(2011) Keith C.Clarke SLEUTH SLEUTH CLUE CLUE- S SLEUTH. Systematic Dynamics (MAS)

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1 31 1 Vol.31 No ECONOMIC GEOGRAPHY Jan (2011) GIS, SLEUTH SLEUTH SLEUTH F301.2 A 20 [1-3] [4-5] Keith C.Clarke C Clarke SLEUTH [29] SLEUTH 19 Reilly centrality [6-8] [26] [9-12] CLUE CLUE- S [18] Systematic Dynamics (MAS) [19] [20-25] [26-31] SLEUTH [13-17] SLEUTH Diffusion coefficient 2Breed coefficient 3Spread coefficient 4Slope coefficient 5Road coefficient GIS, SLEUTH SLEUTH ; KZCXZ- YW- Q

2 ArcGIS9.2 / Lee- salee Lee- salee Compare Fig.1 Urban maps input of ETM CBERS B 8 CCD DEM SRTM ( org) ETM+ 2 CBERS Fig.2 Land use maps input ENVI m 2003 CBERS

3 SLEUTH TEST Fig Transportation maps input left:2003;right: DEM a DEM b c m Deriving forecasting coefficients 4 coefficient 82 Slop Hillshade Excluded SLEUTH a b c Fig.4 Slop, hillshade and excluded maps input Compare Lee- sale ARC/INFO LINEGRID POLYGRID Compare Lee- salee 1 Diffusion coefficient Breed coefficient Spread coefficient Slope coefficient Road coefficient SLEUTH Tab.1 Calibration results of input data Compare Compare Compare = = = Lee- Sallee Lee- Sallee Lee- Sallee = = = avg.log Diffusion coefficient 31 2Breed coefficient 62 3 Spread coefficient 45 4Slope coefficient 42 5Road Compare % Lee- sale m 240m 360m GRIDIMAGE TIFF GIF best ARCGIS test 2008 ENVI4.3 5 SRTM m 240m 360m DEM Albers

4 Fig.6 Simulation and prediction on study area urbanization 6 SLEUTH Fig.6 Comparison of calibration results and actual situation in the study area Lee- sale 0.59 gif ARCGIS Tiff ARCINFO imagegrid grid ARCGIS % 2.66% 4.61% 8.30% 2.81% 3.14% predict final best 2008 Fig.7 Land use simulate maps of study area 120m 2008 from 2009 to Tab.2 Predicted urban areas in Jing- jin- lang Area in 2010, 2015 and /km 2 /km 2 /km 2 /% /km 2 /km 2 /% /km 2 /km 2 /%

5 km km km km km km % 9.04% 23.06% Tab.3 Factors of urban land change / B1 C1 / C2 / /km 2 B2 C3 / C4 /% C5 /% B3 C6 / C7 / / 15 C8 / C9 / C10 / C11 / 15 C13 C14 C16 C17 B4 C18 C19 B5 C C22 C24 /t C25 / m 2 B6 C26 C27 C28 / C29 B7 C30 / C31 / 4 C36 B9 C37 C38 C B8 C12 C15 C21 C23 C32 C33 C34 C35 / / / / / / GDP /% GDP /% / kw h / t / m 2 /t / / / / / t / / / / m 2 /hm 2 /%

6 [1] [2] [J] (2) [3] 1992 [J] (3) [4] (2) [J] (2) [7] Schweitzer F. Steinbrink., 18(1) GDP [8] (1) [9] M Batty 5 SLEUTH (8) [12] (16) [13] [14] SLEUTH (3) [16]. SLEUTH [J] (6) [17]. [J] (1) [J] (1) [J]. [5]. [6] Zanette H.D. Role of intermittency in urban development A model of large-scale city formation[j]. Physical Review Letters J.Estimation of megacity growth simple rules versus complex phenomena[j].applied Geography 1998,. [J]. Y Xie. From cells to cities[j]. Environment and Planning and Design 1994(21) [10]. [J] (1) [11]. [J].. [J]. CLUE - S [D].. [J] (6) [15]. CLUE - S [J]. [19] CLUE - S CLUE - S [18]. [J] (2) [J].

7 ( ) (5) [25] Straatman B White R Engelen G. Towards an automatic calibration procedure for constrained cellular automata[j]. Computers [20]. CA [J] (4) Environment and Urban Systems (1-2) [21]. Logistic CA [26]. [J] (10) [22] K C Clarke S Hoppen L Gaydos. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area[j]. Environment and Planning B Planning and Design 1997 (24) [23] Barredo J I Kasanko M Mccormick N. Modeling dynamic spatial processes simulation of urban future scenarios through cellular automata [J]. Landscape and Urban Planning (3) [24] Goldstein N.C. Brains Brawn. Comparative strategies for the calibration of a cellular automata-based urban growth model[c]// in GeoDynamics Atkinson P. Foody G Darby S. and Wu F. Eds. CRC Press Boca Raton FL [D] [27]. SLEUTH [J] [28]. SLEUTH [J] (5) [29]. SLEUTH [D] [30]. [J] (7) [31]. SLEUTH [J] (10) SIMULATION AND PREDICTION OF URBAN SPATIAL GROWTH IN THE PAST 20 YEARS IN JING- JIN- LANG AREA HE Dan 1,2,JIN Feng - jun 1,2,CAI Jian - ming 1,2 (1. Key Laboratory of Regional Sustainable Development Modeling,Chinese Academy of Sciences,Beijing ,China; 2. Institute of Geography Science and Natural Resources Research,Chinese Academy of Sciences,Beijing ,China) Abstr act: Based on the historical data that are extracted from a time series of satellite images, SLEUTH has been employed to simulate the urban growth and land use evolution in Jing-Jin-Lang area. To make SLEUTH applicable to Jing-Jin-Lang area,a number of calibrations should be done for the model. After calibrations, SLEUTH is used for backcasting of urban extent and land use/cover change with spatiotemporal interpolation during 1985 to 2008.The simulated results are analyzed with historical data. On this basis, SLEUTH used as a forecasting tool to simulate the process of LUCC during 2009 to 2020 respectively. Using SLEUTH model, urban growth tendency in future was forecasted. The results indicated that, margin growth occupied dominant station relative to other growth method. In future, it was going to grow outward continually taking old urban area as the center all the same. At the same time, it was traffic system nowadays that was going to have an important influence to urban development in Jing-Jin-Lang area. At last, through using qualitative and quantitative method, urban expansion driving factors were discussed. Natural environment, national macroscopic policy regulation, traffic, development zone construction and special factors were important to urban growth in Jing-Jin-Lang area. The principal component analysis showed that, population-culture-economy factor and economy factor and economy-environment factor was respectively the first principal component to influence urban growth in Beijing and Tianjin and Langfang. Key words: urban growth simulation; SLEUTH model; cellular automata; Jing-Jin-Lang area 1980 E- mail hedan@igsnrr.ac.cn