Relevant bibliographies by topics / Spatial data and applications

Academic literature on the topic 'Spatial data and applications'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Spatial data and applications"

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Nikparvar, Behnam, and Jean-Claude Thill. "Machine Learning of Spatial Data." ISPRS International Journal of Geo-Information 10, no. 9 (September 12, 2021): 600. http://dx.doi.org/10.3390/ijgi10090600.

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Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spatial machine learning.
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Geymen, A., and T. Yomralioglu. "Spatial data-based e-municipality applications." Proceedings of the Institution of Civil Engineers - Municipal Engineer 163, no. 2 (June 2010): 77–88. http://dx.doi.org/10.1680/muen.2010.163.2.77.

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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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Ravada, Siva. "Big data spatial analytics for enterprise applications." SIGSPATIAL Special 6, no. 2 (March 10, 2015): 34–41. http://dx.doi.org/10.1145/2744700.2744705.

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Li, Deren, Shuliang Wang, Hanning Yuan, and Deyi Li. "Software and applications of spatial data mining." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6, no. 3 (April 14, 2016): 84–114. http://dx.doi.org/10.1002/widm.1180.

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Hunter, Gary J., Monica Wachowicz, and Arnold K. Bregt. "Understanding Spatial Data Usability." Data Science Journal 2 (2003): 79–89. http://dx.doi.org/10.2481/dsj.2.79.

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Gbenga, Folami. "Building GIS Applications using Spatial Network Data Models." International Journal of Computer Applications 181, no. 48 (April 11, 2019): 22–25. http://dx.doi.org/10.5120/ijca2019918655.

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Nievergelt, J. "Special Issue Editorial Spatial data: applications, concepts, techniques." Computer Journal 37, no. 1 (January 1, 1994): 1–2. http://dx.doi.org/10.1093/comjnl/37.1.1.

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Nguyen, Hai, Noel Cressie, and Amy Braverman. "Spatial Statistical Data Fusion for Remote Sensing Applications." Journal of the American Statistical Association 107, no. 499 (September 2012): 1004–18. http://dx.doi.org/10.1080/01621459.2012.694717.

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Jain, Khushboo, and Anoop Kumar. "Energy-Efficient Data-Aggregation Technique for Correlated Spatial and Temporal Data in Cluster-Based Sensor Networks." International Journal of Business Data Communications and Networking 16, no. 2 (July 2020): 53–68. http://dx.doi.org/10.4018/ijbdcn.2020070103.

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Continuous-monitoring applications in sensor network applications require periodic data transmissions to the base-station (BS), which may lead to unnecessary energy depletion. The energy-efficient data aggregation solutions in sensor networks have evolved as one of the favorable fields for such applications. Former research works have recommended many spatial-temporal designs and prototypes for successfully minimizing the data-gathering overheads, but these are constrained to their relevance. This work has proposed a data aggregation technique for homogeneous application set-ups in sensor networks. For this, the authors have employed two ways of model generation for reducing correlated spatial-temporal data in cluster-based sensor networks: one at the Sensor nodes (SNs) and the other at the Cluster heads (CHs). Building on this idea, the authors propose two types of data filtration, first at the SNs for determining temporal redundancies (TRs) in data readings by both relative deviation (RD) and adaptive frame method (AFM) and second at the CHs for determining spatial redundancies (SRs) by both RD and AFM.
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Dissertations / Theses on the topic "Spatial data and applications"

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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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Embleton, Nina Lois. "Handling sparse spatial data in ecological applications." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5840/.

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Estimating the size of an insect pest population in an agricultural field is an integral part of insect pest monitoring. An abundance estimate can be used to decide if action is needed to bring the population size under control, and accuracy is important in ensuring that the correct decision is made. Conventionally, statistical techniques are used to formulate an estimate from population density data obtained via sampling. This thesis thoroughly investigates an alternative approach of applying numerical integration techniques. We show that when the pest population is spread over the entire field, numerical integration methods provide more accurate results than the statistical counterpart. Meanwhile, when the spatial distribution is more aggregated, the error behaves as a random variable and the conventional error estimates do not hold. We thus present a new probabilistic approach to assessing integration accuracy for such functions, and formulate a mathematically rigorous estimate of the minimum number of sample units required for accurate abundance evaluation in terms of the species diffusion rate. We show that the integration error dominates the error introduced by noise in the density data and thus demonstrate the importance of formulating numerical integration techniques which provide accurate results for sparse spatial data.
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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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Menezes, Kim Anne. "Bayesian spatial models : applications for tropospheric ozone data /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Yu, Jihai. "Essays on spatial dynamic panel data model theories and applications /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1179767430.

