InVEST +VERSION+ documentation

Overlap Analysis Model


Mapping current uses and summarizing the relative importance of various regions for particular activities is an important first step in marine spatial planning. The InVEST Overlap Analysis Model was designed to produce maps that can be used to identify marine and coastal areas that are most important for human use. Initial development of this model was as two separate models for recreation and fisheries. However, since the underlying approach was fundamentally similar, we combined them into one model that can be used to map not only recreation and fisheries, but also other activities. While this model was envisioned for use in marine areas where space is shared commonly, it is also applicable to locations on land where overlapping uses occur. Inputs include information about where human use activities occur (required), weights that reflect the relative importance of different human uses (optional) and information on spatial variability within uses (optional). Because it simply maps current uses and does not model behavior, this model is not well-suited to the evaluation of how human uses may change in response to changes in the coastal and marine environment. However, it can be used to model scenarios that reflect changes in the areas used by different activities or changes in attributes such as total landings or number of trips that are used to weight activities.


Understanding where and how humans use coastal and marine environments is an essential component of marine resource planning and management. Marine and coastal ecosystems are essential places for a variety of activities including fishing (commercial, recreational, subsistence and ceremonial) and recreation (e.g. boating, kayaking, diving, whale-watching). When siting new activities and infrastructure or zoning areas for particular uses, a key step is the identification and visualization of the variety of human uses that occur in the region and the places in which they overlap (e.g., GBRMPA 2003, CDFG 2008, Beck et al. 2009, CRMC 2010). This allows for the identification of hotspots of human use and highlights regions where the compatibility of various activities should be investigated.

The InVEST Overlap Analysis model provides users with a simple framework for mapping and identifying important areas for human use in the marine environment. The model also allows users to include information about a variety of uses of the coastal and marine environment (e.g., commercial fishery logbooks or landings reports data, participation numbers for recreational activities) that can be used to weigh the relative importance of different uses and locations. The model is simple to use, quick to run, and can be applied in any region of the world where there is spatially-explicit information on human uses. The model does not value ecosystem services or estimate the economic value of human uses, but the outputs can be used to identify areas and different user groups that may be affected by policy change. The model produces a map of hotspots for human activities (e.g., fishing activity/fishing grounds) across as many human uses as the user chooses to include. Throughout this chapter we will give examples for both recreation and fisheries. Using the tool across various categories of human uses may make sense in some instances, but devising schemes for weights will likely be difficult. Outputs can be used to help decision-makers weigh potential conflicts between sectors of spatially-explicit management options that may involve new activities or infrastructure.

The Overlap Analysis Model complements the more involved InVEST recreation model and the fisheries model that is in development. The InVEST Marine Fish Aquaculture model is appropriate for use with single species or groups of species and is used to estimate the quantity and value of fish harvested by commercial fisheries. Additionally, a recreational submodel can be used to predict the amount of recreational fishing effort required to catch the quantity of fish output from the InVEST Marine Fish Aquaculture model. Future more advanced fisheries models will include functionality to incorporate impacts of biogenic habitat on the survival and fecundity of different life-stages of target species, and the ability to wrap around outputs from more complex food-web models (e.g., Ecopath with Ecosim and Atlantis).

The Model

The InVEST Overlap Analysis model was designed to identify marine and coastal areas most important for human use. The model combines the different input layers of human use and computes an “Importance Score” for each grid cell or management area. If users only know where activities occur but do not have additional information to weight the relative importance of different activities, the default model computes an “Importance Score” by summing the number of activities that occur in any particular cell or zone. Although not required to run the model, users can input qualitative (e.g., indices, scores) or quantitative (e.g., catches, effort levels, revenues, profits) information to weight the importance of different locations for an individual activity and to weight activities compared to one another. The model also allows users to down-weight areas or zones used for different activities as a function of their distance from important land-based hubs such as ports, marinas, or public access points. Model outputs are mapped in the coastal region of interest over the specified seascape or management zones. The default model map output is a shapefile showing the frequency of occurrence of activities across the area of interest. If additional weighting information is included, the model also produces a shapefile showing the gradation of importance across cells or zones. The resulting maps can then be used to evaluate the relative importance of different areas in the seascape for the set of human activities included in the analysis. See Appendix A for suggestions for data sources.

