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Abstract

Structural models use statistical techniques that base population changes on changes in one or more explanatory variables. They are invaluable for many planning and policy-making purposes because they explicitly account for the influence of factors such as employment, wage rates, land use, housing, and the transportation system. We discuss two types of structural models in this chapter. Economic-demographic models typically focus on larger geographic areas such as counties, metropolitan areas, and states whereas urban systems models typically focus on smaller areas such as census tracts, block groups, and individual blocks. We also discuss microsimulation models, which focus on projections of individual entities (e.g., persons, households, or vehicles). We close with a discussion of the strengths and weaknesses of structural and microsimulation models.

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References

  • Ahlburg, D. A. (1986). Forecasting regional births: An economic approach. In A. Isserman (Ed.), Population change in the economy: Social science theory and models (pp. 31–51). Boston: Kluwer-Nijhoff.

    Google Scholar 

  • Ahlburg, D. A. (1999). Using economic information and combining to improve forecast accuracy in demography. Rochester: Industrial Relations Center, University of Minnesota.

    Google Scholar 

  • Algers, S., Eliasson, J., & Mattsson, L. G. (2005). Is it time to use activity-based urban transport models? A discussion of planning needs and modelling possibilities. Annals of Regional Science, 39, 767–789.

    Google Scholar 

  • Alonso, W. (1964). Location and land use. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • American Statistical Association. (1977). Report on the conference on economic demographic methods for projecting population. Washington, DC.

    Google Scholar 

  • Anas, A. (1982). Residential location models and urban transportation. New York: Academic.

    Google Scholar 

  • Anas, A., & Arnott, R. J. (1997). Dynamic housing market equilibrium with taste heterogeneity, idiosyncratic perfect foresight and stock conversions. Journal of Housing Economics, 1, 2–32.

    Google Scholar 

  • Anas, A., & Liu, Y. (2007). A regional economy, land use, and transportation model (RELU-TRAN): Formulation, algorithm design, and testing. Journal of Regional Science, 47, 415–455.

    Google Scholar 

  • Andreassen, L. (1993). Demographic forecasting with a dynamic stochastic microsimulation model. Discussion Paper No. 85. Oslo: Central Bureau of Statistics.

    Google Scholar 

  • Anjos, C., & Campos, P. (2010). The role of social networks in the projection of international migration flows: an agent-based approach. Lisbon: Conference of European Statisticians.

    Google Scholar 

  • Ashby, N. J. (2007). Economic freedom and migration flows between U.S. states. Southern Economic Journal, 73, 677–697.

    Google Scholar 

  • Astone, N. M., & McLanahan, S. S. (1994). Family structure, residential mobility and school report: A research note. Demography, 31, 575–584.

    Google Scholar 

  • Ballas, D., Clarke, G. P., & Wiemers, E. (2005). Building a dynamic spatial microsimulation model for Ireland. Population, Space and Place, 11, 157–172.

    Google Scholar 

  • Bargain, O. (Ed.). (2007). Microsimulation in action: Policy analysis in Europe using EUROMOD. Amsterdam, Holland: Elsevier.

    Google Scholar 

  • Bates, D. M., & Watts, D. G. (1988). Nonlinear regression analysis & its applications. New York: Wiley.

    Google Scholar 

  • Batty, M. J. (1994). A chronicle of scientific planning: The Anglo American modeling experience. Journal of the American Planning Association, 60, 7–16.

    Google Scholar 

  • Ben-Akiva, M., & Lerman, S. (1985). Discrete choice analysis: Theory and application to travel demand. Cambridge, MA: MIT Press.

    Google Scholar 

  • Bhat, C. R., Guo, J. Y., Srinivasan, S., & Sivakumar, A. (2003). Guidebook on activity-based travel demand modeling for planners (Vol. Product 4080-P3). Austin: Texas Department of Transportation.

    Google Scholar 

  • Birkin, M. (2013). Challenges for spatial dynamic microsimulation modeling. In R. Tanton & K. L. Edwards (Eds.), Spatial microsimulation: A reference guide for users (pp. 223–248). Dordrecht: Springer.

    Google Scholar 

  • Bolton, R. E. (1985). Regional economic models. Journal of Regional Science, 25, 495–520.

    Google Scholar 

  • Borning, A., Waddell, P., & Forster, R. (2008). UrbanSim: Using simulation to inform public deliberation and decision-making. In H. Chen, L. Brandt, V. Gregg, R. Traunmuller, S. Dawes, E. Hovy, A. Macintosh, & C. A. Larson (Eds.), Digital government: E-government research, case studies, and implementation (pp. 439–463). Berlin: Springer.

