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Dissertations / Theses on the topic 'Spatial Data'
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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.
Full textAbstract:
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, such capabilities are essential to support and enhance information retrieval for comprehensive spatial decision making. To facilitate spatial data fusion in current SDIs, this thesis has two main objectives. First, it focuses on the conceptualization of a service-based fusion process to functionally extend current SDI and to allow for the combination of spatial data from different spatial data services. It mainly addresses the decomposition of the fusion process into well-defined and reusable functional building blocks and their implementation as services, which can be used to dynamically compose meaningful application-specific processing workflows. Moreover, geoprocessing patterns, i.e. service chains that are commonly used to solve certain fusion subtasks, are designed to simplify and automate workflow composition. Second, the thesis deals with the determination, description and exploitation of spatial data relations, which play a decisive role for spatial data fusion. The approach adopted is based on the Linked Data paradigm and therefore bridges SDI and Semantic Web developments. Whereas the original spatial data remains within SDI structures, relations between those sources can be used to infer spatial information by means of Semantic Web standards and software tools. A number of use cases were developed, implemented and evaluated to underpin the proposed concepts. Particular emphasis was put on the use of established open standards to realize an interoperable, transparent and extensible spatial data fusion process and to support the formalized description of spatial data relations. The developed software, which is based on a modular architecture, is available online as open source. It allows for the development and seamless integration of new functionality as well as the use of external data and processing services during workflow composition on the WebDie 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
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.
Full textAbstract:
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 autocorrelation among flow residuals. An example using patent
citation data that capture knowledge flows across 112 European regions serves to illustrate the
application and the comparison of the two approaches.(authors' abstract)
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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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 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.
4
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.
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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 and assess the quality of their GIS outputs. Specifically, this thesis aims: 1. To develop a framework for establishing a systematic linkage between data quality indicators and appropriate uncertainty models; 2. To propose an object-oriented data quality model for organising and documenting data quality information; 3. To create data quality schemas for defining and storing the contents of metadata databases; 4. To develop a new conceptual model of data quality management; 5. To develop and implement a prototype system for enhancing the capability of data quality management in commercial GIS. Based on reviews of error and uncertainty modelling in the literature, a conceptual framework has been developed to establish the systematic linkage between data quality elements and appropriate error and uncertainty models. To overcome the limitations identified in the review and satisfy a series of requirements for representing data quality, a new object-oriented data quality model has been proposed. It enables data quality information to be documented and stored in a multi-level structure and to be integrally linked with spatial data to allow access, processing and graphic visualisation. The conceptual model for data quality management is proposed where a data quality storage model, uncertainty models and visualisation methods are three basic components. This model establishes the processes involved when managing data quality, emphasising on the integration of uncertainty modelling and visualisation techniques. The above studies lay the theoretical foundations for the development of a prototype system with the ability to manage data quality. Object-oriented approach, database technology and programming technology have been integrated to design and implement the prototype system within the ESRI ArcGIS software. The object-oriented approach allows the prototype to be developed in a more flexible and easily maintained manner. The prototype allows users to browse and access data quality information at different levels. Moreover, a set of error and uncertainty models are embedded within the system. With the prototype, data quality elements can be extracted from the database and automatically linked with the appropriate error and uncertainty models, as well as with their implications in the form of simple maps. This function results in proposing a set of different uncertainty models for users to choose for assessing how uncertainty inherent in the data can affect their specific application. It will significantly increase the users' confidence in using data for a particular situation. To demonstrate the enhanced capability of the prototype, the system has been tested against the real data. The implementation has shown that the prototype can efficiently assist data users, especially non-expert users, to better understand data quality and utilise it in a more practical way. The methodologies and approaches for managing quality information presented in this thesis should serve as an impetus for supporting further research.
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 analysis of spatial-temporal lattice data. We propose a general asymptotic framework for spatial-temporal linear models and investigate the property of maximum likelihood estimates under such framework. Mild regularity conditions on the spatial-temporal weight matrices will be put in order to derive the asymptotic properties (consistency and asymptotic normality) of maximum likelihood estimates. A simulation study is conducted to examine the finite-sample properties of the maximum likelihood estimates.
For spatial data, aside from traditional likelihood-based method, a variety of literature has discussed Bayesian approach to estimate the correlation (auto-covariance function) among spatial data, especially Zheng et al. (2010) proposed a nonparametric Bayesian approach to estimate a spectral density. We will also discuss nonparametric Bayesian approach in analyzing spatial data. We will propose a general procedure for constructing a multivariate Feller prior and establish its theoretical property as a nonparametric prior. A blocked Gibbs sampling algorithm is also proposed for computation since the posterior distribution is analytically manageable.
