Environmental Modelling & Software 18 (2003) 553–563 www.elsevier.com/locate/envsoft
Modeling growth and densification processes in suburban regions—simulation of landscape transition with spatial agents W. Loibl ∗, T. Toetzer Environmental Planning Department, Austrian Research Centers, Systems Research, Seibersdorf 2444, Austria Received 24 October 2001; received in revised form 23 October 2002; accepted 27 October 2002
Abstract Urban sprawl is an essential environmental issue to be monitored and forecasted in order to think about alternatives that could lead to a more sustainable future development. Thus, the objective of the project presented here is to simulate the past and future transformation of suburban land use patterns in the Vienna Region. (The paper describes some results of the project, “STAU-Wien” (City–Suburb relations and development in the Vienna Region), was carried out during 2000–2002). The paper discusses driving forces of suburban growth, and presents a model that simulates polycentric development of suburban systems. The model introduces different settlement growth velocities within the suburban region considering housing area densification and land use change from open space to built up area. In particular, the model takes into account suburban population migration and commercial start ups controlled by regional and local factors (attractiveness/constraints) in the suburban Vienna Region: large and small scale accessibility (traveling time to the core city, access to motorways), land prices, landscape attractiveness, social and commercial services supply, traffic exposure obstacles as well as (land use) zoning constraints. The approach concentrates on a Spatial Agent Model to stimulate regional migration and allocation decisions of households and commercial enterprises aiming in the selection of target municipalities. Land use change will finally be performed by a cellular automaton to decide on densification and land use change. The model has been developed and applied to simulate prior and future landscape transition processes for the suburban region in the surroundings of Vienna, Austria. 2003 Elsevier Science Ltd. All rights reserved. Keywords: Spatial simulation; Landscape transition; Polycentric development; Urban sprawl; Densification; Multi agent simulation; Cellular automation
1. Introduction—challenges of suburban development Growth of urban regions leads to sprawl and densification of built up area in the surroundings of the core city. The major expression of urban development focuses on what is in German called the ‘between-city’ of single family homes and commercial urban sprawl activities in retail trade, logistics, entertainment and leisure facilities beyond the dense city and the outskirts. The growth of suburban built up area and the increase of suburban traffic are driven by new residents and their decision patterns regarding new living places. Increasing
Corresponding author. Tel.: +43-50550-3875; fax: +43-505503888. E-mail address:
[email protected] (W. Loibl). ∗
economic welfare results in changing life styles, with a high mobility according to the selection of workplace and living place and with higher demands regarding living space and environmental quality. The greater distance between core city and suburban residential areas, in addition to the greater dispersion of service facilities to fulfill the demands of the suburban population, leads to an increase of traffic, as average travel distances and numbers of trips have grown. To minimize the commuting distance has partly lost its prime position as a selection criterion for a new living site. Landscape attractiveness and the accessibility of the core city, rather than distance, might increasingly influence the location of the future living place. The (sub)urban environmental crisis is mainly based on these two aspects: loss of open space and increasing traffic. The investigation of driving forces of suburban development and the understanding of the reaction of the
1364-8152/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S1364-8152(03)00030-6
554
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
population on environmental disadvantages that lead to different migration and allocation patterns and, consequently, to new settlement and traffic patterns related to polycentric dynamics might help to solve the problems within suburban regions. According to the main interest of the readers in environmental informatics of the readers mainly addressed by this contribution, the authors will introduce the basic thematic aspects as well as the basic methodological aspects of the model they have developed. Application issues of the model as a planning tool are not further discussed.