    Google Scholar 

  • Borts, G. H., & Stein, J. L. (1964). Economic growth in a free market. New York: Columbia University Press.

    Google Scholar 

  • Bradley, M., Bowman, J. L., & Griesenbeck, B. (2010). SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution. Journal of Choice Modelling, 3, 5–31.

    Google Scholar 

  • Brown, L., & Harding, A. (2002). Social modeling and public policy: Application of microsimulation modeling in Australia. Journal of Artificial Societies and Social Simulation, 5, from http://jasss.soc.surrey.ac.uk/5/4/6.html

  • Buliung, R. N., Kanaroglou, P. S., & Maoh, H. (2005). GIS objects and integrated urban models. In M. E. Lee-Gosselin & S. T. Doherty (Eds.), Integrated land use and transportation modeling behavioral foundations (pp. 207–230). Amsterdam, Holland: Elsevier.

    Google Scholar 

  • Calthorpe Associates. (2012). Urban footprint: Technical summary model version 1.0., from www.calthorpe.com/files/UrbanFootprint%20Technical%20Summary%20-%20July%202012.pdf

  • Campbell, P. R. (1996). Population projections for states by age, sex, race, and Hispanic Origin: 1995 to 2050. PPL 47. Washington, DC: U.S. Census Bureau.

    Google Scholar 

  • Carlino, G. E., & Mills, E. S. (1987). The determinants of county growth. Journal of Regional Science, 27, 39–53.

    Google Scholar 

  • Center for the Continuing Study of the California Economy. (2010). California county projections 2009/10. Palo Alto, CA: Center for the Continuing Study of the California Economy.

    Google Scholar 

  • Clark, D. E., & Hunter, W. J. (1992). The impact of economic opportunity, amenities, and fiscal factors on age-specific migration rates. Journal of Regional Science, 32, 349–365.

    Google Scholar 

  • Clark, D. E., & Murphy, C. A. (1996). Countywide employment and population growth: An analysis of the 1980s. Journal of Regional Science, 36, 235–256.

    Google Scholar 

  • Clark, D. E., Herrin, W. E., Knapp, T. A., & White, N. E. (2003). Migration and implicit amenity markets: Does incomplete compensation matter. Journal of Economic Geography, 3, 289–307.

    Google Scholar 

  • Clarke, G., & Harding, A. (2013). Conclusion and future research directions. In R. Tanton & K. L. Edwards (Eds.), Spatial microsimulation: A reference guide for users (pp. 259–274). Dordrecht: Springer.

    Google Scholar 

  • Cochran, D., & Orcutt, G. H. (1949). Application of least squares regression to relationships containing autocorrelated error terms. Journal of the American Statistical Association, 64, 32–61.

    Google Scholar 

  • Congdon, P. (1992). Multiregional demographic projections in practice: A metropolitan example. Regional Studies, 26, 177–191.

    Google Scholar 

  • Conway, R. S. (2001). The Puget Sound forecasting model: A model of Ron Miller’s hometown. In M. L. Lahr & E. Dietzenbacher (Eds.), Input-output analysis: Frontiers and extensions (pp. 431–450). Basingstoke: Palgrave.

    Google Scholar 

  • Crecine, J. P. (1968). A dynamic model of urban structure. Santa Monica: The Rand Corporation.

    Google Scholar 

  • Cutler, H., & Davies, S. (2007). The impact of sector-specific changes in employment on economic growth, labor market performance, and migration. Journal of Regional Science, 47, 935–963.

    Google Scholar 

  • DaVanzo, J. (1978). Does unemployment affect migration? Evidence from micro-data. Review of Economics and Statistics, 60, 504–514.

    Google Scholar 

  • DaVanzo, J., & Morrison, P. M. (1978). Dynamics of return migration: Descriptive findings from a longitudinal study. Santa Monica: The Rand Corporation.

    Google Scholar 

  • de la Barra, T. (2005). Integrated land use and transport modelling. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Dekkers, G., & Zaidi, A. (2011). The European network for dynamic microsimulation (EURODYM) – A vision and the state of affairs. International Journal of Microsimulation, 4, 100–105.

    Google Scholar 

  • Echenique, M. (1983). The use of planning models in developing countries. In L. Chatterjee & P. Nijkamp (Eds.), Urban and regional policy analysis in developing countries: Some case studies (pp. 115–158). Hampshire: Gower.

    Google Scholar 

  • Edwards, S. (2010). Techniques for managing changes to existing simulation models. International Journal of Microsimulation, 3, 80–89.

    Google Scholar 

  • Evans, A. W. (1990). The assumption of equilibrium in the analysis of migration and interregional differences: A review of some recent research. Journal of Regional Science, 30, 515–531.