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 thus the derivation of finite sample corrections appears to be a crucially important task in order to obtain reliable tests. In this project we consider finite sample corrections based on formal Edgeworth expansions for the cumulative distribution function of some relevant test statistics.
In Chapters 1 and 2 we present refined procedures for testing nullity of the spatial parameter in pure SAR based on ordinary least squares and Gaussian maximum likelihood, respectively. In both cases, the Edgeworth-corrected tests are compared with those obtained by a bootstrap procedure, which is supposed to have similar properties. The practical performance of new tests is assessed with Monte Carlo simulations and two empirical examples. In Chapter 3 we propose finite sample corrections for Lagrange Multiplier statistics, which are computationally particularly convenient as the estimation of the spatial parameter is not required. Monte Carlo simulations and the numerical implementation of Imhof's procedure confirm that the corrected tests outperform standard ones.
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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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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 PhilosophyDepartment 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.
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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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.
Full textAbstract:
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 automate map
creation the traditional SIR paradigm of matching a query to a single dataset type
must be extended to include discovering relationships between different dataset types.
This thesis presents a Bayesian inference retrieval framework that will incorporate
expert knowledge in order to retrieve all relevant datasets and automatically create a
map given an initial user query. The framework consists of a Bayesian network that
utilises causal relationships between GIS datasets. A series of Bayesian learning
algorithms are presented that automatically discover these causal linkages from
historic expert knowledge about GIS datasets. This new retrieval model improves
support for complex and vague queries through the discovered dataset relationships.
In addition, the framework will learn which datasets are best suited for particular
query input through feedback supplied by the user.
This thesis evaluates the new Bayesian Framework for SIR. This was achieved by
utilising a test set of queries and responses and measuring the performance of the
respective new algorithms against conventional algorithms. This contribution will
increase the performance and efficiency of knowledge extraction from GIS by
allowing users to focus on interpreting data, instead of focusing on finding which data
is relevant to their analysis. In addition, they will allow GIS to reach non-technical
people.
11
Walker, Arron R. "Automated spatial information retrieval and visualisation of spatial data." Queensland University of Technology, 2007. http://eprints.qut.edu.au/17258/.
Full textAbstract:
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 automate map
creation the traditional SIR paradigm of matching a query to a single dataset type
must be extended to include discovering relationships between different dataset types.
This thesis presents a Bayesian inference retrieval framework that will incorporate
expert knowledge in order to retrieve all relevant datasets and automatically create a
map given an initial user query. The framework consists of a Bayesian network that
utilises causal relationships between GIS datasets. A series of Bayesian learning
algorithms are presented that automatically discover these causal linkages from
historic expert knowledge about GIS datasets. This new retrieval model improves
support for complex and vague queries through the discovered dataset relationships.
In addition, the framework will learn which datasets are best suited for particular
query input through feedback supplied by the user.
This thesis evaluates the new Bayesian Framework for SIR. This was achieved by
utilising a test set of queries and responses and measuring the performance of the
respective new algorithms against conventional algorithms. This contribution will
increase the performance and efficiency of knowledge extraction from GIS by
allowing users to focus on interpreting data, instead of focusing on finding which data
is relevant to their analysis. In addition, they will allow GIS to reach non-technical
people.
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.
Full textAbstract:
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 paths within this volume. Analysing the exact motion of features determines their three dimensional position in the scene.
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 organization with spatial support. The aim of the work presented in this thesis was to measure the time needed for rasterization of an image map with different amount of features in a simplified prototype of SMIL with similar data flow organization. This prototype was developed in consultation with Sweco’s software architect and GIS consultant and was tested using the organization’s network capacity. Four different types of tests, which were implemented in order to investigate the presence of possible tipping points, illustrated the similar result that when the number of requested features passes one thousand, both the time needed for rasterization and the size of the raster image increases rapidly. The fifth test, that was implemented in order to analyze the time the involved modules in the system needed to generate a response, identified GeoServer as an apparent critical module in the system that delays data flow when the number of requested features passes one thousand and it can slow down the system when the number of requested features passes ten thousand.
14
Dasari, Vivek. "Platform for spatial molecular data." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/107103.
Full textAbstract:
Thesis: M. Eng. in Computer Science and Molecular Biology, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 49-50).
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 is divided into seven services: raw data handling, a spatial coordinate system, an analysis service, an image service, a molecular data service, a spatial data service and a visualization service. Together, these services compose a modular system for organizing the next generation of spatial molecular data.
by Vivek Dasari.
M. Eng. in Computer Science and Molecular Biology
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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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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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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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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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 methodology of spatial data analysis has been studied only from the beginning of this Millennium. Since only few scientists (Dumbrauskas, A.; Kumetaitis, A.; Kumetaitienė, A. and others) are involved, it is very important to expand this area and develop the existing methods. Also it is essential to study the spatial dada modelling methods throughly and provide general spatial data modelling methodology.
