Understand the context and method

Intensive shrimp farming

Shrimp farming around the world is a large industry and the market for farmed cheap shrimp is growing each year. As world consumption has risen and costs have gone down, commercial shrimp farming has grown rapidly in many countries around the world. Shrimp farming mainly takes place along the coast where briny water is used to fill ponds where shrimp are raised. Traditionally, ponds are established in locations where mangrove forests are located and as a result, commercial farming destroys mangroves that serve as natural nurseries for wild shrimp and act as a buffer to sea level rise.

Shrimp farm
Some ponds in a shrimp farm. (Credit: U.S. NOAA, via Wikimedia Commons.)

Often, to boost production, farmers increase shrimp density in ponds, apply antibiotics and pesticides to reduce shrimp mortality, and kill algae resulting from high nitrates that develop as a result of the high density and feeding regimen. Shrimp in these farms are basically living on top of each other in their own waste. In these cases, before harvesting, ponds are drained of chemicals, and antibiotic-laced water is pumped into the rivers and canals that flow into the surrounding sea. For a few weeks, the shrimp are kept in clean ponds to clear chemicals from their system and then they are harvested and shipped to consumers around the world.

Shrimp farming in Costa Rica

Costa Rica has abundant freshwater resources, including mountain ranges, a tropical climate, and a marine exclusive economic zone of almost 600,000 km2 that make the country suitable for aquaculture development and thus ideal for shrimp production.

Initial shrimp production from 2009 to 2015 was associated with the presence of diseases, a low level of technical skills of farmers, few environmental policies, little local trade, and strong competition from South East Asian sources. However, since 2018, the industry has developed an organic supply chain that has achieved certification under the European organic regulations. Many large-scale operators and most small farmers have joined the initiative and are now operating organic shrimp farms.

As a result, within Costa Rica, an organic production protocol was developed, organic nauplii (shrimp larvae) and feed were organized, and some hatcheries converted to organic production. Farmers have been trained in organic production and are now producing, selling, and marketing organic shrimp. As the organic market is growing significantly, it is logical to assume that local farmers, many of whom rely on fishing and tourism for a livelihood, would want to establish new farms and ponds to take advantage of the world interest and demand for sustainably farmed organic shrimp.

Suitability model

You'll use the ArcGIS Pro Suitability Modeler to locate new potential farming sites in the Gulf of Nicoya, Costa Rica.

Gulf of Nicoya
Coco Bay in the Gulf of Nicoya. (Credits: Pacificorealty via Wikimedia Commons.)

Developing a suitability model is a well-known GIS approach to identify the best locations for a site based on several criteria. In this model, you'll use five criteria of suitability:

  • Be in close proximity to salt water, which is needed to grow shrimp. In this context, this means being close to the Gulf of Nicoya, which opens to the Pacific Ocean.
  • Be on specific land use types: the most suitable is land currently covered by shrub/scrub or used for agriculture purposes. Mangrove forests should be avoided.
  • Have access to the road network to get the shrimps to processing plants and market.
  • Be located near rivers to flush shrimp ponds regularly with fresh water.
  • Be in or near areas that are sheltered from north and south trade winds.

Using the ArcGIS Pro Suitability Modeler, you will do the following:

  • Generate a suitability map indicating the most favorable areas based on the five criteria.
  • From the suitability map, identify the five best potential shrimp locations.

There are four main steps for creating a suitability model:

  • Determine and prepare the criteria data.
  • Transform the values of each criterion to a common suitability scale.
  • Weight the criteria relative to one another and combine them to create a suitability map.
  • Locate the areas that best match your criteria.

In this module, you learned about shrimp farming in Costa Rica and reviewed the criteria that you'll use in the suitability model to locate new potential farming sites in the Gulf of Nicoya.


Explore shrimp farm suitability data

In this module, you'll first download the data and set up your project. Then, you'll explore the data layers.

Get started

The data layers used in this lesson are hosted on ArcGIS Online. You will download a zip folder containing the ArcGIS Pro project and the data needed to complete the lesson.

  1. Download ShrimpFarm.zip

    Note:

    Depending on your web browser, you may be prompted to choose a file location before you begin the download. Most browsers download to your computer's Downloads folder by default.

  2. Locate and unzip ShrimpFarm.zip to the desired location on your computer.
  3. In the unzipped folder location, double-click Shrimpfarm.aprx.

    Folder content

    Note:

    If your computer doesn't display the file extensions (.aprx), you can recognize the file by the icon.

  4. When ArcGIS Pro opens, if prompted, sign in to your ArcGIS account.
    Note:

    If you don't have ArcGIS Pro or an ArcGIS account, you can sign up for an ArcGIS free trial.

    The application opens to display the Gulf of Nicoya map.

    Initial map

    The map includes a LandCover layer, a Hillshade layer giving a sense of the region's elevation, and the gulf water body, represented in dark blue (GulfWater). The background is the World Imagery basemap.

  5. Review the Contents pane.

    You can see that several other layers are included, all relevant to the suitability analysis you will perform in the lesson. You'll now review those layers.

    Initial contents

    Note:

    All source data layers have been clipped to the extent of the study area.

Explore the criteria layers

You'll start with exploring the LandCover layer.

