1006-3862 2012 02-0116 - 09 UrbanSim 100084 UrbanSim GIS / UrbanSim UrbanSim F291. 1 A office building 2010 31. 2% 32. 8% 25. 9% 14. 4% Location location and location / UrbanSim 70973065 4D-GIS 116
UrbanSim 1 UrbanSim 1. 1 2010 Waddell 2002 1 / Amstrong 1994 Benjamin Sirmans 1996 Cervero Duncan 2002 Kim Zhang 2005 2004 2005 2010 1 1. 2 GIS UrbanSim CUF MEPLAN / TRANUS DREAM / EMPAL METROSIM / RELU 2007 UrbanSim UrbanSim Waddell 117
UrbanSim UrbanSim 2 2. 1 2. 1. 1 UrbanSim UrbanSim 1 UrbanSim 1 1 2 2 Urbansim CubeLand 3 / UrbanSim 4 3 / UrbanSim 1 64 3 UrbanSim UrbanSim 2 1 UrbanSim 1 2 3 3 64 4 / 2 UrbanSim 2. 1. 2 UrbanSim 18 1 2 3 UrbanSim 18 2 118
UrbanSim 2 Ⅰ Ⅱ Ⅰ Ⅱ 2. 2 UrbanSim 12001 22004 3 2004 3 UrbanSim POTENTIAL ACC D_SUBWAY D_MALL NS_DUMMY O_DUMMY / km 2 64 167. 28 204. 31 min 64 40. 85 7. 03 km 64 4. 11 3. 46 km 64 3. 68 3. 26 / m 2 64 784 1135 1-2- 64 0. 63 0. 49 1-0- 64 0. 55 0. 50 1 3 D _MALL 3. 1 NS_DUMMY 3. 1. 1 2 Utility = β 0 + β 1 + lnpotentlal + β 2 lnacc + β 3 lnd_subway + β 4 lnd_mall + β 5 + β 6 NS_DUMMY + β 7 O_DUMMY 2 UrbanSim 1 Utility = β 0 + β 1 lnpotentlal + β 2 lnaac + β 3 lnd_subway + β 4 + β 5 O_DUMMY 1 3. 1. 2 119
UrbanSim 5 2004 UrbanSim LnPOTENTIAL UrbanSim Cubeland - 0. 064 * 3. 2 2004 UrbanSim 4 5 4 2004 64 NS _DUMMY Equations 1 2 3 4 LnPOTENTIAL LnACC LnD_SUBWAY O_DUMMY 0. 940 *** 0. 945 *** 0. 929 *** 0. 863 *** 22. 27 22. 02 19. 71 17. 38-3. 654 *** - 3. 740 *** - 3. 062 *** - 20. 75-20. 27-11. 14-0. 611 *** - 16. 55-0. 178 *** - 3. 221-0. 0001 * - 0. 0001 * - 0. 0001 ** - 0. 0001 ** - 1. 212-1. 712-2. 104-1. 640 0. 168 ** 0. 134 ** 0. 178 ** 2. 066 1. 607 2. 138 Log-likelihood - 2542-2765 - 2887-3018 Obs. 873 873 873 873 *** ** * 99% 95% 90% 64 Equations 1 2 3 4 0. 407 *** 0. 386 *** 0. 396 *** 9. 177 7. 709 9. 018 2004 64 4 64 LnACC LnD_SUBWAY LnD_MALL O_DUMMY - 1. 164-0. 125 ** - 0. 029 * - 2. 200-0. 106 * - 1. 756-1. 596-0. 0001 * - 0. 0001 * - 0. 0001 * - 0. 0001 *** - 1. 492-1. 221-1. 055-4. 304 0. 458 *** 0. 358 *** 0. 473 *** 1. 126 *** 3. 434 2. 629 3. 557 8. 330 0. 454 ** 1. 791 Log-likelihood - 2632-2833 - 2749-3126 *** ** * Obs. 215 215 215 215 99% 95% 90% 4 2004 120
UrbanSim 4 2007 ~ 2009 5 8 10 4 5 4 4. 1 5 4. 2 6 5 6 121
UrbanSim 6 2009 5 Ⅲ II III 3. 05km 2 UrbanSim 76. 4% 700m 2 50% 2007 10 2004 10 2005 ~ 2014 2014 30 1. 18km 2 2. 59km 2 1. 41km 2 2150 m 2 119. 5% 260 m 2 80% 2 2014 350 m 2 208 m 2 10 23 Ⅰ Ⅱ 3 14 122
UrbanSim 30 II III 43891 5013 7946 64 UrbanSim 7 2004 1. 50% 64 1. 56% 5 1 2 http / / www. bfscc. com / 1 O'Sullivan A. Urban Economics M. McGraw-Hill International Edition 2007. 7 2 Henderson J V. Urban Development Theory Fact and Illusion M. Oxford University Press 1988. 3 Glaeser E. Kallal H. Scheinkaman J. and Shleifer A. Growth in Cities J. Journal of Political Economy 1992 100 126-142. Organization and Agglomeration J Statistics. 2003 85 2 377-393. 4 Rosenthal S. S. and W. C. Strange. Geography Industrial. Review of Economics and 5 Sivitanidou R. Do office-commercial firms value access to service 123
UrbanSim employment centers A hedonic value analysis within polycentric Los Angeles J. Journal of Urban Economics 1996 40 2 125-149. 6. D. J. 2010 9 25-30. 2009. 11. M. 7 Waddell P. UrbanSim Modeling Urban Development for Land Use Transportation and Environmental Planning J. Journal of the American Planning Association 2002 68 3 297-314. 8 Kim J. And Zhang M. Determining Transit's Impact on Seoul Commercial Land Values J. International Real Estate Review 2005 8 1 1-26. 13 J. 2010 65 2 213-223. 2011-09 - 20 9. 10. 2007. Spatial Integrated Modeling of Beijing's Office Market Model Calibration and Simulations Based on UrbanSim ZHENG Siqi HUO Yi Abstract UrbanSim provides a good platform for the spatial integrated modeling of land use and transportation in a city or a region. In this project authors focus on the spatial modeling in Beijing's office market. Taking advantage of the firm location choice and real estate development location choice theories in office market they set up a spatial integrated model of the demand and supply of office spaces in Beijing. This model is calibrated using a GIS-based database of Beijing's office building developments prices firm locations infrastructure and public services. They employ this model to simulate the outcomes of several urban development policies in Beijing i. e. new urban subway system construction and the spatial expansions of the CBD area and Financial Street area. Keywords Office Market Spatial Integrated Modeling UrbanSim Simulation and Scenario Analysis 107 Building "New Towns" from Industrial Zones An UrbanSim Application in Yizhuang Beijing SHI Jin TONG Xin ZHANG Hongmou TAO Dongyan Abstract This paper takes Yizhuang one of the three new towns in Beijing as an example and explores changes in land use and energy consumptions in different scenarios of industrial restructuring during its transition from the Beijing Economic and Technological Development Area to Yizhuang the planned new town using UrbanSim a large scale urban model based on microsimulation. The results highlight the structural mismatch between supply and demand in the housing market in Yizhuang on condition that the visible hand of the government is absent. That mismatch gives rise to the decrease in the ratio of the number of placed households to that of planned households. Moreover the restructuring of industries exerts significant impacts on the amount of energy consumptions in the new town the structural change towards a service economy helps reduce the total energy consumption. Keywords Urban Planning Methods Land Use Changes UrbanSim 124