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Books on the topic 'Spatial and temporal modelling'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Location, transport and land-use: Modelling spatial-temporal information. Berlin: Springer, 2005.

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2

Guignard, Fabian. On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95231-0.

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3

Watts, S. Contributions to the modelling of the temporal and spatial statistics of radar sea clutter and the establishment of new detection performance limits. Birmingham: University of Birmingham, 1987.

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4

Toxopeus, A. G. ISM: An interactive spatial and temporal modelling system as a tool in ecosystem management : with two case studies : Cibodas Biosphere Reserve, West Java, Indonesia, Amboseli Biosphere Reserve, Kajiado District, Central-Southern Kenya. Enschede, the Netherlands: ITC, 1996.

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5

Zoidis, Georgios. Temporal data modelling. Manchester: UMIST, 1997.

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6

Oliviero, Stock, ed. Spatial and temporal reasoning. Dordrecht: Kluwer Academic Publishers, 1997.

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7

Stock, Oliviero. Spatial and Temporal Reasoning. Dordrecht: Springer, 1997.

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8

Stock, Oliviero, ed. Spatial and Temporal Reasoning. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-0-585-28322-7.

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9

Roddick, John F., and Kathleen Hornsby, eds. Temporal, Spatial, and Spatio-Temporal Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45244-3.

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10

Andrew, Lawson, and Denison David G. T, eds. Spatial cluster modelling. Boca Raton, FL: Chapman & Hall/CRC, 2002.

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11

Roy, John R. Spatial Interaction Modelling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24807-1.

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12

Tiefelsdorf, Michael, ed. Modelling Spatial Processes. Berlin/Heidelberg: Springer-Verlag, 2000. http://dx.doi.org/10.1007/bfb0048754.

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13

Ligozat, Gérard. Qualitative Spatial and Temporal Reasoning. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118601457.

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14

Ligozat, Gérard. Qualitative spatial and temporal reasoning. London, UK: ISTE, 2011.

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15

Meen, Geoffrey. Modelling Spatial Housing Markets. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1673-6.

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16

Patuelli, Roberto, and Giuseppe Arbia, eds. Spatial Econometric Interaction Modelling. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30196-9.

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17

Petrounias, Ilias. Conceptual modelling for temporal informationsystems. Manchester: UMIST, 1994.

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18

Luo, Ling. Temporal Modelling of Customer Behaviour. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-18289-2.

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19

Jensen, Christian S., Markus Schneider, Bernhard Seeger, and Vassilis J. Tsotras, eds. Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47724-1.

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20

Papadias, Dimitris, Donghui Zhang, and George Kollios, eds. Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73540-3.

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21

Gertz, Michael, Matthias Renz, Xiaofang Zhou, Erik Hoel, Wei-Shinn Ku, Agnes Voisard, Chengyang Zhang, et al., eds. Advances in Spatial and Temporal Databases. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64367-0.

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22

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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23

Nascimento, Mario A., Timos Sellis, Reynold Cheng, Jörg Sander, Yu Zheng, Hans-Peter Kriegel, Matthias Renz, and Christian Sengstock, eds. Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40235-7.

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24

Bauzer Medeiros, Claudia, Max J. Egenhofer, and Elisa Bertino, eds. Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11535331.

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25

Mamoulis, Nikos, Thomas Seidl, Torben Bach Pedersen, Kristian Torp, and Ira Assent, eds. Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02982-0.

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26

Peters, Gareth William, and Tomoko Matsui, eds. Theoretical Aspects of Spatial-Temporal Modeling. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55336-6.

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27

Pfoser, Dieter, Yufei Tao, Kyriakos Mouratidis, Mario A. Nascimento, Mohamed Mokbel, Shashi Shekhar, and Yan Huang, eds. Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22922-0.

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28

Claramunt, Christophe, Markus Schneider, Raymond Chi-Wing Wong, Li Xiong, Woong-Kee Loh, Cyrus Shahabi, and Ki-Joune Li, eds. Advances in Spatial and Temporal Databases. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22363-6.

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29

Hadzilacos, Thanasis, Yannis Manolopoulos, John Roddick, and Yannis Theodoridis, eds. Advances in Spatial and Temporal Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b11839.

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30

Schneider, David C. Quantitative ecology: Spatial and temporal scaling. San Diego: Academic Press, 1994.

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31

Aura, Reggiani, and Nijkamp Peter, eds. Spatial dynamics, networks and modelling. Cheltenham, UK: Edward Elgar, 2006.

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32

Hendry, Ruth Juliet. Spatial modelling in plant ecology. [s.l.]: typescript, 1995.

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33

Li, Guangquan, and Robert P. Haining. Modelling Spatial and Spatial-Temporal Data. Taylor & Francis Group, 2021.

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34

Li, Guangquan, and Robert P. Haining. Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2020.

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35

Regression Modelling Wih Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2020.

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36

Li, Guangquan, and Robert P. Haining. Regression Modelling Wih Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2019.

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37

Li, Guangquan, and Robert P. Haining. Regression Modelling Wih Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2020.

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38

Li, Guangquan, and Robert P. Haining. Regression Modelling Wih Spatial and Spatial-Temporal Data: A Bayesian Approach. Taylor & Francis Group, 2020.

