7b, Land‐use allocation exercise

learning objective: land-use exercise

Modelling the future of the Netherlands

Now that you are familiar with the model, it is time to bring this knowledge into practice. You are provided with a fully functional copy of the Land Use Scanner that already contains a basic reference to four socio-economic scenarios (see Appendix 7II for a description). Scenario-specific land-use claims have been added to the model, and all available spatial datasets are included in the suitability maps. No weights have been added to these maps, however. The land-use claims and suitability maps are combined in the allocation module, and results are available from the Simulations container.

Assignment 1. Exploring suitability factors

To generate maps related to Dutch land use in 2040 in a specific scenario, we request you do the following:

  1. Discuss within your group what suitability factors are important to you. How should the country look in the future? What factors could play a role? Does it have a positive or negative effect on the suitability?
  2. The next step is adjusting the suitability maps for all land-use types in your scenario. You can do this by changing the appropriate values in the scenario script (e.g. A1.dms) in the text editor. As a start, you can use the values you agreed upon in the previous step. Keep in mind that the total suitability value of a land-use type reflects the net benefits of a certain location for that specific land-use type. This should provide a rationale (as well as limits) for your range of suitability values. Keep the range between -20 and +40. Finally, save the script and re-start Land Use Scanner.
  3. Look at the resulting suitability maps per land-use function and discuss whether these are coherent with your initial thoughts on the scenario. Results can be analysed by comparing saved bitmaps and statistics of the situation before and after you change the script.
  4. Simulate future land use by running the appropriate simulation (e.g. Simulations > A1_GE). Check the resulting maps on consistency with the storylines of the scenario, e.g. by making maps that depict dominant land use and the changes from current land use. Discuss which adjustments are needed.
  5. Adjust the suitability maps and rerun the model until the results correspond with your views on the scenario.
  6. Summarize the results by making characteristic maps of future land use. Make use of the possibilities for creating evaluation maps that describe dominant land use, the changes compared to the current situation and impacts on natural areas and landscape values. You can export maps by right-clicking a layer and use them in, for example, ArcGIS to create more elaborate impact assessments and/or more refined maps.

Assignment 2. Adjusting claims

Now that you understand the impact of suitability factors, it is time to look into the effect of regional demand (or claims) on the outcome. The claims are defined per claim region (Randstad, intermediate zone, and peripheral zone) per land-use type in a .csv-file (e.g. …\lus_demo\data\Scenarioclaims\Residential_rstintper.csv) that looks like this:

RstIntPer Regio A1 A2 B1 B2
0 Exterior 0 0 0 0
1 Randstad 39481.46516 26902.14096 20722.64149 5381.116004
2 Intermediate zone 25409.43429 17430.2063 11185.06832 5048.757996
3 Peripheral zone 16016.07455 11109.5091 7134.264191 0
  1. Save the predominant land-use map to, for example, a Word document. Then, change the claim for a region for a scenario and run again. What are the impacts?

Assignment 3. Using restriction layers as suitability factors

Now that you understand the interplay of suitability and regional demand, we can think more in terms of policy analysis. An increasingly important factor in residential development in the Netherlands is climate adaptation. So, we want to investigate the impact of a policy measure, such as not building on too wet, too weak, or too subsidence-prone soils, or in flood-prone areas.

The model already contains the grid layers we need for this:

  • The soil layers (read from TooWetSoil.tif, TooWeakSoil.tif and TooSubsidenceProneSoil.tif in …\lus_demo\data\Current_situation\Thematic_maps\NatureLandscape) are configured as the attributes IsWetSoil, IsWeakSoil and IsSubsidenceProne in /Current_Situation/Thematic_maps/NatureLandscape.
  • The flood danger layers (read from WaterDepth_cm.tif and FloodHazardZones.tif in …\lus_demo\data\Current_situation\Thematic_maps\Safety) are configured as floodWaterdepth, FloodHazardZones and HasFloodHazard in /Current_Situation/Thematic_maps/Safety.
  1. First, inspect these layers in the TreeView and view them on the map. What value type do these layers have? How can you discern that? Hint: look at the detail pages in the GeoDMS GUI, or find the attribute declarations in …\lus_demo\cfg\demo\Current_situation\thematic_maps.dms.
  2. Now how to use these data sets? They are already present as suitability factors in the scenario scripts (e.g. /Scenario_Components/Local_Suitability/A1_GE/residential in A1.dms), but with a weight of 0.0 [Eur_M2], so they currently have no effect at all. For example: ```
    • 0.0 [Eur_M2] * NatureLandscape/IsWetSoil[Float64] ``` Implement the policy measure by giving these factors a clearly negative weight for the urban land-use types. What value should you give them? And should a yes/no layer such as IsWetSoil get the same weight as a rescaled continuous one such as floodWaterdepth?
  3. Now, determine the impact of this policy measure by rerunning the simulation and comparing the results before and after the change. Hint: look here /Simulations/A1_GE/Results/Continuous/Evaluation/Floodrisk/Result/LU_Damage.

Assignment 4. Add your own additional suitability factor

Maybe you can think of other suitability factors that might be relevant. Find a dataset, maybe manipulate it, and add it. For example, maybe add a distance map to a buffered road network.

Go to previous module: 7a, Getting started

Go to next page: Appendix 7I, Land Use Scanner Background