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Tesi sul tema "Spatial Data"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

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.

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Abstract (sommario):

Over the past decade, the public awareness and availability as well as methods for the creation and use of spatial data on the Web have steadily increased. Besides the establishment of governmental Spatial Data Infrastructures (SDIs), numerous volunteered and commercial initiatives had a major impact on that development. Nevertheless, data isolation still poses a major challenge. Whereas the majority of approaches focuses on data provision, means to dynamically link and combine spatial data from distributed, often heterogeneous data sources in an ad hoc manner are still very limited. However,

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2

Fischer, Manfred M., and 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.

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Spatial interaction models of the gravity type are widely used to model origindestination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterise an origin region of a flow, variables that characterise a destination region of a flow, and finally variables that measure the separation between origin and destination regions. This paper outlines and compares two approaches, the spatial econometric and the eigenfunction-based spatial filtering approach, to deal with the issue of spatial autocorrel

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3

Da, 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.

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<p> Today, 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 m

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4

He, Ying Surveying &amp 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.

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The applications of geographic information systems (GIS) in various areas have highlighted the importance of data quality. Data quality research has been given a priority by GIS academics for three decades. However, the outcomes of data quality research have not been sufficiently translated into practical applications. Users still need a GIS capable of storing, managing and manipulating data quality information. To fill this gap, this research aims to investigate how we can develop a tool that effectively and efficiently manages data quality information to aid data users to better understand a

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5

Zhang, Xiang. "Analysis of Spatial Data." UKnowledge, 2013. http://uknowledge.uky.edu/statistics_etds/4.

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In many areas of the agriculture, biological, physical and social sciences, spatial lattice data are becoming increasingly common. In addition, a large amount of lattice data shows not only visible spatial pattern but also temporal pattern (see, Zhu et al. 2005). An interesting problem is to develop a model to systematically model the relationship between the response variable and possible explanatory variable, while accounting for space and time effect simultaneously. Spatial-temporal linear model and the corresponding likelihood-based statistical inference are important tools for the analysi

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6

ROSSI, FRANCESCA. "Inference for spatial data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/25536.

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It is well known that econometric modelling and statistical inference are considerably complicated by the possibility of correlation across data data recorded at different locations in space. A major branch of the spatial econometrics literature has focused on testing the null hypothesis of spatial independence in Spatial Autoregressions (SAR) and the asymptotic properties of standard test statistics have been widely considered. However, finite sample properties of such tests have received relatively little consideration. Indeed, spatial datasets are likely to be small or moderately-sized and

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7

Alkhaldi, 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.

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8

Li, Xintong. "Modeling for Spatial and Spatio-Temporal Data with Applications." Diss., Kansas State University, 2018. http://hdl.handle.net/2097/38749.

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Doctor of Philosophy<br>Department of Statistics<br>Juan Du<br>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

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9

Chaudhary, Amitabh. "Applied spatial data structures for large data sets." Available to US Hopkins community, 2002. http://wwwlib.umi.com/dissertations/dlnow/3068131.

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10

Walker, 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.

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An increasing amount of freely available Geographic Information System (GIS) data on the Internet has stimulated recent research into Spatial Information Retrieval (SIR). Typically, SIR looks at the problem of retrieving spatial data on a dataset by dataset basis. However in practice, GIS datasets are generally not analysed in isolation. More often than not multiple datasets are required to create a map for a particular analysis task. To do this using the current SIR techniques, each dataset is retrieved one by one using traditional retrieval methods and manually added to the map. To aut

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11

Walker, Arron R. "Automated spatial information retrieval and visualisation of spatial data." Queensland University of Technology, 2007. http://eprints.qut.edu.au/17258/.

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Abstract (sommario):

An increasing amount of freely available Geographic Information System (GIS) data on the Internet has stimulated recent research into Spatial Information Retrieval (SIR). Typically, SIR looks at the problem of retrieving spatial data on a dataset by dataset basis. However in practice, GIS datasets are generally not analysed in isolation. More often than not multiple datasets are required to create a map for a particular analysis task. To do this using the current SIR techniques, each dataset is retrieved one by one using traditional retrieval methods and manually added to the map. To aut

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12

Williams, Mark, and n/a. "Spatial data from image sequences." University of Otago. Department of Computer Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20080130.131733.