In order to apply the methods of spatial statistics, it is necessary to know the location of data in space, which is usually expressed in geographic coordinates. Thus, one of the main distinctions of spatial statistics which makes it different from the classical is the ability to model both spatial trend and spatial autocorrelation.
One of the main objectives of spatial statistics is creating a mathematical model of spatial data, which can be used for interpolation (extrapolation) or for... [to full text]Disertacijoje nagrinėjama erdvinių duomenų su stacionariomis klaidomis modeliavimo per semivariogramas ir tiesinio prognozavimo metodika. Erdvinių duomenų skiriamasis bruožas – jų išsidėstymas erdvėje, kuris dažniausiai aprašomas geografinėmis koordinatėmis. Tokių duomenų modeliavimas semivariogramomis, ir prognozavimas krigingu yra vienas iš svarbių geostatistikos mokslo uždavinių. Krigingas yra stochastinis prognozavimo metodas, kuris prie tam tikrų salygų pateikia geriausią tiesinę nepaslinktą prognozę. Krigingo rezultatų paklaidos priklauso nuo to kaip tiksliai erdvinių duomenų sklaida aprašoma kovariacine funkcija arba semivariograma. Darbe dėmesys skiriamas semivariogramoms, nes jos aprašo platesnę erdvinių procesų klasę. Pagrindinis disertacijos tikslas yra apibendrinti ir realizuoti vieningą erdvinių duomenų su stacionariomis klaidomis modeliavimo metodiką, pagrįstą semivariogramomis. Darbo objektai yra semivariogramos, jų modeliai, įvairūs erdvinių duomenų prognozavimo metodai bei erdvinių duomenų modeliavimo, prognozavimo etapai. Šių objektų analizė bei interpretacija prie tam tikrų sąlygų leidžia gauti geriausius erdvinių duomenų modeliavimo bei prognozavimo rezultatus. Taip pat disertaciniame darbe empiriniam Materon‘o semivariogramų įvertiniui MoM pateikta dispersijų-kovariacijų matricos išraiška per teorines semivariogramas stacionaraus Gauso duomenų modelio atvejui. Tiriami erdvinių duomenų vidurkio modelio parametrų bei semivariogramų vertinimo metodai... [toliau žr. visą tekstą]
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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 illustrate how to identify the parametric/nonparametric components in the proposed semiparametric model. We also show how many unknown parameters an unknown bivariate function amounts to, and propose an AIC/BIC nonparametric model selection. Simulation studies are conducted to examine the performance of the proposed methods, and their results show that the methods work very well. Finally, we apply the proposed methods to analyze the Boston House Price data, which lead to some interesting findings.Although, the proposed model and methodology are stimulated by the Boston House Price data, they could be widely used in many other scientific problems.
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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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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.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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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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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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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 perfectly vectorizable problems. We thus investigate the construction of linear quadtrees, R-trees, and linear k-d trees using the GPU for location code and bounding box calculation, and parallel CPU algorithms for sorting. In this endeavor, we show how existing GPU linear quadtree and R-tree construction algorithms may be modified to be heterogeneous, and we develop a novel linear k-d tree construction algorithm which uses an existing parallel CPU quicksort partition algorithm. We implement these heterogeneous construction algorithms, and we show that heterogeneous construction of spatial data structures can approach the speeds of homogeneous GPU algorithms, while freeing the GPU to be used for better vectorizable problems.
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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, the ecological fallacy problem, the modifiable
areal unit problem, boundary and frame effects, and the spatial dependence problem. The
problem of spatial dependence is at the core of modern spatial analysis and requires the use of
specialised techniques and models in the data analysis. The discussion focuses on exploratory
techniques and model-driven [confirmatory] modes of analysing spatial point patterns and
area data. In closing, prospects are given towards a new style of data-driven spatial analysis
characterized by computational intelligence techniques such as evolutionary computation and
neural network modeling to meet the challenges of huge quantities of spatial data
characteristic in remote sensing, geodemographics and marketing. (author's abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScience
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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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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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Wang, Cunyi. "Spatial clustering algorithms for areal data." Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/39041/.
