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2010 INTERNATIONAL CONFERENCE ON COMPUTATIONAL
SCIENCE AND APPLICATIONS (ICCSA 2010)


     Using Cellular A
     U i C ll...
OUTLINE

   Research Background and Purpose

   Cellular Automata,CA

   Introduction of Study area- Danshuei Township
...
RESEARCH PURPOSE
    Increasing demands of economic growth accelerate the rate of
    over development of land resources. ...
CELLULAR AUTOMATA,CA
 CA, hi h is
  CA which i a grid-based model, considers spatial organization
                     id...
LOCATION OF TAIWAN
LOCATION OF DANSHUEI TOWNSHIP
        •Area: 70.65 km2
        •Population
        density :
        1968.5 p/km2
        ...
URBAN DEVELOPMENT PROCESS OF DANSHUEI
   Taipei County attracted enormous immigrants from the southern parts of
    Taiwa...
DATA PROCESSING AND LAND USE/COVER
IDENTIFICATION
   Official topographic maps of 1985, 1993
    and 2001 and National La...
BUILDING COVERAGE CHANGE OVER TIME
Year 1986
                                     Year 2008
            Year 2003




Year...
PARAMETERS SET IN NETLOGO
   Starting points (seeds):
    Original settlements recorded by historical literatures
   Gro...
URBAN GROWTH SCENARIO SETTING

                                                          Growth          Growth Scenarios
...
SIMULATION RESULT : SIMULATION RESULT 1
   Scenario 1 only considers coefficient of slope. Non of Policy Restricted Contr...
SIMULATION RESULT : SIMULATION RESULT 2 _( )
   U    O    SU        U    O    SU      (1)

                               ...
SIMULATION RESULT : SIMULATION RESULT 2 _( )
           U    O    SU        U    O    SU      (2)
       0.6              ...
SIMULATION RESULT : SCENARIO 3 AND 4
                              Excluded Element From Development
                     ...
COMPARISON OF SIMULATION RESULTS
Comparing scenario 3 with scenario 1 suggests that the environmental restriction policy
...
DISCUSSION AND CONCLUSION
 From Scenario Slope factor is the influential driving force to
  urban growth model, comparing...
RECOMMENDATION
Three major aspects will be addressed and further investigated
in this urban growth modeling study:
(1) mod...
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Using Cellular Automata Model to Simulate the Impact of Urban Growth PolicyonEnvironmentin Danshuei Policy on Environment in Danshuei township, Taiwan

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Using Cellular Automata Model to
Simulate the Impact of Urban Growth
PolicyonEnvironmentin Danshuei Policy on Environment in Danshuei
township, Taiwan

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Transcript of "Using Cellular Automata Model to Simulate the Impact of Urban Growth PolicyonEnvironmentin Danshuei Policy on Environment in Danshuei township, Taiwan"

