Determine the currently protected area

The first task will be to determine the currently protected area in Kenya. You will search for data, explore its documentation, and use it in ArcGIS Pro to determine the percentage of Kenya that is currently protected. You will use this information later to help determine which areas to prioritize for conservation.

Search for data in Living Atlas

First, you’ll examine data layers available in ArcGIS Living Atlas that may be useful for your analysis.

ArcGIS Living Atlas is a collection of curated authoritative data layers, maps, and apps hosted in ArcGIS Online by Esri and the GIS community. If you have an ArcGIS organization account, you have access to this vast collection with a browser and in ArcGIS Pro.

  1. In a browser, go to https://livingatlas.arcgis.com/en/home/
  2. Sign in to ArcGIS Online.

    Note:

    If you don't have an organizational account, see options for software access.

  3. In the ArcGIS Living Atlas search box, type world countries generalized, and in the search results, click World Countries Generalized.

    Search for world countries generalized

  4. In the search results, click World Countries Generalized.

    World Countries Generalized in the results

    This layer is a feature layer, hosted by Esri and shared as authoritative content with ArcGIS Living Atlas.

    Properties page for the World Countries Generalized layer

    From this page, you can get more information about the layer, open it in the map viewer or a scene, and add it to ArcGIS Pro.

    The country polygon features in this layer can be used in analyses. They can be extracted and used to define a study area, and to clip other layers. Later in this tutorial, you'll use a polygon from this layer to determine the conserved area in Kenya and calculate how much more needs to be conserved to meet the 30x30 goals.

    You will explore a bit more before beginning your analysis.

    ArcGIS Online layers can be found using their name or their layer ID, which is shown in the URL:

    https://arcgis.com/home/item.html?id=2b93b06dc0dc4e809d3c8db5cb96ba69
  5. Make a note of the layer name, World Countries Generalized, and the Item ID from the URL: 2b93b06dc0dc4e809d3c8db5cb96ba69
  6. In the ArcGIS Living Atlas search box, type world database of protected areas, and click WDPA – World Database of Protected Areas in the results.

    Search for world database of protected areas

  7. In the search results, click the WDPA – World Database of Protected Areas layer.

    This feature layer is a joint project between UN Environment World Conservation Monitoring Centre (UNEP-WCMC) and the International Union for Conservation of Nature (IUCN). It is an authoritative source, showing marine and terrestrial areas of conservation with a variety of different environmental attributes.

    The layer is provided by IUCN and WCMC to support scientific analysis and conservation planning, policy, and management. This data may not be used for commercial purposes without written permission. The data used in this tutorial is used with permission of UNEP-WCMC.

    Note:
    When you are investigating a data layer that might be useful to you, it is important to study its details page, which provides important context information about the data, including who compiled it, how it was collected, what it is intended to be used for, and whether or not there are use constraints on the data or who can use it.

    The layer is available at: https://arcgis.com/home/item.html?id=ae78aeb913a343d69e950b53e29076f7

  8. Make a note of the layer name and the Item ID value.

    WDPA - World Database of Protected Areas

    ae78aeb913a343d69e950b53e29076f7

    Later in this tutorial you'll use the polygons of protected areas in Kenya to determine the fraction of Kenya that is under conservation management, and you'll determine how much more needs to be conserved to meet the 30x30 goals.

    You have identified some useful data layers in ArcGIS Living Atlas. Next, you’ll open an ArcGIS Pro project package with these and other layers and explore the data.

Add data from Living Atlas to ArcGIS Pro

You've seen data in the ArcGIS Living Atlas website. Now you'll open ArcGIS Pro, add some ArcGIS Living Atlas data to a project, and examine the data.

  1. Download the Model conservation suitability.ppkx project package and locate the downloaded file on your computer.

    Note:
    Most web browsers download files to your computer's Downloads folder by default.

  2. Double-click the Model conservation suitability.ppkx project package.

    Tutorial project package

    Project packages are a way of sharing ArcGIS Pro projects and data. They are compressed files that, when opened, extract a copy of the project to your C:\Users\[user name]\Documents\ArcGIS\Packages folder.

    The project package extracts, and the project opens to a map showing a basemap. A number of layers are in the Contents pane, but they are currently turned off. Next you’ll add a layer from ArcGIS Living Atlas to this map.

