Skip to content

Land Use Scanner

Jump to bottom
Jip Claassens edited this page Jan 16, 2024 · 36 revisions

VU University Amsterdam and Object Vision have long-standing experience in integrated land use modelling. Research is done in both a national and an international context. Applications include studies into the future of agriculture in the Netherlands, deforestation in Surinam, climate impacts and adaptation measures. Most of this research was carried out for and with an operational land use model that integrates urban and non-urban types of land use, the Land Use Scanner.

model outline

Land Use Scanner is a geographical information system (GIS)-based model developed by Hilferink and Rietveld (1999) as an operational tool for the integrated simulation and evaluation of future land-use patterns. Depending on the application, between 10 and 20 land-use classes are distinguished, typically ranging from agricultural and natural on the one hand to urban (industrial, residential, et cetera) on the other. The model input is specified per land-use class and consists of (i) local suitability maps, usually per 1-hectare grid cells, and (ii) regional land-use demand. The model produces projections of future land use per grid cell. In addition to local conditions (e.g., soil type), the suitability maps may also contain reference to the wider spatial context (e.g., land use in neighbouring grid cells or the density of specific facilities within a specified radius). The regional demand for different types of land use is usually derived from external, sector-specific models. The model can apply different procedures to allocate land to the most suitable locations depending on the objective of the study at hand. When land use is described in a discrete manner (with only one type of dominant use per cell), the allocation ensures that (i) aggregated (i.e., for the study area at hand) suitability is maximised; and (ii) regional land-use demand is met (Koomen et al., 2008). The model is also able to deal with a continuous description of land use per grid cell (with different fractions devoted to individual uses) and simulate the competition between different land-use types for limited space. This process is based on the discrete choice theory formulated by McFadden (1978) that explains the choice behaviour between mutually exclusive alternatives from an economic perspective. An extensive description of the different model versions is provided elsewhere (Koomen et al., 2011a).

One of the main strengths of the model is the option to generate ‘what-if’ type simulations to explore different future scenarios or policy interventions and to visualise and communicate expected outcomes. It can do so more or less interactively. As such, the model is not merely a modelling tool but, more importantly, a communication tool stretching different domains that connect analysts with policymakers (Koomen et al., 2011a). See, for example, future land-use projections in Flanders (De Moel et al., 2012), simulations of residential land use in the Elbe River Basin (Hoymann, 2010), assessments of flood risk along the river Rhine (Te Linde et al., 2011), and climate adaptation measures in the European Union (Verburg et al., 2012).

Furthermore, note that the Land Use Scanner in most applications relies strongly on external model results as inputs, while its outputs are regularly used in dedicated impact assessment models. The model is used, for example, in all land-use-related impact assessment studies by PBL Netherlands Environmental Assessment Agency that study future flood risk scenarios (Rijken et al., 2013) and potential urban transformation processes (van Duinen et al., 2016). Moreover, the Joint Resource Centre of the European Commission uses a very similar approach in their ‘Land-Use-based Integrated Sustainability Assessment’ (LUISA) modelling platform, which is based on the Land Use Scanner (EC-JRC, 2016; Lavalle et al., 2011). Figure 1 provides a graphical depiction of the structural design of the Land Use Scanner modelling framework.

The model integrates the best available knowledge and insights from the various fields involved. The output from impact models can be iteratively incorporated in Land Use Scanner to, for example, update local suitability, regional land demand, or both. Another feature of the model is that it integrates developments in all types of land use, making it different from the many models that mostly focus on developments in urban land use, e.g., UrbanSim by Waddell (2002) or SLEUTH by Clarke et al. (1997) or rural types of use, e.g., ProLand by Möller and Kuhlmann (1999). In most applications, Land Use Scanner simulates transitions from one distinct type of use to another, while UrbanSim, for example, also models changes within specific land-use classes and their function. Land Use Scanner is thus better equipped to simulate more drastic transitional processes, whereas UrbanSim can be used, for example, to model more gradual transformation processes such as locational choices of households within urban areas. To some extent, these differences can be classified as focussing on land cover and land function, respectively, as was discussed extensively by (Jacobs-Crisioni et al., 2017). This distinction will be further blurred, however, as ongoing model development work results in a Land Use Scanner version that incorporates more economic logic and better replicates the land market (Borsboom-van Beurden and Zondag, 2011; Koomen et al., 2015).

This section is an excerpt from Claassens, J., Koomen, E., & Rijken, B. (2020). Linking socio-economic and physical dynamics in spatial planning. In S. Geertman, & J. Stillwell (Eds.), Handbook of Planning Support Science (pp. 383-396). Edward Elgar Publishing.

development

This integrated GIS-based land use model was developed in close cooperation with the Netherlands Environmental Assessment Agency (PBL), Geodan and the Agricultural Economics Research Institute (LEI). It has been under constant development since its conception, and most recently by the Netherlands Environmental Assessment Agency (PBL) and Object Vision for several urbanisation studies (e.g. PBL, 2021).

The GeoDMS framework underlying Land Use Scanner is developed and maintained by the Object Vision company and also applied in other spatially explicit models. Examples include a land-use modelling framework for the European Commission that covers all member states of the European Union at a 100-metre resolution (Lavalle et al., 2011) and the 2UP model that simulates urban development and population change around the globe (Koomen et al., 2023). A full account of the original Land Use Scanner is provided by Hilferink and Rietveld (1999), whereas recent applications are documented in a book by Koomen and Borsboom-van Beurden (2011).