  1. In the Contents pane, expand the LandCover layer.

    Landcover layer

    This layer contains different types of land cover categories. Some categories are more suitable for shrimp farming than others and at varying degrees. Shrub/Scrub and Agriculture are most suitable, followed by Barren/Minimal Vegetation, Grassland, and Evergreen Forest that are moderately suitable, with Mangrove, Urban, and Water being the least suitable.

    Note that there are many mangroves (symbolized in darker green) near the coast. A mangrove is a shrub or small tree that grows in coastal saline or brackish water. It is salt-tolerant and adapted to life in harsh coastal conditions. Mangrove forests act as buffers from cyclones and are important to mitigate the impact of climate change. As mentioned, the Costa Rican government wants to protect mangroves and move shrimp farming activities out of them.

    Note:

    Land cover rasters are usually derived from multispectral imagery, using image classification techniques to determine the cover types, based on their spectral signatures. Check out the lesson Calculate impervious surfaces from spectral imagery for an example of such a classification workflow.

    Other layers in this lesson were also derived from remote sensing data, for instance GulfWater, NorthSouthSheltered, and Hillshade.

  2. Zoom in and out with the mouse wheel button to explore the LandCover layer further.
  3. When you are finished, on the Contents pane, collapse the LandCover layer. Right-click the LandCover layer and choose Zoom To Layer.

    Zoom To Layer option

  4. Turn the GulfWater layer off and back on to better visualize its extent.

    Gulfwater

    Salt water is needed to fill the ponds where the shrimp are grown. As salt water can be found in the gulf, your suitability model needs to ensure that the new farm sites are as close as possible to the gulf coast (while still avoiding the protected mangroves). Next, you'll become familiar with the road network.

  5. In the Contents pane, turn on the Roads layer. Explore the layer symbolized in dark gray.

    Roads layer on the map

    Shrimp farmers need to get their shrimp to processing plants and markets for sale and redistribution. As a result, sites with access to the road network are preferred.

  6. When you are finished exploring, in the Contents pane, turn off the Roads layer. If necessary, right-click Landcover and choose Zoom To Layer.

    Next, you'll explore the Rivers layer.

  7. In the Contents pane, turn the Rivers layer on. Explore the layer, symbolized in medium blue.

    Rivers layer on the map

    Shrimp farming requires access to fresh water to flush the ponds regularly, so proximity to a river is highly desirable.

  8. When you are done exploring, in the Contents pane, turn off the Rivers layer. If necessary, right-click Landcover and choose Zoom To Layer.

    Finally, you'll review the wind-sheltered areas.

  9. Turn the NorthSouthSheltered layer on. Explore the layer, symbolized in purple.

    NorthSouthSheltered layer

    The climate of the basin in the Gulf of Nicoya is determined by wind patterns, temperature, and rainfall. In particular, the north and south trade winds have a strong influence and need to be avoided. This means that shrimp farms in this area should be located in areas sheltered from those winds.

  10. When you are finished exploring, in the Contents pane, turn off the NorthSouthSheltered layer. If necessary, right-click Landcover and choose Zoom To Layer.

Next, you review distance rasters that were derived from the layers explored so far.

Explore the distance rasters

In your suitability model, you can use the land cover layer directly, as different suitability levels will be assigned to different land cover types. For instance, Shrub/Scrub will have a very high suitability, and Mangrove a very low suitability.

However, other criteria require that you generate a derived raster layer. For instance, for the salt water criterion, it is not the case that being exactly on the gulf water is suitable and not being on the gulf water is unsuitable. Instead, the change in suitability is incremental. The closer a location is to the gulf water, the more suitable it is. The farther away a location is from the gulf water, the less suitable it is. This means that the suitability level of a location depends on how far it is from the gulf. To determine that distance value, you need to derive a distance raster, which identifies how far each location is from the gulf water. Similarly, you also need to derive distance rasters for the roads, rivers, and wind sheltered criteria. Those distance rasters will be used in your suitability model.

This lesson is not focused on the preparation of the data, so those derived rasters were generated for you.

  1. In the Contents pane, turn on the Processed group layer and expand it.

    Turn on and expand the Processed group layer

  2. In the Contents pane, in the Processed group layer, turn on the Dist_Salty_Water layer and expand it.

    Salty water legend

    From the legend, you can see that the different locations in the study area can be from 0 to 14,344 meters (or 14,3444 kilometers) from the gulf. On the map, verify that the low values (dark blue) are closer to the gulf and the higher values (light blue) are farther away.

    Salty distance water displayed on the map

  3. Click some of the locations to see the distance value displayed in the pop-up.

    Salty water pop up

  4. Close the pop-up panel. In the Contents pane, collapse the Dist_Salty_Water layer.
  5. On your own, review the additional distance layers that represent the following:
    • Distance to roads (Dist_Roads)
    • Distance to rivers (Dist_Rivers)
    • Distance to wind-sheltered areas (Dist_Sheltered)

      You can also turn on the original layers Roads, Rivers, and NorthSouthSheltered to better understand how original and derived layers relate to each other. For instance, Dist_Roads and Roads.

      Dist_Road and Roads

  6. Note how every layer has a different distance range.

    For instance, Dist_Roads ranges from 0 to 4.7 kilometers, and Dist_Rivers varies from 0 to almost 22 kilometers. Later you will need to transform these into a common scale to combine them in your suitability model.