  3. On the ribbon, on the View tab, click Catalog Pane.

    Catalog Pane button

  4. In the Catalog pane, click Portal.

    Portal tab in the Catalog pane

  5. Click Living Atlas.

    Living Atlas button

  6. Copy the Item ID value for the World Countries Generalized layer, 2b93b06dc0dc4e809d3c8db5cb96ba69, paste it in the Search Living Atlas box, and press Enter.

    Item ID in the search box

  7. In the search results, right-click World Countries Generalized and click Add to current map.

    Add To Current Map to add the layer to the map

    The layer is added to the map.

    The countries layer appears on the map.

  8. In the Catalog pane, in the Search Living Atlas box, type World Database of Protected Areas and press Enter.

    Search for World Database of Protected Areas.

  9. In the search results, right-click WDPA - World Database of Protected Areas, and click Add To Current Map.

    Add To Current Map for adding the WDPA layer to the map

    The WDPA – World Database of Protected Areas layer is added to the map.

    The layer is added to the map.

    You've seen how to search for data from ArcGIS Living Atlas and add it to ArcGIS Pro. Next, you’ll use this data to analyze the protected area of Kenya and determine how much more needs to be conserved to reach the 30x30 goal.

Extract a polygon

You’ll use the generalized Kenya polygon to calculate the area currently conserved.

  1. On the ribbon, on the Map tab, in the Inquiry section, click Locate.

    Locate button

    The Locate tool is useful for finding places.

  2. In the Locate pane, click Layer Search.

    Layer Search tab

    The Layer search option searches within the layers currently on the map.

  3. In the search box, type Kenya and press Enter.

    Search for Kenya

  4. In the results, right-click Kenya and click Add To Selection.

    Add To Selection for Kenya

    The Kenya polygon is selected.

  5. In the results, right-click Kenya and click Zoom To.

    Zoom To Kenya

    The map zooms to Kenya.

    The map zooms to Kenya.

  6. Close the Locate pane.
  7. In the Contents pane, right-click the World_Countries_Generalized layer, point to Data, and click Export Features.

    Export Features option

    The Export Features tool opens.

    The Input Features parameter is set to the World_Countries_Generalized layer.

    The tool detects that there is a single feature selected. It will export only the selected features by default, although you can use the switch to ignore the selection and export all of the features.

  8. For the Output Feature Class name, type KenyaFeature.

    Export Features tool ready to run

    The default location for the new feature class is the project geodatabase, which is good.

  9. Click OK to run the tool.

    The new KenyaFeature layer is added to the map. You no longer need the World_Countries_Generalized layer.

  10. In the Contents pane, right-click the World_Countries_Generalized layer, and click Remove.

    Remove the World_Countries_Generalized layer from the map

  11. Click the color patch for KenyaFeature.

    Symbology for the KenyaFeature layer

  12. In the Symbology pane, click Black Outline.

    Black Outline symbol

    The country polygon is drawn with the black outline symbol and no fill, so you can see the other layers.

    KenyaFeature is dawn on the map with a black outline.

  13. Close the Symbology pane.

    The next step to determining how much of Kenya is currently protected is to extract those areas from the WDPA layer.

Extract protected areas

The WDPA layer has data for the entire world. You do not need the full layer to answer your question about protected areas in Kenya, so you will select the features that are relevant for Kenya and export them to a new layer.

  1. On the ribbon, click the Map tab, and in the Selection section, click Select By Location.

    Select By Location

    You will use the KenyaFeature layer to select features from the WDPA layer.

  2. For Input Features, choose the WDPA – World Database of Protected Areas\WDPA_poly_Latest layer.

    WDPA – World Database of Protected Areas\WDPA_poly_Latest layer

  3. Accept the default selection relationship, Intersect.
  4. For Selecting Features, choose the KenyaFeature layer.

    Select By Location tool

  5. Click OK.

    The WDPA polygon features that overlap Kenya are selected.

    For purposes of this simplified analysis, it does not matter what type of protection each area is under. In addition, the polygons in this layer overlap, and you do not want to double-count the areas that fall under multiple conservation jurisdictions, so you will dissolve the features.

    Note:
    For official calculation and reporting of protected areas, some other steps are usually taken to prepare the data. For example, the Man and the Biosphere (MAB) areas are typically excluded, and protected areas for a specific country are selected by attribute selection on areas with an ISO3 value of the country's code (in this case, it would be KEN), rather than by clipping to the generalized country boundary. For more details on WDPA reporting procedures, please see Calculating protected area and OECM coverage and the User Manual for the World Database on Protected Areas and world database on other effective area-based conservation measures.

