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Books on the topic 'Spatial Modeler'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Spatial transportation modeling. Beverly Hills: Sage Publications, 1985.

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2

Spatial statistics and modeling. New York: Springer, 2010.

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3

LeSage, James P. Introduction to spatial econometrics. Boca Raton, FL: CRC Press, 2009.

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4

1940-, Radcliffe John, ed. Spatial deterministic epidemics. Providence, R.I: American Mathematical Society, 2003.

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5

Jonathan, Raper, ed. Spatial evolutionary modeling. New York: Oxford University Press, 2000.

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6

Urban spatial traffic patterns. London: Pion, 1987.

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7

Krugman, Paul R. A dynamic spatial model. Cambridge, MA: National Bureau of Economic Research, 1992.

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8

Jalan, Jyotsna. Spatial poverty traps? Washington, DC: World Bank, Development Research Group, 1997.

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9

Wolfgang, Keller. The origins of spatial interaction. Cambridge, Mass: National Bureau of Economic Research, 2003.

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10

Michael, Sonis, ed. Chaos and socio-spatial dynamics. New York: Springer-Verlag, 1990.

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11

Haynes, Kingsley E. Gravity and spatial interaction models. Beverly Hills, Calif: Sage, 1985.

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12

Weber, Shlomo. On hierarchical spatial competition. Toronto, Ont: Department of Economics, York University, 1989.

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13

Karssenberg, Derek. Building dynamic spatial environmental models. Amsterdam: Koninklijk Nederlands Aardrijkskundig Genootschap, 2002.

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14

W, Perry George L., ed. Spatial simulation: Exploring pattern and process. Chichester, West Sussex, UK: John Wiley & Sons Inc., 2013.

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15

Laan, L. van der. Spatial labour markets in the Netherlands. Delft: Eburon, 1991.

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16

Spatial econometrics: Methods and models. Dordrecht: Kluwer Academic Publishers, 1988.

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17

Scott, Kelso J. A., Volchenkov Dimitri, and SpringerLink (Online service), eds. Mathematical Analysis of Urban Spatial Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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18

Cliff, A. D. Spatial aspects of influenza epidemics. London, England: Pion Ltd., 1986.

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19

Peter, Haggett, and Ord J. K, eds. Spatial aspects of influenza epidemics. London: Pion Ltd., 1986.

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20

Brase, Terry A. Fundamentals of agricultural spatial analysis. Clifton Park, NJ: Thomson Delmar Learning, 2005.

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21

Michael, Bevers, ed. Spatial optimization for managed ecosystems. New York: Columbia University Press, 1998.

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22

Beckmann, Martin. Spatial economics: Density, potential, and flow. Amsterdam: North-Holland, 1985.

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23

Aura, Reggiani, ed. The economics of complex spatial systems. New York: Elsevier Science B.V., 1998.

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24

Wal F. J. van Lierop. Spatial interaction modelling and residential choice analysis. Aldershot, Hants, England: Gower, 1986.

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25

Spatial interaction modelling and residential choice analysis. Aldershot: Gower, 1986.

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26

E, Smith Tony, ed. Gravity models of spatial interaction behavior. Berlin: Springer-Verlag, 1995.

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27

Local models for spatial analysis. 2nd ed. Boca Rataon, Fla: CRC Press, 2011.

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28

Bosch, Frank van den. The velocity of spatial population expansion. [Amsterdam, Netherlands]: F. van den Bosch, 1990.

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29

Hellerstein, Judith K. Spatial mismatch or racial mismatch? Cambridge, Mass: National Bureau of Economic Research, 2007.

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30

P, Carlin Bradley, and Gelfand Alan E. 1945-, eds. Hierarchical modeling and analysis for spatial data. Boca Raton: Chapman & Hall, 2004.

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31

Dunant, Halim, and Fleming Andrew J, eds. Spatial control of vibration: Theory and experiments. River Edge, N.J: World Scientific, 2003.

