3 Models under review
With respect to the model evaluation criteria mentioned in previous reviews and for the “ideal urban model” (Sections 1.2 and 1.3), we solely focus on causalities and feedback loops in the models under review, as we believe that alongside a good description of model components (human sphere, land use, environment), representation of the linkages between the components (= impacts and feedback loops) make up the comprehensiveness and the explanatory strength of the models. The models included in this review were selected in order to represent the most influential streams of urban land use change modelling. First, the review includes models well known within the community, such as those which are discussed in the related literature on urban land use change, e.g., by being referenced in other reviews. Second, system and land use approaches which are not discussed at great length in the literature were included, because system dynamics as a method forces modellers to think in a systemic way and easily allows for the inclusion of feedback mechanisms. For system-oriented, causality-driven models on at least one dimension of urban land-use change, a search on the ISI Web of Science was performed. This procedure led to a total of 19 models, which were also included in this review. These models are listed in the form of a comprehensive overview in Table 1. Details are given in the Annex 7.Roughly four different modelling approaches can be distinguished. Two of the models under review belong to the class of spatial economics/econometric models (SE_1 and SE_2: Nijkamp et al., 1993*; Mankiw and Weil, 1989*). These models mainly look at demography and household-driven demand-supply relations in urban regions, such as housing market developments. Seven models included in this review (SD_1 to SD_7: Forrester, 1969*; Haghani et al., 2003a*,b*; Eppink et al., 2004*; Sanders and Sanders, 2004*; Onsted, 2002*; Eskinasi and Rouwette, 2004*; Raux, 2003*) are system dynamics or causality-driven models (Table 1). System dynamics is an approach which models complex systems using stocks and flows and by explicitly including feedback loops in the model (Sterman, 2000). System dynamics models are – in their standard application – not spatially explicit. Rather, the structure of combining stocks, flows and feedback mechanisms leads to a set of differential equations. The outcome of these equations can be simulated, given values for parameters and initial conditions. The classical approach to modelling urban systems using system dynamics is Forrester’s book on “Urban Dynamics” (Forrester, 1969*): He linked the three subsystems “business,” “housing” and “population” to describe and model urban systems in general, subsequently differentiating each of the three subsystems in very detailed sub-models. Five models included in this review (CA_1 to CA_5: Verburg and Overmars, 2007*; Landis and Zhang, 1998a*,b*; Landis et al., 1998*; Engelen et al., 2007*; Dietzel and Clarke, 2007*) use cellular automata as the main modelling technique (Table 1). A cellular automaton consists of an n-dimensional grid of cells. Each cell has a finite number of states. Cells change their state simultaneously according to the same rules coded in the model, and the state of a cell in time t solely depends on the state of neighbouring cells in t−1 (cf. Clarke et al., 1997*; Landis and Zhang, 1998a*,b*; Silva and Clarke, 2002*). Land use change models use cellular automata with 2-dimensional grids which represent the majority of land use. Each cell symbolises a patch of land, and states of cells are the land use options. Five models in this review (ABM_1 to ABM_5: Strauch et al., 2003*; Salvini and Miller, 2005*; Ettema et al., 2007*; Loibl et al., 2007*; Waddell et al., 2003*) use agent-based approaches as the main modelling technique (Table 1). Agent-based models consist of autonomous individuals (agents) who perceive their environment and interact with one another (Parker et al., 2003). Applications of agent-based modelling in land use change are usually spatially explicit, and agents represent, for example, households relocating their homes or individuals using transport systems, but also governmental and other institutional bodies.