2. An overview of urban dynamics models—from global accounting to microsimulation Urban models that have been developed to explain and simulate urban dynamics and, further on, to serve as a planning tool are based on various objectives, paradigms and methodologies. In the past, urban dynamic models focused on modeling only coarse urban structures, treating city districts as homogenous spatial entities. Models developed between the 1960s and the 1980s were based on the concepts of Forrester (1969) and Lowry (1964): macrosimulation models that focus on population–employees— equilibrium assumptions and bid-curve-gradients. Employment and population numbers are regarded in relation to quite large statistical entities, with the restriction to minimize commuting distances within the city. As these models concentrate on allocation of different land use activities within the city, these models are not able to consider processes of polycentric suburban development. These models treat cities as closed systems and do not allow to consider inter-regional system relations within these regions. Cellular automata (CA) models, applied for urban growth simulation, were disseminated since the 1980s (e.g. Alberti and Waddell, 2000; Batty et al., 1997; Clarke et al., 1996; Couclelis, 1985, 1997; White et al., 1997) and turned out to be a big step forward to simulate urban growth in detail. Cellular settlement growth simulation in a bottom–up approach, based on neighborhood land use pattern and local transition rules, appears as fractal land use change that looks similar to real world processes. This approach seems to be very powerful considering locally differentiated conditions of urban development: general trends of (sub)urban development are modeled by identifying transitions rules which define on the local scale of neighborhoods the potential for each cell to keep its status or to change its status of land use in a certain period of time (Torrens, 2000; Loibl, 2000). Accordingly, this approach produces very satisfying results if these general trends—as driving forces of suburban development—do have the same impact all over the suburban region. Anyhow, very often such general
trends are differentiated over urban systems. Then, a polycentric development is observed which in particular can hardly be modeled with the CA approach because of different velocities of suburbanization and even with time lags between the development of certain urban nuclei. Consequently, just reaching similarity of land use patterns does not meet the needs for today’s city planners, particularly in complex polycentric landscapes. As city planners expect accurate high resolution future scenarios on a local scale, valid for every municipality, one has to rethink the CA model approach. A hybrid approach (Portugali, 1999) has to be considered in order to simulate landscape transition processes based on real behavior of local actors which allows to model the processes of land use change as well as of densification. As landscape transition is the result of decisions and activities of many single actors, not only neighborhoods (or cell states of a cellular land use map) but also interactions between local actors and their environment must be considered in order to ‘forecast’ landscape transition with higher accuracy. Regarding migration dynamics the actors are migrating people. Behavior and movement of these actors depend on their knowledge about the region, their perception of the surrounding area, their desires and (financial) constraints, their decisions and finally, on their actions to overcome discrepancies between desires and housing conditions in their (former) residential environment. These actors are integrated by spatial simulation models as ‘spatial agents’. Franklin and Graesser (1996) defined an ‘agent as a system situated within and a part of an environment that senses that environment and acts on it, over time…’. Agents react on environmental disadvantages by responsive behavior. Although each agent acts individually, the sum of all actions leads to collective behavior patterns (with some stochasticity!) and thus effects changes in developing spatial patterns. As agent-based models describe decisions and behavior of single autonomous objects leading to multiple and even contradicting spatial effects, the model results come close to real world behavior. Referring to urban systems, spatial agent models are applied in order to simulate the movement of households, enterprises etc., which is taken as basic reason for landscape transition. Models that consider this paradigm of more or less autonomous agents were developed in the late 1990s (cf. Portugali, 1999; Torrens, 2001; Wegener and Spiekermann, 1997). 3. Model background The model objective is to simulate suburban development in the Greater Vienna Region (outside the core city) in Austria during the past and for the near future (see Fig. 1).
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
Fig. 1. Location of the study region in Austria, Europe.