  7. When you are done, turn off all layers in the Processed group layer and collapse the Processed group layer. Ensure that Roads, Rivers, and NorthSouthSheltered are turned off.
  8. In the Contents pane, right-click Landcover and choose Zoom To Layer.
    Note:

    Here are some hints if you want to generate such distance rasters with your own data. The four distance layers used in this lesson were generated using the Distance Accumulation Spatial Analyst tool. For instance, to generate the Dist_Roads raster, the tool calculated the straight-line or Euclidean distance from each cell in the raster to the gulf water, as represented by the GulfWater layer.

    In addition, only the land around the gulf and not the gulf itself is of interest. For that reason, the ShoreLineMaskRaster layer was set as the analysis mask. This means that only the cells within the ShoreLineMaskRaster shape received a distance value. Finally, it is also useful to use the same cell size for all distance rasters, which helps with the suitability analysis.

    As requested by some of our users, here are some more details, using the Roads example. In the Distance Accumulation tool, in the Parameters tab, for Input raster or feature source data, choose the Roads raster. For Output distance accumulation raster, type Dist_Roads. Leave the other parameters blank. In the Environments tab, for Extent, Mask, and Snap raster, choose ShoreLineMaskRaster, and for Cell size type 30 (meters).

  9. In the Contents pane, turn on the ShoreLineMaskRaster layer to see its extent and shape. When you are done, turn it off.
  10. Save the project.

    Save button

In this module, you set up the project and explored the criteria layers.


Develop the suitability model and transform the layers

Now that you have explored the shrimp farm suitability criteria and layers, you are ready to begin creating a suitability model using the Suitability Modeler to identify the five best potential sites for new shrimp farms.

The basic premise behind the Suitability Modeler is that a model is an iterative, nonlinear dynamic process. To generate the model, you will interact with panes, plots, and maps and receive immediate feedback that will help you make decisions such as defining model parameters and seeing how those decisions will affect the final results.

The general steps to create a suitability model are the following:

  • Identify criteria.
  • Transform the values within each criterion onto a common scale.
  • Weight the criteria relative to one another and combine them to create a suitability map.
  • Locate the areas that best match your criteria.

Create the model and add the criteria

You'll start with creating the model and adding the criteria to it.

  1. On the ribbon, on the Analysis tab, in the Workflow group, click Suitability Modeler.

    Suitability Modeler button

    The Suitability Modeler pane displays. Note the Settings, Suitability, and Locate tabs.

    Tabs on the Suitability Modeler pane

  2. In the Suitability Modeler pane, verify that the Settings tab is active and update the following parameters:
    • For Model Name, type ShrimpFarm.
    • Verify that Set suitability scale is set as 1 to 10.
    • Ensure that Weight by is set to Multiplier.
    • For Output suitability raster, click Browse and double-click Databases and ShrimpFarm.gdb. For Name, type NicoyaSuitability. Click Save.

    Suitability Modeler pane settings

  3. On the ribbon, on the Suitability Modeler tab, in the Suitability Model group, click Save.

    Save suitability model

    Note:

    If you need to take a break and close ArcGIS Pro, you can always open this project again, open Suitability Modeler, and click the Browse button near the model name to locate your model (click Folders>ShrimpFarm>ShrimpFarm.sam>ShrimpFarm). Then continue from where you left off.

  4. In the Contents pane, verify the addition of a new group layer named ShrimpFarm.

    It is currently empty.

    New Shrimpfarm layer group

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

    The tab displays a Criteria parameter. You'll add the model criteria into the table.

    Suitability tab of the Suitability Modeler

    As a reminder, the following are the five criteria defining the best areas to locate sustainable organic shrimp farms:

    • Be in close proximity to salt water, that is, to the gulf of Nicoya.
    • Be on specific land use types: the most suitable is land currently covered by shrub/scrub or used for agriculture purposes. Mangrove forests should be avoided.
    • Have access to the road network to get the shrimp to processing plants and market.
    • Be located near rivers to flush shrimp ponds regularly with fresh water.
    • Be in or near areas that are sheltered from north and south trade winds.

    You'll add the five relevant raster layers for these criteria.

  6. In the Suitability Modeler pane, for Criteria, click the Add raster criteria as layers from Contents list.

    Add raster criteria

  7. In the Criteria layer list, check the following layers:
    • Dist_Salty_Water
    • Dist_Roads
    • Dist_Rivers
    • LandCover
    • Dist_Sheltered
  8. In the Criteria layer list, click Add.

    Criteria list

  9. In the Suitability Modeler pane, for Criteria, verify the five criteria layers have been successfully added to the list.

    Five rasters listed as criteria

  10. In the Contents pane, verify that the ShrimpFarm group layer is turned on and expanded and that the five criteria layers have been successfully added to it.
    Note:

    If you added a layer by mistake, you can remove it from the Contents pane. Under ShrimpFarm, right-click the layer and click Remove.

  11. In the Contents pane, drag the ShrimpFarm group layer to the top so that you can see it better. If necessary, for each criteria layer, click the arrow to display the legend.

    Suitability criteria listed in the Contents pane

    This group layer stores all the layers relevant to the model, and more will be added as you progress through the workflow.