    Google Scholar 

  • Fischer, M. M., & Nijkamp, P. (1987). Spatial labor market analysis: Labor and scope. In M. Fischer & P. Nijkamp (Eds.), Regional labor markets: Analytical comparisons and cross-national comparisons (pp. 1–33). Amsterdam, Holland: North Holland.

    Google Scholar 

  • Foot, D. K., & Milne, W. J. (1989). Multiregional estimation of gross internal migration flows. International Regional Science Review, 12, 29–43.

    Google Scholar 

  • Freeman, D. G. (2001). Sources of fluctuations in regional growth. Annals of Regional Science, 35, 249–266.

    Google Scholar 

  • Fuguitt, G. V., & Brown, D. L. (1990). Residential preferences and population redistribution. Demography, 27, 589–600.

    Google Scholar 

  • Gallin, J. H. (2004). Net migration and state labor market dynamics. Journal of Labor Economics, 22, 1–21.

    Google Scholar 

  • Gibbs, R. M. (1994). The information effects of origin on migrants’ job search behavior. Journal of Regional Science, 34, 163–178.

    Google Scholar 

  • Gilbert, N. (2008). Agent-based models. Thousand Oaks: Sage.

    Google Scholar 

  • Gordon, I. (1985). The cyclical relationship between regional migration, employment and unemployment: A time series analysis for Scotland. Scottish Journal of the Political Economy, 32, 135–158.

    Google Scholar 

  • Gouldner, W., Rosenthal, S., & Meredith, J. (1972). Projective land use model-PLUM: Theory and practice. Berkeley: Institute for Transportation and Traffic Engineering, University of California.

    Google Scholar 

  • Graves, P. E. (1980). Migration and climate. Journal of Regional Science, 20, 227–237.

    Google Scholar 

  • Graves, P. E. (1983). Migration with a composite amenity. Journal of Regional Science, 23, 541–546.

    Google Scholar 

  • Graves, P. E., & Knapp, T. A. (1988). Mobility behavior of the elderly. Journal of Urban Economics, 24, 1–8.

    Google Scholar 

  • Graves, P. E., & Linneman, P. D. (1979). Household migration: Theoretical and empirical results. Journal of Urban Economics, 6, 383–404.

    Google Scholar 

  • Graves, P. E., & Mueser, P. R. (1993). The role of equilibrium and disequilibrium in modeling growth and decline. Journal of Regional Science, 33, 69–84.

    Google Scholar 

  • Greenberg, M., Krueckeberg, D. A., & Michaelson, C. (1978). Local population and employment projection techniques. New Brunswick: Rutgers University, Center for Urban Policy and Research.

    Google Scholar 

  • Greenwood, M. J. (1975). Simultaneity bias in migration models: An empirical investigation. Demography, 12, 519–536.

    Google Scholar 

  • Greenwood, M. J. (1981). Migration and economic growth in the United States: National, regional and metropolitan perspectives. New York: Academic.

    Google Scholar 

  • Greenwood, M. J. (1985). Human migration: Theory, models, and empirical studies. Journal of Regional Science, 25, 521–544.

    Google Scholar 

  • Greenwood, M. J. (1997). Internal migration in developed countries. In M. Rosenzweig & O. Stark (Eds.), Handbook of population and family economics (pp. 647–720). Amsterdam, Holland: Elsevier.

    Google Scholar 

  • Greenwood, M. J., & Hunt, G. L. (1984). Migration and interregional employment distribution in the United States. American Economic Review, 74, 957–969.

    Google Scholar 

  • Greenwood, M. J., & Hunt, G. L. (1989). Jobs versus amenities in the analysis of metropolitan migration. Journal of Urban Economics, 25, 1–16.

    Google Scholar 

  • Greenwood, M. J., & Hunt, G. L. (1991). Forecasting state and local population growth with limited data: The use of employment migration relationships and trends in vital rates. Environment and Planning A, 23, 987–1005.

    Google Scholar 

  • Greenwood, M. J., Hunt, G. L., & McDowell, J. M. (1986). Migration and employment change: Empirical evidence on the spatial and temporal dimensions of the linkages. Journal of Regional Science, 26, 223–234.

    Google Scholar 

  • Greenwood, M. J., Hunt, G. L., Rickman, D. S., & Treyz, G. I. (1991). Migration, regional equilibrium, and the estimation of compensating differentials. American Economic Review, 81, 1382–1390.

    Google Scholar 

  • Hamalainen, K., & Bockerman, P. (2004). Regional labor market dynamics, housing, and migration. Journal of Regional Science, 44, 543–568.

    Google Scholar 

  • Harding, A. (2007). Challenges and opportunities of dynamic microsimulation modelling. Paper presented at the 1st general conference of the International Microsimulation, Vienna.