3.1. Study area, simulation time and basic data sets The time-frame for the first simulation ranges from 1968–1999. The comparison of the observed land use patterns for 1999 with the model results for 1999 allows a validation of the model and an optimization of the control parameters. Future landscape transition scenarios for the suburban region of Vienna had been calculated for 2011 in order to examine the consequences of different densification and zoning strategies. To simulate growth of built up area in detail, it is necessary to know about the local dynamics of the urban and suburban settlement area and the related demographic and economic dynamics within the study region. Therefore spatial and statistical data sets shall refer to the same or at least similar years in order to be compared. For the Greater Vienna Region, grid cell land use maps with a spatial resolution of 100 × 100 m2 could be made available for 1968 and for 1999 (Steinnocher et al., 2000). US satellite photographs (1968: Corona) and satellite images (1999: IRCS-1C and Landsat 7) were applied to generate a land use classification divided into several land use classes, two of them contain built up areas: housing areas, commercial areas (summing up industry and supermarkets). Statistical data regarding demographics, employment and housing refer to the Austrian census for 1971, 1981 1991 and (preliminary data) 2001. The study area consists of some 1000 census units, 184 municipalities, and nine districts. Employment and demographic information is provided for census units, while data on migration relations and preliminary census population numbers 2001 are provided for municipalities. Fig. 2 allows to compare the built up area pattern in the study region between 1968 and 1999. Both data sets are used to detect rules of former land use change with respect to neighborhood structure and landscape attractiveness. Additional spatial data sets are a transportation network and accessibility patterns, infrastructure location (related to municipality centers), land price pattern and land use zoning (see Figure 4). To estimate future settlement growth, population and
555
employment increase that drive the demand for new built up area have to be considered. Population and economics forecast data were available only at the district level. The spatial distribution of the area-demanding actors is performed by the model. Table 1 shows the population and employment growth within the study region and for Vienna itself. Prior migration data show that the in-migration into the study region is mainly nurtured by the core city: by excluding intra-regional migration ca. 60% of the inmigrants came from Vienna, 40% were migrating from outside the Greater Vienna Region into the study area. Income data are useful to investigate migration in relation to socio-economic status. As they are not available at the municipality level, school education data are used to substitute for the lack of income variables. Usually, school education highly correlates with income and financial capabilities that definitely influence the choice or residence. As the population appears quite mobile concerning workplaces, workplace location is not a constraint for living place choice in a suburban migration context. Instead of considering local workplace supply, the whole region is treated as open system that serves as a reservoir for employment possibilities. The increase of employees is depending on single decisions referring to enterprise start ups or enterprise growth made by enterprise owners, where the factor of location is only one control. 3.2. Driving forces for suburban development The shape and spatial structure of urban systems represent the expression of urban life with the elements: 앫 built up area, hosting different functions of supply and demand for human actors, 앫 traffic infrastructure, providing accessibility that influences speed and direction of spatial growth, 앫 spatial interactions as relations between the built up areas that link locations of demand with locations of supply and lead to traffic. The general driving forces of suburban development are listed below: 앫 Large scale influences on urban dynamics in the study area. The basic drive that enforces urban dynamics is the general commercial success of the larger region, which, in turn, attracts employees and enterprises that further attract potential migrants from economically weak regions. As the population in the Vienna Region does not grow by birth rates, the base for spatial population dynamics is migration. 앫 City–suburb migration as the major influence on intra-regional growth patterns. Densification, the increase of rents and of land prices, of traffic and,
556
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
Fig. 2.
Land use maps of 1968 and 1999 covering the study area.
Table 1 Population and employment in the Greater Vienna Region
Population Greater Vienna region Vienna core city Suburban study area Employees Greater Vienna region Vienna core city Suburban study area a
1981
1991
2001
2,060,000 1,530,000 530,000
2,110,000 1,540,000 570,000
2,180,000 1,560,000 620,000
878,000 710,000 168,000
938,000 744,000 194,000
966,000a 755,000a 204,000a
land use changes. For the cellular automaton, attractiveness patterns reflect the driving forces that directly steer land use changes. 3.3. Model structure
Preliminary data.
