Take this paper with a grain of salt. They compare global gridded datasets with on-the-ground population surveys from dam construction projects. Gridded datasets do indeed underestimate population in these areas compared to global grids. But the authors use this as evidence that the global grids are biased against all rural areas.
Given that these regions were clearly valuable real estate - valleys with waterways near urban centers - it's very hard to make the case that these 307 flooded valleys represent all rural areas on earth. It seems like the authors conveniently skip the bias they themselves introduced. A few hundred dam sites are not representative of rural land on earth, and this paper is sketchy AF for trying to make that claim without supporting their assumption.
Furthermore, and this might be more a chip on my personal shoulder, they fail to outline their methods or do adequate sensitivity analysis. The vector footprint of the reservoirs and the raster population grid are disparate data models and there are many different approaches for determining their overlap. Do you include the center of the cell? Only 50% coverage? Any coverage? 100% coverage only? How are the data reprojected to align spatially? And given the coarse resolution and spatial arrangement (fractal coastlines, high perimeter:area), those details are not just GIS nerdery, they have massive impacts on the results. At least the code is available so it could potentially be reviewed.
onecommentman · 3h ago
A cute thought: what if a global initiative could hand out candles and ask the population in a given area to go to a clearing during a clear night and light the candles at the same time (an individual could hold more to represent children, elderly, the sick)? Satellites and drones could do the counts.
Exquisitely low tech on the ground, and a fun, engaging, global exercise. May not be perfect for total population figures, but may identify where undercounting is occurring (and overcounts). A nice image: hundreds of millions of lights at the same time, each representing a single human life, spanning large portions of the globe. The spy satellite folks would get a kick out of it.
Given that these regions were clearly valuable real estate - valleys with waterways near urban centers - it's very hard to make the case that these 307 flooded valleys represent all rural areas on earth. It seems like the authors conveniently skip the bias they themselves introduced. A few hundred dam sites are not representative of rural land on earth, and this paper is sketchy AF for trying to make that claim without supporting their assumption.
Furthermore, and this might be more a chip on my personal shoulder, they fail to outline their methods or do adequate sensitivity analysis. The vector footprint of the reservoirs and the raster population grid are disparate data models and there are many different approaches for determining their overlap. Do you include the center of the cell? Only 50% coverage? Any coverage? 100% coverage only? How are the data reprojected to align spatially? And given the coarse resolution and spatial arrangement (fractal coastlines, high perimeter:area), those details are not just GIS nerdery, they have massive impacts on the results. At least the code is available so it could potentially be reviewed.
Exquisitely low tech on the ground, and a fun, engaging, global exercise. May not be perfect for total population figures, but may identify where undercounting is occurring (and overcounts). A nice image: hundreds of millions of lights at the same time, each representing a single human life, spanning large portions of the globe. The spy satellite folks would get a kick out of it.