  6. Click the Analysis tab, and in the Geoprocessing section, click Tools.

    Tools button

  7. In the Geoprocessing pane, in the search box, type Dissolve.
  8. In the search results, click the Dissolve tool.
  9. For Input Features, choose the WDPA – World Database of Protected Areas\WDPA_poly_Latest layer.

    WDPA – World Database of Protected Areas\WDPA_poly_Latest selected in the Dissolve pane on the Parameters tab

    These are the polygon features representing protected areas. The layer name includes Latest because this hosted feature service contains the most current version of the data.

    Because some features have been selected, the tool indicates that it will use the selected features.

  10. For Output Feature Class, type WDPA_Dissolve_ and add the month and year, in this case 04_24.

    Dissolve tool and output name

    The World Database of Protected Areas layer is updated monthly. The terms of use advise only using the latest version. It is recommended that you add the month and year (for this example, 04_24, but you should use the current month and year when you extract this data) to the extracted output of the tool to keep track of when this version of the data was extracted. You will need this information for citing the data, if you publish any work based on this layer.

    This tool will create multipart features by default. For this case, that is what you want; all the conserved areas should be merged into a single feature, with many geographically separate parts. If you turn off the Create multipart features parameter, only features that touch or overlap will be merged.

  11. Click Run.

    The WDPA features that touch Kenya are dissolved, now you’ll clip them to the Kenya boundary.

  12. At the top of the Dissolve tool, click the back button.

    Back button

  13. In the Geoprocessing pane, in the search box, type pairwise clip.
  14. In the results, click Pairwise Clip.

    Pairwise Clip tool

  15. For Input Features, choose WDPA_Dissolve_04_24.

    These are the polygon features that you dissolved, representing any protected areas within or touching the border of Kenya.

  16. For Clip Features, choose KenyaFeature.
  17. For Output Feature Class, type WDPA_Kenya_ and add the month and year, in this case 04_24.

    The Pairwise Clip tool is read to run.

    It is recommended that you add the month and year, in this case, 04_24, to the extracted output of the tool to keep track of when this version of the data was extracted.

  18. Click Run.

    The tool runs and the dissolved protected areas are clipped to the Kenya boundary.

    You no longer need the source WDPA – World Database of Protected Areas and the WDPA_Dissolve_04_24 layers on your map.

  19. In the Contents pane, right-click WDPA – World Database of Protected Areas and click Remove.
  20. Right-click WDPA_Dissolve_04_24 and click Remove.

Calculate the area of Kenya

Now that you have a polygon representing the country of Kenya and a polygon representing the protected areas in Kenya, you can calculate the percentage of Kenya that is currently under some protection.

  1. In the Contents pane, right-click KenyaFeature and click Attribute Table.

    Attribute Table for the KenyaFeature layer

  2. In the attribute table, scroll to the Shape_Area column and examine the value for the area of the feature.

    The area of the feature is 586683013946.813599 square meters, as meters are the linear units of the coordinate system of this layer.

    Note:
    The coordinate system for this data is Web Mercator, which is not ideal for area calculations, so you will add a field to contain a geodetically calculated area.

  3. Click Add.

    Add button to a field to the table

  4. In the Fields list, for Field Name, type Area_KM.

    Area_KM field name

  5. Double-click in the Data Type column for the new field and click Double.

    Double option

  6. On the ribbon, on the Fields tab, in the Manage Edits section, click Save.

    Save button to save the edits to the fields in the attribute table

  7. Right-click the Area_KM field name and click Calculate Geometry.

    Calculate Geometry selected

    The Calculate Geometry tool allows you to add calculated data based on feature geometry to an attribute.

  8. On the Calculate Geometry tool, in the Geometry Attributes section, for Field, choose Area_KM.
  9. For Property, choose Area (geodesic).
  10. For Area Unit, choose Square Kilometers.

    Calculate geometry tool ready to run

    Using geodesic area provides a more accurate area value.

    Setting the Area Unit parameter to Square Kilometers simplifies the calculation you’ll make later of what percentage of Kenya is currently protected.

  11. Click OK.

    The area is calculated at 581,864 square km. This differs from the official area of Kenya by a small amount, due to the fact that the country boundaries in this layer are generalized. For the purposes of this analysis, it is accurate enough.

Calculate the protected area

Now you'll calculate the area of the protected areas layer. You will repeat the process you used to calculate the area of Kenya on the WDPA_Kenya_04_24 layer.

