Academic literature on the topic 'Spatial data and applications'
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Journal articles on the topic "Spatial data and applications"
Nikparvar, Behnam, and Jean-Claude Thill. "Machine Learning of Spatial Data." ISPRS International Journal of Geo-Information 10, no. 9 (September 12, 2021): 600. http://dx.doi.org/10.3390/ijgi10090600.
Full textGeymen, A., and T. Yomralioglu. "Spatial data-based e-municipality applications." Proceedings of the Institution of Civil Engineers - Municipal Engineer 163, no. 2 (June 2010): 77–88. http://dx.doi.org/10.1680/muen.2010.163.2.77.
Full textOsborn, Wendy. "Unbounded Spatial Data Stream Query Processing using Spatial Semijoins." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 02 (March 1, 2021): 33–41. http://dx.doi.org/10.5383/juspn.15.02.005.
Full textRavada, Siva. "Big data spatial analytics for enterprise applications." SIGSPATIAL Special 6, no. 2 (March 10, 2015): 34–41. http://dx.doi.org/10.1145/2744700.2744705.
Full textLi, Deren, Shuliang Wang, Hanning Yuan, and Deyi Li. "Software and applications of spatial data mining." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6, no. 3 (April 14, 2016): 84–114. http://dx.doi.org/10.1002/widm.1180.
Full textHunter, Gary J., Monica Wachowicz, and Arnold K. Bregt. "Understanding Spatial Data Usability." Data Science Journal 2 (2003): 79–89. http://dx.doi.org/10.2481/dsj.2.79.
Full textGbenga, Folami. "Building GIS Applications using Spatial Network Data Models." International Journal of Computer Applications 181, no. 48 (April 11, 2019): 22–25. http://dx.doi.org/10.5120/ijca2019918655.
Full textNievergelt, J. "Special Issue Editorial Spatial data: applications, concepts, techniques." Computer Journal 37, no. 1 (January 1, 1994): 1–2. http://dx.doi.org/10.1093/comjnl/37.1.1.
Full textNguyen, Hai, Noel Cressie, and Amy Braverman. "Spatial Statistical Data Fusion for Remote Sensing Applications." Journal of the American Statistical Association 107, no. 499 (September 2012): 1004–18. http://dx.doi.org/10.1080/01621459.2012.694717.
Full textJain, Khushboo, and Anoop Kumar. "Energy-Efficient Data-Aggregation Technique for Correlated Spatial and Temporal Data in Cluster-Based Sensor Networks." International Journal of Business Data Communications and Networking 16, no. 2 (July 2020): 53–68. http://dx.doi.org/10.4018/ijbdcn.2020070103.
Full textDissertations / Theses on the topic "Spatial data and applications"
Li, Xintong. "Modeling for Spatial and Spatio-Temporal Data with Applications." Diss., Kansas State University, 2018. http://hdl.handle.net/2097/38749.
Full textDepartment of Statistics
Juan Du
It is common to assume the spatial or spatio-temporal data are realizations of underlying random elds or stochastic processes. E ective approaches to modelling of the underlying autocorrelation structure of the same random eld and the association among multiple processes are of great demand in many areas including atmospheric sciences, meteorology and agriculture. To this end, this dissertation studies methods and application of the spatial modeling of large-scale dependence structure and spatio-temporal regression modelling. First, variogram and variogram matrix functions play important roles in modeling dependence structure among processes at di erent locations in spatial statistics. With more and more data collected on a global scale in environmental science, geophysics, and related elds, we focus on the characterizations of the variogram models on spheres of all dimensions for both stationary and intrinsic stationary, univariate and multivariate random elds. Some e cient approaches are proposed to construct a variety of variograms including simple polynomial structures. In particular, the series representation and spherical behavior of intrinsic stationary random elds are explored in both theoretical and simulation study. The applications of the proposed model and related theoretical results are demonstrated using simulation and real data analysis. Second, knowledge of the influential factors on the number of days suitable for fieldwork (DSFW) has important implications on timing of agricultural eld operations, machinery decision, and risk management. To assess how some global climate phenomena such as El Nino Southern Oscillation (ENSO) a ects DSFW and capture their complex associations in space and time, we propose various spatio-temporal dynamic models under hierarchical Bayesian framework. The Integrated Nested Laplace Approximation (INLA) is used and adapted to reduce the computational burden experienced when a large number of geo-locations and time points is considered in the data set. A comparison study between dynamics models with INLA viewing spatial domain as discrete and continuous is conducted and their pros and cons are evaluated based on multiple criteria. Finally a model with time- varying coefficients is shown to reflect the dynamic nature of the impact and lagged effect of ENSO on DSFW in US with spatio-temporal correlations accounted.
Embleton, Nina Lois. "Handling sparse spatial data in ecological applications." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5840/.
Full textDavies, Jessica. "Expanding the spatial data infrastructure model to support spatial wireless applications /." Connect to thesis, 2003. http://eprints.unimelb.edu.au/archive/00001044.