  12. In the ShrimpFarm group layer, review each criteria raster layer.

    In your suitability model, you will want to combine all these criteria together. But first, you need to convert each criterion to a common 1-to-10 suitability scale, so that they can all contribute equally to the model. The most preferred value within a criterion is assigned a 10; and the least desirable value a 1. This transformation can be done differently based on the raster types and criterion's meaning.

    One important raster type distinction is between continuous and categorical rasters. For instance, the Dist_Salty_Water is a continuous raster: it has a numeric value range that represents distances from 0 to 14,344.8 meters, and its cells can take any decimal value within that range. Similarly, Dist_Roads, Dist_Rivers, and Dist_Sheltered are also continuous rasters. In contrast, LandCover is a categorical raster: its cells have values that represent categories, such as Mangrove or Grassland, and there are only 11 possible categories for that raster.

    Note:

    In continuous data, the values have meaning relative to one another. For instance, a distance of 50 meters from a road is half as far as a distance of 100 meters. Within categorical data, you don't have those types of relationships between the values. A land use of 10 (Mangrove) is not half the land use assigned a 20 (Urban).

    Continuous is generally represented with floating-point, but it does have to be (it is about the relative meaning of the values) and Categorical is generally integers (categories)

    The transformation for continuous and categorical rasters will be handled differently. You will also see two different ways of transforming continuous rasters, based on the specific data they represent: applying a continuous function or a range of classes.

    Layer nameTypeTransformation method

    Dist_Salty_Water

    Continuous

    Continuous function

    LandCover

    Categorical

    Unique categories

    Dist_Roads

    Continuous

    Range of classes

    Dist_Rivers

    Continuous

    Continuous function

    Dist_Sheltered

    Continuous

    Continuous function

Transform continuous data

You'll start with transforming the continuous raster Dist_Salty_Water. You'll do that by applying a continuous function.

  1. In the Suitability Modeler pane, for Criteria, click the circle next to the Dist_Salty_Water criterion.

    Select the Dist_Salty_Water criteria

    The circle turns green and the Transformation Pane appears.

  2. If necessary, resize and reposition the Transformation Pane below the Gulf of Nicoya map, so you can see both the pane and the map.

    Resize pane

    In the Contents pane, in the ShrimpFarm group layer, two additional layers were also added: Suitability map andTransformed Dist_Salty_Water. The original Dist_Salty_Water layer has also been moved just above Suitability map to make it easier to see.

    New layers added for the suitability analysis

    The Transformed Dist_Salty_Water layer shows the Dist_Salty_Water layer transformed to a 1-to-10 scale. For now, it is using a default transformation. The Suitability map layer will show the combination of all the transformed criteria layers. At the moment, there is only one transformed layer, so Suitability map is just a duplicate of Transformed Dist_Salty_Water.

  3. In the Contents pane, right-click Suitability map and choose Zoom to Layer. Make sure that Transformed Dist_Salty_Water is turned on, and turn off Suitability map, as well as any other layers in the ShrimpFarm group layer.

    You'll use the Transformation Pane to refine the transformation applied to the Dist_Salty_Water layer.

  4. Review the content of the Transformation Pane.

    Transformation Pane for Dist_Salty_Water

    The Transformation Pane has three primary sections, which all provide information to assist you with selecting the most appropriate transformation. The center section is used to specify the transformation method: currently the MSSmall function is used. The section on the right displays a transformation plot, and the section on the left displays a suitability plot.

  5. In the Transformation Pane, in the Continuous Functions tab, click the Function drop-down list and review the available functions. Ensure MSSmall is selected.

    MsSmall function

    Since the Dist_Water layer is a continuous raster, the Continuous Functions method MSSmall was applied by default. Depending on your specific data, other continuous functions listed in the drop-down list may be more appropriate.

  6. Observe the Transformation of Dist_Salty_Water plot.

    Transformation plot for Dist_Salty_Water

    The plot shows how the values of the original raster (x axis) are transformed into 1-to-10 suitability values (y axis). The transformation function is displayed as a blue line. For instance, you can see that the original value of 5,737.9 (meters) is transformed into a suitability of about 7. Applying a continuous function means that with each meter we move away from the gulf, the preference continuously decreases, with the closer distances being more desirable. With the MSSmall function, the closer distances receive the highest suitability (a value of 10) and then after about 4,500 meters, the preference sharply decreases.

    The bars of the plot show a histogram indicating the relative number of cells for the different value ranges on the x axis. The bar colors correspond to suitability: green is most preferred and red least preferred.

  7. Look at the Transformation of Dist_Salty_Water plot in conjunction with the Transformed Dist_Salty_Water layer on the map.

    Transformed Dist_Salty_Water on the map

    The same color symbolization is applied on the plot and on the map.

  8. On the map, click several locations in the area of interest to see their value in the Pop-up pane. As expected, they vary from 1 to 10. Close the Pop-up pane.
  9. In the Transformation Pane, review the Distribution of Suitability plot.

    Distribution of Suitability graph

    This histogram shows the distribution of suitability values in the final suitability map: the x axis shows the range of suitability values (currently 1 to 10), and the y axis shows how many cells were assigned to each value. The histogram and suitability map are updated with each change in the model. They provide feedback on how changes in each criteria transformation will affect the final output.

    You'll now explore different transformations that can be applied to Dist_Salty_Water.

  10. In the Transformation Pane, for Function, choose Linear.

    The two plots and the map update.

