Literatura académica sobre el tema "Spatial Data"
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Artículos de revistas sobre el tema "Spatial Data"
Ivanov, Sabin. "SPATIAL DATA MODELS". Journal Scientific and Applied Research 20, n.º 1 (1 de diciembre de 2020): 40–46. http://dx.doi.org/10.46687/jsar.v20i1.303.
Texto completoOsborn, Wendy. "Unbounded Spatial Data Stream Query Processing using Spatial Semijoins". Journal of Ubiquitous Systems and Pervasive Networks 15, n.º 02 (1 de marzo de 2021): 33–41. http://dx.doi.org/10.5383/juspn.15.02.005.
Texto completoBarkworth, M. E. y J. Mcgrew. "Combining herbarium data with spatial data: potential benefits, new needs". Czech Journal of Genetics and Plant Breeding 41, Special Issue (31 de julio de 2012): 59–64. http://dx.doi.org/10.17221/6136-cjgpb.
Texto completoLee. "A study on the Spatial Sampling Method to Minimize Spatial Autocorrelation of Spatial and Geographical Data". Journal of the Korean Society of Civil Engineers 34, n.º 4 (2014): 1317. http://dx.doi.org/10.12652/ksce.2014.34.4.1317.
Texto completoWiemann, Stefan y Lars Bernard. "Spatial data fusion in Spatial Data Infrastructures using Linked Data". International Journal of Geographical Information Science 30, n.º 4 (24 de septiembre de 2015): 613–36. http://dx.doi.org/10.1080/13658816.2015.1084420.
Texto completoKovaříček, P. y J. Hůla. "Field capacity determination from GPS spatial data". Research in Agricultural Engineering 49, No. 3 (8 de febrero de 2012): 75–79. http://dx.doi.org/10.17221/4955-rae.
Texto completoKlimešová, D. y E. Ocelíková. "Spatial data modelling and maximum entropy theory". Agricultural Economics (Zemědělská ekonomika) 51, No. 2 (20 de febrero de 2012): 80–83. http://dx.doi.org/10.17221/5080-agricecon.
Texto completoK, Sivakumar. "Spatial Data Mining: Recent Trends in the Era of Big Data". Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25 de julio de 2020): 912–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202182.
Texto completoUSUI, Teruko. "Spatial Data Transfer Standard (SDTS) and Spatial Data Model". Theory and Applications of GIS 2, n.º 1 (1994): 1–8. http://dx.doi.org/10.5638/thagis.2.1.
Texto completoWang, Ting. "Adaptive Tessellation Mapping (ATM) for Spatial Data Mining". International Journal of Machine Learning and Computing 4, n.º 6 (2015): 478–82. http://dx.doi.org/10.7763/ijmlc.2014.v6.458.
Texto completoTesis sobre el tema "Spatial Data"
Wiemann, Stefan. "Data Fusion in Spatial Data Infrastructures". Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-216985.
Texto completoDie Entwicklung des Internet im Laufe des letzten Jahrzehnts hat die Verfügbarkeit und öffentliche Wahrnehmung von Geodaten, sowie Möglichkeiten zu deren Erfassung und Nutzung, wesentlich verbessert. Dies liegt sowohl an der Etablierung amtlicher Geodateninfrastrukturen (GDI), als auch an der steigenden Anzahl Communitybasierter und kommerzieller Angebote. Da der Fokus zumeist auf der Bereitstellung von Geodaten liegt, gibt es jedoch kaum Möglichkeiten die Menge an, über das Internet verteilten, Datensätzen ad hoc zu verlinken und zusammenzuführen, was mitunter zur Isolation von Geodatenbeständen führt. Möglichkeiten zu deren Fusion sind allerdings essentiell, um Informationen zur Entscheidungsunterstützung in Bezug auf raum-zeitliche Fragestellungen zu extrahieren. Um eine ad hoc Fusion von Geodaten im Internet zu ermöglichen, behandelt diese Arbeit zwei Themenschwerpunkte. Zunächst wird eine dienstebasierten Umsetzung des Fusionsprozesses konzipiert, um bestehende GDI funktional zu erweitern. Dafür werden wohldefinierte, wiederverwendbare Funktionsblöcke beschrieben und über standardisierte Diensteschnittstellen bereitgestellt. Dies ermöglicht eine dynamische Komposition anwendungsbezogener Fusionsprozesse über das Internet. Des weiteren werden Geoprozessierungspatterns definiert, um populäre und häufig eingesetzte Diensteketten zur Bewältigung bestimmter Teilaufgaben der Geodatenfusion zu beschreiben und die Komposition und Automatisierung von Fusionsprozessen zu vereinfachen. Als zweiten Schwerpunkt beschäftigt sich die Arbeit mit der Frage, wie Relationen zwischen Geodatenbeständen im Internet erstellt, beschrieben und genutzt werden können. Der gewählte Ansatz basiert auf Linked Data Prinzipien und schlägt eine Brücke zwischen diensteorientierten GDI und dem Semantic Web. Während somit Geodaten in bestehenden GDI verbleiben, können Werkzeuge und Standards des Semantic Web genutzt werden, um Informationen aus den ermittelten Geodatenrelationen abzuleiten. Zur Überprüfung der entwickelten Konzepte wurde eine Reihe von Anwendungsfällen konzipiert und mit Hilfe einer prototypischen Implementierung umgesetzt und anschließend evaluiert. Der Schwerpunkt lag dabei auf einer interoperablen, transparenten und erweiterbaren Umsetzung dienstebasierter Fusionsprozesse, sowie einer formalisierten Beschreibung von Datenrelationen, unter Nutzung offener und etablierter Standards. Die Software folgt einer modularen Struktur und ist als Open Source frei verfügbar. Sie erlaubt sowohl die Entwicklung neuer Funktionalität durch Entwickler als auch die Einbindung existierender Daten- und Prozessierungsdienste während der Komposition eines Fusionsprozesses
Fischer, Manfred M. y Daniel A. Griffith. "Modelling spatial autocorrelation in spatial interaction data". WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/3948/1/SSRN%2Did1102183.pdf.