  1. 1. 2010 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND APPLICATIONS (ICCSA 2010) Using Cellular A U i C ll l Automata Model to M d l Simulate the Impact of Urban Growth Policy on Environment in Danshuei township, Taiwan Feng-Tyan Lin i 1. Dean, College of Design and Planning, National Cheng Kung University. 2. Professor, Graduate Institute of Building and Planning, National Taiwan University Wan-Yu Tseng Ph.D. student, Graduate Institute of Building and Planning, National Taiwan f g g University 2010/03
  2. 2. OUTLINE  Research Background and Purpose  Cellular Automata,CA  Introduction of Study area- Danshuei Township area  Data Processing and Land Use/Cover Identification  Simulation Achievement of Urban Growth Model and E i d Environmental Impacts lI  Discussion, Conclusion and Recommendation ,
  3. 3. RESEARCH PURPOSE Increasing demands of economic growth accelerate the rate of over development of land resources. Inappropriate urban sprawl and unsuitable configuration of land use destructively impact the g y p ecological environment.  Danshuei is a good case showing how to balance between urban development and natural resource protection.  This study applies CA model to simulate urban growth models and environmental impacts using different growing scenarios.  Find out main driving forces of urban growth.  Evaluate the effectiveness of land use policy.
  4. 4. CELLULAR AUTOMATA,CA  CA, hi h is CA which i a grid-based model, considers spatial organization id b d d l id ti l i ti in terms of evolution and provides another point of view to spatial evolution.  The status of each grid is decided by rule of status of transformation. Its mathematical form is defined in equation q  F ( S ij ,  ij ) t 1 t t S ij  NetLogo is adopted in this study because it can simulate and dynamically display the spatial changes of urban. (NetLogo simulation software was developed by Northwest University , US . This system is widely applied to medical science, biology, environment and education; NetLogo was used to simulate the dissemination mode and situation of epidemic SARS)
  5. 5. LOCATION OF TAIWAN
  6. 6. LOCATION OF DANSHUEI TOWNSHIP •Area: 70.65 km2 •Population density : 1968.5 p/km2 Danshuei Township Taipei city
  7. 7. URBAN DEVELOPMENT PROCESS OF DANSHUEI  Taipei County attracted enormous immigrants from the southern parts of Taiwan, since 1895 when Taipei city has been the political and financial center of Taiwan.  The population growth of Danshuei township has increased by 87.30% from 1986 to 2009 reaching 139,073.  The building coverage has also been significantly increased by 1.7 times from 2.8 km2 to 7.6 km2 during 1986 to 2009 . 28 76
  8. 8. DATA PROCESSING AND LAND USE/COVER IDENTIFICATION  Official topographic maps of 1985, 1993 and 2001 and National Land Use Survey data of 2008 as basic inputs to acquire the land use data.  These maps were scanned to digital files, overlaid with 100 by 100 meters square grid in GIS software. (whereas Danshuei occupied 7211 cells) p )  Classified terrain features into 9 categories comprising built-up, infrastructure, road, agricultural land, forest, grassland, barren land forest grassland land, wetland and water land. The coverage contains five categorizations from 0 4 respectively 0-4, presents 0%, 1~20% (10%), 21~50% (35%), 51~80% (65%) and 81~100% (90%). Legend presents as from lighter red to darker red.
  9. 9. BUILDING COVERAGE CHANGE OVER TIME Year 1986 Year 2008 Year 2003 Year 1994 Building coverage change ratio between 2003 and 2008
  10. 10. PARAMETERS SET IN NETLOGO  Starting points (seeds): Original settlements recorded by historical literatures  Growth Rule 1. Slope: considers a seed cell to seek the nearest unoccupied neighbor cell with the lowest slope 2.Neighbor 2 Neighbor effect: considers the summation of building coverage of nine cells, which are the seed cell and the eight neighboring cells. 3.Road gravity: considers the distance from each cell to the nearest cell with road.  Policy Restricted Condition 1. None 2. 2 Land use control-”moderate protection” and “restricted protection” control  Environmental Impacts Agricultural and Forest resources g  Validation 1. The actual growth in 2008 of Danshuei township is used to compare with the ultimate urban development form simulated results. results 2.The model accuracy is assessed based on three parameters: predicted accuracy rate, over-predicted rate and less-predicted rate.
  11. 11. URBAN GROWTH SCENARIO SETTING Growth Growth Scenarios Six of starting points Parameters Coefficients 1 2 3 4 Starting points six six six six Slope ▲ ▲ ▲ ▲ Neighbor ▲ Growth rules 淡海新市鎮effect Road gravity d i ▲ Non ▲ Managed ▲ growth with Policy moderate Restricted p protection Condition Managed ▲ growth with six original settlements of Pepo frau Pepo-frau restricted aboriginal residents recorded by protection historical literatures
