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Letteratura scientifica selezionata sul tema "Spatial Data"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "Spatial Data"

Ivanov, Sabin. "SPATIAL DATA MODELS." Journal Scientific and Applied Research 20, no. 1 (December 1, 2020): 40–46. http://dx.doi.org/10.46687/jsar.v20i1.303.

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Spatial data represents the shape, location, and spatial relationships of geographic features to other features. The form represents the geometry of the objects, the location is described by a list of x, y coordinates of discrete points of the objects, and the spatial connections (topological information) of the geographical objects determine the interaction between them. Spatial (coordinate) information can also include time-related data.

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Osborn, Wendy. "Unbounded Spatial Data Stream Query Processing using Spatial Semijoins." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 02 (March 1, 2021): 33–41. http://dx.doi.org/10.5383/juspn.15.02.005.

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In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.

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Lee. "A study on the Spatial Sampling Method to Minimize Spatial Autocorrelation of Spatial and Geographical Data." Journal of the Korean Society of Civil Engineers 34, no. 4 (2014): 1317. http://dx.doi.org/10.12652/ksce.2014.34.4.1317.

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Barkworth, M. E., and J. Mcgrew. "Combining herbarium data with spatial data: potential benefits, new needs." Czech Journal of Genetics and Plant Breeding 41, Special Issue (July 31, 2012): 59–64. http://dx.doi.org/10.17221/6136-cjgpb.

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Wiemann, Stefan, and Lars Bernard. "Spatial data fusion in Spatial Data Infrastructures using Linked Data." International Journal of Geographical Information Science 30, no. 4 (September 24, 2015): 613–36. http://dx.doi.org/10.1080/13658816.2015.1084420.

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Kovaříček, P., and J. Hůla. "Field capacity determination from GPS spatial data." Research in Agricultural Engineering 49, No. 3 (February 8, 2012): 75–79. http://dx.doi.org/10.17221/4955-rae.

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For agricultural machinery management the actually reached machines capacity has a considerable importance. The data recorded by GPS monitoring enable to correct machines work productivity under concrete operational conditions. Assessment of machine aggregates operation records has proved effect of the operational factors onto operational efficiency reached on particular plots. The theoretical efficiency given by exploitation characteristics of machines has decreased effect of higher share of non-productive travels within small and irregular plots almost by 25%. In this paper we are dealing with searching for correlation between field speed and travelled unit path and defined classes of size, length and plot shape. The resulting knowledge of field efficiency on plots properties will enable to make more accurate the machines planned operation.

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Klimešová, D., and E. Ocelíková. "Spatial data modelling and maximum entropy theory." Agricultural Economics (Zemědělská ekonomika) 51, No. 2 (February 20, 2012): 80–83. http://dx.doi.org/10.17221/5080-agricecon.

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Spatial data modelling and consequential error estimation of the distribution function are key points of spatial analysis. For many practical problems, it is impossible to hypothesize distribution function firstly and some distribution models, such as Gaussian distribution, may not suit to complicated distribution in practice. The paper shows the possibility of the approach based on the maximum entropy theory that can optimally describe the spatial data distribution and gives&nbsp; the actual error estimation.&nbsp;

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K, Sivakumar. "Spatial Data Mining: Recent Trends in the Era of Big Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 912–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202182.

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USUI, Teruko. "Spatial Data Transfer Standard (SDTS) and Spatial Data Model." Theory and Applications of GIS 2, no. 1 (1994): 1–8. http://dx.doi.org/10.5638/thagis.2.1.

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Wiwin, Sulistyo, Sulistyo Wiwin, and Pulungan Reza. "Development of a Spatial Path-Analysis Method for Spatial Data Analysis." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (August 1, 2018): 2456–67. https://doi.org/10.11591/ijece.v8i4.pp2456-2467.

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Path analysis is a method for identifying and analyzing direct and indirect relationship between independent and dependent variables. This method was developed by Sewal Wright and initially only used correlation analysis results in identifying the variables’ relationship. So far, path analysis has been mostly used to deal with variables of non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to enhance path analysis to identify the effects of spatial dependencies. This paper proposes a method derived from path analysis that can process data with spatial elements and furthermore can be used to identify and analyze the spatial effects on the data; we call this method spatial path analysis.

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Tesi sul tema "Spatial Data"

Wiemann, Stefan. "Data Fusion in Spatial Data Infrastructures." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-216985.

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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 Web Die 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

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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 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)

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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.

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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 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.

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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.

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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 Philosophy Department 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.

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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.

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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 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.

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Più fonti

Libri sul tema "Spatial Data"

Patanè, Giuseppe, and Michela Spagnuolo, eds. Heterogeneous Spatial Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02589-1.

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Mamoulis, Nikos. Spatial Data Management. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01884-8.

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Li, Deren, Shuliang Wang, and Deyi Li. Spatial Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5.

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Oliver, Dev. Spatial Network Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39621-7.

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Fischer, Manfred M., and Jinfeng Wang. Spatial Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21720-3.

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Fisher, Peter F., and Michael F. Goodchild. Spatial Data Quality. Edited by Wenzhong Shi. Abingdon, UK: Taylor & Francis, 2002. http://dx.doi.org/10.4324/9780203303245.

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Wenzhong, Shi, Goodchild Michael F, and Fisher Peter, eds. Spatial data quality. London: Taylor & Francis, 2002.