Full textAbstract:
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 different types of clustering data and the Glasgow housing market data which will be used in the thesis’s application. At the end of this chapter, I will outline the structure of this thesis. In Chapter 2, I will give the general statistical theory and inference methodologies used across this thesis, including frequentist and Bayesian statistical inferences, multidimensional scaling and the Procrustes transformation. In Chapter 3, I will introduce techniques that could be used in transforming between two types of clustering data introduced in Chapter 1. Chapter 4 will define some cluster and graph terminology and will also introduce different clustering techniques, such as hierarchical clustering, Chameleon hierarchical clustering and model-based clustering. In this chapter, I will also cover some techniques used in cluster comparisons, methods for number of clusters decisions and number of dimensions decisions. Chapter 6 will introduce more detail about spatial hierarchical clustering. The simulation results from spatial hierarchical clustering will be used as the reference results for comparison with the results from the proposed novel spatial clustering techniques in later chapters. The newly proposed clustering techniques, Chameleon spatial hierarchical clustering, spatially constrained finite mixture model with noise component or with priors and spatially constrained Bayesian model-based clustering with dissimilarities, in clustering areal data will be introduced in Chapters 7, 8 and 9 respectively. Also, the simulations and the application in Glasgow housing market will be given at the end of each of these three chapters. Chameleon spatial hierarchical clustering combined the spatial contiguity with Chameleon hierarchical clustering, so areas grouped together are spatially contiguous. Spatially constrained finite mixture models incorporate the spatial prior distribution into the classical finite mixture model to deal with the spatial contiguity issue. Also, I will make the spatially constrained finite mixture model more robust by incorporating a uniform distribution to model the noise points or adding prior distributions to the model. In Chapter 9, I will add a spatial prior to the model-based clustering with dissimilarities model and then will use a Bayesian approach to obtain a spatial contiguous clustering. Chapter 10 will be conclusions and discussion about the newly proposed clustering methods.
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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.
Full textAbstract:
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 to augment GPS. However, the potential of using various types of existing and newly acquired spatial data for enhancing GPS performance has not been fully realised. This is particularly true due to the fact that higher accuracy digital surface models (DSMs), which include buildings and vegetation, and digital maps, have only been made widely available in recent years. This thesis will report on a number of experiments that used spatial data of various complexity and accuracy for enhancing GPS performance. These experiments include height aiding with different scale digital terrain models (DTMs); map-matching using odometer data, DTM and road centrelines; modelling and prediction of GPS satellite visibility using DSMs; and prediction of GPS multipath effect using DSMs and building footprints. These experiments are closely related to each other in the sense that GPS and spatial data are combined to provide value-added information for improved modelling and prediction of GPS positioning accuracy and reliability, for applications such as transport navigation and tracking ... Extensive fieldwork has been carried out to verify the developed techniques and methods. The results show that the accuracy of a standalone GPS receiver can be improved by height aiding using a higher resolution DTM and map-matching especially when the satellite geometry is poor. The mean error of single receiver GPS positioning for one particular dataset, on which the described map-matching algorithm was developed, is 8.8m compared with 53.7m for GPS alone. This work was carried out in collaboration with London Transport. In terms of satellite visibility analysis, the results obtained from the fieldwork indicate that greater modelling accuracy has been achieved when using higher resolution DSMs. Furthermore, a ray tracing model was implemented in a 3D GIS environment in order to model reflected and diffracted GPS signals. The Double Differencing (DD) residuals were used to give an indication of the magnitude of the possible pseudorange multipath error caused by diffraction. A single-knife diffraction model was first implemented on 1m Light Detection And Ranging (LiDAR) DSMs, and verified by post-processing (i.e. large DD residuals occurred when the satellites are partially masked and unmasked by buildings), which indicate that GPS multipath prediction with LiDAR data and building footprints is feasible, and has the potential to offer greater modelling accuracy.
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Kuznetsova, O. "Spatial data infrastructure for ecological environment." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/31623.
Full textAbstract:
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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Kou, Yufeng. "Abnormal Pattern Recognition in Spatial Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.
Full textAbstract:
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 congestion points, pinpoint military targets in satellite images, determine possible locations of oil reservoirs, and detect water pollution incidents.
Numerous traditional outlier detection methods have been developed, but they cannot be directly applied to spatial data in order to extract abnormal patterns. Traditional outlier detection mainly focuses on "global comparison" and identifies deviations from the remainder of the entire data set. In contrast, spatial outlier detection concentrates on discovering neighborhood instabilities that break the spatial continuity. In recent years, a number of techniques have been proposed for spatial outlier detection. However, they have the following limitations. First, most of them focus primarily on single-attribute outlier detection. Second, they may not accurately locate outliers when multiple outliers exist in a cluster and correlate with each other. Third, the existing algorithms tend to abstract spatial objects as isolated points and do not consider their geometrical and topological properties, which may lead to inexact results.
