6a: Getting started

learning objective: How to use the Land Use Scanner model

To familiarize yourself with the Land Use Scanner model, we ask you to go through this simple exercise. Read the exercise carefully! After completing all steps, you should be able to use the basic functions of the model.

Getting started

Start the Land Use Scanner model. The model should now display a blank starting page entitled demo.dms (aka LUS_demo). Beneath this title bar, you find:

• The menu bar with several pull-down menus;
• A dark grey toolbar that can contain window-specific tools;
• On the left-hand side, a TreeView that allows you to navigate through the spatial data collection;
• In the middle, a currently empty grey data view area (for displaying tables and maps);
• On the right-hand side, a light-brownish panel titled Generic properties will later show all kinds of detailed information.
• At the bottom is a status bar presenting hints and status information.

We will concentrate on the TreeView since that provides the easiest access to the model.

Viewing spatial data

The Land Use Scanner contains a number of spatial data sets. These can be viewed by browsing through the TreeView.

1. Start by clicking on the arrow in front of the container (directory) called Current_situation and again on the arrow in front of the subsequent Current_landuse container. This container includes nine data layers describing the land use in 2017. The small globe indicates that a map of the data layers can now be drawn in the data-view area.
2. View several land-use maps by trying out all two possible ways to draw a map view:
   1. double-clicking on any data layer names. This option allows multiple data layers to be drawn in the same map view window, and
   2. by activating (clicking on) the data layer and then simultaneously pressing the Ctrl-M key combination or giving a right-mouse-click on the data layer and selecting the Map View option from the appearing menu. This option will open a new map view window for every data layer; Note that when you view a current land use layer, a legend appears on the right-hand side, which indicates the number of hectares (ha) and that values range from 0 to 1, the maximum value for the 100x100-meter grid cells. The last column in this legend contains each class's count (i.e., the number of cells). Right mouse-click on the legend area. This allows you to select the statistics function in the appearing menu, providing you with some basic statistics on the selected land-use function.
3. The toolbar now contains several standard GIS functions like zoom in, zoom out, pan, get info, full extent, copy visible area to clipboard, and toggle scalebar. Explore these functions to familiarise yourself with their possibilities.
4. The individual land-use maps do not offer a coherent view of land use in the country, so we will try a different way of looking at these data layers. Close all opened map views (menu option Window > Close All), then click on the data layer Predominant. This layer describes the predominant land use per cell. This map shows the land-use function that covers the most hectares for every grid cell. It clearly indicates the most notable features of the country: a network of big cities in the west of the country, a number of large nature areas along the coast and in the middle of the country, and lots (two-thirds of the land surface!) of agricultural land in between.
5. The Land Use Scanner also contains a collection of relevant spatial data sets for future land-use simulation. These sets are considered when suitability is defined.

Take 10 minutes to browse the containers containing policy, thematic, and distance decay maps. See the description of all available data layers in Appendix 3 for information about the available data sets.

Defining suitability

We will now explore the way suitability is included in the model.

1. Start by opening the A1_GE scenario in the Scenario_Components > Local_Suitability container. Now open the Residential land-use suitability map and write down the minimum and maximum value in the statistics. Zoom in at a city and save an image of that particular situation. Inspect the definition of the suitability map that you opened by right-clicking on the appropriate data layer and selecting the edit config source option. You may also hit the Crtl+E keys after selecting the data layer. This will open a text editor at the line of the script that defines the data layer you just opened. Note that this option will only work when you properly refer to the text editor of your choice see this page for instructions.
2. All suitability maps of a scenario are defined in a single compact text file that holds the scenario name. It contains a header and a series of text lines relating to the suitability of the various land-use functions. See, for example, the suitability script of the residential land-use function.

A function’s suitability is clearly distinguished in values related to:

1. current land use;
2. policy maps;
3. thematic maps, and
4. distance decay maps.

Note that most available spatial datasets are included in the script, but all values are set to zero apart from the one related to current land use. This means that the suitability of a land-use function is currently only related to present land use. This is a good start because we normally want to preserve land-use functions in their current location. Remember that we will simulate the total of current land use and additional claims on a blank map. We will need to add values to the relevant datasets to include the preferred locations for the future development of specific land-use functions. The upcoming assignment's objective is to do this in a structured, coherent way with the scenario's storyline. For now, we will only practice adjusting the script.

Please only adjust the values at the start of the line (before the [Eur_M2]), so do not, for example, change the km or Percent values at the end of the lines. You may, however, also change the signs of the values in front of the lines, indicating whether a map increases (“ + value …”) or decreases (“ - value …”) the suitability of a land-use function.

As a guideline, you can use the following values to indicate the importance of the various datasets/policies/maps:

• Value not important 0.0
• Somewhat important 1.0
• Important 3.0
• Very important 5.0

Of course, you are free to choose values outside these guidelines. For example, if you really want to stress the importance, you could set the value to 15.0 and see what the effect is on your simulation result. Since the Land Use Scanner uses the suitability in an exponential function, high values for this variable will cause a numerical overflow error and stop the simulation. Therefore, try to keep the range of the suitability values of your land-use types between –20 and +40 Euro per m2, as the legend of the suitability maps also indicates.

