What’s the correct way to reproject a population raster?

Reprojecting Population Rasters: A Guide to Keeping Your Data Honest

Population rasters: they’re gold when you need to understand where people are, whether you’re shaping policy, or diving into research. But here’s the thing – these datasets often need a little makeover, a transformation from one coordinate system to another. We call this reprojection. Mess it up, and suddenly your analysis is built on shaky ground. Trust me, I’ve seen it happen! So, how do you reproject a population raster the right way? Let’s break it down and keep your data singing the truth.

First things first, let’s talk basics.

Coordinate Systems 101: Why Reprojection Matters

Think of coordinate reference systems (CRSs) as different ways of flattening the Earth onto a map. Each one uses a different projection, and guess what? Each projection distorts things in its own special way. Reprojection? That’s just moving data from one of these “flattened” worlds to another. It’s essential when you’re trying to mash up data from different sources. But, and this is a big but, you’ve gotta be careful not to introduce a bunch of errors in the process.

The Population Raster Puzzle

Population rasters are like grids, each square holding a population count. Now, when you reproject, these squares morph in size and shape. If you’re not careful, those population counts can get all wonky. The trick? Keep the total population the same while spreading it accurately across the new grid. Easier said than done, right?

Resampling: Choosing Your Weapon

Resampling is where the rubber meets the road. It’s how you calculate those new pixel values in your reprojected raster, using the original values as a guide. There are several methods, but not all are created equal when it comes to population data.

• Nearest Neighbor: Imagine just grabbing the value from the closest original pixel and slapping it onto the new one. Quick? Yes. Accurate for population? Absolutely not! It’s like taking a blurry photo and making it even blockier. Stick to using this for things like land cover categories, not population numbers.
• Bilinear Interpolation and Cubic Convolution: These methods are a bit smoother. They average out the values from surrounding pixels. But here’s the catch: they can change the original population numbers. Not ideal! Better for continuous data like elevation.
• Sum: This is the game changer. It’s like it was made for population datasets. It adds up the values of all the original pixels that fall within the new pixel, weighting them based on how much they overlap. Perfect for when you’re making your raster coarser, ensuring that the total population stays put.
• Average: Imagine you’re working with tree canopy cover data, where each pixel represents the percentage of cover. If you want to downsample this data, the average resampling method comes to the rescue! It calculates the new pixel value by simply averaging all the overlapping pixels.

Reprojection: A Step-by-Step

Alright, let’s get practical. Here’s how to do this thing right:

A Few Things to Keep in Mind

• Data Types Matter: Make sure your population data is stored as integers (whole numbers). No half-people allowed! If decimals creep in during reprojection, convert back to integer.
• Cell Size Shenanigans: A drastically different cell size will change the population counts in each cell. Obvious, but worth saying!
• Edge Cases: Sometimes reprojection leaves blank spots (NoData) around the edges. You can fill these in if needed.
• Top-Down vs. Bottom-Up: Where did your population data come from? If it’s top-down (disaggregated from larger areas), you might need extra steps to keep things accurate.
• Step-by-Step is Key: If the pixel sizes between your source and target are vastly different, you might run into errors. Break down the aggregation into smaller, more manageable steps to avoid issues.

Your Reprojection Toolkit

You’ve got options, people:

• GDAL: The command-line master. Powerful, precise, but requires a bit of coding know-how.
• QGIS: Free, open-source, and user-friendly. A great all-around GIS package.
• ArcGIS Pro: The commercial powerhouse. Packed with features, but comes with a price tag.
• R: For the stats-inclined. Use packages like raster and sf to handle reprojection in a statistical environment.
• Google Earth Engine: A cloud-based option for large-scale geospatial analysis.

Density: Your Backup Plan

If the “sum” resampling method isn’t available, don’t despair! Convert your population counts to density (population per unit area) before reprojecting. Then, use a smoother resampling method like bilinear interpolation. After reprojection, just multiply by the new cell area to get your population counts back.

The Takeaway

Reprojecting population rasters isn’t rocket science, but it does demand attention to detail. Nail the basics, choose the right resampling method, and double-check your results. Do that, and you’ll keep your data honest, your analysis sound, and your insights sharp. Now go forth and reproject with confidence!