This dissertation reports a study of the problem of abnormal spatial pattern recognition, and proposes a suite of novel algorithms. Contributions include: (1) formal definitions of various spatial outliers, including single-attribute outliers, multi-attribute outliers, and region outliers; (2) a set of algorithms for the accurate detection of single-attribute spatial outliers; (3) a systematic approach to identifying and tracking region outliers in continuous meteorological data sequences; (4) a novel Mahalanobis-distance-based algorithm to detect outliers with multiple attributes; (5) a set of graph-based algorithms to identify point outliers and region outliers; and (6) extensive analysis of experiments on several spatial data sets (e.g., West Nile virus data and NOAA meteorological data) to evaluate the effectiveness and efficiency of the proposed algorithms.Ph. D.
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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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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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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.
Full textAbstract:
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 exist an R&D spillover effect. These different results were the motivation
to run a spatial analysis, which can be done by considering the countries as
regions and using an adequate spatial link matrix. The used methods from
the field of spatial econometrics are described briefly and quite general, and
finally the results from the spatial models (the ones which correspond to the
non-spatial ones) are compared with the results from the non-spatial analysis.
The preferred model supports the position that R&D spillover effects exist.
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Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.
Full textAbstract:
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 Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.
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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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Wang, Boyang, and Boyang Wang. "Secure Geometric Search on Encrypted Spatial Data." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625567.
Full textAbstract:
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 geometric area to predict whether there will be a dangerous outbreak of a particular disease (e.g., Ebola or Zika).
With the dramatic increase on the scale and size of data, many companies and organizations are outsourcing significant amounts of data, including significant amounts of spatial data, to public cloud data services in order to minimize data storage and query processing costs. For instance, major companies and organizations, such as Yelp, Foursquare and NASA, are using Amazon Web Services as their public cloud data services, which can save billions of dollars per year for those companies and organizations. However, due to the existence of attackers (e.g., a curious administrator or a hacker) on remote servers, users are worried about the leakage of their private data while storing and querying those data on public clouds.
Searchable Encryption (SE) is an innovative technique to protect the data privacy of users on public clouds without losing search functionalities on the server side. Specifically, a user can encrypt its data with SE before outsourcing data to a public server, and this public server is able to search encrypted data without decryption. Many SE schemes have been proposed to support simple queries, such as keyword search. Unfortunately, how to efficiently and securely support geometric queries over encrypted spatial data remains open.
In this dissertation, to protect the privacy of spatial data in public clouds while still maintaining search functions without decryption, we propose a set of new SE solutions to support geometric queries, including geometric range queries and nearest neighbor queries, over encrypted spatial data. The major contributions of this dissertation focus on two aspects. First, we enrich search functionalities by designing new solutions to carry out secure fundamental geometric search queries, which were not supported in previous works. Second, we minimize the performance gap between theory and practice by building novel schemes to perform geometric queries with highly efficient search time and updates over large-scale encrypted spatial data.
Specifically, we first design a scheme supporting circular range queries (i.e., retrieving points inside a circle) over encrypted spatial data. Instead of directly evaluating compute-then-compare operations, which are inefficient over encrypted data, we use a set of concentric circles to represent a circular range query, and then verify whether a data point is on any of those concentric circles by securely evaluating inner products over encrypted data.
Next, to enrich search functionalities, we propose a new scheme, which can support arbitrary geometric range queries, such as circles, triangles and polygons in general, over encrypted spatial data. By leveraging the properties of Bloom filters, we convert a geometric range search problem to a membership testing problem, which can be securely evaluated with inner products. Moving a step forward, we also build another new scheme, which not only supports arbitrary geometric range queries and sub-linear search time but also enables highly efficient updates.
Finally, we address the problem of secure nearest neighbor search on encrypted large-scale datasets. Specifically, we modify the algorithm of nearest neighbor search in advanced tree structures (e.g., R-trees) by simplifying operations, where evaluating comparisons alone on encrypted data is sufficient to efficiently and correctly find nearest neighbors over datasets with millions of tuples.
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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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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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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.
Full textAbstract:
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)Series: Discussion Papers of the Institute for Economic Geography and GIScience
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Al-Naymat, Ghazi. "NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA." Thesis, The University of Sydney, 2009. http://hdl.handle.net/2123/5295.
Full textAbstract:
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 association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002.
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Al-Naymat, Ghazi. "NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA." University of Sydney, 2009. http://hdl.handle.net/2123/5295.
Full textAbstract:
Doctor of Philosophy (PhD)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 association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002.
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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.
Full textAbstract:
Thesis (M.S. in Information Technology Management) Naval Postgraduate School, Sept. 1997.Thesis advisors, Daniel R. Dolk, George W. Thomas, and Kathryn Kocher. Includes bibliographical references (p. 203-206). Also available online.
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Duan, Yuanyuan. "Statistical Predictions Based on Accelerated Degradation Data and Spatial Count Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/56616.
