Bibliografie tematiche / Spatial Data

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Letteratura scientifica selezionata sul tema "Spatial Data"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Spatial Data"

1

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

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Abstract (sommario):
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 (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 s
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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 (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 (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 (2012): 75–79. http://dx.doi.org/10.17221/4955-rae.

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Abstract (sommario):
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 wi
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Klimešová, D., and E. Ocelíková. "Spatial data modelling and maximum entropy theory." Agricultural Economics (Zemědělská ekonomika) 51, No. 2 (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  the actual error estimation. 
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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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K, Sivakumar. "Spatial Data Mining: Recent Trends in the Era of Big Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 912–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202182.

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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 (2018): 2456–67. https://doi.org/10.11591/ijece.v8i4.pp2456-2467.

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Abstract (sommario):
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 e
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Tesi sul tema "Spatial Data"

1

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

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Over the past decade, the public awareness and availability as well as methods for the creation and use of spatial data on the Web have steadily increased. Besides the establishment of governmental Spatial Data Infrastructures (SDIs), numerous volunteered and commercial initiatives had a major impact on that development. Nevertheless, data isolation still poses a major challenge. Whereas the majority of approaches focuses on data provision, means to dynamically link and combine spatial data from distributed, often heterogeneous data sources in an ad hoc manner are still very limited. However,
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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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Abstract (sommario):
Spatial interaction models of the gravity type are widely used to model origindestination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterise an origin region of a flow, variables that characterise a destination region of a flow, and finally variables that measure the separation between origin and destination regions. This paper outlines and compares two approaches, the spatial econometric and the eigenfunction-based spatial filtering approach, to deal with the issue of spatial autocorrel
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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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Abstract (sommario):
<p> Today, a large amount of spatial data is generated from a variety of sources, such as mobile devices, sensors, and satellites. Traditional spatial data processing techniques no longer satisfy the efficiency and scalability requirements for large-scale spatial data processing. Existing Big Data processing frameworks such as Hadoop and Spark have been extended to support effective large-scale spatial data processing. In addition to processing data in distributed schemes utilizing computer clusters for efficiency and scalability, single node performance can also be improved by making use of m
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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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Abstract (sommario):
The applications of geographic information systems (GIS) in various areas have highlighted the importance of data quality. Data quality research has been given a priority by GIS academics for three decades. However, the outcomes of data quality research have not been sufficiently translated into practical applications. Users still need a GIS capable of storing, managing and manipulating data quality information. To fill this gap, this research aims to investigate how we can develop a tool that effectively and efficiently manages data quality information to aid data users to better understand a
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Zhang, Xiang. "Analysis of Spatial Data." UKnowledge, 2013. http://uknowledge.uky.edu/statistics_etds/4.

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In many areas of the agriculture, biological, physical and social sciences, spatial lattice data are becoming increasingly common. In addition, a large amount of lattice data shows not only visible spatial pattern but also temporal pattern (see, Zhu et al. 2005). An interesting problem is to develop a model to systematically model the relationship between the response variable and possible explanatory variable, while accounting for space and time effect simultaneously. Spatial-temporal linear model and the corresponding likelihood-based statistical inference are important tools for the analysi
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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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Abstract (sommario):
It is well known that econometric modelling and statistical inference are considerably complicated by the possibility of correlation across data data recorded at different locations in space. A major branch of the spatial econometrics literature has focused on testing the null hypothesis of spatial independence in Spatial Autoregressions (SAR) and the asymptotic properties of standard test statistics have been widely considered. However, finite sample properties of such tests have received relatively little consideration. Indeed, spatial datasets are likely to be small or moderately-sized and
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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<br>Department of Statistics<br>Juan Du<br>It is common to assume the spatial or spatio-temporal data are realizations of underlying random elds or stochastic processes. E ective approaches to modelling of the underlying autocorrelation structure of the same random eld and the association among multiple processes are of great demand in many areas including atmospheric sciences, meteorology and agriculture. To this end, this dissertation studies methods and application of the spatial modeling of large-scale dependence structure and spatio-temporal regression modelling
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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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Abstract (sommario):
An increasing amount of freely available Geographic Information System (GIS) data on the Internet has stimulated recent research into Spatial Information Retrieval (SIR). Typically, SIR looks at the problem of retrieving spatial data on a dataset by dataset basis. However in practice, GIS datasets are generally not analysed in isolation. More often than not multiple datasets are required to create a map for a particular analysis task. To do this using the current SIR techniques, each dataset is retrieved one by one using traditional retrieval methods and manually added to the map. To aut
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Libri sul tema "Spatial Data"

1

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

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Mamoulis, Nikos. Spatial Data Management. 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. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5.

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Oliver, Dev. Spatial Network Data. 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. 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. 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. Taylor & Francis, 2002.

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8

Mamoulis, Nikos. Spatial data management. Morgan & Claypool, 2012.

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9

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

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

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Capitoli di libri sul tema "Spatial Data"

1

Trauth, Martin H. "Spatial Data." In MATLAB® Recipes for Earth Sciences. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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"

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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. 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"

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Mulgaonkar, Prasanna. Data Driven Spatial Reasoning. Defense Technical Information Center, 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), 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. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-1316.

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Williams, R. J. Data Quality Statements for Spatial Databases. Defense Technical Information Center, 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. 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. National Bureau of Economic Research, 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. Defense Technical Information Center, 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., 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. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada231165.

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