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Honnor, Thomas R. "Some spatial statistical techniques with applications to cellular imaging data." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/97940/.

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The aim of this thesis is to provide techniques for the analysis of a variety of types of spatial data, each corresponding to one of three biological questions on the function of the protein TACC3 during mitosis. A starting point in each investigation is the interpretation of the biological question and understanding of the form of the available data, from which a mathematical representation of data and corresponding statistical problem are developed. The thesis begins with description of a methodology for application to two collections of (marked) point patterns to determine the significance of differences in their structure, achieved through comparison of summary statistics and quantification of the significance of such differences by permutation tests. A methodology is then proposed for application to a pair spatio-temporal processes to estimate their individual temporal evolutions, including ideas from optimal transportation theory, and a test of dependence between such estimators. The thesis concludes with a proposed model for line data, designed to approximate the mitotic spindle structure using trajectories on the surface of spheroids, and a comparison score to compare model t between models and/or observations. The results of methodologies when applied to simulated data are presented as part of investigations into their validity and power. Application to biological data indicates that TACC3 influences microtubule structure during mitosis at a range of scales, supporting and extending previous investigations. Each of the methodologies is designed to require minimal assumptions and numbers of parameters, resulting in techniques which may be applied more widely to similar biological data from additional experiments or data arising from other fields.
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Vuorio, R. (Riikka). "Use of public sector’s open spatial data in commercial applications." Master's thesis, University of Oulu, 2014. http://urn.fi/URN:NBN:fi:oulu-201311201883.

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The objective of this study was to analyse how young Finnish information technology (IT) companies utilize the public sector’s open spatial data. The aim was to find out to what extent companies use public sector’s open spatial data in products and how companies are using it. In addition, defects related to data and its use and companies’ awareness of public sector open data were canvassed. Defects and unawareness might prevent or retard the utilization of public sector’s data. Public sector is collecting vast amount of data from various areas when performing public tasks. The major part of the data is spatial, meaning the data has a location aspect. Public sector is opening the data for everybody to use freely and companies could use this open spatial data for commercial purposes. High expectations have been set for the data opening: along with it, innovations and business — new companies and digital products — will be created. The European Union has promoted greatly the public sector data opening with its legislative actions. First with the PSI directive (directive on re-use of public sector data) and later with the INSPIRE directive (directive on establishing and Infrastructure for Spatial Information in the European Community). The both directives are aiming to facilitate the re-use and dissemination of public sector data, whereas the INSPIRE directive has focused on the use of interoperable spatial data by creating the spatial data infrastructure. Even if the developments are still on going, these undertakings have already created possibilities for companies to use public sector data. This applies especially to the spatial data. This study was quantitative by nature and the empirical data for the study was collected through online survey, which was targeted to randomly selected Finnish IT companies established during the years 2009–2012. Data was analyzed by descriptive statistics. The results can be generalized to the whole target population in Finland. The results of this study shows that the number of companies utilizing public sector’s open spatial data is small and the public sector’s open spatial data has not yet enabled establishing of new companies. However, companies have developed few new products with the contribution of public sector’s open spatial data and the value of the data for the products is not minor. The thesis concludes that there is a need for greater investment in promoting the public sector’s open data amongst companies: the awareness of public sector’s open spatial data could be increased. In addition, coverage of datasets and interface services could be improved. Perhaps by eliminating these defects, the number of utilizers of public sector’s open spatial data would increase. Now there is a quiet sign of awakening of the business to utilize public sector’s data.
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Martin, Elaine B. "The detection of change in spatial processes with environmental applications." Thesis, University of Glasgow, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361063.

