Nighttime Glow: Moscow, Russian Federation
For today’s mapping challenge, I needed to make a map using a raster dataset and I wanted to see if I could map artificial light at night. In thinking through this mapping project and exploring publicly available raster data, I came across the Visible Infrared Imaging Radiometer Suite (VIIRS) layer, a beautifully detailed dataset showing nocturnal visible and near-infrared light that is built and maintained by The Payne Institute for Public Policy at The Colorado School of Mines Earth Observations Group. This layer is accessible in Esri’s Living Atlas and provides near global and current coverage. Here is a short Esri blog post with more detailed information about this dataset.
Approach
For this project, I wanted to use the VIIRS dataset to show the glow of artificial nocturnal light being emitted from a larger urban area and decided to highlight Moscow, Russia, a city of over 11 million residents. I centered my map’s subject area over Moscow and began examining how the VIIRS data appeared on my map, but I was not satisfied with the flat 2-dimensial look of it. I decided to tinker around with different map projections to see if I could address this concern.
I wanted to provide a 3D-like effect, while keeping the map 2 dimensional. To solve this concern, I ended up pulling in The World From Space Projection, which produced the effect of being in near Earth orbit and looking down on Moscow. I like this overall visual effect and it makes the light data look less flat. Now that my projection is set and the map has a 3D feel to it, I explored the VIIRS layer’s symbology.
When I first incorporated the VIIRS data layer into ArcGIS Pro from Esri’s Living Atlas, I liked the vibrant color scheme that comes natively as a symbology style for this layer and found that it is incredibly effective at showing areas of high, medium, and low light pollution and corresponding dense and sparse areas of human settlement. The color spectrum of this layer’s native symbology starts with a dark hue of blue to indicate low nocturnal light levels, then progresses to a green color that corresponds with areas of medium nocturnal light levels, and finally peaks with a bright yellow color that corresponds with areas of high nocturnal light levels. After experimenting with different symbology schemes, I came back to this one and decided to use it because of how effective it is.
Below is my map.
This map clearly delineates the areas of Moscow that are more built up, and areas in the city’s hinterland that are more sparely populated. The areas of low light levels also correspond to less dense areas of human settlement and the areas of high light levels correspond to more dense areas of human settlement. Using this light, one can begin to make inferences on patterns of urban development and get a clear understanding of Moscow’s greater metropolitan area.
Overall, I really like working with this dataset and hope to incorporate it into a new project soon. If you’re a GIS professional, I recommend checking it out.