Day 4: Hexagons

Did you know “Gold Mountain” (Chinese: 金山; pinyin: Jīnshān) is a commonly used nickname for San Francisco, especially among the Chinese? It all started during the California Gold Rush when thousands of Chinese began to travel to the West in search of gold and riches. If you know your US geography then you’ll notice that the area that has the most mines is located in Sierra Nevada where gold was discovered in 1848, and more than 25 million ounces of gold were extracted from the foothills!

Work Flow:

Collected data from MRDS for mines and US Census Bureau for boundaries
Set project CRS to one that matches the proportion of base maps. I used EPSG:3857.
I uploaded the US national boundary map. I tried states but I didn’t like that it showed the state lines and this would be confusing with all the hexagons later on.
Using MMQGIS, I created a hexagon grid layer within the layer extent.
- There was a lot of trial and error to see which hexagon size I was happy with.
- If you make it too small, you are going to wait for a LOOONG time or the program will keep crashing.
- Finally, I was happy with X-spacing: 70,000, Y-spacing: 100,000.
Then I used Geoprocessing Tools > Intersection to create a new layer with only hexagons that intersected the U.S.
Now, I uploaded the MRDS shp file and filtered it to keep only mines that included “Au” or gold.
Counting Points (under Vector Analysis in tools):
- On the first try, it took a VERY very long time (maybe an hour?)
- On the second map, it didn’t take as long maybe under 5 minutes? Is it because I used smaller hexagons?
- This basically allowed me to have data on how many gold mines were in each hexagon. With this data, I can symbolise the map to become the chloropeth map you see above!
To obtain the area of one hexagon, I didn’t trust the data being shown on qGIS so I googled the total area of USA and divided it by the number on hexagons in my map. I know this is not the most accurate because there are some incomplete ones but it’s just an approximation.