accordingly, the decrease of environmental quality in the core city pushes parts of the population to leave the core city and to move into a more comfortable and green municipality in the surroundings. The expansion of industrial area is based on start ups or the migration of enterprises searching for better accessibility and lower land prices in the outskirts or surroundings of the core city. 앫 Reasons for regional distinctions concerning the speed and direction of settlement growth. Since we deal with a polycentric settlement structure, it seems that there are different subregions obviously displaying different degrees of attractiveness, resulting in differentiated migration patterns. These attractiveness-gradients within the larger urban system induce different population and commercial dynamics and land use changes. Thus, attractiveness patterns have to be derived in order to model the effects of the driving forces of intra-regional migration. For spatial agent modeling, attractiveness patterns serve as a spatial knowledge base of environment and neighborhood conditions that provoke decisions that lead to
Here a simple but effective agent-based model is introduced with some extensions referring to the development of polycentric settlements in order to gain higher accuracy regarding the location of transition and the speed of settlement development. The basic assumptions are: in our case agents are enterprises and households with different socio-economic characteristics (and thus different desires, decisions and actions), who want to migrate into the respective suburban region. The landscape of the suburban region is modeled as cellular space with n-tuple cells: several grid cell layers contain different local information for each respective cell. Agents move virtually within the cellular landscape. Fig. 3 depicts the concept as overview. The model steps are: 앫 Task 0: Initialization of the reservoir of migrating households and ‘growing’ employee numbers which consists of the agents that have to be moved. This step is performed once to define the general set up. 앫 Task 1: Municipality choice model. In a first task each agent decides to which municipality he wants to move. This decision is assumed to be driven by regional attractiveness criteria. The selection of the target municipality by each single agent is performed by taking into account a municipality choice probability distribution. The probability for each suburban municipality to be selected by the agents as migration target was estimated on the basis of former migration numbers and employee-growth numbers and by
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
Fig. 3.
557
Model structure and model steps referring to household migration simulation.
attractiveness data reflecting main selection criteria on a municipality scale. The probability distribution controls the frequency of how often each municipality will be selected as potential migration target. 앫 Task 2: Local search, agents allocation and landscape transition. In a second task, after the selection of the municipality, the search for an ideal or at least appropriate residential (or commercial site) area will be continued within the target municipality. The local search depends on corresponding desires of the different agent classes regarding housing type and population density or (referring to commercial site selection) on local accessibility and neighboring land use pattern. Thus, the search is stopped and the agent is allocated if a respective cell shows high local attractiveness and the appropriate built up area zoning (to estimate housing area demand it is also considered whether there exists the possibility of densification in the housing cell and whether the agents accept a higher densification). If the single households search is successful, then the household will settle and the population density in the respective cell increases by the household size (and if necessary/possible, the land use class will change—performed by a cellular automaton). If the commercial enterprise search is successful, then land use change will be performed by CA. If the search is not successful, a different municipality has to be selected according to a choice probability distribution. Task 1 and task 2 are carried out one by one, for all agents to simulate the migration of single households or the start up of enterprises. The decision of each agent where to settle is influenced by the actions of the former migrating agents as they cause new population density and/or land use patterns. In addition each agent’s action
might also influence the migration decision of the ‘later’ moving agents.
4. Model details 4.1. Agent classes and their characteristics regarding suburban migration Inside the model several agent classes were introduced to simulate households with different socio-economic characteristics and also enterprises that will start up. The agents have different characteristics and, accordingly, different desires/constraints and will decide on municipalities as appropriate future residence. Six agent classes were defined, four of them are different household types related to socio-economic characteristics and different behavior patterns. The behavior patterns were derived from prior migration data related to socio-economic characteristics (regarding attractiveness layers see Section 4.2): 1. High income, highly educated households: they prefer single family houses in municipalities with high levels of core city accessibility, high standards of supply of social services, and high levels of landscape attractiveness and they do not mind high levels of land prices. 2. Moderate to higher income households: they can afford single family homes in municipalities with low levels of land prices, moderate standards in accessibility of the core city and in supply of social services. 3. Moderate income, highly educated younger households: they prefer municipalities with high levels of core city accessibility and of social services, but
558
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
Fig. 4. Layers indicating different regional attractiveness aspects.