  1. In the Contents pane, right-click the WDPA_Kenya_04_24 layer and choose Attribute Table.

    Attribute Table.

  2. On the attribute table, click Add Field.

    Add Field

  3. Add a new field named Area_KM of type Double, and save the change to the attribute table.
  4. Close the Fields pane.
  5. In the WDPA_Kenya_04_24 table, right-click the Area_KM field name and click Calculate Geometry.
  6. On the Calculate Geometry tool, set Field to Area_KM, Property to Area (geodesic), and Area Unit to Square Kilometers, and click OK.

    Calculate Geometry parameters to calculate the area of the conserved area

  7. Examine the resulting value in the Area_KM field. It should be about 91,382.

    The protected area is calculated in square kilometers.

    The total area is 581,864 and 91,382 of that, or 15.7%, is currently protected.

  8. Close the attribute table for the WDPA_Kenya_04_24 layer.
  9. Close the attribute table for the KenyaFeature layer.
  10. Click Save to save your project.

    Save button to save the project

Kenya is currently about halfway to its UN 30x30 target. To reach 30 percent, another 87,000 square kilometers or so will need to be protected. Now the question is, which 87,000 km² should be conserved? Where are the greatest biodiversity conservation opportunities in Kenya?

In the next section, you will use Suitability Modeler in the ArcGIS Spatial Analyst extension to help answer that question.


Determine the high priority areas

Now that you’ve identified how much more area needs to be conserved to reach the UN 30x30 target, you will work on the problem of determining which areas are highest priority to add to the protected area. You will use data from ArcGIS Living Atlas and the ArcGIS Spatial Analyst extension Suitability Modeler to determine additional areas of high importance for conservation.

Examine the data layers

Earlier, you reviewed some layers in ArcGIS Living Atlas and learned how to add those layers to ArcGIS Pro. Now you’ll examine the data to learn about the layers you’ll be considering in your analysis.

  1. In the Contents pane, uncheck the WDPA_Kenya_04_24 layer.

    Uncheck the WDPA layer.

  2. In the Contents pane, check the GlobalTerrRaR_025deg layer.

    Turn on the GlobalTerrRaR_025deg layer.

  3. In the Contents pane, expand the GlobalTerrRaR_025deg layer to see its symbology.

    Expand the layer to see the symbology.

    This layer has bivariate symbology. Bivariate symbology allows you to see a quantitative relationship between two variables. The GlobalTerrRaR_025deg layer shows species richness and species rarity calculated for terrestrial vertebrate species — birds, reptiles, mammals, and amphibians. Species richness quantifies how much species diversity a given location has, which is an important measure to consider, as protecting an area with high richness protects many species. Species rarity quantifies how uncommon the species found in a given area are. This is also an important measure, because if a given area is one of a few areas suitable for an uncommon species, protecting that area may be your only opportunity to protect the rare species.

    The species richness and rarity layer

    Inspecting this layer shows that the southwestern portion of Kenya is high in both species richness and species rarity (dark blue), while the region in the northeast generally has moderate species rarity and low species richness (light blue).

    As you can see, there are certain areas, particularly along the coastline and around north of Metro Nairobi, that stand out as areas of high species rarity.

    This layer is published by the Map of Life and is a product of Half-Earth Project, the EO Wilson Foundation, and Yale University to support conservation of biodiversity.

    It is available in ArcGIS Living Atlas.

    The layer’s terms of use prohibit use for commercial purposes or personal gain. The layer is used in this tutorial with the permission of Map of Life and Yale University.

  4. Uncheck the GlobalTerrRaR_025deg layer.
  5. Check the World Distance to Water layer.

    This layer provides a landscape-scale estimate of the distance from large water bodies such as lakes and rivers, including the ocean. The distribution of water affects the distribution of many species. In addition, a component of Target 3 of the GBF is to integrate terrestrial and marine protected areas.

    The distance to water layer on the map

    The map shows distance to coastlines and inland water sources like Lake Victoria and rivers.

    The northeastern portion of the country stands out as the area that is more arid in comparison to the rest.

    This layer is published by Esri, USGS, and ESA and is shared with ArcGIS Living Atlas at:

    https://www.arcgis.com/home/item.html?id=46cbfa5ac94743e4933b6896f1dcecfd

  6. Uncheck the World Distance to Water layer.
  7. Check and expand the European Space Agency WorldCover 2021 Land Cover layer.

    Land cover layer in the Contents pane

    This layer shows land cover classes derived from satellite imagery at a 10-meter resolution. This is highly accurate information about the current landscape worldwide.