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There are many existing methods for capturing three dimensional data from two dimensional images. Methods based on images captured from multiple view-points require solving the correspondence problem: establishing which points in each image represent the same points in the scene. Most attempts at solving the correspondence problem require carefully controlled lighting and reference points within the scene. A new method captures many consecutive images to form a dense spatiotemporal volume as the camera-or scene-undergoes controlled motion. Feature points in the scene move along predictable p

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13

Dahlberg, Marina. "Rasterization of Fragmented Spatial Data." Thesis, Umeå universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-85411.

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The Geographical Information Systems (GIS) is nowadays a large industry that has evolved from a highly specialized niche to a technology that affects nearly every aspect of our lives. There is a big challenge to use the functionality of GIS within the organization that works with data in the ordinary old-fashioned way using files stored locally in the computer. The availability of sharing and visualizing data forces an organization to invest in modern software solutions. Sweco is one of the organizations which offer the software solution SMIL to complement the information stored in the organiz

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14

Dasari, Vivek. "Platform for spatial molecular data." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/107103.

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Thesis: M. Eng. in Computer Science and Molecular Biology, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 49-50).<br>I designed and implemented a comprehensive platform for storing, analyzing, visualizing, and interacting with spatial molecular data. With the advent of high throughput in situ sequencing methods, such as fluorescent in situ sequencing (FISSEQ), the need for a platform to organize spatial molecular data has become paramount. The platform

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15

Onnen, Nathaniel J. "Estimation of Bivariate Spatial Data." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1616243660473062.

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16

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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17

Love, Alison L. "Visualizing spatial multi-valued data /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2004. http://uclibs.org/PID/11984.

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18

Borisenko, Ingrida. "Modelling of Spatial Data Using Semivariograms of Stationary Spatial Processes." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2009~D_20100303_113358-73668.

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Abstract (sommario):

Spatial statistics is one of the youngest trends in the science of statistics. First, it has been applied in mining, during the fifth decade of the last century. In fifty years after this trend of science had been discovered, the circle of the scientists involved in it has grown drastically as well as areas of application. Also, a wide range of theoretical and practical material has been issued. Nowadays, spatial statistics methods are used in: ecology, quantity geology, image processing and analysis, epidemiology, studying global climate change and even cosmology. However, in Lithuania, the m

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19

Yan, Hongjia. "Statistical analysis of spatial dynamic pattern in spatial data analysis." Thesis, University of York, 2013. http://etheses.whiterose.ac.uk/4495/.

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In this thesis, inspired by the Boston House Price data, we propose a semiparametric spatial dynamic model, that extends the ordinary spatial autoregressive models to accommodate the effects of some covariates associated with the House price. A profile likelihood-based estimation procedure is proposed and the asymptotic normality of the proposed estimators are derived. We also investigate the connection between cross-validation method and AIC/BIC methods in the semiparametric family. In the proposed model, it is easier to apply the AIC/BIC method than the 'cross-validation' method. We illustra

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20

Beaton, Duncan. "Integration of data description and quality information using metadata for spatial data and spatial information systems." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321263.

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21

Bai, Ping Truong Young K. Smith Richard L. "Temporal-spatial modeling for fMRI data." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1481.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.<br>Title from electronic title page (viewed Apr. 25, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.

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22

Shimakura, Silvia Emiko. "Statistical methods for spatial survival data." Thesis, Lancaster University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418824.

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23

Butts, Robert O. "Heterogeneous construction of spatial data structures." Thesis, University of Colorado at Denver, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1588178.

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<p> Linear spatial trees are typically constructed in two discrete, consecutive stages: calculating location codes, and sorting the spatial data according to the codes. Additionally, a GPU R-tree construction algorithm exists which likewise consists of sorting the spatial data and calculating nodes' bounding boxes. Current GPUs are approximately three orders of magnitude faster than CPUs for perfectly vectorizable problems. However, the best known GPU sorting algorithms only achieve 10-20 times speedup over sequential CPU algorithms. Both calculating location codes and bounding boxes are perfe

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24

Fischer, Manfred M. "Recent Advances in Spatial Data Analysis." WU Vienna University of Economics and Business, 2000. http://epub.wu.ac.at/4243/1/WGI_DP_7000.pdf.

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This article views spatial analysis as a research paradigm that provides a unique set of specialised techniques and models for a wide range of research questions in which the prime variables of interest vary significantly over space. The heart of spatial analysis is concerned with the analysis and modeling of spatial data. Spatial point patterns and area referenced data represent the most appropriate perspectives for applications in the social sciences. The researcher analysing and modeling spatial data tends to be confronted with a series of problems such as the data quality problem, th

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25

Buchanan, Hugh. "Transfer and modelling of spatial data." Thesis, University of Newcastle Upon Tyne, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360266.