    Google Scholar 

  • Harding, A., & Gupta, A. (2007). Modeling our future: Population ageing, social security and taxation. Amsterdam, Holland: Elsevier.

    Google Scholar 

  • Harding, A., Keegan, M., & Kelly, S. (2010). Validating a dynamic population microsimulation model: Recent experience in Australia. International Journal of Microsimulation, 3, 46–64.

    Google Scholar 

  • Harris, B. (1965). New tools for planning. Journal of the American Institute for Planners, 30, 90–95.

    Google Scholar 

  • Harris, B. (1994). The real issues concerning Lee’s requiem. Journal of the American Planning Association, 60, 31–34.

    Google Scholar 

  • Haurin, D. R., & Haurin, R. J. (1988). Net migration, unemployment and the business cycle. Journal of Regional Science, 28, 239–254.

    Google Scholar 

  • Henderson, J. V. (1982). Evaluating consumer amenities and interregional welfare differences. Journal of Urban Economics, 11, 32–59.

    Google Scholar 

  • Heppenstall, A. J., Crooks, A. T., See, L. M., & Batty, M. (Eds.). (2012). Agent-based models of geographical systems. Dordrecht: Springer.

    Google Scholar 

  • Hunt, G. L. (1993). Equilibrium and disequilibrium in migration modelling. Regional Studies, 27, 341–349.

    Google Scholar 

  • Hunt, J. D., & Abraham, J. E. (2005). Design and implementation of PECAS: A generalized system for allocating economic production, exchange, and consumption categories. In M. E. Lee-Gosselin & S. T. Doherty (Eds.), Integrated land use and transportation modeling behavioral foundations (pp. 253–274). Amsterdam, Holland: Elsevier.

    Google Scholar 

  • Hunt, J. D., Kriger, D. S., & Miller, E. J. (2005). Current operational urban land use–transport modelling frameworks: A review. Transport Reviews, 25, 329–376.

    Google Scholar 

  • Isserman, A. M. (1985). Forecasting regional population change with endogenously determined birth and migration rates. Environment and Planning A, 17, 25–45.

    Google Scholar 

  • Isserman, A. M., Plane, D. A., Rogerson, P. A., & Beaumont, P. M. (1985). Forecasting interstate migration with limited data: A demographic-economic approach. Journal of the American Statistical Association, 80, 277–285.

    Google Scholar 

  • Jin, L., & Fricker, J. D. (2008). Development of integrated land-use transportation model for Indiana (Vol. Publication FHWA/IN/JTRP-2008/15). West Lafayette: Joint Transportation Research Program, Indiana Department of Transportation and Purdue University.

    Google Scholar 

  • Johnston, R. A., & McCoy, M. C. (2005). Assessment of integrated land use and transportation models: Final report. Davis: Department of Environmental Science & Policy, University of California, Davis.

    Google Scholar 

  • Joun, R. P., & Conway, R. (1983). Regional economic-demographic forecasting models: A case study of the Washington and Hawaii models. Socio-Economic Planning Sciences, 17, 345–353.

    Google Scholar 

  • Judd, K. L. (1998). Numerical methods in economics. Cambridge, MA: MIT Press.

    Google Scholar 

  • Krieg, R. G., & Bohara, A. K. (1999). A simultaneous probit model of earnings, migration, job change with wage heterogeneity. The Annals of Regional Science, 33, 453–467.

    Google Scholar 

  • Kriesberg, E. M., & Vining, D. R. (1978). On the contribution of outmigration to changes in net migration: A time series analysis of Beale’s cross-sectional results. Annals of Regional Science, 12, 1–11.

    Google Scholar 

  • Krupka, D. J. (2009). Location-specific human capital, location choice, and amenity demand. Journal of Regional Science, 49, 833–854.

    Google Scholar 

  • Lee, D. B. (1973). Requiem for large-scale models. Journal of the American Institute of Planners, 39, 163–178.

    Google Scholar 

  • Levernier, W., & Cushing, B. (1994). A new look at the determinants of intrametropolitan distribution of population and employment. Urban Studies, 31, 1391–1405.

    Google Scholar 

  • Long, L. H., & Hansen, K. A. (1979). Reasons for interstate migration. Washington, DC: U.S. Census Bureau.

    Google Scholar 

  • Lowry, I. S. (1964). A model of metropolis. Santa Monica: The Rand Corporation.

    Google Scholar 

  • Mainzer, K., & Chua, L. (2012). The universe as automaton: From simplicity and symmetry to complexity. Dordrecht: Springer.