    ESA landcover layer on the map

    To prioritize ecosystem functions and services, in accordance with 30x30 targets, you want to prioritize areas with tree cover and shrub land for conservation over crop lands or urban areas.

    This layer is from the WorldCover Consortium and European Space Agency are for the years 2020/2021. The data was derived from worldwide Sentinel satellite imagery.

    It is available in ArcGIS Living Atlas.

    https://www.arcgis.com/home/item.html?id=e28b7e1da5414010ba4f47dd5a3c3ebb

  8. Uncheck the European Space Agency WorldCover 2021 Land Cover layer.
  9. Check and expand the Land Cover Vulnerability Change 2050 - Country layer.

    Land Cover Vulnerability Change 2050 - Country layer in Contents pane

    This layer was produced by Clark Labs at Clark University, looking at trends in landcover change and extrapolating those trends into the future to the year 2050.

    They created a model to understand which landscapes and landcover types are most vulnerable to change over the next 30 or so years.

    Land Cover Vulnerability layer on map

    Some areas are more vulnerable to change given pressures like urbanization or deforestation.

    Dark purple areas are less vulnerable, while the ones drawn in bright yellow on the map show areas that are most imperiled in need of conservation before they are converted into something less sustainable. Using this layer supports GBF Target 1, which relates to preventing the loss of areas of high biodiversity importance.

    This layer is available in Living Atlas at:

    https://www.arcgis.com/home/item.html?id=20bfd812017e4bc1a241d2581c156bcd

    It is shared under a Creative Commons By Attribution license.

    Now that you have investigated the data, you’ll use Suitability Modeler to weight them against each other to determine which additional 15 percent of the country to recommend for conservation to support the 30x30 goals.

Rasterize data for the analysis

You’ll work on a clipped, locally saved set of layers in Suitability Modeler to speed processing.

  1. In the map pane, click the Kenya map tab.

    Kenya map tab

    This map contains a copy of the layers from the previous map, with some small changes:

    • For processing speed, each of the layers was clipped using the Kenya boundary feature and stored as local data in the project. The suffix _Kenya was added to the layer names to indicate the change.

      Kenya map layers in the Contents pane

    • To simplify the suitability analysis, the species richness and rarity bivariate layer was duplicated and each layer was independently symbolized by the richness or rarity attributes, with symbology adjusted to emphasize each attribute separately.

      Richness and rarity layers split

    • The distance to water layer was symbolized with a stretch and new color scheme.

      Distance to water on map with new symbology

    There are two more data preparation steps before you can start the analysis. Suitability Modeler works with raster data. The species richness and species rarity layers are feature layers, so you’ll need to convert them to rasters.

  2. Open the Geoprocessing pane and in the search box, type feature to raster.
  3. Click Feature to Raster.

    Feature to Raster tool

  4. On the Feature to Raster tool, for Input features, choose GlobalTerrRaR_025_Richness_Kenya.
  5. For Field, choose Rich_all.
  6. For Output raster, type GlobalTerrRaR_025_Richness_Raster_Kenya.
  7. For Output cell size, type 1000.

    The cell size of 1,000 meters, or 1 km, is a compromise between the relatively large polygons and the need to capture their boundaries.

    The Feature to Raster tool is ready to run.

  8. Click Run.

    The raster layer, GlobalTerrRaR_025_Richness_Raster_Kenya, is added to the map.

    Before closing the tool, you will run it again on the Species rarity layer.

  9. For Input features, choose GlobalTerrRaR_025_Rarity_Kenya.
  10. For Field, choose Rar_all.
  11. For Output raster, type GlobalTerrRaR_025_Rarity_Raster_Kenya.

    The Feature to Raster tool is ready to run.

  12. Click Run.

    The GlobalTerrRaR_025_Rarity_Raster_Kenya raster layer is added to the map.

    The symbology of these two new layers will differ from the original layers.

    You no longer need the original richness and rarity feature layers.

  13. In the Contents pane, right-click GlobalTerrRaR_025_Rarity_Kenya and click Remove.
  14. In the Contents pane, right-click GlobalTerrRaR_025_Richness_Kenya and click Remove.

    Now that these layers have been converted to raster format, you are ready to begin using Suitability Modeler.

Start a suitability analysis

Now you'll start a new suitability analysis model and add rasters to it.

  1. On the ribbon, on the Analysis tab, in the Workflows section, click Suitability Modeler.

    Suitability Modeler button

    The ribbon shows the Suitability Modeler tab, and the Suitability Modeler pane appears.