  11. Review the Transformation of Dist_Salty_Water plot.

    Plot with linear function

    In the plot, the blue line now shows the progression of a typical linear function. The locations closer to the water are now less preferred and the preference increases as you move away from the water. This is not what you want, so you'll invert the transformation.

  12. In the Transformation Pane, check the Invert function box.

    Invert function checkbox

    The transformation is now back in the right direction (lower distances are preferred). The main difference with the MSSmall transformation is that the suitability values drop more steadily (linearly).

  13. Look at the map to see how the MSSmall and Linear transformations change the Transformed Dist_Salty_Water layer.

    You can go back and forth between the functions a few times.

  14. In the Transformation Pane, using the Function drop-down list, explore some of the other functions to see their effect.

    MSSmall is actually a good choice for this layer. The suitability sharply decreases as the distance from the coast increases. However, the sharp drop needs to happen sooner, because the shrimp farm should really be located close to the coast for easy access to briny water. You'll achieve this with the Mean multiplier parameter.

  15. In the Transformation Pane, using the Function drop-down list, select the MSSmall function.
  16. For Mean multiplier, type 0.2, and click anywhere on the pane for the model to update.

    Mean multiplier parameter

    The sharp drop now happens much sooner both on the transformation plot and the map. Areas very close to the ocean are most preferred (green), while farther areas quickly drop to less preferred values (yellow and red).

    Final map for the Dist_Salty_Water suitability

In this section, you saw how the Suitability Modeler allows you to receive dynamic feedback on your transformation choices, with a focus on continuous functions. In the next section, you'll explore categorical transformations.

Transform landcover to unique categories

Now, you'll transform the second criterion, LandCover, onto the common scale. Certain land cover types are more suitable to develop shrimp ponds on than others.

  1. In the Suitability Modeler pane, for Criteria, check the circle next to the LandCover criterion.

    Activate LandCover criteria

    Note:

    The button turns green indicating it is the active criterion in the Transformation Pane. The button turns gray once the criterion has been transformed and is no longer active in the Transformation Pane.

    The Transformation Pane and the Contents pane update.

  2. In the Contents pane, review the ShrimpFarm group layer.

    An additional layer named Transformed LandCover has been added. The Suitability map layer has now a range of 1 to 20 as it combines the first two criteria.

  3. Make sure that Transformed LandCover is turned on. Turn off all other layers in the ShrimpFarm group layer.

    Landcover criteria in contents pane

  4. Explore the Transformation Pane.

    Since LandCover is categorical data, the Unique Categories transformation method has been applied by default. In the table, the Class and the Category columns the numeric value and name of each land cover type. The Suitability column shows the result of the one-to-one transformation method that was applied: each land cover category is assigned a suitability value, based on how preferred it is for developing a shrimp farm. For now, the suitability values have been assigned blindly in the order the classes are listed. Next, you'll enter your desired values.

    Unique Categories tab

    You'll turn off Auto Calculate to prevent the Suitability Modeler from applying immediate updates each time you change a single value..

  5. On the ribbon, on the Suitability Modeler tab, in the Suitability Analysis group, uncheck Auto Calculate.

    Auto Calculate button

  6. In the Unique Categories table, update the Suitability for each landcoverCategory.

    Use the following table to assign the desired suitability values.

    CategorySuitability

    Evergreen Forest

    4

    Shrub/Scrub

    9

    Grassland

    4

    Barren/Minimal Vegetation

    5

    Agriculture, General

    6

    Agriculture, Paddy

    6

    Wetland

    3

    Mangroves

    1

    Water

    1

    Urban, High Density

    1

    Urban, Medium to Low Density

    1

    The most suitable land cover types such as Shrub/Scrub and Agriculture are assigned the higher values, and the least suitable are assigned the lowest values.

  7. On the ribbon, on the Suitability Modeler tab, click Calculate and check Auto Calculate.

    Calculate button

    The new suitability values are now applied to the LandCover types, and subsequent calculations will now be applied automatically when a change is made to the model parameters. You'll inspect the updated plots and layers.

  8. In the Transformation Pane, explore the updated Transformation of LandCover bar chart.

    Transformation of LandCover

    The colors on the transformation bar chart depict the suitability preference for each land use type. The green bars are the most preferred land use types moving to the red bars being assigned the lower suitability values. The height of the bars identifies the number of raster cells for each land use. Taller bars indicate that land use covers more area within the study area and is more common.

  9. On the map, explore the Transformed LandCover layer.

    LandCover transformation map

    Notice the red areas along the coast. They represent mangrove forests and some urban areas and are the least suitable. In contrast, shrub/scrub and agricultural land is most suitable (in dark and light green).

  10. In the Transformation Pane, explore the updated Distribution of Suitability plot.

    Distribution of Suitability graph

    The suitability plot shows the distribution of the combined suitability with the first two criteria. You can see that the value range is now about from 3.2 to about 20. The medium suitability (in yellow) is currently the most often assigned.

  11. In the Contents pane, in the ShrimpFarm group layer, turn on the Suitability map layer.

    Two criteria combined in the Suitability map

    You can see that the suitability map is now a combination of the first two transformed criteria layers.

  12. Turn off the Suitability map layer.
  13. On the ribbon, on the Suitability Modeler tab, click Save to save the model.
  14. Save the project.