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32

Janis, Michael J. Multivariate spatial interpolation of monthly precipitation. Elmer, N.J: C.W. Thornthwaite Associates, Laboratory of Climatology, 1995.

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33

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

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34

Desmet, Klaus. Spatial growth and industry age. Cambridge, Mass: National Bureau of Economic Research, 2007.

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35

Fotheringham, A. Stewart. Spatial interaction models: Formulations and applications. Dordrecht: Kluwer Academic Publishers, 1989.

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36

Wasmer, Etienne. Equilibrium search unemployment with explicit spatial frictions. Bonn, Germany: IZA, 2005.

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37

P, Ancot J., ed. Eléments d'analyse économique spatiale. [Paris]: Editions régionales européennes, 1985.

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38

Copeland, Brian Richard. Trade, spatial separation, and the environment. Cambridge, MA: National Bureau of Economic Research, 1995.

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39

The random spatial economy and its evolution. Aldershot, Hants, England: Ashgate Pub., 1998.

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40

Stephen, Cantrell Robert, Cosner Chris, and Ruan Shigui 1963-, eds. Spatial ecology. Boca Raton: Chapman & Hall/CRC, 2010.

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41

Cosner, Chris, Stephen Cantrell, and Shigui Ruan. Spatial Ecology. Taylor & Francis Group, 2009.

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42

Stephen, Cantrell Robert, Cosner Chris, and Ruan Shigui 1963-, eds. Spatial ecology. Boca Raton: CRC Press, 2010.

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43

Colaresi, Michael, and 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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44

Krzanowski, Roman M., and Jonathan Raper. Spatial Evolutionary Modeling (Spatial Information Series). Oxford University Press, USA, 2001.

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45

Franzese, Robert J., and Jude C. Hays. Empirical Models of Spatial Inter‐Dependence. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0025.

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This article discusses the role of ‘spatial interdependence’ between units of analysis by using a symmetric weighting matrix for the units of observation whose elements reflect the relative connectivity between unit i and unit j. It starts by addressing spatial interdependence in political science. There are two workhorse regression models in empirical spatial analysis: spatial lag and spatial error models. The article then addresses OLS estimation and specification testing under the null hypothesis of no spatial dependence. It turns to the topic of assessing spatial lag models, and a discussion of spatial error models. Moreover, it reports the calculation of spatial multipliers. Furthermore, it presents several newer applications of spatial techniques in empirical political science research: SAR models with multiple lags, SAR models for binary dependent variables, and spatio-temporal autoregressive (STAR) models for panel data.

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46

Guyon, Xavier, and Carlo Gaetan. Spatial Statistics and Modeling. Springer, 2009.

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47

Guyon, Xavier, and Carlo Gaetan. Spatial Statistics and Modeling. Springer, 2012.

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48

Schmidt, Alexandra, Jennifer Hoeting, João Batista M. Pereira, and Pedro Paulo Vieira. Mapping malaria in the Amazon rain forest: A spatio-temporal mixture model. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.5.

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This article focuses on the use of a spatio-temporal mixture model for mapping malaria in the Amazon rain forest. The spatio-temporal model was developed to study malaria outbreaks over a four year period in the state of Amazonas, Brazil. The goal is to predict malaria counts for unobserved municipalities and future time periods with the aid of a free-form spatial covariance structure and a methodology that allows temporal prediction and spatial interpolation for outbreaks of malaria over time. The proposed structure is unique in that it is not a distance- or neighbourhood-based covariance model. Instead, spatial correlation is allowed among all locations to be estimated freely. To model the temporal correlation between observations, a Bayesian dynamic linear model is incorporated into one level of the spatio-temporal mixture model. The model also provides sensible ways of malaria mapping for municipalities which were not observed.

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49

Exploring Spatial Scale. John Wiley & Sons Inc, 2014.

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50

Peletier, L. A., and W. C. Troy. Spatial Patterns. Springer, 2012.

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