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Libros sobre el tema "Spatial and temporal scaling"

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Publicado: 4 de mayo de 2024

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1

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

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2

Quattrochi, Dale A. Spatial and temporal scaling of thermal infrared remote sensing data. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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3

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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4

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

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5

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

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6

Roddick, John F. y 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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7

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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8

Lovett, Sarah Breen y Claudia Perren. Expanded architecture: Temporal spatial practices. Braunach, Germany: Spurbuchverlag, 2016.

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9

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

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10

Makela, Pia Kristiina. Spatial scaling in human peripheral vision. Birmingham: Aston University. Department of Vision Science, 1994.

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11

Jensen, Christian S., Markus Schneider, Bernhard Seeger y 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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12

Papadias, Dimitris, Donghui Zhang y 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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13

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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14

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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15

Nascimento, Mario A., Timos Sellis, Reynold Cheng, Jörg Sander, Yu Zheng, Hans-Peter Kriegel, Matthias Renz y 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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16

Bauzer Medeiros, Claudia, Max J. Egenhofer y 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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17

Mamoulis, Nikos, Thomas Seidl, Torben Bach Pedersen, Kristian Torp y 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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18

Peters, Gareth William y 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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19

Pfoser, Dieter, Yufei Tao, Kyriakos Mouratidis, Mario A. Nascimento, Mohamed Mokbel, Shashi Shekhar y 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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20

Claramunt, Christophe, Markus Schneider, Raymond Chi-Wing Wong, Li Xiong, Woong-Kee Loh, Cyrus Shahabi y 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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21

Hadzilacos, Thanasis, Yannis Manolopoulos, John Roddick y 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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22

Vittoria, Giuliani Maria, ed. Home: Social, temporal and spatial aspects. San Giuliano Milanese: Progette Finalizatto Edilizia, 1992.

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23

Batagelj, Vladimir, Patrick Doreian, Anus̆ka Ferligoj y Natas̆a Kejz̆ar, eds. Understanding Large Temporal Networks and Spatial Networks. Chichester, United Kingdom: John Wiley & Sons Ltd, 2014. http://dx.doi.org/10.1002/9781118915370.

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24

Miles, J., W. Schmidt y E. van der Maarel, eds. Temporal and Spatial Patterns of Vegetation Dynamics. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2275-4.

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25

Verma, Desh Pal S. y Robert B. Goldberg, eds. Temporal and Spatial Regulation of Plant Genes. Vienna: Springer Vienna, 1988. http://dx.doi.org/10.1007/978-3-7091-6950-6.

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26

Fan, L. T., D. Neogi y M. Yashima. Elementary Introduction to Spatial and Temporal Fractals. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-45690-9.

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27

Izadi, Dariush. The Spatial and Temporal Dimensions of Interactions. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-19584-7.

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28

Smith, Kelly Jane. Spatial and temporal aspects of visual binding. Birmingham: University of Birmingham, 1997.

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29

1941-, Miles John, Schmidt W y Maarel E. van der, eds. Temporal and spatial patterns of vegetation dynamics. Dordrecht: Kluwer Academic, 1989.

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30

Verma, D. P. S. 1944- y Goldberg Robert B, eds. Temporal and spatial regulation of plant genes. Wien: Springer-Verlag, 1988.

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31

Temporal and spatial patterns in carbonate platforms. Berlin: Springer-Verlag, 1993.

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32

ECSA Symposium (19th 1989 University of Caen, France). Estuaries and coasts: Spatial and temporal intercomparisons. Fredensborg, Denmark: Olsen & Olsen, 1991.

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33

Fan, L. T. Elementary introduction to spatial and temporal fractals. Berlin: Springer-Verlag, 1991.

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34

Peters, Gareth William y Tomoko Matsui, eds. Modern Methodology and Applications in Spatial-Temporal Modeling. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55339-7.

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35

Montero, José-María, Gema Fernández-Avilés y Jorge Mateu. Spatial and Spatio-Temporal Geostatistical Modeling and Kriging. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118762387.

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36

Ando, Yoichi. Brain-Grounded Theory of Temporal and Spatial Design. Tokyo: Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-55891-0.

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37

Community Criminology: Fundamentals of Spatial and Temporal Scaling, Ecological Indicators, and Selectivity Bias. NYU Press, 2015.

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38

Taylor, Ralph B. Community Criminology: Fundamentals of Spatial and Temporal Scaling, Ecological Indicators, and Selectivity Bias. New York University Press, 2015.

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39

Bonds, Matthew H., Andres Garchitorena, Paul E. Farmer y Megan B. Murray. Ecology of poverty, disease and health care delivery: lessons for planetary health. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789833.003.0018.

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Resumen
Over the past two decades, the global health agenda has increasingly embraced the concept of sustainable development in pursuit of solutions at the “systems” level. A central challenge is that the relevant social, economic and biophysical systems that influence human health and well-being operate at difference spatial and temporal scales and scopes of problem solving. Here, we explore three interconnected self-reinforcing systems of central importance to planetary health: the ecology of poverty, the ecology of disease, and systems of health care delivery. We frame these issues to inform how practical interventions can be implemented and studied to create practical systems-level change at the ground level and establish methods for evaluating that change and produce transferable knowledge for scaling or replication.
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40

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

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41

Colaresi, Michael y Jude C. Hays. Spatial and Temporal Interdependence. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.301.

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Time and space are two dimensions that are likely to provide the paths—either singly or in tandem—by which international policy decisions are interdependent. There are several reasons to expect international relations processes to be interdependent across space, time, or both dimensions. Theoretical approaches such as rational expectations models, bureaucratic models of decision-making, and psychological explanations of international phenomena at least implicitly assume—and in many cases explicitly predict—dependence structures within data. One approach that researchers can use to test whether their international processes of interest are marked by dependence across time, space, or both time and space, is to explicitly model and interpret the hypothesized underlying dependence structures. There are two areas of spatial modeling at the research frontier: spatial models with qualitative and limited dependent variables, an co-evolution models of structure and behavior. These models have theoretical implications that are likely to be useful for international relations research. However, a gap remains between the kinds of empirical models demanded by international relations data and theory and the supply of time series and spatial econometric models that are available to those doing applied research. There is a need to develop appropriate models of temporal and spatial interdependence for qualitative and limited dependent variables, and for better models in which outcomes and structures of interdependence are jointly endogenous.
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42

Spatial Temporal Information Systems. Taylor & Francis Inc, 2013.

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43

Stock, O. Spatial and Temporal Reasoning. Springer, 1998.

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44

Ligozat, Gérard. Qualitative Spatial and Temporal Reasoning. Wiley & Sons, Incorporated, John, 2013.

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45

Ligozat, Gérard. Qualitative Spatial and Temporal Reasoning. Wiley & Sons, Incorporated, John, 2013.

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46

Ligozat, Gérard. Qualitative Spatial and Temporal Reasoning. Wiley & Sons, Incorporated, John, 2011.

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47

Ligozat, Gérard. Qualitative Spatial and Temporal Reasoning. Wiley & Sons, Incorporated, John, 2013.

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48

Ligozat, G. Qualitative Spatial and Temporal Reasoning. Wiley & Sons, Incorporated, John, 2013.

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49

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

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50

Crop photosynthesis: Spatial and temporal determinants. New York: Elsevier, 1992.

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