Transform a range of classes

Next you will transform the third criterion, distance to roads.

  1. In the Suitability Modeler pane, for Criteria, check the circle next to the Dist_Roads criterion.

    Select Dist_Roads criteria

    As usual, the Transformation Pane and the Contents pane update.

  2. In the Contents pane, under the ShrimpFarm group layer, make sure that Dist_Roads is on, and turn off all of the other layers in the group layer.
  3. Explore the Transformation Pane.

    Since Dist_Roads is a continuous raster, the continuous function MSSmall was applied by default. However, the cost to get shrimp to processing plants using the roads does not change with each meter travelled. Instead, the costs can be grouped into distance groups of equal cost, and each range will be assigned a single suitability value.

  4. In the Transformation Pane, click the Range of Classes transformation tab.

    Range of classes tab

    The range of distance-to-roads values is now divided into 10 classes. The first class is for distances from about 0 to 475 meters; the second class, 475 to 948 meters; and so forth.

  5. Review the Transformation of Dist_Roads plot.

    Transformation of Dist_Roads bar chart

    You can see that currently the closest distances are the least preferred and appear in red. This is the reverse of what you want, so you'll invert the suitability assignment.

  6. In the Transformation Pane, on the Range of Classes tab, click the Reverse button.

    Reverse button

    The plots and the map are updated to the desired effect.

  7. In the Transformation Pane, review the updated Transformation of Dist_Roads plot.

    Distances close to the roads are now classified as most suitable (green).

  8. In the Contents pane, drag the Roads layer above the Shrimpfarm group layer and turn it on.

    The roads now display on top of the Transformed Dist_Roads layer.

  9. On the map, explore the Transformed Dist_Roads and Roads layers.

    Dist_Roads transformation map

  10. Zoom in to verify that the distance values are grouped into ten discrete classes of distinct ranges around the location of roads.

    Ten discrete classes of distinct ranges

    The range closest to the roads is the greenest because it is the most suitable.

  11. In the Contents pane, turn off the Roads layer, and in the ShrimpFarm group layer, turn on the Suitability map layer. Right click the Suitability map layer and choose Zoom to Layer.

    Three criteria combined in the Suitability map

    The suitability map now shows the combination of the first three criteria. The same is true for the Distribution of Suitability plot, which now ranges from 8.5 to about 30.

  12. In the Contents pane, turn off the Suitability Map layer.
  13. Save the model.
  14. Save the project.

Transform using the power function

The fourth criterion is the distance to rivers. Before transforming this criterion, explore some additional functionality of the Suitability Modeler. You'll open two maps side-by-side to view both the current criteria transformation and final suitability maps at the same time. This will give you even more feedback on the impact of your choices.

  1. Click on the Gulf of Nicoya map pane to select it.

    The Gulf of nicoya map is selected

  2. On the ribbon, on the Suitability Modeler tab, in the Views group, expand the Map views drop-down list, and select Two maps.

    Map views drop-down list

    A second map, Suitability-TopRight, appears. The ShrimpFarm suitability group layer is copied to the Contents pane of the second map.

  3. If necessary, position and dock the Suitability-TopRight map pane to the right of the Gulf of Nicoya map, as shown in the example image.

    Side-by-side windows

  4. Click the Gulf of Nicoya map pane to select it. On the Contents tab, under the ShrimpFarm group layer, right-click the Suitability map layer, and choose Zoom To Layer. Do the same for the Suitability-TopRight map pane.
  5. Make sure the Suitability-TopRight map pane is selected. On the ribbon, on the Map tab, in the Layer group, click Basemap and choose Imagery to change the basemap.

    Change the basemap

  6. For the Gulf of Nicoya map pane, turn off the GulfWater layer, so that the two maps look completely similar.
  7. For the Gulf of Nicoya map pane, turn on the Suitability map layer.

    Side-by-side windows after some adjustments

    You can now see the final suitability and current transformation maps at a glance. Next, you'll examine the transformation for the Dist_Rivers criterion.

  8. In the Suitability Modeler pane, in the Criteria table, check the circle next to the Dist_Rivers criterion.

    Since the Dist_Rivers raster is continuous data, as expected, the continuous function MSSmall is applied by default. The Gulf of Nicoya map pane now displays the Suitability map layer, while the Suitability-TopRight map pane displays the Transformed Dist_Rivers layer.

    Initial maps for Dist_Rivers

  9. In the Transformation Pane, review the current transformation plot.

    Transformation of Dist_Rivers plot

    Notice that, with the MSSmall function, the distance up to around 6,500 meters is considered most suitable and then the suitability sharply declines. This does not capture the desired suitability, because the shrimp farm should be very close to a river for easy access to fresh water. Instead, you'll use the Power function.

  10. In the Transformation Pane, in the Function drop-down list, select the Power function.

    Choose power function

  11. In the Transformation Pane, review the updated transformation plot.

    Transformation of Dist_Rivers plot power

    The locations closer to the rivers are less preferred when the Power function is first applied. You need to invert it.

  12. In the Transformation Pane, on the Continuous Functions tab, check the Invert function box.

    Invert the power function

    The plots and the maps update.

  13. In the Transformation Pane, review the updated transformation plot.

    Transformation of Dist_Rivers graph

    As you can see, the locations very close to rivers (in green) are highly preferred, and then the preference drops quickly and eventually slows down. The inverted Power function best captures the desired suitability transformation.