    The first step is to create a new model.

  2. For Model name, type Kenya conservation priorities.

    The model name should be Kenya conservation priorities.

  3. For Output suitability raster, type Conservation_suitability_Kenya.

    The name of the output raster is set to Conservation_suitability_Kenya.

    The Kenya conservation priorities model layer is added to the Contents pane.

    The next step is to add the layers of information to the model.

  4. In the Suitability Modeler pane, click the Suitability tab.

    Suitability tab

    The next step is to add the layers of information to the model.

  5. In the Criteria section, for Input rasters, click the down arrow of the Add from Contents button.

    Add from Contents button on the Suitability tab in the Criteria section

  6. To the left of the Add button, click the Select all button.

    Select all button

  7. Click Add.

    Add button to add the layers to the model

    The input rasters are added to the model.

    Input rasters in the suitability model

    You’ve added rasters to the suitability model. Next, you’ll transform the layers to a common 1-10 suitability scale.

Transform layers

The next step in the suitability modeling workflow is to transform the rasters. The first raster you’ll transform is the distance to water raster. This is a continuous raster, and you will transform it to use 10 categories of distance to water.

  1. In the Criteria table, click the circle beside Distance_to_Water_Kenya.

    Circle selected to transform the Distance to water raster

    The Transformation pane opens for the Distance_to_Water_Kenya layer.

    In the Transformation pane, the distribution of the data in the raster is shown as a histogram.

    The histogram appears on the Transformation pane.

    The Transformation pane also shows the field being shown and the range of values for each class.

    The ranges of classes for the distance to water transformation

    The Range of Classes transformation was selected for this data, based on its properties. If you examine the default mapping of values, the low values in the input raster are assigned low suitability values. You should examine the transformation for each layer and determine if it works well for the problem that you are modeling. For this particular raster, low values mean short distances to water, which should be ranked as high suitability. If you were going to use the Range of Classes transformation for this data, you would click the Reverse button to reverse the ordering of the classes. This would rank locations close to water high and locations far from water low.

    Instead of doing this, you will choose a different type of transformation.

  2. Click Continuous Functions.

    Continuous Functions tab

    The MSSmall function was determined to be the best fit.

    The Transformation pane shows the curve that fits the data a histogram of the transformed values.

    MSSmall is chosen.

    The histogram updates to show the fit line of the function.

    The histogram for the transformed distance to water appears.

    The function that fits this data is MSSmall. This function rescales input data based on the mean and standard deviation where smaller values in the input raster have higher preference. Under this function, the short and intermediate distance areas are classified as highly suitable, while the long distance areas are classified as low suitability. Learn more about transformation functions.

    This is a more accurate model of suitability for this problem.

  3. In the Contents pane, ensure that the Kenya conservation priorities layer is checked.
  4. In the Contents pane, uncheck the Conservation_suitability_kenya output raster layer.

    Unchecked output raster

    You’ll examine the Conservation_suitability_kenya output raster at the end of the suitability analysis.

    When the Transformed Distance_to_Water_Kenya layer was added in the Transformation pane, it was also added to the Contents pane and to the Kenya map.

    Transformed Distance_to_Water_Kenya in the Contents pane

    Transformed distance to water on map

    In this layer, red represents greater distances from water, and green represents lesser distances.

    Note:
    Make sure the map does not look like this:

    The distance to water layer with the default Range of Classes transformation

    This shows the distance to water layer with the default Range of Classes transformation.

    If the southern portion of the map appears to be mostly red, with some areas of green in the northeast, the suitability values are reversed, and places with long distances to water are being ranked high, while places closer to water are ranked low. To fix this, in the Transformation pane, click the Continuous Functions tab. Verify that the histogram with the fit line looks like this:

    The histogram for the transformed distance to water appears.

    If you do this, the map should update to look like this:

    Transformed distance to water on map

    This transformation converts the linear distance values in the original raster to a 1-10 scale to allow it to be added to the other layers in the overlay.

    Transformed distance to water on map

    Next, you’ll transform another layer.

  5. In the Criteria table, click the circle beside GlobalTerrRaR_025_Rarity_Raster_Kenya.

    Circle selected for the GlobalTerrRaR_025_Rarity_Raster_Kenya layer to transform it

    The Transformation pane updates to show the range of classes for the rarity layer.

    The range of classes table for the rarity layer

    For this layer, a higher value for rarity should receive a higher suitability value.