Full textMenezes, Kim Anne. "Bayesian spatial models : applications for tropospheric ozone data /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.
Full textYu, Jihai. "Essays on spatial dynamic panel data model theories and applications /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1179767430.
Full textHonnor, Thomas R. "Some spatial statistical techniques with applications to cellular imaging data." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/97940/.
Full textVuorio, R. (Riikka). "Use of public sector’s open spatial data in commercial applications." Master's thesis, University of Oulu, 2014. http://urn.fi/URN:NBN:fi:oulu-201311201883.
Full textMartin, Elaine B. "The detection of change in spatial processes with environmental applications." Thesis, University of Glasgow, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361063.
Full textBerndt, Christian [Verfasser]. "Spatial interpolation of climate data for hydrological and environmental applications / Christian Berndt." Hannover : Technische Informationsbibliothek (TIB), 2016. http://d-nb.info/1124166823/34.
Full textZborovskiy, Marat. "Representing and manipulating spatial data in interoperable systems and its industrial applications." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35099.
Full textIncludes bibliographical references (p. 123-126).
Introduction: The amount of information available nowadays is staggering and increases exponentially. Making sense of this data has become increasingly difficult because of the two factors: · The sheer volume of data · The lack of interoperability between disparate data sources and models While one can do little about the former factor, the latter one can be mitigated by advancing solutions that make data easy to work with and ensure the interoperability among data sources and models in intelligent networks. One way to achieve interoperability is to force every entity involved in the data exchange to adopt the same standard. However, organizations have heavily invested in proprietary data standards and are unlikely to replace their existing standards with a new one. Therefore, another solution is to create a standard, through which organizations can translate their data sources and share them with their customers or general community. The MIT Data Center is spearheading an initiative to create M - a language that is capable to provide the much needed interoperability between divergent data sources and models with an ultimate goal of creating a new intelligent information infrastructure (Brock, Schuster and Kutz 2006).
by Marat Zborovskiy.
S.M.
Books on the topic "Spatial data and applications"
Sibley, David. Spatial applications of exploratory data analysis. Norwich: Geo Books, 1988.
Find full textN, Boots B., and Sugihara Kōkichi 1948-, eds. Spatial tessellations: Concepts and applications of Voronoi diagrams. Chichester, England: Wiley, 1992.
Find full textStillwell, John C. H. Technologies for migration and commuting analysis: Spatial interaction data applications. Hershey, PA: Business Science Reference, 2010.
Find full textOort, Pepijn van. Spatial data quality: From description to application. Delft: NCG, Nederlandse Commissie voor Geodesie, 2005.
Find full textAlbert, Donald Patrick, and G. Rebecca Dobbs. Emerging methods and multidisciplinary applications in geospatial research. Hershey PA: Information Science Reference, 2013.
Find full textSamet, Hanan. Applications of spatial data structures: Computer graphics, image processing, and GIS. Reading, Mass: Addison-Wesley, 1990.
Find full textDiaz, Laura, and Carlos Granell. Discovery of geospatial resources: Methodologies, technologies, and emergent applications. Hershey PA: Information Science Reference, 2012.
Find full textFischer, Manfred M. Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.
Find full textZoran, Stančič, and Veljanovski Tatjana, eds. Computing archaeology for understanding the past: CAA 2000 : computer applications and quantitative methods in archaeology : proceedings of the 28th Conference, Ljubljana, April 2000. Oxford, England: Archaeopress, 2001.
Find full textCentre national d'études spatiales (France)., ed. Systèmes informatiques temps réel pour les applications spatiales =: Real-time embedded processing for space applications. Toulouse: Cépaduès, 1993.
Find full textBook chapters on the topic "Spatial data and applications"
Mamoulis, Nikos. "Applications of Spatial Data Management Technology." In Spatial Data Management, 85–118. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01884-8_6.
Full textPeri, M., C. Regazzoni, A. Tesei, and G. Vernazza. "Spatial Fusion of Multisensor Visual Information for Crowding Evaluation." In Data Fusion Applications, 155–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84990-9_15.
Full textVaisman, Alejandro, and Esteban Zimányi. "Spatial and Mobility Data Warehouses." In Data-Centric Systems and Applications, 437–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65167-4_12.
Full textWaller, Lance A. "Applications of Big Spatial Data: Health." In Encyclopedia of Big Data Technologies, 1–4. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_72-1.
Full textWaller, Lance A. "Applications of Big Spatial Data: Health." In Encyclopedia of Big Data Technologies, 1–5. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-63962-8_72-2.
Full textWaller, Lance A. "Applications of Big Spatial Data: Health." In Encyclopedia of Big Data Technologies, 86–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_72.
Full textAkiki, Pierre, and Hoda Maalouf. "Incorporating Spatial Data into Enterprise Applications." In Information Systems Development, 695–704. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/b137171_72.