    Google Scholar 

  • Malenfant, E. C., Martel, L., & Lebel, A. (2011). An overview of DEMOSIM: Statistics Canada’s microsimulation model for population projections. In N. Hoque & D. A. Swanson (Eds.), Opportunities and challenges for applied demography in the 21st century (pp. 371–384). Dordrecht: Springer.

    Google Scholar 

  • Massey, D. S., Alarcon, R., Durand, J., & Gonzalez, H. (1987). Return to Aztlan: The social process of international migration from western Mexico. Berkeley: University of California Press.

    Google Scholar 

  • Mathur, V. K., & Song, F. M. (2000). A labor market based theory of regional economic development. The Annals of Regional Science, 34, 131–145.

    Google Scholar 

  • McFadden, D. (1974). The measurement of urban travel demand. Journal of Public Economics, 3, 303–328.

    Google Scholar 

  • McNalley, M. G. (2007). The four step model. In D. A. Hensher & K. J. Button (Eds.), Handbook of transportation modeling (2nd ed., pp. 35–52). Amsterdam, Holland: Elsevier.

    Google Scholar 

  • Messen, D., & Joshi, H. (2010). Demographic microsimulation and long-range regional population forecasting, from http://www.h-gac.com/community/socioeconomic/documents/demographic_microsimulation_and_long-range_population_forecasting.pdf

  • Meuser, P. R., & White, M. J. (1989). Explaining the association between rates of in-migration and out-migration. Papers of the Regional Science Association, 67, 121–134.

    Google Scholar 

  • Meyer, M. D., & Miller, E. J. (2001). Urban transportation planning: A decision-oriented approach. Boston: McGraw-Hill.

    Google Scholar 

  • Miller, R. E., & Blair, P. D. (2009). Input-output analysis: Foundations and extensions. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Mills, E. S., & Lubuele, L. S. (1995). Projecting growth in metropolitan areas. Journal of Urban Economics, 37, 344–360.

    Google Scholar 

  • Mincer, J. (1978). Family migration decisions. Journal of the Political Economy, 86, 749–773.

    Google Scholar 

  • Mitton, L., Sutherland, H., & Weeks, M. (Eds.). (2000). Microsimulation modeling for policy analysis: Challenges and innovations. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Moeckel, R., Schwarze, B., Spiekermann, K., & Wegener, M. (2007). Microsimulation for integrated urban modelling. Paper presented at the 10th international conference of Computers in Urban Planning and Urban Management, Iguassu Falls.

    Google Scholar 

  • Morciano, M., Hancock, R., & Pudney, S. (2012). Disability costs and equivalence scales in the older population. ISER Working Paper Series, 2012-09. Colchester: Institute for Social and Economic Research, University of Essex.

    Google Scholar 

  • Muller, K., & Axhausen, K. W. (2011). Population synthesis for microsimulation: State of the art. Paper presented at the 90th Transportation Research Board Meeting, Washington, DC.

    Google Scholar 

  • Murdock, S. H., & Ellis, D. R. (1991). Applied demography: An introduction of basic concepts, methods, and data. Boulder: Westview.

    Google Scholar 

  • Murdock, S. H., Leistritz, F. L., Hamm, R. R., Hwang, S. S., & Parpia, B. (1984). An assessment of the accuracy of a regional economic-demographic projection model. Demography, 21, 383–404.

    Google Scholar 

  • Murdock, S. H., Jones, L. L., Hamm, R. R., & Leistritz, F. L. (1987). The Texas assessment modeling system (TAMS): Users guide. College Station: Texas Agricultural Experiment Station, Texas A&M University.

    Google Scholar 

  • Muth, R. F. (1971). Migration: Chicken or egg. Southern Economic Journal, 37, 295–306.

    Google Scholar 

  • O’Neill, B. C., Balk, D., Brickman, M., & Ezra, M. (2001). A guide to global population projections. Demographic Research, 4, 203–388.

    Google Scholar 

  • Orcutt, G. H., Caldwell, S., & Wertheimer, R. F. (1976). Policy exploration through microanalytic simulation. Washington, DC: The Urban Institute.

    Google Scholar 

  • Pagliara, F., Preston, J., & Simmons, D. (2010). Residential location choice. Dordrecht: Springer.

    Google Scholar 

  • Panis, C., & Lillard, L. (1999). Near term model development: Part II. Santa Monica: The Rand Corporation.

    Google Scholar 

  • Partridge, M. D., & Rickman, D. S. (2003). The waxing and waning of regional economies: The chicken–egg question of jobs versus people. Journal of Urban Economics, 76, 76–97.

    Google Scholar 

  • Partridge, M. D., & Rickman, D. S. (2006). An SVAR model of fluctuations in U.S. migration flows and state labor market dynamics. Southern Economic Journal, 72, 958–980.