accept flats in multi-story buildings as they cannot afford single family houses. 4. Lower income households: they accept flats in multistory buildings in municipalities with low land prices or rents and even with low levels of general accessibility. 5. Weekend-home seekers: they behave like agent class (2) but accept longer distance to the core city. 6. Enterprise founders/owners looking for appropriate commercial lots: they behave differently and look for good local accessibility, motorway access, open space and commercial lots in the neighborhood. 4.2. Regional attractiveness as knowledge base for selection of target location The spatial knowledge base for moving decisions is provided as ‘attractiveness maps’. We identify two groups of attractiveness. Regional attractiveness: 앫 accessibility of core city, of district centers, 앫 landscape attractiveness (open space characteristics, topography), 앫 services supply (social, economic, cultural, educational, recreational facilities), 앫 land prices/rents. Local attractiveness: 앫 neighborhood land use pattern, distance to motorways with high traffic density,
앫 population density, 앫 availability of lots (houses, flats, commercial areas), 앫 land use zoning constraints. Fig. 4 shows the most important attractiveness layers. Attractiveness surface layers were generated as follows (Loibl and Kramar, 2001): 앫 Landscape attractiveness is derived from the land use maps: the fraction of those land use classes that were known to be attractive for land use change was calculated for all cells in order to estimate the probability to serve as a residential area for potential movers. 앫 Indicators for local services supply were derived from the numbers of facilities of different services within the vicinity of each cell. In this context, attorneys, pharmacies, medical service, and colleges were considered as suitable indicators. 앫 Accessibility of the Vienna city center was calculated applying a shortest path model in order to find the shortest travel time. Accessibility was calculated for the years 1968, 1999. For future scenarios the future accessibility of the core city as expected for 2011 was calculated, applying the expected future road network for the shortest path calculations. 앫 Availability of lots is represented by the total built up area per municipality as proxy data set. 앫 Average land prices per municipality were provided from real estate sale statistics per municipality and further information sources.
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
4.3. Task 1: municipality choice
according to different accessibility of the core city due to new motorway segments.
The postulated dependence of population growth/employment growth and built up area growth on attractiveness patterns has to be verified. This was performed with the help of regression functions with inmigration numbers or employment growth numbers at target municipality i as predictor variable Mi and the attractiveness layers as explanatory variables Mki ⫽ DcixkD ⫹ LixkL, ⫹ SixkS ⫹ AixkA,
559
(1)
where Dci is the distance (accessibility) between center c and target municipality i; Li the landscape attractiveness at target i (forest quota, elevation difference); Si the services supply at target i; Ai the availability of lots, houses, etc. at target i; xk equals weight coefficients of variable D, L, S and A for agent class k. The finally selected variables for the regression functions for each agent class are chosen among alternatives that show high correlation with the migrant numbers/employment growth numbers and serve as best explanatory variables within the regression function. The correlation coefficients R2 vary between 0.67 and 0.88 (Loibl and Kramar, 2001). In order to define choice probability P for each municipality i such regression models were estimated for every agent and Eq. (1) is applied with normalized x→x∗. The decision, which municipality will be selected, is performed randomly referring to the choice probability distribution Pki for each agent (see Fig. 5). The probability distribution for every agent class can be overruled by thresholds for two selection criteria in order to exclude municipalities because of unacceptable characteristics like too long distance to the core city. It is assumed that the single household or enterprise has only few influences on attractiveness at the regional scale. The most attractiveness layers are rather stable, just the accessibility indicator for the validation model run and for the future scenario model runs changes over time. So the probability distribution for different simulation periods will remain static during the simulation period, but will be different for future simulation periods
Fig. 5. Choice probability distribution across municipalities per agent class (for 180 municipalities).
