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

Zhao Xu1 & Volker Coors2

1, Polytechnic university of Turin , Turin, Italy2, 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

Outline

Introduction

Study area & Data

Methodology

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

Outline

Introduction

Study area & Data

Methodology

Conclusion

Introduction (GISSD SYSTEM)

Outline

Introduction

Study area & Data

Methodology

Conclusion

Functions1, Designed for sustainability assessment of urban residential development2, Linking residential housing prediction and sustainability indicators

Advantages1, 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.

GISSD

Methods

GIS1, 2D in ArcMap2, 3D in CityEngine

System dynamics1, DPSIR framework 2, Multicriteria analysis3, SD model

Data sourcesRelated statistical data was

collected from many sources, for example yearly reports on

local economic-social development published by the State Statistic Bureau

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.

Outline

Introduction

Study area & Data

Methodology

Conclusion

Study area and Data

Outline

Introduction

Study area & Data

Methodology

Conclusion

Stuttgart Region is located in the northeast of Baden-Württemberg in

the southern Germany.

Plieningen is a southern district of Stuttgart city and 10 kilometers from the city center with a

district area of around 13.07 square kilometers and 13.035 thousand residents.

The State Statistic Bureau of Baden-Württemberg “Structural and Regional Database”

“State Database” “Statistic report of Baden-Württemberg for housing

sample” “Economic and social development in Baden-Württemberg”

Ministry of Economy Baden-Württemberg“Economic facts and figures Baden-Württemberg 2010”

“Energy report 2010” “State development report Baden-Württemberg (LEB)

2005”

Methodology (Indicator Selection)

Organization for Economic

Cooperation and Development

(OECD) environmental

indicator system

United Nations Commission on

Sustainable Development

(UNCSD) indicator system

European System of

Social Indicators

(EUSI)

Outline

Introduction

Study area & Data

Methodology

Conclusion

Driving forces-Pressures-State-Impacts-Response (DPSIR) framework

Sustainability issue – Economic, Environment, and Society

Methodology (Index Weight)

Sustainability Assessment

Driving

forces

I1-I4

Pressures

I5-I10

State

I11-I15

Impact

I16-I20

Response

I21-I24

Level 1, Objective

Level 2, Criteria

Level 3, Alternatives

Outline

Introduction

Study area & Data

Methodology

Conclusion

AHPWithin 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)

Outline

Introduction

Study area & Data

Methodology

Conclusion

weighted priority of indicators

Methodology (SD Model)

Outline

Introduction

Study area & Data

Methodology

Conclusion

stock (or level)

flow

converter

connector

SD model unit

59 variables including 4

stock variables, 6 rate variables

and 49 convertor variables

Hou

sing

se

ctor

Econ

omic

sec

tor

Envi

ron

men

t se

ctor

Soci

ety

sect

or

Sustainability indicators

30-year tim

e horizon

Methodology (SD Model)

Outline

Introduction

Study area & Data

Methodology

Conclusion

stock-flow diagrams of four sectors in ithink

Methodology (SD results)

Outline

Introduction

Study area & Data

Methodology

Conclusion

Simulation results of Housing Supply

Simulation results of Housing

supply anddemand

Line chart of

sustainability index and

DPSIR categories

Methodology (GIS)

Outline

Introduction

Study area & Data

Methodology

Conclusion

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)

Outline

Introduction

Study area & Data

Methodology

Conclusion

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)

Outline

Introduction

Study area & Data

Methodology

Conclusion

• Plieningen has a residential stock of 335.132 thousand m2 in 2009 which can be expected to rise to 350.097 thousand m2 in 2020 by assuming the same average growth rate as in Stuttgart Region.

• The number of new residential buildings required up to 2020 is obtained assuming all new buildings are built in the form of the chosen standard one “ID_351600539503”.

Two assumption

• The Compact City is an urban planning and urban design concept, characterized by relatively high residential density with mixed land uses•outward development is a multifaceted concept, which includes the spreading outwards of a city and its suburbs to its outskirts to low-density and auto-dependent development.

Two urban development

patterns

Methodology (GIS)

Outline

Introduction

Study area & Data

Methodology

Conclusion

The estimated results of the stock of housing supply in Plieningen

Methodology (GIS-Density Map)

Outline

Introduction

Study area & Data

Methodology

Conclusion

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)

Outline

Introduction

Study area & Data

Methodology

Conclusion

• 3D visualization provides a more comprehensive approach to observe the prediction of urban development.

3D GIS

• CityEngine is a standalone software that provides professional users in entertainment, architecture, urban planning, GIS and general 3D content production.

CityEngine software

• The CGA shape grammar is a unique programming language specified to generate architectural 3D content based on the shapes which uses a different syntax but provides the same functionality with the widely used GML shape grammar

CGA (Computer Generated

Architecture)

Methodology (GIS – 3D)

Outline

Introduction

Study area & Data

Methodology

Conclusion

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)

Outline

Introduction

Study area & Data

Methodology

Conclusion

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

Outline

Introduction

Study area & Data

Methodology

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

Outline

Introduction

Study area & Data

Methodology

Conclusion

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!!Outline

Introduction

Study area & Data

Methodology

Conclusion