    Google Scholar 

  • Paxton, P. M., Hipp, J. R., & Marquart-Pyatt, S. (2011). Nonrecursive models: Endogeneity, reciprocal relationships, and feedback loops. Los Angeles: Sage.

    Google Scholar 

  • Pettit, C. J., & Wyatt, R. (2009). A planning support system toolkit approach for formulating and evaluating land-use change scenarios. In S. Geertman & J. Stillwell (Eds.), Planning support systems: Best practice and new methods (pp. 69–90). Dordrecht: Springer.

    Google Scholar 

  • Pinto, N. N., & Antunes, A. P. (2007). Modeling and urban systems: An introduction. Architecture, City and Environment, 2, 471–485.

    Google Scholar 

  • Plane, D. A. (1989). Population migration and economic restructuring in the United States. International Regional Science Review, 12, 263–280.

    Google Scholar 

  • Plane, D. A. (1993). Demographic influences on migration. Demography, 27, 375–383.

    Google Scholar 

  • Plane, D. A., Rogerson, P. A., & Rosen, A. (1984). The cross-regional variation of in-migration and out-migration. Geographical Analysis, 16, 162–175.

    Google Scholar 

  • Poot, J., Waldorf, B., & van Wissen, L. (Eds.). (2009). Migration and human capital. Cheltenham, UK: Edward Elgar.

    Google Scholar 

  • Pozoukidou, G. (2007). Facilitating land use forecasting in planning agencies. In A. G. Kungolos, C. A. Brebbia, & E. Beriatos (Eds.), Sustainable development III (Vol. I, pp. 67–80). Southampton: WIT Press.

    Google Scholar 

  • Prastacos, P. (1986). An Integrated land use and transportation model for the San Francisco region: Empirical results and estimation. Environment and Planning A, 18, 511–528.

    Google Scholar 

  • Putman, S. H. (1991). Integrated urban models: II. London: Pion Limited.

    Google Scholar 

  • Putman, S. H. (2010). DRAM residential location and land use model: 40 years of development and application. In F. Pagliara, J. Preston, & D. Simmons (Eds.), Residential location choice: Models and applications (pp. 61–76). Dordrecht: Springer.

    Google Scholar 

  • Radu, D. (2008). Social interactions in economic models of migration: A review and appraisal. Journal of Ethnic and Migration Studies, 34, 531–548.

    Google Scholar 

  • Rahman, A., Harding, A., Tanton, R., & Liu, S. (2010). Methodological issues in spatial microsimulation modelling for small area estimation. International Journal of Microsimulation, 3, 3–22.

    Google Scholar 

  • Ravenstein, E. G. (1889). The laws of migration. The Journal of the Royal Statistical Society, 10, 241–301.

    Google Scholar 

  • Reeve, T. R., & Perlich, P. S. (1995). State of Utah demographic and economic projection modeling system. Salt Lake City: Governor’s Office of Planning and Budget.

    Google Scholar 

  • Rickman, D. S., & Rickman, S. D. (2011). Population growth in high-amenity nonmetropolitan areas: What’s the prognosis? Journal of Regional Science, 51, 863–879.

    Google Scholar 

  • Rogers, A. (1967). A regression analysis of interregional migration in California. Review of Economics and Statistics, 49, 262–267.

    Google Scholar 

  • Rogers, A., & Williams, P. (1986). Multistate demoeconomic modeling and projection. In A. Isserman (Ed.), Population change in the economy: Social science theory and methods (pp. 177–202). Boston: Kluwer-Nijhoff.

    Google Scholar 

  • Salvini, P., & Miller, E. J. (2005). ILUTE: An operational prototype of a comprehensive microsimulation model of urban systems. Networks and Spatial Economics, 5, 217–234.

    Google Scholar 

  • Sanderson, W. C. (1999). Knowledge can improve forecasts: A review of selected socioeconomic population projection models. In W. Lutz, J. Vaupel, & D. Ahlburg (Eds.), Frontiers of population forecasting (pp. 88–117). New York: The Population Council (A supplement to Population and Development Review, 24).

    Google Scholar 

  • Schachter, J., & Althaus, P. G. (1989). An equilibrium model of gross migration. Journal of Regional Science, 29, 134–159.

    Google Scholar 

  • Schmidt, R., Barr, C. F., & Swanson, D. A. (1997). Socioeconomic impacts of the proposed federal gaming tax. International Journal of Public Administration, 20, 1675–1698.

    Google Scholar 

  • Simmonds, D. C. (1999). The design of the DELTA land-use modeling package. Environment and Planning B, 26, 665–684.