How it Works

Calculating Frequencies (Model Default)

Users input maps of the locations of multiple human activities. Data is input in a vector format as polygons or points; vector data are rasterized after they are input. In the simplest (default) model, all activities and locations are weighted equally and the model calculates an Importance Score (IS), which is a count of how many activities take place in each grid cell or management zone \(i\):

(1)\[IS_i=\sum_{i,j} U_{ij} I_j\]

where \(U_{ij}\) = usage of activity \(j\) in grid cell or management zone \(i\). \(U_{ij}\) is scored by the presence (\(U_{ij}\) = 1) or absence (\(U_{ij}\) = 0) of the activity in the cell or zone.

Including Weights (Optional)

Users are also given the option to apply different weights to each activity. The two ways in which users can provide these weights are as inter- or intra-activity weights:

  1. Inter-activity weight: this allows users to weight the importance of activities relative to one another. Users may choose to give more weight in the analysis to certain activities (e.g., those that generate the highest profits of all fleets in the analysis, or are key employers in the region) and less to other activities. For example, if the user is examining 3 activities (1. commercial salmon fishing, 2. commercial crab fishing, and 3. commercial kelp harvest) and commercial salmon fishing is deemed to be twice as important as either commercial crab fishing or commercial kelp harvest, then the user would provide weights of (commercial salmon fishing, commercial crab fishing, commercial kelp harvest)= (2,1,1). Inter-activity weights are included in an input .csv table (see “Running the Model” section below); and/or
  2. Intra-activity weight: Spatially explicit information about the relative importance of various locations (points or polygons on the map) for a particular activity can be used to weight the scores used in the model calculations. Importance can be measured several ways. For fisheries, weights might be informed by the amount of fish caught or landed, profits earned, safety or accessibility of the fishing ground, or the cultural value of the area. For recreation, they might be determined by the number of visitors or trips to different areas. For example, if the user is examining three commercial harvesting activities and has catch data for each polygon representing those activities, these intra-activity weights can be included by adding a column to the shapefile attribute table of each input activity layer. The name of this column should have no spaces, and this column name will need to be given as an input so that the model knows where these weights are stored.

If intra- or inter-activity weights are included, IS is weighted by the importance of the cell (or zone) relative to other cells (or zones) with that activity occurring, and/or the importance of the activity relative to other activities included in the analysis. Please see Appendix A for guidance on preparing and including information on intra- and inter-activity weights using qualitative (i.e., scores of ‘more’ or ‘less’ fishing in a cell, visitation or trip numbers for recreational activities) or quantitative (i.e., commercial fishing catch, effort level, revenues, profits) data.

Functionally, \(IS\) of pixel or management zone \(i\) is:

(2)\[IS_i = \frac{1}{n}\sum_{i,j}U_{ij}I_j\]


\(n\) = number of human use activities included in the analysis.

\(U_{ij}\) = usage or intra-activity weight (optional) of activity \(j\) in pixel or management zone \(i\). If the user does not include intra-activity weights (i.e., model default), \(U_{ij}\) represents usage and is scored by presence (\(U_{ij}\) = 1) or absence (\(U_{ij}\) = 0) of the activity in the cell or zone. When intra-activity weights are included, \(U_{ij}\) reflects the weights as \(U_{ij}\) = \(X_{ij}\) / \(Xmax_j\), where \(X_{ij}\) is the intra-activity weight of activity \(j\) in pixel or management zone \(i\) and \(Xmax_j\) is the maximum intra-activity weight for all cells or zones where the activity occurs.