Full textAbstract:
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 describe the degradation path. Unit-to-unit variabilities
are modeled as random effects. The maximum likelihood approach is used to estimate
parameters based on the accelerated test data from laboratory. The developed model is then
extended to allow for time-varying covariates and is used to predict outdoor degradation where
the explanatory variables are time-varying.
Chapter 3 introduces a class of models for analyzing degradation data with dynamic covariate
information. We use a general path model with random effects to describe the degradation
paths and a vector time series model to describe the covariate process. Shape restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters of these models are estimated by using the maximum likelihood method. Algorithms for computing the estimated lifetime distribution are also described. The proposed methods are applied to predict the photodegradation path of an organic coating in a
complicated dynamic environment.
Chapter 4 investigates the Lyme disease emergency in Virginia at census tract level. Based
on areal (census tract level) count data of Lyme disease cases in Virginia from 1998 to 2011,
we analyze the spatial patterns of the disease using statistical smoothing techniques. We also use the space and space-time scan statistics to reveal the presence of clusters in the spatial and spatial/temporal distribution of Lyme disease.
Chapter 5 builds a predictive model for Lyme disease based on historical data and environmental/demographical information of each census tract. We propose a Divide-Recombine method to take advantage of parallel computing. We compare prediction results through simulation studies, which show our method can provide comparable fitting and predicting accuracy but can achieve much more computational efficiency. We also apply the proposed method to analyze Virginia Lyme disease spatio-temporal data. Our method makes large-scale spatio-temporal predictions possible. Chapter 6 gives a general review on the contributions of this dissertation, and discusses directions for future research.Ph. D.
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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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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.
Full textAbstract:
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. Per questo motivo, negli ultimi anni sono stati pubblicati diversi paper che mostrano le difficoltà principali legate alla natura stessa della network. Inoltre, il recente sviluppo di database spaziali open source (quali Open Street Map) ha permesso il download e la creazione di dataset che coprono le reti stradali di quasi tutto il mondo. L’enorme mole di dati e gli (inevitabili) errori geometrici presenti nei database di Open Street Map rappresentano due problematiche ulteriori. Infine, dato che al momento la maggior parte dei pacchetti R per l’analisi di dati su network sono ancora in fase di sviluppo, esistono anche diverse difficoltà computazionali e problemi nell’implementazione di metodologie nuove.
Questo lavoro di tesi riassume quattro articoli che presentano strutture dati e metodologie statistiche per l’analisi di dati spaziali aventi supporto di network, considerando sia un approccio di tipo network-lattice che un approccio di tipo point-pattern.
Il primo paper presenta una revisione bibliografica dei pacchetti R che implementano classi e funzioni per l’analisi di network stradali, concentrandosi in particolare su stplanr e dodgr. Vengono introdotte le principali routines legate al calcolo di shortest paths e centrality
measures utilizzando dataset via via più complessi.
Il secondo lavoro presenta un modello di Poisson Dinamico Zero Inflated per la stima di due indici di rischiosità relativi agli incidenti stradali avvenuti nel network di Milano dal 2015 al 2017. L’unità statistica elementare è rappresentata dal singolo segmento di strada, mentre la variabile risposta misura il numero di incidenti avvenuti in ognuno dei tre anni. Viene impiegato un insieme di covariate demografiche e strutturali estratte da Open Street Map e dai dati del censimento italiano avvenuto nel 2011.
Il terzo paper introduce un modello Bayesiano gerarchico multivariato per la stima della rischiosità stradale tramite un approccio di tipo network-lattice. Ci si è concentrati sul network stradale della città di Leeds e su due diverse tipologie di incidenti. La componente spaziale è stata modellata tramite un errore casuale di tipo Multivariate CAR, mentre le correlazioni residue sono state catturate tramite un errore casuale non strutturato. Infine, si è anche sviluppata una metodologia nuova per l’analisi di MAUP su dati di tipo network-lattice.
Per concludere, il quarto articolo presenta un insieme di risultati preliminari relativi all’analisi spazio-temporale di point pattern su network tramite processi di Poisson non-omogenei. In particolare, si è analizzata la distribuzione degli interventi delle ambulanze nel comune di Milano tra il 2015 ed il 2017, sviluppando un modello a fattori latenti per la componente temporale ed uno stimatore kernel non-parametrico per l’intensità spaziale, riadattato nel caso di dati su reticolo.
La tesi si compone anche di tre appendici. Le prima riassume le caratteristiche di base del software e della metodologia INLA, la seconda presenta i materiali addizionali legati al quarto capitolo, mentre la terza appendice introduce un pacchetto R chiamato osmextract, utilizzato per manipolare dati da Open Street Map.