  12. 12. SIMULATION RESULT : SIMULATION RESULT 1  Scenario 1 only considers coefficient of slope. Non of Policy Restricted Control.  the predicted accuracy rate reaches 59.43% and the over-predicted rate reaches 33.59%.  Forest and agriculture loss rates reach 27.12% and 38.78% respectively. (the loss rate of agricultural and forest areas are calculated based on the data of 1986. The Th area circled in i l di yellow could not be regarded as over- Proposed New Township prediction as it will Planning Site be developed in future.
  13. 13. SIMULATION RESULT : SIMULATION RESULT 2 _( ) U O SU U O SU (1)  Scenario 2 considers slope, neighbor effect (200 m2) , road gravity (500m) simultaneously.  Although the predicted accuracy rate goes slightly down to 51.23% from 59 43% th over- 51 23% f 59.43%, the predicted rate is also down to 18.74% from 33.59% comparing to Scenario1. Legend accurate prediction over-prediction under-prediction
  14. 14. SIMULATION RESULT : SIMULATION RESULT 2 _( ) U O SU U O SU (2) 0.6 Considers slope and neighbor effect: 0.5 when the value is equal or greater than 0.4 accuracy rate 1,000, the predicted accuracy rate reduces 0.3 over-predicted… significantly from 50% to 37%. Based on this observation, it is suggested rate 0.2 that the urban development in D h i h h b d l i Danshuei r 0.1 township would spread over an area instead 0 100 200 300 400 500 600 700 800 900 1000 of concentrated at a particular place. Area (square meter) 0.7 Considers slope and road g gravity: y 0.6 the urban development in 0.5 accuracy rate Danshuei township tends to occur 0.4 over-predicted rate at a place which is 250m or above p forest-damged f td d away from a road system. 0.3 agriculture-damaged The loss rate of natural 0.2 future-forest-damaged resources is noticeable when the future-agriculture-damaged 0.1 01 distance between new urban development and a road system is 0 rrate 200 250 500 1000 2000 3000 Distance (meter) greater than 2000m.
  15. 15. SIMULATION RESULT : SCENARIO 3 AND 4 Excluded Element From Development Element Scenario 3 Scenario 4 moderate restricted protection protection g Agriculture x District of Urban Planning General x Agricultural Zone Special x x Agricultural A i lt l Zone Recreational x Scenario 3 area General x forest district Urban parks x National x x Parks Slop land x x conservation zone Scenario 4 Wetland x x River District x x
  16. 16. COMPARISON OF SIMULATION RESULTS Comparing scenario 3 with scenario 1 suggests that the environmental restriction policy can reduce the forest loss rate by 9.2% and the agricultural land loss rate of 8.03%. As in scenario 4, the reduction for forest loss rate and agricultural land loss rate is 11.17% and 9.33% respectively. Scenario 1 Scenario 2-1 21 Scenario 3 Scenario 4 Accuracy rate 0.5943 0.5123 0.5943 0.5943 Over-predicted rate* 0.3359 0.1874 0.3359 0.3359 Forest-damaged rate 0.2712 0.3274 0.2712 0.2712 Agriculture-damaged 0.3878 0.4656 0.3878 0.3878 rate Future damaged rate of 0.1341 0.1168 0.0421 0.0224 forest ** Future damaged rate of 0.1247 0 1247 0.1611 0 1611 0.0444 0 0444 0.0314 0 0314 agriculture** * over-predicted areas may indicate that the places have a high potential to be urbanized in the future. ** assumes that over-predicted areas are feasible to be urbanized, then predicts forest and agriculture loss rates.
  17. 17. DISCUSSION AND CONCLUSION  From Scenario Slope factor is the influential driving force to urban growth model, comparing to neighbor effect and road gravity. More driving forces inputted would not increase the correct prediction rate.  Over-predicted areas may indicate that the places have a high potential to be urbanized in the future, where should be controlled or protected carefully. carefully  Scenarios of environmental impact analyses show that restricted zoning control (Scenario 4) could have considerable preservation of forest and agriculture land in the future.  Protection policy strategies assumed in Scenario 3 and 4 could be further applied as Performance measurement/ indicators of land use control.
  18. 18. RECOMMENDATION Three major aspects will be addressed and further investigated in this urban growth modeling study: (1) model calibration: more historical images/data will be collected to undergo a detailed calibration as well as to improve the model capability in modelling urban transformation process. (2)investigation of other driving forces/growth rules: • urban density and other social factors will be included in future simulation models. • Combination of the strategies of development constraints will be considered. (3)methods for accuracy assessment: further assessment methods such as Kappa, f th d h K fuzzy KKappa statistic and other t ti ti d th index will be examined in future research.
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