\(I_j\) = inter-activity weight (optional) of activity \(j\) relative to other activities included in the analysis. If the user treats all activities as equally important (model default), \(I_j\) is ignored (i.e., \(I_j\) = 1). When inter-activity weights are included, \(I_j\) reflects the weights as \(I_j\) = \(Y_j\) / Ymax, where \(Y_j\) is the inter-activity weight of activity \(j\) and \(Ymax\) is the maximum inter-activity weight for all activities.

Limitations and Simplifications

This model is a very simple framework that provides little insight into how human activities might change under different scenarios of change in the coastal and marine environment. Such insights are best gleaned from models that include descriptors of human behavior. However, scenarios that add or remove activities or change weights of various activities and/or locations can be used to explore change.


the model is very sensitive to inter- and intra-activity weights. Therefore, the assumptions you make when including these optional inter- and/or intra-activity weights will strongly affect model outputs. If you are unsure of how to appropriately include inter- or intra-activity weights, we encourage you to conduct several model runs to see how different weighting schemes affect model outputs.

Data Needs

The model uses an interface to input all required and optional model data. There are two options: the standard overlap analysis model that computes use intensity for each raster pixel, and an option to compute intesity by larger management zones. Each is a standalone model in InVEST, however the inputs required have the same descriptions and requirements so they are not reft below. Here we outline the options presented to the user via the interface, and the maps and data tables used by the model. First we describe required inputs, followed by a description of optional inputs.

Required Inputs

The required inputs are the minimum data needed to run this model. The minimum input data allows the model to run without importance weights or distance decay, both of which are optional parameters.

  1. Workspace Location (required). Users are required to specify a workspace folder path. We recommend that you create a new folder for each run of the model. For example, by creating a folder called “runBC” within the “OverlapAnalysis\Recreation” folder, the model will create “intermediate” and “output” folders within this “runBC” workspace. The “intermediate” folder will compartmentalize data from intermediate processes. The model’s final outputs will be stored in the “output” folder.

    Name: Path to a workspace folder.  Avoid spaces.
    Sample path: \InVEST\OverlapAnalysis\BCrun
  2. Analysis Zones Layer (required). A polygon shapefile that defines the area of interest for the standard analysis. The AOI must be projected with linear units equal to meters. For the management zones model, a similar shapefile is needed except the AOI should be divided into appropriate management zones.

    Name: File can be named anything, but no spaces in the name
    File type: Polygon shapefile (.shp)
    Sample path: \InVEST\OverlapAnalysis\Input\AOI_WVCI.shp
  3. Analysis Cell Size (required). This determines the spatial resolution at which the model runs and at which the results are summarized. For example, if you want to run the model and see results at a 100m x 100m pixel size then enter “100.”

    Name: A numeric text string (positive integer)
    File type: text string (direct input)
    Sample (default): 1000
  4. Overlap Analysis Data Directory (required). Users are required to specify the path on their system to a folder containing only the input data for the Overlap Analysis model. Input data can be point, line or polygon data layers indicating where the human use activity takes place (e.g., whale watching, diving, or kayaking in a marine setting). Please note that optional intra-activity importance information, described below for optional input #1, can be associated with each layer. In InVEST 3.1.0 and earlier, there may be no more than 32 layers in this directory.

    Name: Path to an activity data folder.  Avoid spaces.
    Sample path: \InVEST\OverlapAnalysis\Input\RecreationLayers_RIS\


All data in this folder must be shapefiles and projected in meters. For general help with creating and editing shapefiles, try documentation provided by ArcGIS or QGIS. For more specific InVEST-related GIS video tutorials, consider enrolling in the online course, Introduction to the Natural Capital Project Approach.

Optional Inputs

The next series of inputs are optional for added model functionality.

  1. Intra-Activity Attribute Name (optional). The user has the option of providing information on the importance of locations (i.e., polygons or points) within a layer of human use data (e.g., one fishing ground may be much more valuable than another; certain kayaking routes may be more popular than others). These intra-activity importance scores can be qualitative or quantitative (see Appendix for further description of data inputs) and must be listed in a new column of the attribute tables for each layer included in the Overlap Analysis (see intra-activity weighting in The Model section). The name given to the column that contains the intra-activity importance scores must be the same for all layers contained within the directory specified by input #4. The model uses this information to weight the importance of areas found within each input layer.