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Ever since Halley (1686) superimposed onto a map of land forms. the direction of trade winds and monsoons between and near the tropics and attempted to assign them a physical cause. homo-sapiens has attempted to develop procedures which quantify the level of change in a spatial process. or assess the relationship between associated spatially measured variables. Most spatial data. whether it be originally point. linear or areal in nature. can be converted by a suitable procedure into a continuous form and plotted as an isarithmic map i.e. points of equal height are joined. Once in that form it may be regarded as a statistical surface in which height varies over area in much the same way as the terrain varies on topographic maps. Particularly in environmental statistics. the underlying shape of the surface is unknown. and hence the use of non-parametric techniques is wholly appropriate. For most applications. the location of data points is beyond the control of the map-maker hence the analyst must cope with irregularly spaced data points. A variety of possible techniques for describing a surface are given in chapter two, with attention focusing on the methodology surrounding kernel density estimation. Once a surface has been produced to describe a set of data. a decision concerning the number of contours and how they should be selected has to be taken. When comparing two sets of data. it is imperative that the contours selected are chosen using the same criteria. A data based procedure is developed in chapter three which ensures comparability of the surfaces and hence spurious conclusions are not reached as a result of inconsistencies between surfaces. Contained within this chapter is a discussion of issues which relate to other aspects of how a contour should be drawn to minimise the potential for inaccuracies in the swface fitting methodology. Chapter four focuses on a whole wealth of techniques which are currently available for comparing surfaces. These range from the simplest method of overlaying two maps and visually comparing them to more involved techniques which require intensive numerical computation. It is the formalisation of the former of these techniques which forms the basis of the methodology developed in the following two chapters to discern whether change/association has materialised between variables.One means of quantifying change between two surfaces, represented as a contoured surface, is in terms of the transformation which would be required for the two surfaces to be matched. Mathematically, transformations are described in terms of rotation, translation and scalar change. Chapter five provides a geometrical interpretation of the three transformations in terms of area, perimeter, orientation and the centre of gravity of the contour of interest and their associated properties. Although grid resolution is fundamentally a secondary level of smoothing, this aspect of surface fitting has generally been ignored. However to ensure consistency across surfaces, it is necessary to decide firstly, whether data sets of different sizes should be depicted using different mesh resolutions and secondly, how fine a resolution provides optimal results, both in terms of execution time and inherent surface variability. This aspect is examined with particular reference to the geometric descriptors used to quantify change. The question of random noise contained within a measurement process has been ignored in the analysis to this point. However in practice, some form of noise will always be contained within a process. Quantifying the level of noise attributable to a process can prove difficult since the scientist may be over optimistic in his evaluation of the noise level. In developing a suitable set of test statistics, four situations were examined, firstly when no noise was present and then for three levels of noise, the upper bounds of which were 5, 15 and 25%. Based on these statistics, a series of hypothesis tests were developed to look at the question of change for individual contour levels Le. local analysis. or alternatively for a whole surface by combining the statistics and effectively performing a multivariate test. A number of problems are associated with the methodology. These difficulties are discussed and various remedial measures are proposed. The theoretical derivation of the test statistic, both in the absence and presence of random noise, has proved mathematically to be extremely complex, with a number of stringent assumptions required to enable the theoretical distribution to be derived. A major simulation study was subsequently undertaken to develop the empirical probability distribution function for the various statistics defining change for the four levels of noise. Also for each of the statistics, the resultant power of the test was examined.The remaining chapter explicitly examines two case studies and how the methodology developed in the preceding two chapters may be implemented. The first example cited raises the question, 'Has a seasonal temperature change resulted during the fifty year span, 1930 to 1980, within the contiguous United States of America?' The data base was provided by the United States Historical Climatology Network (HCN) Serial Temperature and Precipitation Data, Quinlan et al (1987). The second problem examines whether there is an association between background radiation levels, within three regions of the south-west England, and the location of various fonns of leukaemia or whether case location is a product of the population distribution. Differences between this example and the previous illustration materialise in terms of the spatial resolution of the data; the leukaemia data are defined as punctual data points and are extremely sparse; the population distribution is defined as areal regions; with the radiation data being of a more continuous format. The methodology developed required modification, but aside of this a preliminary set of conclusions were reached.
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Berndt, Christian [Verfasser]. "Spatial interpolation of climate data for hydrological and environmental applications / Christian Berndt." Hannover : Technische Informationsbibliothek (TIB), 2016. http://d-nb.info/1124166823/34.