| Model |
Main purpose |
Components |
Reference |
|
Spatial Economics / Econometric models
|
|||
| SE_1 |
Modelling household life cycles and their impact on residential relocation behaviour and the urban housing market for a European capital city. |
Human sphere (population, migration, household, transportation, housing market, prices, dwellings, vacancies) |
|
| SE_2 |
Simulation of demographic changes (baby boom and baby bust) and its influences on the housing market in the U.S. |
Human sphere (population, migration, household, housing market, prices, dwellings, vacancies) |
|
|
System Dynamics
|
|||
| SD_1 |
Modelling urban system in general, explicitly including “urban decline.” Examples: focus on a specific topic, e.g., rapid population growth, demolition, et cetera and therefore need specific models. |
Human sphere (business, housing, population) |
|
| SD_2 |
Integrated land-use and transportation model for estimating scenarios regarding transport policies |
Human sphere (population, migration, household, job growth, employment and commercial land development, housing development, travel demand, congestion) |
|
| SD_3 |
Assessing the impact of urban sprawl on wetland biodiversity and social welfare |
Human sphere (population) Land use (agricultural land, wetlands) Environment (wetlands, nature protection) |
|
| SD_4 |
Redefining the model of urban dynamics by Forrester (1969*), including: 1. spatial dimension (16 squares) and 2. disaggregation: different types of housing, industry, and people in zones |
Human sphere (population, housing availability, houses, land availability, business structures, and job availability, labour market and housing market) |
|
| SD_5 |
Simulation model to provide scenarios for future land use in Santa Barbara, e.g., with restrictions to urban growth |
Human sphere (housing, population, business) Land use Quality of life |
|
| SD_6 |
Assessing the impact of future policy interventions on the social housing market (specific: rate of building new dwellings) |
Human sphere (commercial housing stock, social housing stock, waiting families, supply of available social houses; migration, demolition, construction) |
|
| SD_7 |
Simulating medium- and long-term effects of urban transportation policies with reference to sustainable travel |
Human sphere (urbanisation, internal travel demand, car ownership, external travel demand, transportation, socio-economic evaluation) Environment (environmental appraisals) |
|
|
Cellular Automata
|
|||
| CA_1 |
Tool for understanding land-use patterns, possible future scenarios for given demand |
Human sphere (demand rules) Land use (suitability rules) |
|
| CA_2 |
Simulating urban growth, scenarios for future development |
Human sphere (population, household, jobs, employment) Land use (single-family residential, multi-family residential, commercial, industrial, transportation, public) Environment (undeveloped land) |
|
| CA_3 |
Development of policy scenarios of urban growth, impact on habitat change/biodiversity |
Human sphere (urban growth, policy simulation and evaluation) Environment (habitat change and habitat fragmentation) |
|
| CA_4 |
Monitoring developments of urban areas and identifyng trends at the European level, focus is on growth scenarios |
Human sphere (population, economy, planning, accessibility via transportation network) Land use (land use functions) |
|
| CA_5 |
Modelling urban growth, scenarios for future development of an urban region |
Land use (urban or non urban, roads, different land use types) Environment (topography)
|
|
|
Agent-Based Models
|
|||
| ABM_1 |
Dynamic simulation model with a focus on urban traffic flows, including activity behaviour, changes in land use, and effects on environment |
Human sphere (activity patterns and travel demand, traffic flows, goods transport, accessibility of locations, location decisions of households, firms, developers) Land use (moving households, location of firms, investment of developers, new industrial area) Environment (clean air, traffic noise) |
|
| ABM_2 |
Evolution of an entire urban region with emphasis on transportation |
Human sphere (location choice, activity schedule, activity patterns, automobile ownership, travel demand) Land use (land development, transportation network) |
|
| ABM_3 |
Predicting urbanisation with behavioural agents |
Human sphere (demographic change, decisions of individuals) |
|
| ABM_4 |
Development of built-up area in peri-urban region, driven by households and entrepreneurs; urban growth with different growth rates |
Human sphere (households, jobs, numbers of people, households and workplaces at the start of the year, average travel time to district centres and capital city) Land use (urban land, open space, forest area) |
|
| ABM_5 |
Link between transport and land use; impact of different planning strategies |
Human sphere (population, households, employment, travel demand, accessibility, mobility, real estate, land price) Land use |
|
 |
 |
 |
Living Rev. Landscape Res., 3 (2009), 2, doi:10.12942/lrlr-2009-2, URL (accessed <date>): http://lrlr.landscapeonline.de/lrlr-2009-2.
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