DOI: 10.5061/dryad.sj3tx96bs
title: Future land use maps for the Netherlands for the Dutch One Health SSPs

description: We have created future land use maps for the Netherlands for 2050, based
 on the Dutch One Health Shared Socio-economic Pathways (SSPs). This was
 done using the DynaCLUE modelling framework. Future land use is based on
 altitude, soil properties, groundwater, salinity, flood risk, agricultural
 land price, distance to transport hubs and climate. We also account for
 anticipated demand for different land use types, historic land use changes
 and potential spatial restrictions. These land maps will enable detailed
 modelling of a wide variety of future health challenges in the
 Netherlands, such as disease risk, water quality and pollution. In
 addition, the methodology and assumptions used can inform research in
 other urban deltas. Here we provide all model input and output files.
description: We used the DynaCLUE modelling framework. A full description of
 how all model inputs were derived is provided in the accompanying
 paper. We made future land use maps for the Netherlands
 for 2050, based on the Dutch One Health Shared Socio-economic Pathways
 (SSPs) (https://doi.org/10.1007/s10113-023-02169-1). Maps were created for SSP1, 3, 4 and 5. We considered five land use types: urban, pasture, crops, forest and non-forest nature.  The maps were created on a 1km grid. The main steps were: Classify land use types Use historic Dutch land use data to determine the likelihood of changes Determine demand for each land use type under each scenario Determine possible predictors of change (from literature) and use logistic regression to determine how they affect land use type Define spatial restrictions and preferences affecting land use change under each scenario Define additional model parameters and inputs Run the model to create land use maps for each SSP scenario We also validated the model by starting it in the year 1990 and seeing how well it predicted land use in 2018.
description: # Future land use maps for the Netherlands for the Dutch One Health SSPs
 [https://doi.org/10.5061/dryad.sj3tx96bs](https://doi.org/10.5061/dryad.sj3tx96bs) These files include everything needed to run the DynaCLUE model to replicate our results. This includes the inputs for each scenario and for the model validiation. These files also include the model outputs, i.e. land use maps for the Netherlands for 2050 under each of four scenarios. ## Description of the data and file structure **DynaCLUE** The DynaCLUE model is available to download here: [https://www.environmentalgeography.nl/site/data-models/data/clue-model/](https://www.environmentalgeography.nl/site/data-models/data/clue-model/) We used version 2.0. To run the model with our inputs, save all the model inputs for one scenario in the same folder as the DynaCLUE executable file. Then open DynaCLUE, select the region and demand files, and click 'Run'. The outputs will be saved in the same folder. **Model inputs** The model inputs are contained in 5 zip files: SSP1, SSP3, SSP4, SSP5 and Validation. Each of these contains the following: | File name | File type | Description | | :---------------------- | :-------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | age.0 (validation only) | raster | Length of time each grid cell has held its current land use type | | alloc1.reg | text | Regression coefficients for each land use type | | allow\.txt | text | Describes which land use changes are permissible | | cov\_all.0 | raster | Starting land use | | demand.in1 | text | Demand for each land use type per year simulated | | locspec\.fil | raster | Describes areas with increased probability for the given land use type (location specific preference addition). X is the land use type (0: Urban, 1: Pasture, 2: Crops, 3: Forest, 5: Non-forest nature) | | main.1 | text | Lists model parameters | | region\_\.fil | raster | Describes areas where no land use change is possible. X is the scenario or validation | | sc1gr\.\ | raster | Files for each predictor (altitude, flood risk, etc.). X is the predictor number (see predictor\_names.txt in *Additional files*). Y is either ‘fil’ or a number indicating the year of the simulation; ‘fil’ is used for constant predictors and also for the final year simulated. | Land use types are referred to by numbers. These are as follows: 0 - Urban, 1 - Pasture, 2 - Crops, 3 - Forest, 5 - Non-forest nature All raster files are on a 1km grid with CRS EPSG: 28992. Full details of how DynaCLUE input files are structured is available here: [https://www.environmentalgeography.nl/site/data-models/data/clue-model/ ](https://www.environmentalgeography.nl/site/data-models/data/clue-model/) **Model outputs** Model outputs are in the following files: | File name | File type | Description | | :------------------------------------- | :-------- | :------------------------------------------------------ | | SSP1.asc, SSP3.asc, SSP4.asc, SSP5.asc | raster | Final land use maps for each scenario | | validation\_CORINE.asc | raster | Used for validation: CORINE 2018 land use | | validation\_prediction.asc | raster | Output of validation: model prediction of 2018 land use | **Additional files** * rasterTemplate.asc * land_types.txt * currentLU.asc * correlations.xlsx * predictors.txt * predictor_names.txt * predictorFiles.zip * regressionOutput.csv * LandPrice_RegressionOutput.csv * SSP4demand.tif * SSP5demand.tif rasterTemplate.asc is a blank raster, providing the grid which is used in all the analysis land_types.txt lists how the CORINE land use types were classified into our 5 land use types. currentLU.asc is a raster showing current (2018) land use in the Netherlands, using our 5 land use types. correlations.xlsx is a spreadsheet containing the correlations between all the different predictors which were considered for use. predictors.txt is the list of predictors used for each land use type for the logistic regression analysis. Each environmental predictor used in the model is referred to by a number. Predictor_names.txt shows which predictor has which number. predictorFiles.zip includes all the predictor data used in the logistic regression analysis. regressionOutput.csv contains the output of the logistic regression analysis which was used to establish relationships between the land use types and the different environmental predictors. LandPrice_RegressionOutput.csv contains the output of the regression analysis which was performed to enable the prediction of future agricultural landprices. SSP4demand.tif was used for calculating potential forest succession under SSP4. It shows the area that was deemed to have the potential for succession. This is the pasture, crop and non-forest nature area from 2018, excluding the wealthy areas and areas near centres for knowledge and business. Comparing this with the total pasture, crop and non-forest nature area from 2018 (see cov_all.0 in above table) suggests that 87% of this area has the potential for succession. SSP5demand.tif was used for calculating potential forest succession under SSP5. It shows the area that was deemed to have the potential for succession. This is the non-forest nature area from 2018, excluding areas near major population centres. Non-forest nature which has the potential for succession has value '4', while non-forest nature near population centres has value '5'. This suggests that 67% of 2018 non-forest nature area has the potential for succession. ## Code/Software Two R scripts are included: * historicChanges.R * regression.R historicChanges.R determines historic land use changes in the Netherlands from 1990 to 2018 and uses this to calculate the conversion elasticities used in the DynaCLUE model. In addition to the files included here, this also requires the CORINE land use maps for the Netherlands. regression.R performs logistic regression analysis to determine land use suitability and creates the alloc1.reg file to use as an input to DynaCLUE. All analysis was performed using R v4.0.4. These scripts require the following packages: * dplyr (v1.0.7) * MASS (v7.3-56) * pROC (v1.18.0) * raster (v3.5-2) * rgdal (1.5-23) ## Additional files * Supplementary_tables.docx This includes several tables complementing the journal article.