Full textShekhar, Shashi, Yan Huang, Weili Wu, C. T. Lu, and S. Chawla. "What’s Spatial About Spatial Data Mining: Three Case Studies." In Data Mining for Scientific and Engineering Applications, 487–514. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1733-7_26.
Full textYuan, Huaqiang, Yaxun Wang, Jie Zhang, Wei Tan, Chao Qu, and Wenbin He. "Spatial Fuzzy Clustering Using Varying Coefficients." In Advanced Data Mining and Applications, 183–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73871-8_18.
Full textWang, Shuliang, and Haning Yuan. "View-Angle of Spatial Data Mining." In Advanced Data Mining and Applications, 1065–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_116.
Full textConference papers on the topic "Spatial data and applications"
Ginige, Athula, Marco Romano, Monica Sebillo, Giuliana Vitiello, and Pasquale Di Giovanni. "Spatial data and mobile applications." In the International Working Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2254556.2254591.
Full textRahman, Yitoshee, Sarker Monojit Asish, Adil Khokhar, Arun K. Kulshreshth, and Christoph W. Borst. "Gaze Data Visualizations for Educational VR Applications." In SUI '19: Symposium on Spatial User Interaction. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357251.3358752.
Full textShi, Qingdong, Jiaguo Qi, Lijun Chen, Shunli Chang, Qingsan Shi, and Guanghui Lv. "Validating classification accuracy of low spatial resolution data by using high spatial resolution data." In Optical Engineering + Applications, edited by Wei Gao and Susan L. Ustin. SPIE, 2007. http://dx.doi.org/10.1117/12.741540.
Full textZeng, Chen, Yanfang Liu, Yaolin Liu, Xing Li, and Ya Tang. "Application of high-resolution RS image in settlement extraction." 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/10.1117/12.838545.
Full textXia, Yin, Yaolin Liu, Xiaofeng Hong, and Dianfeng Liu. "Application of binary tree based SVMs approach to land grade 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/10.1117/12.838414.
Full textYuan, Hanning, and Shuliang Wang. "Data preprocessing of spatial data mining." 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/10.1117/12.650728.
Full textHuang, Zhengdong, Jie Li, and Xiaotang Xia. "Representation and application of bus system at the lowest level of detail." 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/10.1117/12.837290.
Full textLiu, Fucheng, Xuezhao He, and Li Zhou. "Application of generalized regression neural network residual kriging for terrain surface interpolation." 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/10.1117/12.837425.
Full textLan, Qiuping, Lifan Fei, Yining Liu, and Kun Zhang. "Application of multi-temporal DEM data in calculating the Earth's surface deformation." 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/10.1117/12.837926.
Full textLi, Wenjing, Qiao Wang, and Zhiyong Lin. "Application research of rough sets in the multi-elemental cooperative spatial analysis." 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/10.1117/12.838284.
Full textReports on the topic "Spatial data and applications"
Connell, S., J. Ernsting, D. Kukan, and A. Currie. GEOSIS project-integration of text and spatial data for geoscience applications. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128099.
Full textBorgoni, Riccardo, and Francesco C. Billari. Bayesian spatial analysis of demographic survey data: an application to contraceptive use at first sexual intercourse. Rostock: Max Planck Institute for Demographic Research, October 2002. http://dx.doi.org/10.4054/mpidr-wp-2002-048.
Full textLewis, David J., and Ralph J. Alig. A spatial econometric analysis of land-use change with land cover trends data: an application to the Pacific Northwest. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2014. http://dx.doi.org/10.2737/pnw-rp-600.
Full textBelles, Randy, Gary T. Mays, Olufemi A. Omitaomu, and Willis P. Poore III. Updated Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Small Modular Reactors. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1052267.
Full textMays, Gary T., Randy Belles, Brandon R. Blevins, Stanton W. Hadley, Thomas J. Harrison, Warren C. Jochem, Bradley S. Neish, Olufemi A. Omitaomu, and Amy N. Rose. Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources. Office of Scientific and Technical Information (OSTI), May 2012. http://dx.doi.org/10.2172/1032036.
Full textReyes, Julian, Jeb Williamson, and Emile Elias. Spatio-temporal analysis of Federal crop insurance cause of loss data: A roadmap for research and outreach effort. U.S. Department of Agriculture, April 2018. http://dx.doi.org/10.32747/2018.7202608.ch.
Full textХолошин, Ігор Віталійович, Ірина Миколаївна Варфоломєєва, Олена Вікторівна Ганчук, Ольга Володимирівна Бондаренко, and Андрій Валерійович Пікільняк. Pedagogical techniques of Earth remote sensing data application into modern school practice. CEUR-WS.org, 2018. http://dx.doi.org/10.31812/123456789/3257.
Full textKholoshyn, Ihor V., Iryna M. Varfolomyeyeva, Olena V. Hanchuk, Olga V. Bondarenko, and Andrey V. Pikilnyak. Pedagogical techniques of Earth remote sensing data application into modern school practice. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3262.
Full textCohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.
Full textTarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.
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