Il quinto capitolo conclude la tesi, riassumendo i risultati principali e introducendo alcuni sviluppi futuri.In the last years, we observed a surge of interest in the statistical analysis of spatial data lying on or alongside networks. Car crashes, vehicle thefts, bicycle incidents, roadside kiosks, neuroanatomical features, and ambulance interventions are just a few of the most typical examples, whereas the edges of the network represent an abstraction of roads, rivers, railways, cargo-ship routes or nerve fibers. This type of data is interesting for several reasons. First, the statistical analysis of the events presents several challenges because of the complex and non-homogeneous nature of the network, which creates unique methodological problems. Several authors discussed and illustrated the common pitfalls of re-adapting classical planar spatial models to network data. Second, the rapid development of open-source spatial databases (such as Open Street Map) provides the starting point for creating road networks at a wide range of spatial scales. The size and volume of the data raise complex computational problems, while common geometrical errors in the network’s software representations create another source of complexity. Third, at the time of writing, the most important software routines and functions (mainly implemented in R) are still in the process of being re-written and readapted for the new spatial support. This manuscript collects four articles presenting data structures and statistical models to analyse spatial data lying on road networks using point-pattern and network-lattice approaches. The first paper reviews classes, vital pre-processing steps and software representations to manipulate road network data. In particular, it focuses on the R packages stplanr and dodgr, highlighting their main functionalities, such as shortest paths or centrality measures, using a range of datasets, from a roundabout to a complete network covering an urban city. The second paper proposes the adoption of two indices for assessing the risk of car crashes on the street network of a metropolitan area via a dynamic zero-inflated Poisson model. The elementary statistical units are the road segments of the network. It employs a set of open-source spatial covariates representing the network’s structural and demographic characteristics (such as population density, traffic lights or crossings) extracted from Open Street Map and 2011 Italian Census. The third paper demonstrates a Bayesian hierarchical model for identifying road segments of particular concern using a network-lattice approach. It is based on a case study of a major city (Leeds, UK), in which car crashes of different severities were recorded over several years. It includes spatially structured and unstructured random effects to capture the spatial nature of the events and the dependencies between the severity levels. It also recommends a novel procedure for estimating the MAUP (Modifiable Areal Unit Problem) for network-lattice data. Finally, the fourth paper summarises a set of preliminary results related to the analysis of spatio-temporal point patterns lying on road networks using non-homogeneous Poisson processes. It focuses on the ambulance interventions that occurred in the municipality of Milan from 2015 to 2017, developing two distinct models, one for the spatial component and one for the temporal component. The spatial intensity function was estimated using a network readaptation of the classical non-parametric kernel estimator. The first two appendices briefly review the basics of INLA methodology, the corresponding R package and the supplementary materials related to the fourth chapter, while the third appendix briefly introduces an R package, named osmextract, that was developed during the PhD and focuses on Open Street Map data. The fifth chapter concludes the manuscript, summarising the main contributions and emphasising future research developments.
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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, generating a parallel-friendly quad-based representation; a new parallel hashing scheme compacting spatial data with high load factors, which has the unique advantage of exploiting spatial coherence in input data and access patternsLa qüestió principal explorada en aquesta tesi doctoral és la forma de definir noves formes d'accés aleatori paral•lel en estructures de dades amb informació de superfícies i d'imatge. La nostra principal aportació és un conjunt de mètodes paral•lels i eficients per avaluar imatges i geometries irregulars, i proposem: un mètode per a separar la forma i els detalls d'aparença visual partint de malles d'alta resolució, mapejant de manera interactiva la informació en dominis més simples de baixa resolució; un marc d'edició geomètrica per convertir malles irregulars de triangles d'alta resolució en representacions més simples basades en un domini de cubs, generant una estructura fàcilment paral•lelitzable basada en primitives quadrangulars; un nou esquema de hashing paral•lel per a la organització i compactació de dades espacials amb un elevat factor de càrrega, explotant la coherència espacial de les dades d'entrada i els seus patrons d'accés a memòria
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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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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 these data sets reside within one organisation. However, there are persistent challenges that limit the utilization of available spatial data across organizations. The existence of these challenges is a global phenomenon and Brunei, a small country in south East Asia is no exception. The main purpose of this research is to identify factors that impede spatial data sharing within government departments in Brunei and use the findings to develop a framework for sharing spatial data within the government departments. The proposed framework took the innovative approach of combining both technical and non- technical factors, which have not been currently addressed. This research adopted multiple holistic case studies in 3 selected government departments in Brunei. A comprehensive literature review of relevant topics helped in designing a preliminary guideline for research in spatial data sharing. This guideline is used as a basis for data collection and at the same time refined by the case studies. Both content analysis and cognitive mapping techniques were applied to help in customizing the framework for sharing spatial data in Brunei, the ultimate product of this research. The framework comprises of two different components, which include the contextual component and the collaborative process component. Under the collaborative process component, there were 5 key elements that are crucial in spatial data sharing. This includes leadership, formal agreement, IT structure, monitoring and review and security. The framework was not empirically validated due to the immaturity of spatial data sharing in Brunei, unable the framework to be validated empirically. At such, opinions from the experts on the appropriateness of the framework were elicited as an initial validation. Efforts were also made by assessing the impact of each key element to the past data sharing projects in Brunei. The research concluded that the proposed framework offers a viable and effective formal mechanism for data sharing and coordination of spatial activities within government departments in Brunei. It is envisaged that with minor amendments to the policy aspects, the framework is expandable for application to private sectors.