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26

Williams, Richard David. "Organisation and analysis of spatial data." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304464.

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27

Wang, Cunyi. "Spatial clustering algorithms for areal data." Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/39041/.

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The main aim of this thesis is to develop new spatial clustering approaches which can simultaneously identify different areal clusters and guarantee their geographical contiguity. The second aim is to adjust the finite mixture model in order to cope with the issues caused by outliers or singletons (clusters with only one object). In addition, the thesis also aims to extend the applications of these newly proposed spatial clustering techniques from univariate to multivariate space. In Chapter 1, I will review some available clustering techniques in grouping spatial data and will also introduce

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28

Li, Jing. "GPS augmentation using digital spatial data." Thesis, University of South Wales, 2006. https://pure.southwales.ac.uk/en/studentthesis/gps-augmentation-using-digital-spatial-data(5142f388-0940-4f56-aa85-bb36f46a5a58).html.

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The primary aim of this research is to develop and assess the innovative methods and techniques which are used to augment GPS using a variety of digital spatial data. It is well known that the use of GPS can be severely compromised by various error sources such as signal obstructions, multipath and poor satellite geometry etc., especially in highly built-up areas. In order to improve the accuracy and reliability of GPS, complementary data is often combined with GPS data for enhancing the performance of a standalone GPS receiver. Spatial data is one type of complementary data that can be used t

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29

Kuznetsova, O. "Spatial data infrastructure for ecological environment." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/31623.

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The main objective is to ensure the sustainable development of Energy, requires the development of enterprise geographic information systems (GIS) for the modeling of energy systems. The corporate GIS in Ukraine will increase the energy efficiency of the management of energy companies. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/31623

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30

Kou, Yufeng. "Abnormal Pattern Recognition in Spatial Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.

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In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic

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31

Davies, Jessica. "Expanding the spatial data infrastructure model to support spatial wireless applications /." Connect to thesis, 2003. http://eprints.unimelb.edu.au/archive/00001044.

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32

Yudono, Adipandang. "Enhancing democracy in spatial planning through spatial data sharing in Indonesia." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17306/.

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33

Gumprecht, Daniela. "R&D Spillovers: A Non-Spatial and a Spatial Examination." Austrian Statistical Society, c/o Bundesanstalt Statistik Austria, 2007. http://epub.wu.ac.at/5388/1/316%2D1053%2D1%2DSM.pdf.

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In recent years there were many debates and different opinions whether R&D spillover effects exist or not. In 1995 Coe and Helpman published a study about this phenomenon, based on a panel dataset, that supports the position that such R&D spillover effects are existent. However, this survey was criticized and many different suggestions for improvement came from the scientific community. Some of them were selected and analysed and finally led to a new model. And even though this new model is well compatible with the data, it leads to different conclusions, namely that there does not exi

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34

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 Bi

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35

Kang, Lei. "Reduced-Dimension Hierarchical Statistical Models for Spatial and Spatio-Temporal Data." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1259168805.

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36

Wang, Boyang, and Boyang Wang. "Secure Geometric Search on Encrypted Spatial Data." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625567.

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Spatial data (e.g., points) have extensive applications in practice, such as spatial databases, Location-Based Services, spatial computing, social analyses, computational geometry, graph design, medical imaging, etc. Geometric queries, such as geometric range queries (i.e., finding points inside a geometric range) and nearest neighbor queries (i.e., finding the closest point to a given point), are fundamental primitives to analyze and retrieve information over spatial data. For example, a medical researcher can query a spatial dataset to collect information about patients in a certain geometri

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37

Najar, Christine Ruth. "A model-driven approach to management of integrated metadata-spatial data in the context of spatial data infrastructures /." Zürich : ETH, 2006. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=16474.

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38

Ajayakumar, Jayakrishnan. "Context in Geographic Data: How to Explore, Extract and Analyze Data from Spatial Video and Spatial Video Geonarratives." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1560165020968485.

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39

Fischer, Manfred M., and Henk J. Scholten. "Geographic Information Systems, Spatial Data Analysis and Spatial Modelling. - Problems and Possibilities -." WU Vienna University of Economics and Business, 1994. http://epub.wu.ac.at/4194/1/WSG_DP_3794.pdf.