Texto completoDa, Yanan. "A Big Spatial Data System for Efficient and Scalable Spatial Data Processing". Thesis, Southern Illinois University at Edwardsville, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10682760.
Texto completoToday, a large amount of spatial data is generated from a variety of sources, such as mobile devices, sensors, and satellites. Traditional spatial data processing techniques no longer satisfy the efficiency and scalability requirements for large-scale spatial data processing. Existing Big Data processing frameworks such as Hadoop and Spark have been extended to support effective large-scale spatial data processing. In addition to processing data in distributed schemes utilizing computer clusters for efficiency and scalability, single node performance can also be improved by making use of multi-core processors. In this thesis, we investigate approaches to parallelize line segment intersection algorithms for spatial computations on multi-core processors, which can be used as node-level algorithms for distributed spatial data processing. We first provide our design of line segment intersection algorithms and introduce parallelization techniques. Then, we describe experimental results using multiple data sets and speed ups are examined with varying numbers of processing cores. Equipped with the efficient underlying algorithm for spatial computation, we investigate how to build a native big spatial data system from the ground up. We provide a system design for distributed large-scale spatial data management and processing using a two-level hash based Quadtree index as well as algorithms for spatial operations.
He, Ying Surveying & Spatial Information Systems Faculty of Engineering UNSW. "Spatial data quality management". Publisher:University of New South Wales. Surveying & Spatial Information Systems, 2008. http://handle.unsw.edu.au/1959.4/43323.
Texto completoZhang, Xiang. "Analysis of Spatial Data". UKnowledge, 2013. http://uknowledge.uky.edu/statistics_etds/4.
Texto completoROSSI, FRANCESCA. "Inference for spatial data". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/25536.
Texto completoAlkhaldi, Rawan. "Spatial data transmission security authentication of spatial data using a new temporal taxonomy /". abstract and full text PDF (free order & download UNR users only), 2005. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433280.
Texto completoLi, Xintong. "Modeling for Spatial and Spatio-Temporal Data with Applications". Diss., Kansas State University, 2018. http://hdl.handle.net/2097/38749.
Texto completoDepartment 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.
Chaudhary, Amitabh. "Applied spatial data structures for large data sets". Available to US Hopkins community, 2002. http://wwwlib.umi.com/dissertations/dlnow/3068131.
Texto completoWalker, Arron R. "Automated spatial information retrieval and visualisation of spatial data". Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/17258/1/Arron_Robert_Walker_Thesis.pdf.
Texto completoLibros sobre el tema "Spatial Data"
Patanè, Giuseppe y Michela Spagnuolo, eds. Heterogeneous Spatial Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02589-1.
Texto completoMamoulis, Nikos. Spatial Data Management. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01884-8.
Texto completoLi, Deren, Shuliang Wang y Deyi Li. Spatial Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5.
Texto completoOliver, Dev. Spatial Network Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39621-7.
Texto completoFischer, Manfred M. y Jinfeng Wang. Spatial Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21720-3.
Texto completoFisher, Peter F. y Michael F. Goodchild. Spatial Data Quality. Editado por Wenzhong Shi. Abingdon, UK: Taylor & Francis, 2002. http://dx.doi.org/10.4324/9780203303245.
Texto completoMamoulis, Nikos. Spatial data management. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2012.
Buscar texto completoWenzhong, Shi, Goodchild Michael F y Fisher Peter, eds. Spatial data quality. London: Taylor & Francis, 2002.
Buscar texto completoSherman, Michael. Spatial Statistics and Spatio-Temporal Data. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470974391.
Texto completoRoddick, John F. y 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.
Texto completoCapítulos de libros sobre el tema "Spatial Data"
Trauth, Martin H. "Spatial Data". En MATLAB® Recipes for Earth Sciences, 249–314. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46244-7_7.
Texto completoTrauth, Martin H. "Spatial Data". En MATLAB® Recipes for Earth Sciences, 293–363. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38441-8_7.