    It also shows the histogram of values.

    The transformed rarity value histogram

    The layer is added to the Kenya map and to the contents in the Kenya conservation priorities group layer.

    The transformed rarity layer in the Contents pane

    The transformed rarity layer in the map

  6. In the Criteria table, click the circle beside GlobalTerrRaR_025_Richness_Raster_Kenya.

    Circle to transform the richness layer

    The Transformation pane updates to show the range of classes for the richness layer.

    The range of classes table for the richness layer

    For this layer, higher values for richness are mapped to higher values for suitability.

    The Transformation pane also shows the histogram of values.

    Histogram for the transformed richness layer

    Transformed richness map

  7. In the Criteria table, click the circle beside LandCoverVulnerability_Kenya.

    Transform the vulnerability to change layer

    For this layer, higher values for vulnerability should have higher values for suitability.

    Range of classes for vulnerability to change

    Transformed vulnerability histogram

    Transformed vulnerability map

    You've transformed most of the input criteria. The Landcover layer is a categorical variable, so you will transform it a bit differently.

Reclassify the land cover variable

The next data preparation step for this analysis is to specify a categorical transformation for the landcover layer. To do so, you will prioritize certain landcover classes over others, based on their value for biodiversity.

The raster contains unique categories. To make it easier to reclassify them, you’ll change the field that will be used for the transformation.

  1. In the Criteria table, click the circle beside EuropeanSpaceAgencyWorldcover_Kenya.

    Transform the landcover layer

    The Transformation pane shows the current default mapping, but it is based on the object ID values of the raster attribute table. You’ll change it to use the landcover class name.

  2. In the Transformation pane, click Value and click ClassName.

    ClassName field for classification

    Now the Transformation pane shows the mapping of class names to values.

    Initial mapping of the landcover class names

    To prioritize ecosystem functions and services, you will rank tree covered land highest.

  3. In the Suitability column, for TreeCover, click the value, type 10, and press Enter.

    Tree cover set to the highest suitability value of 10

    This is the highest value. Shrubland is also important from a biodiversity perspective, so you will give it a slightly lower suitability ranking of 9.

  4. In the Suitability column, for Shrubland, click the value, type 9, and press Enter.

    Shrubland set to the next-highest suitability value of 9

    Grassland is also important, so you will give it a slightly lower suitability ranking of 8.

  5. In the Suitability column, for Grassland, click the value, type 8, and press Enter.

    The least important areas to conserve are urban built-up land.

  6. In the Suitability column, for Built-up, click the value, type 1, and press Enter.

    Crop land is also less important in this analysis, so you will give it a low value of 2.

  7. In the Suitability column, for Cropland, click the value, type 2, and press Enter.

    Bare or sparsely vegetated areas are also less important, so you will give that class a value of 3.

  8. In the Suitability column, for Bare or sparse vegetation, click the value, type 3, and press Enter.

    This mapping accounts for most of the landcover in Kenya.

    Most landcover classes set

  9. Scroll down to see the remaining values.

    The less common land use classes in Kenya are shown.

    Mangroves and herbaceous wetlands have high value for biodiversity, so you will leave the default suitability values of 10 and 9, respectively.

    Snow and ice covered land is rare in Kenya, and permanent water bodies are important for aquatic biodiversity. You will accept the default values for these.

    The distribution of transformed values is shown in the histogram.

    Distribution of transformed values for land cover

    Now you’ll preview the results of the suitability model.

  10. In the Contents pane, check the Conservation_suitability_Kenya layer.

    Conservation suitability layer in Contents pane

    The map shows the model results.

    Conservation suitability layer on the map.

    These preliminary results look good, so you’ll run the model to produce a raster copy of the output in your project geodatabase.

Specify an output resolution and run the model

Before you run the model, you should set the cell size for the output. By default, the cell size is the maximum of the cell sizes in each input layer. In this analysis, you have several high resolution layers and two very low resolution (species richness and rarity) layers. By default, the output will have a resolution of 1 km, matching the two lower resolution layers.

A low resolution output will be processed more quickly and take up less space on disk but will also be less useful for land management decision making. A high resolution output takes longer to process and more disk space to store, but will be more useful for decision making.

You will set the output resolution to an intermediate value as a compromise.

  1. In the Suitability Modeler pane, click the Environments tab.

    Environments tab

  2. Scroll to and expand the Raster Analysis section.
  3. For Cell Size, choose Same as layer Distance_to_Water_Kenya.

    Set the cell size from the distance to water raster.