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This article is the position paper for the ESF-GISDATA Specialist Meeting on GIS & Spatial Analysis, Amsterdam, 1-5December1993. The focus here is on the two major themes of the meeting: Spatial Data Analysis and Spatial Modelling. Special emphasis is laid on specific problems and possibilities for interfacing spatial analysis tools (i.e. spatial data analysis techniques and spatial models) and GIS. Both GIS application fields, the environmental sciences and the social sciences, are taken into consideration. (authors' abstract)<br>Series: Discussion Papers of the Institute for Economic G

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40

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

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41

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)<br>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 pr

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42

Dill, Robert W. "Data warehousing and data quality for a Spatial Decision Support System." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA336886.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, Sept. 1997.<br>Thesis advisors, Daniel R. Dolk, George W. Thomas, and Kathryn Kocher. Includes bibliographical references (p. 203-206). Also available online.

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43

Duan, Yuanyuan. "Statistical Predictions Based on Accelerated Degradation Data and Spatial Count Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/56616.

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This dissertation aims to develop methods for statistical predictions based on various types of data from different areas. We focus on applications from reliability and spatial epidemiology. Chapter 1 gives a general introduction of statistical predictions. Chapters 2 and 3 investigate the photodegradation of an organic coating, which is mainly caused by ultraviolet (UV) radiation but also affected by environmental factors, including temperature and humidity. In Chapter 2, we identify a physically motivated nonlinear mixed-effects model, including the effects of environmental variables, to des

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44

Coetzee, Serena Martha. "An analysis of a data grid approach for spatial data infrastructures." Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-09272009-152926/.

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45

GILARDI, ANDREA. "Statistical Models and Data Structures for Spatial Data on Road Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/314016.

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Negli ultimi anni è nato un interesse sempre crescente verso l’analisi statistica di dati spaziali aventi supporto di network. Gli esempi più classici di questa tipologia di eventi sono, ad esempio, gli incidenti stradali, i furti di auto, i crimini, e gli interventi delle ambulanze, mentre le linee che compongono la network rappresentano tipicamente le strade, i fiumi, i binari della ferrovia, oppure le terminazioni nervose. L’analisi di questi fenomeni è interessante sotto diversi punti di vista. Innanzitutto, i modelli statistici presentano diverse problematiche legate al supporto spaziale

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46

García, Fernández Ismael. "Parallel spatial data structures for interactive rendering." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/107998.

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The main question explored in this thesis is how to define novel parallel random-access data structures for surface and image spatial data with efficient construction, storage, and query memory access patterns. Our main contribution is a set of parallel-efficient methods to evaluate irregular, sparse or even implicit geometries and textures in different applications: a method to decouple shape and shading details from high-resolution meshes, mapping them interactively onto lower resolution simpler domains; an editable framework to map highresolution meshes to simpler cube-based domains, genera

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47

Haider, Khaled. "Using Spatial Data for Geo-Environmental Studies." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-135089.

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48

Salleh, Norzamni. "Sharing spatial data in Brunei government departments." Thesis, University of Salford, 2010. http://usir.salford.ac.uk/26890/.

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Spatial data are items of information related to a location on earth. The early forms of spatial data included maps, survey plans, coastal charts and geodetic triangulation. With the advancement of technology, this spatial data have increased its importance. Decision makers from many disciplines tend to rely on up-to-date relevant, wider and accessible spatial data as an essential input in supporting their operational and strategic decisions. The demand to access multi-organisations spatial data continue to increase and this has pushed organisation to share their spatial data as rarely all the

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Siqueira, Thiago Luís Lopes. "The design of vague spatial data warehouses." Universidade Federal de São Carlos, 2015. https://repositorio.ufscar.br/handle/ufscar/298.

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Made available in DSpace on 2016-06-02T19:04:00Z (GMT). No. of bitstreams: 1 6824.pdf: 22060515 bytes, checksum: bde19feb7a6e296214aebe081f2d09de (MD5) Previous issue date: 2015-12-07<br>Universidade Federal de Minas Gerais<br>O data warehouse espacial (DWE) é um banco de dados multidimensional integrado e volumoso que armazena dados espaciais e dados convencionais. Já o processamento analítico espacial online (SOLAP) permite consultar o DWE, tanto pela seleção de dados espaciais que satisfazem um relacionamento topológico, quanto pela agregação dos dados espaciais. Deste modo, DWE e SOLAP b

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Xie, Enhai. "Spatial data structure indexing for video databases." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0023/MQ62164.pdf.

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