Posted on December 31, 2022 (Updated on July 19, 2025)

How to create least-cost path between two polygons with GRASS?

Hiking & Activities

Ditching the Detours: Finding the Easiest Route Between Areas with GRASS GIS

Ever wondered how to find the absolute easiest way to get from point A to point B across a complicated landscape? I’m not talking about Google Maps here. I mean truly understanding the path of least resistance, whether you’re planning a road, figuring out where animals like to roam, or even laying down some pipes. That’s where GRASS GIS comes in – it’s a seriously powerful, open-source tool that can help you find those sweet, sweet shortcuts, especially when you’re dealing with entire areas instead of just single points. Let’s dive in, shall we?

The Lowdown on Least-Cost Paths

Okay, so what’s a “least-cost path,” really? Simply put, it’s the route that costs you the least amount of “effort” to travel. Think of it like this: imagine hiking across a landscape. You’re not just worried about distance; you’re also thinking about hills, thick forests, and maybe even grumpy landowners! A least-cost path takes all those “costs” into account to find the easiest overall route. In GRASS GIS, we represent these costs as a “cost surface,” a map where each location is assigned a value based on how difficult or expensive it is to move through.

Getting Your Ducks in a Row: Data Prep

Before GRASS GIS can work its magic, you’ve got to get your data prepped and ready. Trust me, a little prep work here saves a ton of headache later.

  • Define Your Territories: First things first, you need to clearly define the starting and ending areas you’re interested in. Maybe it’s two different forest patches, or two neighborhoods you want to connect with a new bike path. Whatever it is, make sure you know exactly what areas you’re working with.
  • Crafting Your Cost Surface: This is where things get interesting. Your cost surface is a map that shows how “expensive” it is to travel across each part of your study area. The “expense” can be anything that matters to your project. For example, if you’re planning a hiking trail, you might use elevation data to make steep slopes more “expensive” to cross. Or, if you’re worried about environmental impact, you could make sensitive habitats more “expensive” to disturb. I once worked on a project where we combined elevation, land cover, and even property values to create a cost surface for a new power line – talk about complex!
  • Importing into GRASS GIS: Now, get all that data into GRASS GIS. Use the v.in.ogr command for your area data (the starting and ending territories) and r.in.gdal for your cost surface. And double-check that everything is using the same coordinate system and covers the same area. Trust me, this is a common gotcha.

    • GRASS GIS to the Rescue: Finding That Path!

    Alright, time for the fun part – actually finding the least-cost path! GRASS GIS has some seriously cool tools for this. Here’s the breakdown:

  • Turning Areas into Starting Points: GRASS GIS needs to know where your “starting” and “ending” areas are. The r.grow.distance command helps us with that. It basically creates a map showing the distance from every location to your starting and ending areas.

  • Calculating the Cumulative Cost: This is where the magic happens. The r.cost command calculates the total “cost” of moving from your starting area to every other location on the map, taking into account your cost surface. It’s like GRASS GIS is exploring every possible route and adding up the “expense” along the way. The command looks something like this:

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