    Names: Text string containing letters and/or numbers (must start with a letter).
    Field name must correspond to an existing column name in each layer's attribute table
    Sample: RIS
  2. Inter-Activity Weight Table (optional). The model also allows users to provide information on the relative importance of uses. This .csv file lists the activities and gives them a numerical relative importance weighting. The default files demonstrate the required structure; it is recommended that these files not be overwritten. In the .csv table, it is important that the name of each use exactly corresponds to the given name of the shapefile that represents that use.

    Names: File can be named anything, but no spaces in the name
    File type: Comma-separated values file (.csv)
    Sample path: \InVEST\OverlapAnalysis\Input\Recreation_Inputs.csv
  3. Points Layer of Human Use Hubs (optional). The model allows users to down-weight areas or zones used for different activities as a function of the distance from important land-based hubs such as ports, marinas, or public access points. This input GIS layer must be a point shapefile and projected in meters.

    Names: File can be named anything, but no spaces in the name
    File type: Point shapefile (.shp)
    Sample path: \InVEST\OverlapAnalysis\Input\PopulatedPlaces_WCVI.shp
  4. Distance Decay Rate (optional). If a GIS layer is specified for optional input #3, the model will use a decay rate of \(\beta\) =0.025 by default. If this input is not specified, no distance decay occurs and this rate is ignored. See Figure 1 for how changing this parameter changes the decay rate. With a decay rate of 0.025, an importance score of 1 would decrease to ~0.8 at a distance of approximately 10 km from the nearest hub. User judgment should be exercised when using this option. The following scenario illustrates one example of how users might use the distance decay function. Suppose you know that the intensity of human activities is greatest in areas relatively close to the ports, marinas, and other public access points, but you do not have the data necessary to construct spatially-explicit weighting factors to reflect this knowledge. In the absence of these data, the distance decay function could be used to reflect this intensity / distance tradeoff. You can choose a decay rate that reflects your best judgment on how the importance (e.g., intensity) of activities declines with distance from important population centers, marinas, or access points. For example, if most recreational fishing grounds are located within 10 km from the central marina, you could choose a decay parameter of \(\beta\) =0.01 to reflect a gradual threshold in the decline of importance of more distant sites, or \(\beta\) =0.5 to reflect a sharper threshold.

    Names: A string of numeric text with a value between 0 and 1
    File type: Text string (direct input to the ArcGIS interface)
    Sample (default): 0.025

Exponential decay functions used to downweight importance of activities based on distance from land-based access point

Multiple Runs of the Model

The tool setup is the same as for a single run, but you must specify a new workspace for each new run. Make sure each new workspace exists under the main workspace folder (i.e. OverlapAnalysis folder in the example above). As long as all data are contained within the main Input data folder you can use the same Input folder for multiple runs. For example, using the sample data, if you wanted to create two runs of the Overlap Analysis model based on two different weighting systems for fishing fleets, you could use the Input data folder under main Overlap Analysis folder and create two new workspace folders, runFisheries1 and runFisheries2.

Running the Model

The model is available as a standalone application accessible from the Windows start menu. For Windows 7 or earlier, this can be found under All Programs -> InVEST |version| -> Overlap Analysis. Windows 8 users can find the application by pressing the windows start key and typing “overlap” to refine the list of applications. The standalone can also be found directly in the InVEST install directory under the subdirectory invest-3_x86/invest_overlap_analysis.exe.

Viewing Output from the Model

Upon successful completion of the model, you will see new folders in your Workspace called “intermediate” and “Output”. The Output folder, in particular, will contain several types of spatial data, which are described in the Interpreting Results section.


