Howdy OpenStreetMap, I am excited to share that I am working as a Research Fellow with Mapbox this summer! As a research fellow, I am looking to better understand contributions to OSM.
For my first project, I have been using the tile-reduce framework to summarize per-tile visible edits from the Historical OSM-QA-Tiles. These historical tiles are a snapshot of what the map looked like at the time listed on the link.
With this annual resolution, we can visualize the edits (those edits that were visible at the end of that year) that happened on each tile. So far, I’ve summarized them as a) number of editors, b) number of objects, and c) recency of the latest edit (relative to that year).
The OSM-QA-Tiles are all generated at Zoom level 12, which separates the world into 5Million+ tiles. Some tiles have few objects while others have ten-thousand plus.
So far I have created two interactive maps to investigate OpenStreetMap editing behavior at this tile-level analysis:
1. Editor Density (Number of editors active on a tile)
### 2. Edit Recency (Time since last edit on the tile)
This map highlights tiles where multiple editors have been active. The most active editors in most cases are automated bots, especially in the more recent years. For best results, moving the slider in the bottom left for
Minimum Users Per Tile to 2 or 3 will exclude most of these automated edits.
#### 2007: European Hotspots By increasing the minimum object and minimum user thresholds, areas of heavy editing activity pop out:
This image of the activity in the US in 2007 has no threshold on the limited number of objects or users per tile, so you can see all of the tiles affected by the 2007 import. If you increase the threshold, it changes dramatically
This map shows the recency of edits to a tile, relative to the year of analysis. It looks surprising at first how many tiles are edited at the end of the year, but that is most likely a function of automated bots. Again, if you move the threshold for number of editors or objects per tile, interesting patterns pop out across the world where users may have been active early in the year and then are less active later. The 2010 Haiti Earthquake is a good example, as it occurred in January of 2010.
If we view by latest edit date, relative to the year, we see the state-by-state import in the US:
More to come! -Jennings