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Zborovskiy, Marat. "Representing and manipulating spatial data in interoperable systems and its industrial applications." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35099.

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Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2006.
Includes bibliographical references (p. 123-126).
Introduction: The amount of information available nowadays is staggering and increases exponentially. Making sense of this data has become increasingly difficult because of the two factors: · The sheer volume of data · The lack of interoperability between disparate data sources and models While one can do little about the former factor, the latter one can be mitigated by advancing solutions that make data easy to work with and ensure the interoperability among data sources and models in intelligent networks. One way to achieve interoperability is to force every entity involved in the data exchange to adopt the same standard. However, organizations have heavily invested in proprietary data standards and are unlikely to replace their existing standards with a new one. Therefore, another solution is to create a standard, through which organizations can translate their data sources and share them with their customers or general community. The MIT Data Center is spearheading an initiative to create M - a language that is capable to provide the much needed interoperability between divergent data sources and models with an ultimate goal of creating a new intelligent information infrastructure (Brock, Schuster and Kutz 2006).
by Marat Zborovskiy.
S.M.
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Books on the topic "Spatial data and applications"

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

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N, Boots B., and Sugihara Kōkichi 1948-, eds. Spatial tessellations: Concepts and applications of Voronoi diagrams. Chichester, England: Wiley, 1992.

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Stillwell, John C. H. Technologies for migration and commuting analysis: Spatial interaction data applications. Hershey, PA: Business Science Reference, 2010.

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Oort, Pepijn van. Spatial data quality: From description to application. Delft: NCG, Nederlandse Commissie voor Geodesie, 2005.

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Albert, Donald Patrick, and G. Rebecca Dobbs. Emerging methods and multidisciplinary applications in geospatial research. Hershey PA: Information Science Reference, 2013.

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Samet, Hanan. Applications of spatial data structures: Computer graphics, image processing, and GIS. Reading, Mass: Addison-Wesley, 1990.

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Diaz, Laura, and Carlos Granell. Discovery of geospatial resources: Methodologies, technologies, and emergent applications. Hershey PA: Information Science Reference, 2012.

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Fischer, Manfred M. Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Zoran, Stančič, and Veljanovski Tatjana, eds. Computing archaeology for understanding the past: CAA 2000 : computer applications and quantitative methods in archaeology : proceedings of the 28th Conference, Ljubljana, April 2000. Oxford, England: Archaeopress, 2001.

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Centre national d'études spatiales (France)., ed. Systèmes informatiques temps réel pour les applications spatiales =: Real-time embedded processing for space applications. Toulouse: Cépaduès, 1993.

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Book chapters on the topic "Spatial data and applications"

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Mamoulis, Nikos. "Applications of Spatial Data Management Technology." In Spatial Data Management, 85–118. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01884-8_6.

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Peri, M., C. Regazzoni, A. Tesei, and G. Vernazza. "Spatial Fusion of Multisensor Visual Information for Crowding Evaluation." In Data Fusion Applications, 155–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84990-9_15.

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Vaisman, Alejandro, and Esteban Zimányi. "Spatial and Mobility Data Warehouses." In Data-Centric Systems and Applications, 437–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65167-4_12.

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Waller, Lance A. "Applications of Big Spatial Data: Health." In Encyclopedia of Big Data Technologies, 1–4. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_72-1.

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Waller, Lance A. "Applications of Big Spatial Data: Health." In Encyclopedia of Big Data Technologies, 1–5. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-63962-8_72-2.

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Waller, Lance A. "Applications of Big Spatial Data: Health." In Encyclopedia of Big Data Technologies, 86–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_72.

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Akiki, Pierre, and Hoda Maalouf. "Incorporating Spatial Data into Enterprise Applications." In Information Systems Development, 695–704. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/b137171_72.

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Shekhar, Shashi, Yan Huang, Weili Wu, C. T. Lu, and S. Chawla. "What’s Spatial About Spatial Data Mining: Three Case Studies." In Data Mining for Scientific and Engineering Applications, 487–514. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1733-7_26.