4.4. Task 2: target location search After selecting the target municipality, the final decision of the households or enterprise owners for an appropriate residential or commercial site area, the search for the target location within the municipality starts. This task depends on the availability of lots, houses or flats as well as on different local characteristics of probable target cells. The local search takes place in a cellular ‘landscape’ of 100 × 100 m2 grid cells and consists of several steps. Initial local search step: Household agents start with a search for a local population density minimum cell (of cells with housing land use) in a population density surface within the selected municipality. Agents that prefer single family houses start their search in a random cell in the open space landscape within the selected municipality. They move to the nearest settlement border and seek for a built up area cell that shows a potential population density appropriate for single family houses. Agents that accept multi-story buildings start their search in the center of the selected municipality and move in random direction in search of a cell with the lowest potential population density above a threshold that is appropriate for multi-story buildings. If densification is below a certain critical level the household agent settles down. Enterprise agents’ search starts also in a random cell in open space within the selected municipality seeking for the nearest commercial site area or at least the nearest area that is dedicated for industrial use. Further selection step: After the respective agent has searched for the cell i with the minimum potential population density or the enterprise agent has found a appropriate cell adjacent to commercial sites or a cell that show commercial site zoning, a set of additional cells in a neighborhood S will be selected within a defined extent around the cell i. The cells in set S will be examined regarding additional attractiveness criteria: (1) actual population density and (2) potential (future) population within built up area cell, (3) actual land use, (4) land use zoning regulations, (5) distance to next settlement/industrial site area, (6) distance to highway (weighted by traffic load), (7) distance to major road junctions, (8) number of neighboring housing cells, (9) industry cells, (10) open space cells. (The potential population density of open space cells to be covered by residential buildings in next future is derived from the population density of the adjacent or nearby built up area cells.) As the moving households increase local population density or households and enterprise start ups trigger land use change, local attractiveness is changing dynamically. This set of
560
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
local cells S is now described by a set of characteristics cj (j = 1 to n) for each cell i. The final choice: The final choice of a distinct cell from the set S is made by examining the cell with the maximum attractiveness taking into account these criteria. This is performed by multi-criteria decision making, which allows a rational choice within all appropriate alternatives (Jankowski, 1995). Therefore all appropriate cells will be ranked considering each single characteristic cj. To provide a comparison of the different characteristics for each single cell every characteristic cj is standardized to c⬘j. This characteristic c⬘j are now weighted regarding the importance of the single characteristics for residential attractiveness using a set of factors wj (j = 1 to n). The weights are different for every agent group and can be modified interactively. The normalized characteristics c⬘j weighted with the factors wj now deliver the overall attractiveness a for each cell i
冘 n
ai ⫽
c⬘j.wj
(2)
j⫽1
All ai make up the set A—the attractiveness of the cells i (i = 1 to x) for settling down: A = {a1,…an}. The cell that shows highest attractiveness in a multi-criteria evaluation (e.g. low population density, long distance from highways, much open space cells but adjacency to already built up area, etc.) will be selected: ai:afinal ⫽ max(A),
5. Model results The validity of the model results can easily be observed by comparing the landscape transition simulation results 1968–1999 with the observed land use pattern of 1999 as shown in Fig. 6 for a single district. Fig. 6 depicts the increase of the housing area cells in Mo¨ dling—one (prosperous) district, south of Vienna, with some 20 municipalities out of 180. The built up area of each municipality is shown by two bars. The upper bar (with broad stripes) shows the housing area in 1999, the lower bar (with thin stripes) shows the housing area in 1968. Black bars extending the 1968 bars indicate the growth of housing area within each municipality. When both lower bars together reach a similar length as the upper bar, this proves the consistency of observed and simulated growth. As the figure shows, the model results referring to housing area meet the observations very well. The commercial area allocation distribution results look similar. To examine the results on municipality level, Fig. 7 allows a comparison of model results with the observed actual land use patterns for one municipality, Gerasdorf,
(3)
where afinal is member of the set A preferred against ax2… against axn afinal傻ax2傻…傻axn.
(4)
In case there is no cell within set S that allows an increase of population density or the agent does not accept higher density, a cellular automaton will be applied to decide whether land use change will take place in an adjacent attractive open space cell using several neighborhood rules. If the agents’ search is not successful after several trials in an already selected municipality, the search will start again in a different municipality according to the municipality choice probability distribution. A successful search will be marked on a blackboard (one individual for each agent class) in order to provide communication between the agents (of the same class). An agent that wants to move to a respective municipality will first look on the agents class’ blackboard whether there has been a successful search in the target municipality. If yes, the search will continue at the cell that was selected last within the municipality; if not, the search will start at a random cell within the municipality (see Fig. 3).
Fig. 6. Regional distribution of housing area and housing area growth per municipality in a suburban district, south of Vienna, from 1968– 1999.
