicinity of in-service pipelines occurs quite frequently. Consequently, pipeline companies need effective engineering procedures to estimate blast-induced stresses for use in developing realistic blasting guidel
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