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Yuan, Huaqiang, Yaxun Wang, Jie Zhang, Wei Tan, Chao Qu, and Wenbin He. "Spatial Fuzzy Clustering Using Varying Coefficients." In Advanced Data Mining and Applications, 183–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73871-8_18.

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Wang, Shuliang, and Haning Yuan. "View-Angle of Spatial Data Mining." In Advanced Data Mining and Applications, 1065–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_116.

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Conference papers on the topic "Spatial data and applications"

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Ginige, Athula, Marco Romano, Monica Sebillo, Giuliana Vitiello, and Pasquale Di Giovanni. "Spatial data and mobile applications." In the International Working Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2254556.2254591.

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Rahman, Yitoshee, Sarker Monojit Asish, Adil Khokhar, Arun K. Kulshreshth, and Christoph W. Borst. "Gaze Data Visualizations for Educational VR Applications." In SUI '19: Symposium on Spatial User Interaction. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357251.3358752.

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Shi, Qingdong, Jiaguo Qi, Lijun Chen, Shunli Chang, Qingsan Shi, and Guanghui Lv. "Validating classification accuracy of low spatial resolution data by using high spatial resolution data." In Optical Engineering + Applications, edited by Wei Gao and Susan L. Ustin. SPIE, 2007. http://dx.doi.org/10.1117/12.741540.

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Zeng, Chen, Yanfang Liu, Yaolin Liu, Xing Li, and Ya Tang. "Application of high-resolution RS image in settlement extraction." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838545.

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Xia, Yin, Yaolin Liu, Xiaofeng Hong, and Dianfeng Liu. "Application of binary tree based SVMs approach to land grade evaluation." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838414.

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Yuan, Hanning, and Shuliang Wang. "Data preprocessing of spatial data mining." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.650728.

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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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Liu, Fucheng, Xuezhao He, and Li Zhou. "Application of generalized regression neural network residual kriging for terrain surface interpolation." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837425.

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Lan, Qiuping, Lifan Fei, Yining Liu, and Kun Zhang. "Application of multi-temporal DEM data in calculating the Earth's surface deformation." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837926.

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Li, Wenjing, Qiao Wang, and Zhiyong Lin. "Application research of rough sets in the multi-elemental cooperative spatial analysis." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838284.

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Reports on the topic "Spatial data and applications"

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Connell, S., J. Ernsting, D. Kukan, and A. Currie. GEOSIS project-integration of text and spatial data for geoscience applications. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128099.

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Borgoni, Riccardo, and Francesco C. Billari. Bayesian spatial analysis of demographic survey data: an application to contraceptive use at first sexual intercourse. Rostock: Max Planck Institute for Demographic Research, October 2002. http://dx.doi.org/10.4054/mpidr-wp-2002-048.

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Lewis, David J., and Ralph J. Alig. A spatial econometric analysis of land-use change with land cover trends data: an application to the Pacific Northwest. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2014. http://dx.doi.org/10.2737/pnw-rp-600.

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Belles, Randy, Gary T. Mays, Olufemi A. Omitaomu, and Willis P. Poore III. Updated Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Small Modular Reactors. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1052267.

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Mays, Gary T., Randy Belles, Brandon R. Blevins, Stanton W. Hadley, Thomas J. Harrison, Warren C. Jochem, Bradley S. Neish, Olufemi A. Omitaomu, and Amy N. Rose. Application of Spatial Data Modeling and Geographical Information Systems (GIS) for Identification of Potential Siting Options for Various Electrical Generation Sources. Office of Scientific and Technical Information (OSTI), May 2012. http://dx.doi.org/10.2172/1032036.

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Reyes, Julian, Jeb Williamson, and Emile Elias. Spatio-temporal analysis of Federal crop insurance cause of loss data: A roadmap for research and outreach effort. U.S. Department of Agriculture, April 2018. http://dx.doi.org/10.32747/2018.7202608.ch.