Texto completoTrauth, Martin H. "Spatial Data". En MATLAB® Recipes for Earth Sciences, 165–224. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72749-1_7.
Texto completoFox, Charles. "Spatial Data". En Springer Textbooks in Earth Sciences, Geography and Environment, 57–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72953-4_5.
Texto completoTrauth, Martin H. "Spatial Data". En Springer Textbooks in Earth Sciences, Geography and Environment, 251–320. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07719-7_7.
Texto completoTrauth, Martin H. "Spatial Data". En MATLAB® Recipes for Earth Sciences, 193–254. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12762-5_7.
Texto completoMaggio, Sabrina y Claudia Cappello. "Spatial Data". En Encyclopedia of Mathematical Geosciences, 1–6. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_303-1.
Texto completoMamoulis, Nikos. "Spatial Data". En Spatial Data Management, 11–19. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01884-8_2.
Texto completoChen, Jeffrey C., Edward A. Rubin y Gary J. Cornwall. "Spatial Data". En Springer Series in the Data Sciences, 237–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71352-2_12.
Texto completoMa, Xiaogang. "Spatial Data". En Encyclopedia of Big Data, 865–69. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_192.
Texto completoActas de conferencias sobre el tema "Spatial Data"
Wang, Yuan-ni y Fu-ling Bian. "Obstacle constraint spatial clustering". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837648.
Texto completoZhou, Yan, Qing Zhu y Yeting Zhang. "A data skew handling method based on the minimum spatial proximity for parallel spatial database". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837521.
Texto completoSPIE, Proceedings of. "Front Matter: Volume 7492". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.849092.
Texto completoWu, Guofeng. "A review of remote-sensing-based spatial/temporal information capturing for water resource studies in Poyang Lake". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.836771.
Texto completoLi, Deying, Kunlong Yin, Huaxi Gao y Changchun Liu. "Design and application analysis of prediction system of geo-hazards based on GIS in the Three Gorges Reservoir". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837229.
Texto completoHuang, Zhengdong, Jie Li y Xiaotang Xia. "Representation and application of bus system at the lowest level of detail". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837290.
Texto completoSu, Hongjun, Yehua Sheng y Yongning Wen. "Data mining based on spectral and spatial features for hyperspectral classification". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837304.
Texto completoWei, Yingchun, Daiyong Cao y Juemei Deng. "A new practical methodology of the coal bed stability evaluation: the trend and variation method". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837308.
Texto completoSu, Lilan, Yanfang Liu y Xiaoyong Gao. "Entropy-theory-based study on the relationship between land use structure and industry system: a case study of the eastern Hubei metropolitan area". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837337.
Texto completoWu, Xiaofang, Zhiyong Xu, Shitai Bao y Feixiang Chen. "Application of data mining in science and technology management information system based on WebGIS". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837340.
Texto completoInformes sobre el tema "Spatial Data"
Mulgaonkar, Prasanna. Data Driven Spatial Reasoning. Fort Belvoir, VA: Defense Technical Information Center, octubre de 1991. http://dx.doi.org/10.21236/ada242727.
Texto completoStockinger, Kurt y Kesheng Wu. Improved searching for spatial features in spatio-temporal data. Office of Scientific and Technical Information (OSTI), septiembre de 2004. http://dx.doi.org/10.2172/833576.
Texto completoWilliams, R. J. Data Quality Statements for Spatial Databases. Fort Belvoir, VA: Defense Technical Information Center, julio de 1992. http://dx.doi.org/10.21236/ada264125.
Texto completoArmstrong, Marc P., Gerard Rushton, Jayajit Chakraborty, Allen Wayne Ibaugh y Amy J. Ruggles. Spatial Data Systems for Transportation Planning. Iowa City, Iowa: University of Iowa Public Policy Center, 1997. http://dx.doi.org/10.17077/qi9q-uir0.
Texto completoBertanha, Marinho y Petra Moser. Spatial Errors in Count Data Regressions. Cambridge, MA: National Bureau of Economic Research, agosto de 2014. http://dx.doi.org/10.3386/w20374.
Texto completoDove, Linda P. GIS-Assisted Spatial Data Management for Corps of Engineers Real Estate Activities: Spatial Data Conversion Options. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2002. http://dx.doi.org/10.21236/ada409099.
Texto completoGrunsky, E. Spatial factor analysis: a technique to assess the spatial relationships of multivariate data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128074.
Texto completoMcDonnell, Michael. Scan-Line Methods in Spatial Data Systems. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1990. http://dx.doi.org/10.21236/ada231165.
Texto completovan der Pol, L. BO: development of spatial data analysis for (pulse) fisheries data. IJmuiden: Wageningen Marine Research, 2023. http://dx.doi.org/10.18174/631835.
Texto completoMineter, M. J., S. Dowers y B. M. Gittings. Software Instrastructure to Enable Parallel Spatial Data Handling. Fort Belvoir, VA: Defense Technical Information Center, julio de 2001. http://dx.doi.org/10.21236/ada394739.
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