Scroll down to the part of the A1.dms script that relates to the function of Residential land use, which is indicated by the green heading with the word RESIDENTIAL in capitals. Change the values for residential spatial plans and attractiveness surroundings (indicating the public appreciation of the surrounding landscape) from 0 to 5. Make sure you save the changes you made.

Reopen the Land Use Scanner by hitting the ALT and R keys simultaneously or by going to the File dropdown menu, choosing Reopen current configuration. Note: Reopening the Land Use Scanner allows the model to consider your update in the scenario script. You can now redraw the changed suitability map for residential land use in the A1 scenario. This map should include the plans for residential extensions and an indication of attractive landscapes.

Compare the current minimum and maximum values with the original ones that you wrote down in step 1. Have the values changed as you expected them to? Also, check whether the pattern reflects that of the underlying data layers by adding these to the map view and comparing it with the saved image.

Other scenario components

Apart from suitability, scenarios are defined through their exogenous land-use developments and land-use claims.

1. The container Scenarios > Exogenous_Landuse > A1_GE contains the expected exogenous developments, i.e. the expected future land use for the functions that are not simulated. Most of these functions are not expected to change at all. We will not pay any further attention to this issue.
2. Each land-use function demands a certain surface area. For each scenario, the demand per land-use type may vary. For example, in a scenario with higher population growth, it is presumable that more space will be needed for residential areas, thus, the land-use claim (in hectares) for residential will be higher. The container Scenario_Components > Regional_Demand > A1_GE > Additional_Claims lists for each land-use type the area expected to be added between 2017 and 2040 based on the scenario assumptions.

Right-click on the residential map and select Table View (shortcut: press Ctrl-D). A table will be displayed in the DataView listing the land-use claims for 3 regions. Close (exit) the table in DataView and then double-click on the item in TreeView to view the map of the claims. It is not necessary for all land-use claims to balance out (add up to exactly 0). The model is able to cope with over-demand or over-supply of land.

Simulating future land use

1. In the TreeView, click on Simulations > A1_GE > results > continuous (i.e. the continuous allocation algorithm). When you open this container, you will see two sub-containers: LandUse and Evaluation. The container LandUse contains result maps of all nine land-use types for 2040 (subdivided into two sub-containers: Endogenous and Exogenous land use) and a map with the predominant land use per cell only. You can, for example, compare the maps of predominant land use in 2040 with the map of predominant land use in 2017.
2. The container Evaluation contains a set of evaluation measures for different spatial scale levels, which are described below:
   • Total: Four table containers describing national totals in current land use, total demand, allocated land use and the fraction of demand that was realised.
   • Regional: Double-click on the container label AllocatedLanduse, and a table listing allocated land use in hectares will be drawn in the DataView. Copy-paste the contents of this table to Excel by clicking on the Copy-Paste icon in the Toolbar and then selecting Edit > Paste (or CTRL+V) in Excel. Using current and allocated land use, you can numerically see the differences between the endogenous land use in 2017 and 2040, calculate percentages et cetera.
   • Local: The container DifferenceMaps contains difference maps of the six endogenous land-use types. The maps indicate the local (i.e. per grid cell) difference between land use of a certain type in 2040 and 2017. Container Urbanisation contains several evaluation measures developed specifically for analysing the effects of urbanisation (see Ritsema van Eck and Koomen, 2008, for more details). The indicators describe:
     • urban development by creating maps of current and allocated built-up areas and calculating a difference map;
     • urban pressure on nature and landscape, distinguishing pressure on the National Ecological Network (netEHS), BirdHabitat areas, or high-quality landscapes;
     • urban area size, capturing the size of individual urban areas (BuiltupAreaSizePerCel) in both the current and allocated situation and an additional statistic for each situation describing the average size of the urban areas.
   • Flood risk: This set of indicators describes the potential damage that arises from flooding. These calculations build upon land-use-specific damage functions that link land use and expected water depth to potential damage in Euros per hectare. Damage is calculated for individual grid cells based on predominant land use and aggregated to flood risk management zones (dike rings). By comparing current and simulated land-use-based damage per region, an indication of the potential increase in damage due to land-use change is obtained. This change ratio is applied to expert-based damage assessments for the current situation to obtain a more accurate estimate of potential future damage. Similar calculations are applied in many research projects and policy evaluation studies (e.g. de Moel et al., 2011). A more detailed description of the calculation process is provided elsewhere (e.g., Bubeck and Koomen, 2008).
3. Familiarise yourself with these evaluation measures by opening several maps to see if you understand the interpretations of the maps you open. As soon as you draw the map of predominant land use in 2040 or use one of the evaluation measures, the model will run the scenario and calculate the results. This may take several minutes. After the results have been calculated, they are kept in memory and therefore, opening other evaluation measures or displaying other allocated land use will go much faster.

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