Fifty Years of Urban Modelling : Macro Statics to Micro Dynamics , in

This chapter presents both a chronological and conceptual history of urban land use-transportation models movement in the context of current developments. Such models –‘urban models’ for short – first appeared in the 1950s in North America and were made possible by two interrelated forces: the development of digital computing from which large-scale simulation emanated, and policy imperatives for testing the effects of large-scale public investments on cities. Essentially, urban models are still pragmatically motivated tools for testing the impact of changes in the locations of land use and transportation on dense and usually large urban agglomerations. Planning and policy determine their rationale although their foundations are built on theoretical ideas which go back to the roots of modern social science and the influence of physics and mathematics from the time of the Enlightenment. During the brief but turbulent years since this field has developed, there have been substantial shifts in viewpoint. Indeed even the paradigms that condition what attributes of the city are to be modeled, and the way such modeling takes place, have changed. We will chart these changes, beginning with a set of intersecting time lines focusing on theo retical origins and practical applications. We will show how urban models were first conceived in aggregative, static terms when the concern was for simulating the way cities appeared at a cross-section in time. This aggregative, static conception of urban structure has slowly given way to one where much more detailed disaggregate activities appear more important and where dynamics rather than statics is the focus. This reflects as much our abilities t o s imulate m ore e laborate c omputational s tructures a nd c ollect better data as any grand theoretical revision of the way we look at the city, although such a revision is now under way As such, this chapter sets a con text for many of the current advances in urban modeling reported elsewhere in this book. 1 Historical Antecedents Wassily Leontieff is best known as the Russian economist who invented the inputoutput model of the economy in the 1920s before he emigrated to the United States where he subsequently spent his life develop ing the idea. In c ontemporary parlance, an input-output model can best be viewed as a large spreadsheet whose

[1]  Michael Batty,et al.  Progress, Success, and Failure in Urban Modelling , 1979 .

[2]  Douglass B. Lee Requiem for Large-Scale Models , 1973 .

[3]  藤田 昌久,et al.  Urban economic theory : land use and city size , 1989 .

[4]  Graham Clarke,et al.  Microsimulation for urban and regional policy analysis , 1996 .

[5]  Michael Batty,et al.  Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals , 2007 .

[6]  P. Waddell UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning , 2002 .

[7]  J. Landis,et al.  The Second Generation of the California Urban Futures Model. Part 1: Model Logic and Theory , 1998 .

[8]  M Echenique AN INTEGRATED LAND USE AND TRANSPORT MODEL , 1977 .

[9]  Helen Couclelis,et al.  Cellular Worlds: A Framework for Modeling Micro—Macro Dynamics , 1985 .

[10]  Keith C. Clarke,et al.  A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area , 1997 .

[11]  Peter Nijkamp,et al.  Interaction, evolution, and chaos in space , 1992 .

[12]  W. Tobler A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[13]  J. Royce Ginn,et al.  Front matter, The Detroit Prototype of the NBER Urban Simulation Model , 1973 .

[14]  Frank Schweitzer,et al.  Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences , 2003 .

[15]  B. Harris URBAN SIMULATION MODELS IN REGIONAL SCIENCE , 1985 .

[16]  MICHAEL BATTY,et al.  Modelling Cities as Dynamic Systems , 1971, Nature.

[17]  Alan Wilson,et al.  Catastrophe Theory and Bifurcation : Applications to Urban and Regional Systems , 1980 .

[18]  F. Stuart Chapin,et al.  A probabilistic model for residential growth , 1968 .

[19]  D. Dendrinos,et al.  Urban evolution : studies in the mathematical ecology of cities , 1985 .

[20]  A. Venables,et al.  The Spatial Economy: Cities, Regions, and International Trade , 1999 .

[21]  W. Alonso Location And Land Use , 1964 .

[22]  Guy Engelen,et al.  Cellular Automata as the Basis of Integrated Dynamic Regional Modelling , 1997 .

[23]  M. Wegener Operational Urban Models State of the Art , 1994 .

[24]  Alan Wilson,et al.  Entropy in urban and regional modelling , 1972, Handbook on Entropy, Complexity and Spatial Dynamics.

[25]  M. Ben-Akiva,et al.  Discrete choice analysis , 1989 .

[26]  J. Borges,et al.  Cities and Regions as Self-organizing Systems: Models of Complexity , 2003 .

[27]  R. Thom Structural stability and morphogenesis , 1977, Pattern Recognition.

[28]  John R Hamburg,et al.  AN OPPORTUNITY-ACCESSIEILITY MODEL FOR ALLOCATING REGIONAL GROWTH , 1965 .