    Google Scholar 

  • Simmonds, D. C. (2010). The DELTA residential location model. In F. Pagliara, J. Preston, & D. Simmons (Eds.), Residential location choice: Models and applications (pp. 77–97). Dordrecht: Springer.

    Google Scholar 

  • Simmonds, D. C., Waddell, P., & Wegener, M. (2011). Beyond equilibrium: Advances in urban modelling. Paper presented at the 12th international conference on Computers in Urban Planning and Urban Management, Lake Louise.

    Google Scholar 

  • Sjaastad, L. A. (1960). The relationship between income and migration in the United States. Papers and Proceedings of the Regional Science Association, 6, 37–64.

    Google Scholar 

  • Sjaastad, L. A. (1962). The costs and returns of human migration. Journal of Political Economy, 70, 80–93.

    Google Scholar 

  • Smirnov, O. A. (2010). Modeling spatial discrete choice. Regional Science and Urban Economics, 40, 292–298.

    Google Scholar 

  • Southworth, F. (1995). A technical review of urban land use-transportation models as tools for evaluating vehicle travel reduction strategies. Oak Ridge: Oak Ridge National Laboratory.

    Google Scholar 

  • Steinnes, D. N. (1982). Do ‘people follow jobs’ or do ‘jobs follow people’? A causality issue in urban economics. Urban Studies, 19, 187–192.

    Google Scholar 

  • Stevens, D., Dragicevic, S., & Rothley, K. (2007). iCity: A GIS–CA modelling tool for urban planning and decision making. Environmental Modelling & Software, 22, 761–773.

    Google Scholar 

  • Stillwell, J. (2005). Inter-regional migration modelling: A review and assessment. Paper presented at the 45th congress of the European Regional Science Association, Amsterdam, Holland.

    Google Scholar 

  • Stock, J. H., & Watson, M. W. (2010). Introduction to econometrics (3rd ed.). Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Stone, L. O. (1971). On the correlation between metropolitan area in- and out-migration by occupation. Journal of the American Statistical Association, 66, 693–701.

    Google Scholar 

  • Sui, D. Z. (1998). GIS-based urban modelling: Practices, problems, and prospects. International Journal of Geographical Information Science, 12, 651–671.

    Google Scholar 

  • Tanton, R., & Edwards, K. (Eds.). (2013). Spatial microsimulation: A reference guide for users. Dordrecht: Springer.

    Google Scholar 

  • Tayman, J. (1996). Forecasting, growth management, and public policy decision making. Population Research and Policy Review, 15, 491–508.

    Google Scholar 

  • Timmermans, H. (2003). The saga of integrated land use-transport modeling: How many more dreams before we wake up? Paper presented at the 10th international conference on Travel Behaviour Research, Lucerne.

    Google Scholar 

  • Toossi, M. (2012). Labor force projections to 2020: A more slowly growing workforce. Monthly Labor Review, 135. Washington, DC: U.S. Bureau of Labor Statistics.

    Google Scholar 

  • Transportation Research Board. (2007). Metropolitan travel forecasting: Current practice and future direction. Special Report 288. Washington, DC: Transportation Research Board.

    Google Scholar 

  • Treyz, G. I. (1993). Regional economic modeling: A systematic approach to economic forecasting and policy analysis. Boston: Kluwer.

    Google Scholar 

  • Treyz, G. I. (1995). Policy analysis applications of REMI economic forecasting and simulation models. International Journal of Public Administration, 18, 13–42.

    Google Scholar 

  • Treyz, G. I., Rickman, D. S., & Shao, G. (1992). The REMI economic-demographic forecasting and simulation model. International Regional Science Review, 14, 221–253.

    Google Scholar 

  • Treyz, G. I., Rickman, D. S., Hunt, G. L., & Greenwood, M. J. (1993). The dynamics of U.S. internal migration. Review of Economics and Statistics, 75, 209–214.

    Google Scholar 

  • Treyz, F., & Treyz, G. I. (2004). The evaluation of programs aimed at local and regional development: Methodology and twenty years experience using REMI Policy Insight, from http://www.keepeek.com/Digital-Asset-Management/oecd/urban-rural-and-regional-development/evaluating-local-economic-and-employment-development_9789264017092-en

  • Troitzsch, K. G., Mueller, U., Gilbert, N., & Doran, J. E. (2010). Social science microsimulation. Dordrecht: Springer.

    Google Scholar 

  • U.S. Bureau of Economic Analysis. (1995). BEA regional projections to 2045. Volume I: States. Washington, DC: US Government Printing Office.