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Mamoulis, Nikos. Spatial data management. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2012.

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Sherman, Michael. Spatial Statistics and Spatio-Temporal Data. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470974391.

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Sibley, David. Spatial applications of exploratory data analysis. Norwich: Geo Books, 1988.

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Capitoli di libri sul tema "Spatial Data"

Trauth, Martin H. "Spatial Data." In MATLAB® Recipes for Earth Sciences, 249–314. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46244-7_7.

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Trauth, Martin H. "Spatial Data." In MATLAB® Recipes for Earth Sciences, 293–363. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38441-8_7.

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Trauth, Martin H. "Spatial Data." In MATLAB® Recipes for Earth Sciences, 165–224. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72749-1_7.

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Fox, Charles. "Spatial Data." In Springer Textbooks in Earth Sciences, Geography and Environment, 57–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72953-4_5.

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Trauth, Martin H. "Spatial Data." In Springer Textbooks in Earth Sciences, Geography and Environment, 251–320. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07719-7_7.

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Arnold, Taylor, and Lauren Tilton. "Spatial Data." In Quantitative Methods in the Humanities and Social Sciences, 169–99. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-62566-4_9.

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Maggio, Sabrina, and Claudia Cappello. "Spatial Data." In Encyclopedia of Mathematical Geosciences, 1353–58. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_303.

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Rey, Sergio, Dani Arribas-Bel, and Levi John Wolf. "Spatial Data." In Geographic Data Science with Python, 35–66. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9780429292507-4.

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Trauth, Martin H. "Spatial Data." In MATLAB® Recipes for Earth Sciences, 193–254. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12762-5_7.

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Maggio, Sabrina, and Claudia Cappello. "Spatial Data." In Encyclopedia of Mathematical Geosciences, 1–6. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_303-1.

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Atti di convegni sul tema "Spatial Data"

Wang, Yuan-ni, and Fu-ling Bian. "Obstacle constraint spatial clustering." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837648.

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Raman, Balan Sethu, and Mohamed Ali. "Spatial data streaming or streaming spatial data." In the 1st International Conference and Exhibition. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1823854.1823856.

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Zhou, Yan, Qing Zhu, and Yeting Zhang. "A data skew handling method based on the minimum spatial proximity for parallel spatial database." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837521.

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SPIE, Proceedings of. "Front Matter: Volume 7492." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.849092.

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Wu, Guofeng. "A review of remote-sensing-based spatial/temporal information capturing for water resource studies in Poyang Lake." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.836771.

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Li, Deying, Kunlong Yin, Huaxi Gao, and Changchun Liu. "Design and application analysis of prediction system of geo-hazards based on GIS in the Three Gorges Reservoir." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837229.

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Huang, Zhengdong, Jie Li, and Xiaotang Xia. "Representation and application of bus system at the lowest level of detail." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837290.

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Su, Hongjun, Yehua Sheng, and Yongning Wen. "Data mining based on spectral and spatial features for hyperspectral classification." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837304.

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Wei, Yingchun, Daiyong Cao, and Juemei Deng. "A new practical methodology of the coal bed stability evaluation: the trend and variation method." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837308.

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Su, Lilan, Yanfang Liu, and Xiaoyong Gao. "Entropy-theory-based study on the relationship between land use structure and industry system: a case study of the eastern Hubei metropolitan area." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837337.

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Rapporti di organizzazioni sul tema "Spatial Data"

Mulgaonkar, Prasanna. Data Driven Spatial Reasoning. Fort Belvoir, VA: Defense Technical Information Center, October 1991. http://dx.doi.org/10.21236/ada242727.

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Stockinger, Kurt, and Kesheng Wu. Improved searching for spatial features in spatio-temporal data. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/833576.

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Hashemi, Hooman. Spatial and Graphical Data Processing: Spatial Crowdsourcing and Quasi-Clique Enumeration. Ames (Iowa): Iowa State University, January 2021. http://dx.doi.org/10.31274/cc-20240624-1316.

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Williams, R. J. Data Quality Statements for Spatial Databases. Fort Belvoir, VA: Defense Technical Information Center, July 1992. http://dx.doi.org/10.21236/ada264125.

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Armstrong, Marc P., Gerard Rushton, Jayajit Chakraborty, Allen Wayne Ibaugh, and Amy J. Ruggles. Spatial Data Systems for Transportation Planning. Iowa City, Iowa: University of Iowa Public Policy Center, 1997. http://dx.doi.org/10.17077/qi9q-uir0.

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Bertanha, Marinho, and Petra Moser. Spatial Errors in Count Data Regressions. Cambridge, MA: National Bureau of Economic Research, August 2014. http://dx.doi.org/10.3386/w20374.

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Dove, Linda P. GIS-Assisted Spatial Data Management for Corps of Engineers Real Estate Activities: Spatial Data Conversion Options. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada409099.

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Thomas, Robert, and Terry Idol, eds. Development of Spatial Data Infrastructures for Marine Data Management. Open Geospatial Consortium, Inc., August 2019. http://dx.doi.org/10.62973/19-025r1.

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Grunsky, E. Spatial factor analysis: a technique to assess the spatial relationships of multivariate data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128074.

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McDonnell, Michael. Scan-Line Methods in Spatial Data Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada231165.

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