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Federal crop insurance provides a financial safety net for farmers against insured perils such as drought, heat, and freeze. In 2016 over $100 billion dollars of crops were insured through the Federal crop insurance program administered by the U.S. Department of Agriculture Risk Management Agency. In this white paper, we analyze publicly-available Federal crop insurance data to understand how weather and climate-related perils, or causes of loss (COL), change over time and spatial areas. We find that over 75% of all weather/climate-related indemnities (i.e., crop losses) from 2001 to 2016 are due to three COL: drought, excess moisture, and hail. However, the extent to which these top COL and others impact indemnities is highly dependent on the time period, temporal scale, and spatial scale of analysis. Moreover, we identify what COL are region- or season-specific, and visualize COL trends over time. Finally, we offer a road map of research applications to quantify such trends in indemnities, as well as outreach and extension efforts that include an online data portal.
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Холошин, Ігор Віталійович, Ірина Миколаївна Варфоломєєва, Олена Вікторівна Ганчук, Ольга Володимирівна Бондаренко, and Андрій Валерійович Пікільняк. Pedagogical techniques of Earth remote sensing data application into modern school practice. CEUR-WS.org, 2018. http://dx.doi.org/10.31812/123456789/3257.

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Abstract. The article dwells upon the Earth remote sensing data as one of the basic directions of Geo-Information Science, a unique source of information on processes and phenomena occurring in almost all spheres of the Earth geographic shell (atmosphere, hydrosphere, lithosphere, etc.). The authors argue that the use of aerospace images by means of the information and communication technologies involvement in the learning process allows not only to increase the information context value of learning, but also contributes to the formation of students’ cognitive interest in such disciplines as geography, biology, history, physics, computer science, etc. It has been grounded that remote sensing data form students’ spatial, temporal and qualitative concepts, sensory support for the perception, knowledge and explanation of the specifics of objects and phenomena of geographical reality, which, in its turn, provides an increase in the level of educational achievements. The techniques of aerospace images application into the modern school practice have been analyzed and illustrated in the examples: from using them as visual aids, to realization of practical and research orientation of training on the basis of remote sensing data. Particular attention is paid to the practical component of the Earth remote sensing implementation into the modern school practice with the help of information and communication technologies.
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Kholoshyn, Ihor V., Iryna M. Varfolomyeyeva, Olena V. Hanchuk, Olga V. Bondarenko, and Andrey V. Pikilnyak. Pedagogical techniques of Earth remote sensing data application into modern school practice. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3262.

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The article dwells upon the Earth remote sensing data as one of the basic directions of Geo-Information Science, a unique source of information on processes and phenomena occurring in almost all spheres of the Earth geographic shell (atmosphere, hydrosphere, lithosphere, etc.). The authors argue that the use of aerospace images by means of the information and communication technologies involvement in the learning process allows not only to increase the information context value of learning, but also contributes to the formation of students’ cognitive interest in such disciplines as geography, biology, history, physics, computer science, etc. It has been grounded that remote sensing data form students’ spatial, temporal and qualitative concepts, sensory support for the perception, knowledge and explanation of the specifics of objects and phenomena of geographical reality, which, in its turn, provides an increase in the level of educational achievements. The techniques of aerospace images application into the modern school practice have been analyzed and illustrated in the examples: from using them as visual aids, to realization of practical and research orientation of training on the basis of remote sensing data. Particular attention is paid to the practical component of the Earth remote sensing implementation into the modern school practice with the help of information and communication technologies.
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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.
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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.

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To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and converted from the research code to its implementational version written in the C++ programming language. Two trailer-based TScan units have been built. The design of the prototype was iterated multiple times to account for component placement, ease of maintenance, etc. The expansion of the TScan system from a one single-sensor unit to multiple units with multiple LiDAR sensors necessitated transforming all the measurements into a common spatial and temporal reference frame. Engineering applications for performing traffic counts, analyzing speeds at intersections, and visualizing pedestrian presence data were developed. The limitations of the existing SSAM for traffic conflicts analysis with computer simulation prompted the research team to develop and implement their own traffic conflicts detection and analysis technique that is applicable to real-world data. Efficient use of the development system requires proper training of its end users. An INDOT-CRS collaborative process was developed and its execution planned to gradually transfer the two TScan prototypes to INDOT’s full control. This period will be also an opportunity for collecting feedback from the end user and making limited modifications to the system and documentation as needed.
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