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
561
Fig. 7. Local allocation of new built up area: state 1968, state 1999 and simulation 1999.
which is located in the north of Vienna. The simulation map (top right)—the dark areas in the ellipses depict housing and commercial areas in the respective municipality—shows a high spatial coincidence with the 1999 observations (below right). The initial land use pattern can be seen in the below left insert of Fig. 7. (The built up area within the Vienna municipality remains always the same for the simulation run, as the simulation is limited to the municipalities surrounding Vienna.) The areas of interest are marked by ellipses: the gray ellipses mark the areas with commercial site growth. The black circle and ellipse south of the gray ellipse mark the regions that show increasing housing area. Obviously, Fig. 6 and Fig. 7 prove that the spatial agent model approach is well capable to simulate suburban dynamics in a complex and multi-centered landscape. To simulate future landscape transition in the surroundings of Vienna, forecasts for population and employment as driving force for growth of built up area are necessary. Until 2011 an increase of 60,000 inhabitants and of 46,000 employees is forecasted. Scenarios of different preferences regarding population density and acceptance of commuting distance to the core city lead to different built up area increase. If the actual population density (and thus housing density) in the target municipalities will be increased by 30% forcing a change of single family houses to terraced houses this will lead to a growth of 9.3 km2 residential area. If longer commuting distances will be accepted more single family houses in rural municipalities with lower population and housing density will be built in long distance to the core city that lead to a growth of 30 km2. The increase of commercial site area is estimated at 6.2 km2 assuming the actual employee density in commercial site areas.
6. The software There already exists several agent simulation model shells, but until now there is no shell available that considers multiple spatial influences on agents’ decisions and that allows to simulate the effects of these decisions on real landscapes. Therefore, a program was developed that has basic GIS functionality, cellular automaton functionality and autonomous agent functionality. A control file allows to define, which input data sets shall be loaded—a MS Access database with statistical data sets per municipality or district and up to 12 grid cell layers that provide land use, population density and local attractiveness information as Arc/Info ASCII-grid files (the format defined by Environmental Software Systems Research Institute Inc. ESRI, the developer of the Arc/Info GIS software, which was used to prepare the input data sets). Output data sets are exported as Arc/Info ASCII-grids (new land use and population density) and as ASCII-text files containing population numbers, housing cell numbers and commercial site cell numbers per municipality, to be reimported into spread sheets. The agents are objects that behave upon rules: they move within the landscape established as 2D-land use array (and up to 10 cell characteristics—2D-arrays) to seek appropriate cells and change cell characteristics like population density or land use iteratively during the simulation runs. Fig. 8 shows the GUI (Graphical User Interface). The big simulation window shows land use and land use change, and is provided with zoom and pan functions to show details within the study area. Several buttons at the top of the window allow to open additional map windows that show the population density and attractiveness
562
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
Fig. 8.
Program GUI with map and chart windows and control panels.
layers as well as the original land use to compare the results. Other buttons control the input and output, further start and stop of the simulation. The bar chart window on the right shows the increase of built up area cells in commercial and residential areas similar to Fig. 6. The right bottom control panel allows modifications of fractions and parameters for the six agent classes. The panel fields in the extreme right column show the migration numbers per agent class, the fields in the bottom row show the total numbers of commercial and housing cells and population numbers. The left bottom control panel allows to change the weights of the attractiveness variables for the agent classes in various panel fields.
7. Conclusion The general results of the model prove that the spatial agent approach not only allows a simulation of coarse virtual city dynamics but also performs the transition of complex multi-centered landscapes. Besides land use, the inclusion of additional attraction factors for land use transition as well as migration of population and increase of employment leads to more realistic estimations for future development.
The general migration and enterprise start up patterns show very stable conditions as they refer to the municipality choice probabilities of the several agents. If they are estimated correctly the general results are valid. The crucial point is the definition of behavior rules and choice processes, which lead to very different results on the local scale. Here intensive tests to modify the weights and parameters have to be carried out.