You can view the output spatial data in ArcMap using the Add Data button. adddata

You can change the symbology of a layer by right-clicking on the layer name in the table of contents, selecting “Properties”, and then “Symbology”. There are many options here to change the way the data appear in the map.

You can also view the attribute data of output files by right clicking on a layer and selecting “Open Attribute Table”.

Interpreting Results

Model Outputs

The following is a short description of each of the outputs from the Overlap Analysis model. Each of these output files is saved in the “Output” folder that is saved within the user-specified workspace directory:

Output Folder

  • Output\hu_freq
    • This raster layer depicts the frequency of activities for each cell or management zone for the study area. Each layer input is only counted once regardless of the number of features within that layer overlapping a cell. Therefore, if three layers are specified in the input directory, then the max value of this output is 3.
    • This is the default model output that will be generated for each run of the model.
  • Output\hu_impscore
    • This raster layer depicts Importance Scores for each cell or management zone for the study area.
    • This output is only generated if the user includes intra-activity weights defined by optional input #1: “Importance Score Field Name”.
  • overlap_analysis-log-yr-mon-day-min-sec].txt
    • Each time the model is run a text file will appear in the workspace folder. The file will list log information that can be used to identify detailed configurations of each of scenario simulation.

Appendix A

Preparing Input Data

Maps of Fishing Grounds

Users should create a layer of polygons or points to define where individual fishing fleets operate. Fleets can be defined however you deem appropriate. Often, fleets are defined by their sector (e.g., commercial, recreational, subsistence), the species or species complexes they target (e.g., prawn, salmon, groundfish), and the gear that they use (e.g., trawl, seine, longline). For example, fleets might be commercial groundfish trawl, subsistence salmon seine, or recreational tuna hook and line.

For each fleet you decide to include, you must have information on where that fleet fishes. Locations can be points or polygons. You can generate these layers if existing maps of spatial distribution of fishing catch or effort are available to you. These maps are not often readily available, in which case, you can summarize catch, effort, or revenue data by management zone or statistical area. Availability of these data varies regionally – most regional management councils in the U.S. collect these data and make them publicly available through data clearinghouses associated with regional management councils (e.g., Pacific Fisheries Information Network associated with Pacific Fisheries Management Council). When summary by management zone or statistical area is unavailable, information can be solicited from stakeholders through exercises where they draw polygons or points on maps. If none of these are options for you, but you have habitat information available, it is possible to draw habitat-species-gear associations and coarsely estimate where fleet activity may occur.

Recreational Activity Layers

Spatially explicit data on recreation activities can be collected from a variety of sources including local tourism operators, government agencies, and guide books. In most areas, there is no clearinghouse for this type and users will likely need to combine data from a variety of sources.

Importance Data (Optional)

Intra-fleet Weights

Quantitative or qualitative or data on which locations in the coastal and marine environment are most or least important for a human use (i.e., intra-activity weights) can be easily prepared and included in the Overlap Analysis model. Whichever type of data is used does not need to be consistent across human use activities. For example, when spatially-explicit catch data exist for one fishing fleet, and another fishing fleet only has qualitative rankings of importance of different fishing grounds, both data sets can be used. Intra-fleet weights are entered for each polygon or point in each data layer’s attribute table. If intra-fleet weights are missing for one or more data layers in the analysis, users must include a placeholder column (i.e., values for all polygons in the layer = 1) for the model to run correctly.

Quantitative data are likely to be catch, effort, profit, or revenue information for fisheries. For recreation, the number of trips or number of visitors to each site is the suggested metric to be used to weight activities. Alternatively, users may use the number of days that an area is open to particular activities or other metrics that proxy for importance or usage. Higher values should indicate polygons or points of higher importance than those with lower values.

