Application of integrated System Dynamics, GIS and 3D visualization system in a study of residential sustainability
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Application of integrated System Dynamics, GIS and 3D visualization system in a study of residential sustainability

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Application of integrated System Dynamics, GIS and 3D visualization system in a study of residential sustainability...

Application of integrated System Dynamics, GIS and 3D visualization system in a study of residential sustainability
Zhao Xu - Polytechnic of Turin
Volker Coors - University of Applied Sciences Stuttgart

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Application of integrated System Dynamics, GIS and 3D visualization system in a study of residential sustainability Application of integrated System Dynamics, GIS and 3D visualization system in a study of residential sustainability Presentation Transcript

  • Application of System Dynamics GIS and 3D visualization in a study of residential sustainability
    Zhao Xu1 & Volker Coors2
    1, Polytechnic university of Turin , Turin, Italy
    2, Stuttgart University of Applied Sciences, Stuttgart, Germany
  • Outline
    Introduction
    - Motivation and a GISSD system
    Study area and Data
    Integrated Methodology
    - SD model and GIS/3D
    Conclusion
  • Introduction (Motivation)
    Develop appropriate assessment approaches to evaluate the sustainability level of urban residential areas.
    Provide more information about housing equilibrium.
    Forecasting and visualization
  • Introduction (GISSD SYSTEM)
    Advantages
    1, Capacity to explore the housing equilibrium utilizing sustainability indicators.
    2, Economic-social-environmental features especially on residential buildings.
    3, The visualization of the simulation data in GIS technology.
    Functions
    1, Designed for sustainability assessment of urban residential development
    2, Linking residential housing prediction and sustainability indicators
    GISSD
    Data sources
    Related statistical data was collected from many sources, for example yearly reports on local economic-social development published by the State Statistic Bureau
    Methods
    GIS
    1, 2D in ArcMap
    2, 3D in CityEngine
    System dynamics
    1, DPSIR framework
    2, Multicriteria analysis
    3, SD model
  • Introduction (GISSD SYSTEM)
    The structure of GISSD system and Process:
    1, the SD model for forecasting urban development.
    2, a GIS-based analysis and visualization for the spatial pattern of residential development.
  • Study area and Data
  • Methodology (Indicator Selection)
  • Methodology (Index Weight)
    AHP
    Within multicriteria analyses, a very remarkable role is played by the analytic hierarchy process (AHP) which has a linear structure that goes from the top to the bottom.
  • Methodology (Index Weight)
    weighted priority of indicators
  • Methodology (SD Model)
  • Methodology (SD Model)
    stock-flow diagrams of four sectors in ithink
  • Methodology (SD results)
  • Methodology (GIS)
    Urban model was provided by the surveying department of the City of Stuttgart. It was provided as CityGML level of detail 2 (LoD 2) building model in which the geometric representation consists of polygonal ground, wall and roof surfaces per building.
    the area of Plieningen in Stuttgart with 1125 residential buildings in 2009 was considered.
    Polygonal ground plan and point shapefile of the Plieningen data set
  • Methodology (GIS-Density Map)
    In kernel density, the values of point attributes spread out from the point location with the highest value at the center of the surface (the point location) and tapering to zero at the search radius distance.
    For a point in the selected region, the measure of the density at point “s” can be defined as: Density = mean point values of events per unit area at point “s” defined as the limit. And the measure of the density at point “s” can also be marked mathematically as:
    D(S): the density values at point “s”.
    ds: the area of small region around “s”.
    Y(ds): the given attribute values of events at point “s”.
  • Methodology (GIS)
  • Methodology (GIS)
    The estimated results of the stock of housing supply in Plieningen
  • Methodology (GIS-Density Map)
    Density maps of buildings in Plieningen considering 4 different attributes: the total number of buildings, location areas, floor areas and building volumes in 2009 and 2020
  • Methodology (GIS – 3D)
  • Methodology (GIS – 3D)
    The 3 shapefiles (Plieningen in 2009, Pliningen_Compact development in 2020 and Pliningen_Outward development in 2020) containing the shape data and attributes for each shape were imported into CityEngine using the shapefile importer.
  • Methodology (GIS – 3D)
    Two group 3D simulation phenomena were created. On the left we show the 50 new buildings are located centrally in Plieningen on a small scale. On the right we show a scattered distribution of the 50 buildings in Plieningen on a small scale.
  • Conclusion
    In this paper, a GISSD system integrated of system dynamics model, GIS analysis and 3D visualization is developed to assist evaluation of the future trend of the residential development in Plieningen district of Stuttgart Region, Germany.
    The model examines interactions among five subsystems (“Driving forces”, “Pressures”, “State”, “Impact” and “Responses” categories) within a time frame of 30 years, and then the result of this model is discussed with two possible development patterns – compact development and outward development in Plieningen.
    It can also be concluded that the integration of System Dynamics theory and GIS is a useful strategy to study the development of urban residential areas in terms of sustainability.
  • Future work
    There are still a number of opportunities for expanding the study and for validating the results obtained herein.
    Firstly, only core-indicators were considered in this work. It would be of interest to add other indicators resulting from policies and strategies.
    Secondly, further research would be required to collect more historic data and optimize the structure of system dynamics model.
    Finally in this paper we only considered a very small district of Stuttgart as the study object and did not input terrain data and street data in 3D GIS analysis.
  • Thank you for your attention!!