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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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Previous issue date: 2015-12-07Universidade Federal de Minas Gerais
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 beneficiam o suporte a tomada de decisão. As aplicações de DWE e SOLAP abordam majoritarimente fenômenos representados por dados espaciais exatos, ou seja, que assumem localizações e fronteiras bem definidas. Contudo, tais aplicações negligenciam dados espaciais afetados por imperfeições, tais como a vagueza espacial, a qual interfere na identificação precisa de um objeto e de seus vizinhos. Um objeto espacial vago não tem sua fronteira ou seu interior precisamente definidos. Além disso, é composto por partes que certamente pertencem a ele e partes que possivelmente pertencem a ele. Apesar de inúmeros fenômenos do mundo real serem caracterizados pela vagueza espacial, na literatura consultada não se identificaram trabalhos que considerassem a vagueza espacial no projeto de DWE e nem para consultar o DWE. Tal limitação motivou a elaboração desta tese de doutorado, a qual introduz os conceitos de DWE vago e de SOLAP vago. Um DWE vago é um DWE que armazena dados espaciais vagos, enquanto que SOLAP vago provê os meios para consultar o DWE vago. Nesta tese, o projeto de DWE vago é abordado e as principais contribuições providas são: (i) o modelo conceitual VSCube que viabiliza a criação de um cubos de dados multidimensional para representar o esquema conceitual de um DWE vago; (ii) o modelo conceitual VSMultiDim que permite criar um diagrama para representar o esquema conceitual de um DWE vago; (iii) diretrizes para o projeto lógico do DWE vago e de suas restrições de integridade, e para estender a linguagem SQL visando processar as consultas de SOLAP vago no DWE vago; e (iv) o índice VSB-index que aprimora o desempenho do processamento de consultas no DWE vago. A aplicabilidade dessas contribuições é demonstrada em dois estudos de caso no domínio da agricultura, por meio da criação de esquemas conceituais de DWE vago, da transformação dos esquemas conceituais em esquemas lógicos de DWE vago, e do processamento de consultas envolvendo as regiões vagas do DWE vago.
Spatial data warehouses (SDW) and spatial online analytical processing (SOLAP) enhance decision making by enabling spatial analysis combined with multidimensional analytical queries. A SDW is an integrated and voluminous multidimensional database containing both conventional and spatial data. SOLAP allows querying SDWs with multidimensional queries that select spatial data that satisfy a given topological relationship and that aggregate spatial data. Existing SDW and SOLAP applications mostly consider phenomena represented by spatial data having exact locations and sharp boundaries. They neglect the fact that spatial data may be affected by imperfections, such as spatial vagueness, which prevents distinguishing an object from its neighborhood. A vague spatial object does not have a precisely defined boundary and/or interior. Thus, it may have a broad boundary and a blurred interior, and is composed of parts that certainly belong to it and parts that possibly belong to it. Although several real-world phenomena are characterized by spatial vagueness, no approach in the literature addresses both spatial vagueness and the design of SDWs nor provides multidimensional analysis over vague spatial data. These shortcomings motivated the elaboration of this doctoral thesis, which addresses both vague spatial data warehouses (vague SDWs) and vague spatial online analytical processing (vague SOLAP). A vague SDW is a SDW that comprises vague spatial data, while vague SOLAP allows querying vague SDWs. The major contributions of this doctoral thesis are: (i) the Vague Spatial Cube (VSCube) conceptual model, which enables the creation of conceptual schemata for vague SDWs using data cubes; (ii) the Vague Spatial MultiDim (VSMultiDim) conceptual model, which enables the creation of conceptual schemata for vague SDWs using diagrams; (iii) guidelines for designing relational schemata and integrity constraints for vague SDWs, and for extending the SQL language to enable vague SOLAP; (iv) the Vague Spatial Bitmap Index (VSB-index), which improves the performance to process queries against vague SDWs. The applicability of these contributions is demonstrated in two applications of the agricultural domain, by creating conceptual schemata for vague SDWs, transforming these conceptual schemata into logical schemata for vague SDWs, and efficiently processing queries over vague SDWs.
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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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