    This layer has a cell size of about 200 meters, which is between the highest resolution data (10 meters) and the lowest resolution (1 km).

  4. Click the Parameters tab.

    Parameters tab

  5. In the Suitability Modeler pane, click Run.

    Run button to run the suitability model

    The model runs and produces the Conservation_suitability_Kenya raster layer. This may take a few minutes, depending on your computer.

    The result map is exported.

    In the output, you can see the impact of the lower resolution data, in the large blocky 30 km pixel footprints, but the result also retains some of the fine-grained data quality of the higher resolution layers. This output resolution is acceptable for this analysis.

    In the output, the values range from 50, representing 10 points, or maximum value, for each of the input layers, to 16.8 points, representing low scores from each of the input layers. High values in the output represent areas that have a high priority for conservation, based on the values you assigned in the model.

    The result is based on the data but also on your professional knowledge and judgement about which layers to include, the importance of the different layers, and the transformation and the weighting of the values.

    Modeling is a nonlinear, iterative process, and is subject to some inherent constraints. An important step is to use the Evaluate environment to identify how the layers contribute to the result.

    The next step is to identify regions to propose for conservation.

Locate regions

The Suitability Modeler allows you to locate regions, based on the model result layer. Next, you’ll identify four regions that make up a total area equal to the remaining 15 percent needed to meet the 30x30 goal.

  1. In the Suitability Modeler pane, click the Locate tab.

    Locate tab

  2. For Input suitability map, choose Conservation_suitability_Kenya.
  3. For the Total area, type 87000.

    This is the area you calculated would need to be added as conserved land to meet the 30x30 goals.

  4. For Area units, choose Square kilometers.
  5. For Output raster, type Kenya_conservation_areas.

    Locate pane settings

  6. For Number of regions, type 4.

    Number of regions warning

    The warning beside the Number of regions parameter indicates that the tool will use the Combinatorial method of selecting regions, because the value you choose, 4, is less than 8. The Region growth and search parameters section of the Locate pane will allow you to change the method, but it is not necessary in this case. The Combinatorial method is better for small numbers of regions. If you chose to create more than 8 regions, the tool would default to the Sequential selection method, which is more efficient for larger numbers of regions.

  7. For Maximum distance between regions, type 50.
  8. For Distance units, choose Kilometers.

    Maximum distance between regions of 50 kilometers

    You can set a minimum and maximum area for each region, and a minimum distance between regions if you want to ensure that they are dispersed, but it is not necessary for this analysis.

  9. For Input suitability map or feature of existing regions, click the browse button.
  10. Click Databases.

    Databases folder

  11. Double-click the model_conservation_suitability.gdb geodatabase.

    model_conservation_suitability project geodatabase in the Databases folder

  12. Click WDPA_04_24_Kenya, and click OK.

    This layer, which you created earlier in the other map, has the existing conserved areas. This will allow the tool to avoid adding existing conserved areas to the proposed conservation areas.

    Parameters set

  13. Click Run.

    The tool runs and the new Kenya_conservation_areas layer is added to the map.

    The regions map is added.

    The results show four regions, totaling 87,000 km² within the country of Kenya. It does not include areas that are currently conserved, but does prioritize that are continuous. It also prioritizes areas that have high species richness, species rarity, access to water, mostly tree cover, grassland and shrub land, and high vulnerability to change.

    The areas you see in gray, red, blue, and yellow are areas the model has identified as prime conservation areas. These are areas with high biodiversity and important ecosystem functions that could be degraded if not conserved.

    One of them is close to the coastline, which fits into the UN target of integrating into larger seascapes.

    Some of them are close to the urban centers and, and the richly biodiverse and tree-covered parts of Kenya.

In this tutorial, you identified the area of Kenya currently protected and determined the additional area that needs to be protected to meet the UN 30x30 goals. You conducted an analysis aligned with the Global Biodiversity Framework targets to conserve areas with significant biodiversity and importance to ecosystem functions. You used data from ArcGIS Living Atlas in Suitability Modeler to create a weighted overlay of the transformed layers. You used the Locate function to identify compact areas for conservation, building upon existing conserved areas and trading off various factors.

To go further, you could convert the Kenya_conservation_areas raster to a feature layer and share it in a web map. This could be used to communicate your analysis results to your colleagues and to the broader community. You could tell the story of this analysis, 30x30 goals, and conservation in your area by including the map as part of a story using ArcGIS StoryMaps.