This website provides more information on the outline and characteristics of the model. A fully functional demonstration version of the model can be downloaded here.

variants

Over the decades, many versions of the Land Use Scanner have been developed, such as:

bibliography

  • Borsboom-van Beurden, J. A. M., & Zondag, B. (2011). Developing a new, market-based land-use model. In E. Koomen & J. Borsboom-van Beurden (Eds.), Land-use modeling in planning practice (pp. 191-209). Dordrecht: Springer.

  • Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area. Environment and Planning B: Planning and Design, 24(2), 247-261.

  • De Moel, H., Koks, E. E., Dekkers, J. E. C., Lassche, M. R., & Bouwer, L. M. (2012). Methods for future land-use projections for Flanders. Amsterdam, The Netherlands: Institute for Environmental Studies Vrije Universiteit Amsterdam.

  • EC-JRC. (2016). LUISA Territorial Modelling Platform. Retrieved from https://ec.europa.eu/jrc/en/luisa

  • Hilferink, M., & Rietveld, P. (1999). Land Use Scanner: An integrated GIS based model for long term projections of land use in urban and rural areas. Journal of Geographical Systems, 1(2), 155-177.

  • Hoymann, J. (2010). Spatial allocation of future residential land use in the Elbe River Basin. Environment and Planning B: Planning and Design, 37(5), 911-928.

  • Jacobs-Crisioni, C., Diogo, V., & Baranzelli, C. (2017). Bridging the gap between function and cover in spatially explicit land-use models: The LUISA approach. Paper presented at the AGILE 2017: 20th conference on geo-information science, Wageningen.

  • Koomen, E., Diogo, V., Dekkers, J. E. C., & Rietveld, P. (2015). A utility-based suitability framework for integrated local scale land-use modelling. Computers, Environment and Urban Systems, 50, 1-14.

  • Koomen, E., Hilferink, M., & Borsboom-van Beurden, J. (2011a). Introducing Land Use Scanner. In E. Koomen & J. Borsboom-van Beurden (Eds.), Land-use modeling in planning practice (pp. 3-21). Dordrecht: Springer.

  • Koomen, E., Koekoek, A., & Dijk, E. (2011b). Simulating Land-use Change in a Regional Planning Context. Applied Spatial Analysis and Policy, 4(4), 223-247.

  • Koomen, E., Loonen, W., & Hilferink, M. (2008). Climate-change adaptations in land-use planning; a scenario-based approach. In L. Bernard, A. Friis-Christensen, & H. Pundt (Eds.), The European Information Society; Taking Geoinformation Science One Step Further (pp. 261-282). Berlin: Springer.

  • Koomen, E., Opdam, P., & Steingröver, E. (2012). Adapting complex multi-level landscape systems to climate change. Landscape ecology, 27(4), 469-471.

  • Lavalle, C., Baranzelli, C., Batista e Silva, F., Mubareka, S., Rocha Gomes, C., Koomen, E., & Hilferink, M. (2011). A High Resolution Land use/cover Modelling Framework for Europe: introducing the EU-ClueScanner100 model. In B. Murgante, O. Gervasi, A. Iglesias, D. Taniar, & B. O. Apduhan (Eds.), Computational Science and Its Applications - ICCSA 2011, Part I, Lecture Notes in Computer Science vol. 6782 (pp. 60-75). Berlin: Springer-Verlag.

  • McFadden, D. (1978). Modelling the choice of residential location. In A. Karlqvist, L. Lundqvist, S. F., & J. W. Weibull (Eds.), Spatial Interaction Theory and Planning Models (pp. 75-96). Amsterdam: North Holland Publishers.

  • Möller, D., & Kuhlmann, F. (1999). ProLand: A new approach to generate and evaluate land use options. Paper presented at the IX European Congress of Agricultural Economists, Warsaw, Poland.

  • Rijken, B., Bouwman, A., Van Hinsberg, A., Van Bemmel, B., Van den Born, G. J., Polman, N., Lindenhof, V., & Rijk, P. (2013). Regionalisering en kwantificering verhaallijnen Deltascenario's 2012. Technisch achtergrondrapport. Den Haag: P. v. d. L. L. W. UR.

  • Te Linde, A. H., Bubeck, P., Dekkers, J. E. C., De Moel, H., & Aerts, J. C. J. H. (2011). Future flood risk estimates along the river Rhine. Natural Hazards and Earth System Sciences, 11(2), 459-473.

  • van Duinen, L., Rijken, B., & Buitelaar, E. (2016). Transformatiepotentie: woningbouwmogelijkheden in de bestaande stad. Den Haag, Nederland: Uitgeverij PBL.

  • Verburg, P. H., Koomen, E., Hilferink, M., Pérez-Soba, M., & Lesschen, J. P. (2012). An assessment of the impact of climate adaptation measures to reduce flood risk on ecosystem services. Landscape ecology, 27(4), 473-486.

  • Waddell, P. (2002). Urbansim: modelling urban development for land use. Journal of the American Planning Association, 68(3), 297-314.

Land use modelling documentation

© Object Vision BV

Land use modelling

Clone this wiki locally