Qualitative scoring is a good option for users without quantitative input data. Low scores should indicate least important locations for the activity, high scores most important areas, and multiple areas should be allowed to have the same score (i.e., areas are given scores, not ranks). We encourage users to take care in assignment of values to locations as these values strongly influence outcomes. For example, if one fishing area polygon is given a score of 1, and another a score of 2, is the 2nd polygon twice as “important” as the first? If not, and the two polygons are more similar in their importance, the user could considering scoring more closely to one another (e.g., score of 1.75 and 2, instead of 1 and 2) or score on a larger scale (e.g., scores of 4 and 5, instead of 1 and 2). The onus is on the user to decide which range of weights to use. If you are unsure of how to appropriately include these weights, we encourage you to conduct several model runs to see how different weighting schemes affect model outputs. A common method for obtaining qualitative information on the importance of an activity is by querying stakeholders or decision-makers in the region. InVEST will soon include a mapping tool to help collect data from stakeholders. The tool will include functionality for entering intra-activity weights. If using the InVEST drawing tool (forthcoming) while querying stakeholders, importance scores can be input when generating layers.

Once intra-activity weights are input into the model, they are scaled by the maximum value for all locations where the activity occurs. For example, if the user has identified 3 fishing grounds for a fleet, with values of 2, 4, and 5, they will be scaled by 5, to be 0.4, 0.8 and 1.0.

Inter-activity Weights

The user has the option to include information on the importance of activities relative to one another so that all activities are not treated equally. This information is not spatially explicit, rather is in the form of one value for each activity. If the user chooses to include inter-activity weights, they must be included for all activities. Inter-activity weights can be qualitative (e.g., stakeholder designated) or quantitative (e.g., total catch, effort, profit, or revenue; socio-economic assessment of contributions of each fishing fleet to community stability or tax base), but the same metric should be used to weight all activities. For recreation, if the user does not have spatially explicit data on numbers of recreation trips, but does have the aggregate number of trips or participants for each activity, these numbers can be used to construct an importance ranking of each activity by using the percentage of trips / participants in each activity as inter-activity weights. For fisheries, for example, if running the model for three fishing fleets, inter-activity weights could be calculated using total revenue earned by each fleet as is done in the example presented earlier in this chapter. It would be inappropriate to determine weights by comparing one fleet’s catches to the others’ revenues. Given this caution, when determining inter-activity weights, users should choose a common quantitative (e.g., catch, revenue for fishing fleets) or qualitative (e.g., scores from stakeholder input) metric that is applicable across all activities. Similar to the intra-activity weights, inter-activity weights are not ranks (i.e., activities can have the same weights), and must be included for all data layers. Once input into the model, quantitative or qualitative values are scaled by the maximum value for all activities.

The caution in the preceding, intra-activity, section about the numeric scales used for qualitatively weighting activities applies here, as weights strongly affect model outputs. To reiterate, using a hypothetical model run for recreational data, if the inter-activity weight for whale-watching is 1, and kayaking 2, is the kayaking twice as “important” as whale-watching? If the activities are actually more similar, the weights should be closer to one another (e.g., score of 1.75 and 2, instead of 1 and 2) or score on a larger scale (e.g., scores of 4 and 5, instead of 1 and 2). Users are responsible for choosing the range of weights to use, and we encourage you to conduct several model runs to see how different weighting schemes affect model outputs.


Beck, M.W, Z. Ferdana, J. Kachmar, K. K. Morrison, P. Taylor and others. 2009. Best Practices for Marine Spatial Planning. The Nature Conservancy, Arlington, VA. 32 pp.

CDFG (California Department of Fish and Game). 2008. California Marine Life Protection Act. Master Plan for Marine Protected Areas. 110 pp.

CRMC (Coastal Resources Management Council). 2010. Rhode Island Ocean Special Area Management Plan: Adopted by the Rhode Island Coastal Resources Management Council October 2010. 993 pp.

DFO (Department of Fisheries and Oceans). 2008. Canadian Fisheries Statistics 2006. Ottawa: Fisheries and Oceans Canada.

GBRMPA (Great Barrier Reef Marine Park Authority). 2003. Great Barrier Reef Marine Park Zoning Plan 2003. Australian Government. 220 pp.