    Google Scholar 

  • van der Werf, M., van Sonsbeek, J. M., & Gradus, R. H. (2007). The SADNAP modelmicro simulations on the effects of ageing-related policy measures: The Social Affairs Department of the Netherlands ageing and pensions model, from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1010305

  • van Sonsbeek, J. M. (2011). Micro simulations on the effects of ageing-related policy measures: The Social Affairs Department of the Netherlands ageing and pensions model. International Journal of Microsimulation, 4, 72–99.

    Google Scholar 

  • Veldhuisen, J., Timmermans, H., & Kapoen, L. (2000). RAMBLAS: A regional planning model based on the microsimulation of daily activity travel patterns. Environment and Planning A, 32, 427–443.

    Google Scholar 

  • Vijverberg, W. P. M. (1993). Labor market performance as a determinant of migration. Economica, 60, 143–160.

    Google Scholar 

  • Virginia Department of Transportation. (2009). Implementing activity-based models in Virginia. VTM Research Paper 09-01. Richmond: Virginia Department of Transportation.

    Google Scholar 

  • Vovsha, P., Petersen, E., & Donnelly, R. (2002). Microsimulation in travel demand modeling: Lessons learned from the New York best practice model. Transportation Research Record, 1805, 68–77.

    Google Scholar 

  • Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation and environmental planning. Journal of the American Planning Association, 68, 297–314.

    Google Scholar 

  • Waddell, P. (2011). Integrated land use and transportation planning and modelling: Addressing challenges in research and practice. Transport Reviews, 31, 209–229.

    Google Scholar 

  • Waddell, P., & Ulfarsson, G. F. (2004). Introduction to urban simulation: Design and development of operational models. In P. Stopher, K. J. Button, K. E. Haynes, & D. A. Hensher (Eds.), Handbook in transport, Volume 5: Transport geography and spatial systems (pp. 203–236). Oxford: Pergamon.

    Google Scholar 

  • Waddell, P., Borning, A., Noth, M., Freier, N., Becke, M., & Ulfarsson, G. F. (2003). Microsimulation of urban development and location choices: Design and implementation of UrbanSim. Networks and Spatial Economics, 3, 43–67.

    Google Scholar 

  • Walker, D., & Daniels, T. (2011). The planners guide to CommunityViz: The essential tool for a new generation of planning. Chicago: American Planning Association’s Planners Press.

    Google Scholar 

  • Wegener, M. (2004). Overview of land use transport models. In D. A. Hensher & P. R. Stopher (Eds.), Handbook of transport geography and spatial systems (pp. 127–146). Kidlington: Pergamon/Elsevier Science.

    Google Scholar 

  • Williamson, P. (2013). An evaluation of two synthetic small area microdata simulation methodologies: Synthetic reconstruction and combinatorial optimisation. In R. Tanton & K. L. Edwards (Eds.), Spatial microsimulation: A reference guide for users (pp. 19–48). Dordrecht: Springer.

    Google Scholar 

  • Wilson, A. G. (1974). Urban and regional models in geography. London: Wiley.

    Google Scholar 

  • Wingo, L. (1961). Transportation and urban land. Baltimore: The John Hopkins University Press.

    Google Scholar 

  • Wu, B., & Birken, M. (2013). Moses: A dynamic spatial microsimulation model for demographic planning. In R. Tanton & K. L. Edwards (Eds.), Spatial microsimulation: A reference guide for users (pp. 171–194). Dordrecht: Springer.

    Google Scholar 

  • Yano, K., Nakaya, T., Fotheringham, A. S., Openshaw, S., & Ishikawa, Y. (2003). A comparison of migration behaviour in Japan and Britain using spatial interaction models. International Journal of Population Geography, 9(5), 419–431.

    Google Scholar 

  • Zandy, M. M., & Posar, Z. (2010). U.S. macro model system, from http://www.economy.com/home/products/samples/macromodel.pdf

  • Zhang, W. B. (2008). A multi-region economic growth model with migration, housing and regional amenity. Analele Stiintifice ale Universitatii “Alexandru Ioan Cuza” din Iasi, 55, 322–350.

    Google Scholar 

  • Zhou, B., Kockelman, K. M., & Lemp, J. D. (2009). Transportation and land use policy analysis using integrated transport and gravity-based land use models. Transportation Research Record, 2133, 123–132.

    Google Scholar 

  • Zinn, S., Gampe, J., Himmelspach, J., & Uhrmacher, A. M. (2010). A DEVS model for demographic microsimulation. Paper presented at the Spring Simulation Multiconference 2010, Orlando.

    Google Scholar 

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Smith, S.K., Tayman, J., Swanson, D.A. (2013). Structural and Microsimulation Models. In: A Practitioner's Guide to State and Local Population Projections. The Springer Series on Demographic Methods and Population Analysis, vol 37. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7551-0_9

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