  14. On the Suitability-TopRight map pane, explore the updated Transformed Dist_Rivers layer.

    Maps with power function applied

    Now, only the areas very close to the rivers appear in green.

  15. On the Gulf of Nicoya map pane, explore the updated Suitability map layer.

    You can see in the legend that its values now range from 1 to 40, because it represents four criteria combined. You can see how the distance to rivers influences the suitability map: some of the most suitable areas (in green) are now along the rivers.

  16. If necessary, for each map, zoom to the Suitability map layer extent.
  17. Save the model.
  18. Save the project.

Transform using the exponential function

You will now transform the final criteria: the shrimp farm should be in or near locations that are sheltered from the north and south winds.

  1. In the Suitability Modeler pane, in the Criteria table, check the circle next to the Dist_Sheltered criterion.

    Since the Dist_Sheltered raster is continuous data, the continuous function MSSmall is again applied by default.

  2. In the Transformation Pane, review the transformation plot.

    Transformation of Dist_Shetered plot

    The default function does not capture the transformation you desire. The positive impact of wind sheltered areas does not end abruptly, so you want the preference to decrease much slower for the short distances.

  3. In the Transformation Pane, on the Continuous Functions tab, from the Function drop-down list, select the Exponential function. Check the Invert function box.

    Function choice for wind shelter criteria

  4. In the Transformation Pane, review the updated transformation plot.

    Transformation of Dist_Sheltered

    The blue function line shows that the closer locations are preferred, and that the preference decreases slowly at first and faster as the distances get farther. However, to better capture the preference for the criterion, you want to fine-tune the function by making it decrease slightly faster.

  5. In the Transformation Pane, on the Continuous Functions tab, for Base factor, type 0.00014. Click anywhere on the pane to apply the change.

    Base factor parameter

    The plots and maps update.

  6. In the Transformation Pane, review the updated transformation plot.

    Transformation of Dist_Sheltered with the preference decreasing at a slightly more rapid rate

    If you observe the blue line function, you notice that the preference is now decreasing at a slightly more rapid rate.

  7. On the Suitability-TopRight map pane, observe the resulting Transformed Dist_Sheltered layer.
  8. On the Gulf ofNicoya map pane, observe how the addition of the distance from sheltered areas criteria changes the final Suitability map layer.

    Final Dist_Ssheltered maps

    The suitability map now combines all five criteria and ranges from 1 to 50. You can see clearly that the most suitable areas (in green) are close to the gulf, but not on mangroves or urban land, and they tend to be close to rivers. The proximity to roads and wind-sheltered areas is less easy to see at a glance, but it is taken into account.

  9. On the Suitability-TopRight map pane, turn on the different transformed criteria layers one by one, to compare them with the Suitability map layer on the Gulf ofNicoya map pane.

    Observe how each one has influenced the final suitability map.

  10. If necessary, for each map, zoom to the Suitability map layer extent.
  11. Save the model.
  12. Save the project.

In this section, you created a suitability model using the Suitability Modeler, and you transformed all your criteria onto the common scale of 1 to 10.


Complete the suitability analysis

In this module, you'll perform the last steps of the analysis to identify the most suitable locations for new sustainable shrimp farms.

Weight model criteria relative to one another

During the transformation steps, you transformed the values within the criteria onto a common scale. However, with regards to locating a shrimp farm, some criteria may be more important than others. To incorporate the criteria importance, you will assign weights to each layer relative to one another.

For instance, it is critical that shrimp farms be next to the gulf to ensure access to briny water necessary for shrimp development. So, this criterion will receive the highest weight. In contrast, since the road network is good in most of the area of interest, the distance to roads criterion is not as essential, and it can receive a lower weight. But first, you'll go back to a one-map view.

  1. On the ribbon, in the Suitability Modeler tab, in the Views group, click the Map views drop-down list, and select One map.
  2. In the Contents pane, under the ShrimpFarm group layer, right click the Suitability map layer and choose Zoom to Layer.
    Note:

    The Suitability Modeler only processes data within the current extent. If you had changed the extent while exploring the map, processing may take place for a limited extent instead of the full extent of the suitability map layer.

  3. In the Suitability Modeler pane, in the Criteria table, review the Weight field.

    Weights of one

    Currently, all criteria are assigned a weight of 1, and thus are equal in importance. Since the Dist_Salty_Water criterion is critical, you'll assign it a weight of 8. The Dist_Rivers criterion is next in importance and you'll assign it a weight of 3. Dist_sheltered will receive a weight of 2, and all the remaining criteria a weight of 1.

  4. In the Criteria table, update the Weight values as follows.

    Input RasterWeight

    Dist_Salty_Water

    8

    Dist_Rivers

    3

    Dist_sheltered

    2

    LandCover

    1

    Dist_Roads

    1

    Click anywhere on the pane to apply the weights.

  5. In the Criteria table, verify your Weight values before continuing.

    New weights for the criteria

    Note:

    The order in which the criteria are listed may vary.

  6. Review the resulting Suitability map layer.

    Suitability map with weights applied

    Note:

    In the Contents pane, you can see that the suitability value range now goes up to 150. This is because the weights are multipliers: each criterion's suitability value is multiplied by the criterion's weight. Since the maximum value is 10 for each criterion, the total maximum suitability is the following:

    10 * 8 + 10 * 3 + 10 * 2 + 10 *1 + 10 * 1 = 150.