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39

Chan, Yupo. Location, Transport and Land-Use: Modelling Spatial-Temporal Information. Springer, 2004.

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40

Chan, Yupo. Location, Transport and Land-Use: Modelling Spatial-Temporal Information. Springer London, Limited, 2005.

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41

L, Heres, ed. Time in GIS: Issues in spatio-temporal modelling. Delft: NCG, 2000.

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42

Sahu, Sujit Kumar. Bayesian Modelling of Spatio-Temporal Data with R. Taylor & Francis Group, 2022.

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43

Bayesian Modelling of Spatio-Temporal Data with R. Taylor & Francis Group, 2022.

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44

Luc, Heres, ed. Time in GIS: Issues in spatio-temporal modelling. Nederlandse Commissie voor Geodesie, 2000. http://dx.doi.org/10.54419/v5m55p.

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Abstract:

Most Geographic Information Systems started as a substitute for loose paper maps. These paper maps did not have a built-in time dimension and could only represent history indirectly as a sequence of physically separate images. This was in fact imitated by these first generation systems. The time dimension could only be represented by means of separate files. A minority of Geographic Information Systems however, started their life as a substitute for ordered lists and tables with a link to paper maps. In these lists, the inclusion of a time com-ponent in the form of a data field was quite usual. This method too was copied by the systems that replaced these paper tables. The current trend in the development of Geographic Information Systems is towards the inte-gration of the classical map-oriented concepts with the table-oriented concepts. This often leads to the explicit embedding of the time component in the GIS environment. The Subcommission Geo-Information Models of the Netherlands Geodetic Commission has organized a workshop to discuss the theory and practice of time and history in GIS on 18 May 2000. This publication contains 6 articles prepared for the workshop. The first paper, written by Donna Peuquet, gives a bird’s-eye view of the current state of the art in spatio-temporal database technology and methodology. She is a well-known expert in the field of spatio-temporal information systems and the author of many articles in this field. The second article is written by Monica Wachowicz. She describes what you can do with a GIS once it contains a historical dimension and how you can detect changes in geographic phenomena. Furthermore, her article suggests how geographic visualisation and knowledge discovery techniques can be integrated in a spatio-temporal database. How to record the time dimension in a database is one thing, how to show this dimension to users is another one. In his contribution, Menno-Jan Kraak first tells about the techniques, which were used in the age of paper maps and the limitations these methods had. He goes on to explain what kind of cartographic techniques have been developed since the mass introduc-tion of the computer. Finally he describes the powerful animation methods which currently exist and can be used on CD-ROM and Internet applications. Peter van Oosterom describes how the time dimension is represented in the information sys-tems of the Cadastre and how this is used to publish updates. The Cadastre has a very long tradition in incorporating the time component, which has always been an inherent component of the cadastral registration. In former times this was translated in very precise procedures about how to update the paper maps and registers. Today it is translated in spatio-temporal database design. The article of Luc Heres tells about the time component in the National Road Database, origi-nally designed for traffic accident registration. This is one of the systems with ''table'' roots and with quite a long tradition in handling the time dimension. He elucidates first the core objects in the conceptual model and how time is added. Next, how this model is translated in a logical design and finally how this is technically implemented. Geologists and geophysicians also have a respectable tradition in handling the time dimension in the data they collect. This is illustrated in the last paper, which is written by Ipo Ritsema. He outlines how time is handled in geological and geophysical databases maintained by TNO. By means of some practical cases he illustrates which problems can be encountered and how these can be solved.

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45

Sahu, Sujit Kumar. Bayesian Modelling of Spatio-Temporal Data with R. Taylor & Francis Group, 2022.

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46

Sahu, Sujit Kumar. Bayesian Modelling of Spatio-Temporal Data with R. Taylor & Francis Group, 2022.

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47

Lopes, Hedibert, and Nicholas Polson. Analysis of economic data with multiscale spatio-temporal models. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.12.

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This article discusses the use of Bayesian multiscale spatio-temporal models for the analysis of economic data. It demonstrates the utility of a general modelling approach for multiscale analysis of spatio-temporal processes with areal data observations in an economic study of agricultural production in the Brazilian state of Espìrito Santo during the period 1990–2005. The article first describes multiscale factorizations for spatial processes before presenting an exploratory multiscale data analysis and explaining the motivation for multiscale spatio-temporal models. It then examines the temporal evolution of the underlying latent multiscale coefficients and goes on to introduce a Bayesian analysis based on the multiscale decomposition of the likelihood function along with Markov chain Monte Carlo (MCMC) methods. The results from agricultural production analysis show that the spatio-temporal framework can effectively analyse massive economics data sets.

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48

Haider, Murtaza. Spatio-temporal modelling of housing starts in the greater Toronto area. 2003.

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49

Spatio-Temporal Characterisation of Drought: Data Analytics Modelling Tracking Impact and Prediction. Taylor & Francis Group, 2022.

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50

Mercado, Vitali Diaz. Spatio-Temporal Characterisation of Drought: Data Analytics, Modelling, Tracking, Impact and Prediction. Taylor & Francis Group, 2022.

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