Acknowledgements The presented model was developed for the Project, “STAU-Wien” (City–Suburb relations and development in the Greater Vienna Region), a project undertaken by the Austrian Research Center Seibersdorf, the Technical University of Vienna and the University for Economics and Business Administration, Vienna and funded by the Austrian Ministry for Education, Science and Culture. The authors wish to thank the funding institutions for their financial support. Also they wish to thank Klaus Steinnocher and Mario Ko¨ stl for the provision of the land use data sets, Hans Kramar for the accessibility model calculations and Rudolf Giffinger for his comments regarding urban development.
W. Loibl, T. Toetzer / Environmental Modelling & Software 18 (2003) 553–563
References Alberti, M., Waddell, P., 2000. An integrated urban development and ecological simulation model. Part 1, rationale for synthesis. In: CDROM Proceedings of CUPUM 1999, Venice. (http://www. urbansim.org/papers/uecfin.pdf). Batty, M., Couclelis, H., Eichen, M., 1997. Editorial: urban systems as cellular automata. Environment and Planning B: Planning and Design 24 (2), 159–164. Clarke, K.C., Hoppen, S., Perez, S., 1996. Clarke Urban Growth Model. (http://www.geo.arc.nasa.gov/usgs/clarke/hilt.html). Couclelis, H., 1985. Cellular worlds: a framework for modeling micro –macro dynamics. Environment and Planning A 17, 585– 596. Couclelis, H., 1997. From cellular automata to urban models: new prinicples for model development and implementation. Environment and Planning B: Planning and Design 24 (2), 165–174. Forrester, J.W., 1969. Urban Dynamics. MIT Press, Cambridge, MA. Franklin, S., Graesser, A., 1996. Is it an agent, or just a program? A taxonomy for autonomous agents. In: Proceedings of the Third International Workshop on Agent Theories, Architectures and Languages. Springer, Berlin, pp. 21–35 (http://www.msci. memphis.edu/~franklin/AgentProg.html). Jankowski, P., 1995. Integrating geographical information systems and multiple criteria decision making methods. International Journal of Geographical Information Science 9 (3), 251–273. Loibl, W., 2000. Modellierung der Siedlungsdynamik mit einem GISbasierten Zellularen Automaten-Konzeption, GIS-Integration und erste Ergebnisse. In: Strobl, J., Blaschke, T., Griesebner, G. (Eds.),
563
Angewandte Geographische Informationsverarbeitung XII. Wichmann Verlag, Heidelberg, pp. 297–306. Loibl, W., Kramar, H., 2001. Standortattraktivita¨ t und deren Einfluss auf Wanderung und Siedlungsentwicklung. In: Strobl, J., Blaschke, T., Griesebner, G. (Eds.), Angewandte Geographische Informationsverarbeitung XIII. Wichmann Verlag, Heidelberg, pp. 309–315. Lowry, I.S., 1964. A Model of Metropolis. Rand Corporation, Santa Monica (Cal, Memorandum RM.4035-RC). Portugali, J., 1999. Self Organization and the City. Springer, New York. Steinnocher, K., Kressler, F., Ko¨ stl, M., 2000. Erstellung einer Siedlungsmaske aus Fernerkundungsdaten und Integration zusa¨ tzlicher Information aus Zensusdaten. In: Strobl, J., Blaschke, T. (Eds.), Angewandte Geographische Informationsverarbeitung XII. Wichmann Verlag, Heidelberg, pp. 481–488. Torrens, P.M., 2000. How Cellular Models of Urban Systems Work (1. Theory). (http://www.casa.ucl.ac.uk/paper28.pdf). Torrens, P.M., 2001. Can Geocomputation Save Urban Simulation? Throw some agents into the mixture, simmer, and wait…. (http://www.casa.ucl.ac.uk/paper32.pdf). Wegener, M., Spiekermann, K., 1997. The potential of microsimulation for urban modelling. In: Proceedings of the International Workshop on Application of Computers in Urban Planning. Kobe University, Kobe, Japan, pp. 129–143. White, R., Engelen, G., Uljee, I., 1997. The use of constraint cellular automate for high resolution modelling of urban land-use dynamics. Environment and Planning B: Planning and Design 24 (2), 323–343.