    During the exploratory stage of the model creation, you were processing at screen resolution and extent and layers generated were not saved outside of the project. Before moving on to the next phase of your workflow, you must run the model at full resolution and save the Suitability map layer to disk.

  7. In the Suitability Modeler pane, click Run.

    Run button

    Depending upon your machine, the process may take a couple of minutes.

  8. In the Contents pane, review the value range.

    New suitability range

    The range has now been updated to the precise suitability values existing in the model: the lowest is 39.74 and the highest 149.

    On the map, notice that areas located close to the gulf, rivers, and sheltered areas are the most suitable and are symbolized in green. Areas that are farther from these features are least suitable and symbolized in red.

Locate the shrimp farms

You'll now use the Suitability map layer to identify the five best locations for new shrimp farms. This is done in the Suitability Modeler Locate tab. The goal is to find regions that have the highest suitability possible, while also meeting a number of spatial requirements. For instance, spatial requirements can include the total area and number of regions desired, their minimum and maximum sizes, and the ideal distances between the regions.

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

    You'll consider the following spatial requirements for the shrimp farms.

    • Capacity and demand for the processing of shrimp at local plants is limited and thus cannot service more than five new shrimp farms.
    • Each farm must be at least 3,000 hectares in size.
    • To avoid spreading disease between farms, new farms cannot be sited within 5 kilometers of one another.
    • Finally, mangrove areas should be fully avoided. To ensure this, you'll use the NoMangroves raster as a mask, so that Locate only searches for suitable regions in non-mangrove areas.
    Note:

    The NoMangroves raster was generated using the Extract by Attributes Spatial Analyst tool applied to the LandCover layer. Only the raster cells whose values are not equal to the Mangrove land cover type were selected and copied to the new raster.

  2. On the Locate tab, enter the following parameters:
    • For Area units, select Hectares.
    • For Total area, type 3000. (Note: This is best done after selecting the Area units.)
    • For Output raster, type ShrimpFarmLocations.
    • For Number of regions, type 5.
    • For Minimum distance between regions, type 5.
    • For Distance units, ensure Kilometers is selected.
    • For all the other parameters, accept the default.

    Locate parameters

  3. On the Locate tab, click Environments.

    Environments tab

  4. Under Raster Analysis, for Mask, choose NoMangroves.

    Mask set to NoMangroves

  5. On the Locate tab, click Parameters, and click Run.

    Parameters tab

    The tool might take a few moments to run.

    Note:

    The Locate tab calls the Locate Regions geoprocessing tool. To learn how the locate algorithm works, see How the Locate Regions tool works.

    The Suitable locations layer appears.

  6. Verify that 5 locations were identified. The value 0 was assigned to all the areas that were not selected for the desired suitability and spatial configurations.

    Suitable locations layer

    Note:

    The colors are assigned at random and may vary.

    You'll assign no color to value 0, so that you can focus on the five locations of choice and see the Suitability map layer below.

  7. In the Contents pane, for Suitable locations right-click the legend for Value 0, and select No color.

    Set Value 0 to No color.

  8. On the map, review the Suitable locations layer.

    The five suitable locations

    You can turn the Suitable locations layer on and off, as you pan and zoom around the map. Notice that all five regions are located in a highly suitable area (darker green). They also avoid mangrove areas.

  9. Save the model. Save the project.

Going further

Using the Suitability Modeler, you successfully identified the best locations in the Gulf of Nicoya for the development of five new sustainable organic shrimp farms. Because of the interactive, exploratory nature of the Suitability Modeler, you can continue your examination of the model using the Exploratory tools on the Suitability tab to understand how the model components interact to produce the final results. You can create queries to examine how the base input criteria are realized in the final suitability map. For example, you can identify all locations that are within 1,000 meters of the gulf, are 500 meters from a river, and have received a suitability value greater than 100 on the final suitability map.

With the Modeler, you can also explore what-if scenarios to see their impact. For example, what happens if a new treatment comes available that allows you to locate the shrimp farms closer together? You can also run the Suitability Modeler reducing the distance between the farms parameter and see if you get different proposed locations.

Additionally, you could explore adding additional criteria to the model that might influence the siting of the shrimp farms. They may include the following:

  • Slope and aspect—Some sites are on locations where the slope and aspect (orientation) are unsuitable and thus cannot support building ponds. Slope and aspect can be derived from elevation data.
  • Soil and geology—The characteristics of the underlying geology and soil type and depth may not be suitable for pond development.
  • Water salinity—Shrimp cultivation takes place in briny water, thus if water salinity varies, it should be incorporated into the model.

These criteria may or may not affect the model results. Either way, the Suitability Modeler will allow you to assess if they do.

In this lesson, you explored five criteria relevant for shrimp farming. You created a suitability model with the help of Suitability Modeler, added the five criteria to it, transformed the criteria to a common 1-to-10 scale, weighted them, and located the five best locations for new shrimp farms. Because of the dynamic feedback you receive in the Suitability Modeler, you can be more confident of your input criteria, the model parameters, and final result obtained. As a result, you will make more informed decisions.

You can find more lessons like this on the Introduction to Imagery & Remote Sensing page.