Let’s take a closer on the distribution / proximity of settlements with respect to forest regions. Download “natural” = “wood” from OSM using the “QuickOSM” plug-in. This will create a new polygon vector layer with forest regions. See Image 1 below.
Open up “Symbology” and visualize forests using “Fill Style” = “BDiagonal” in green color. When overlaying on a Google Satellite base map we can see that the OSM forest regions roughly match with dark green forests visible on the sat map. See Images 2+3 below.
Forest regions downloaded from OSM for the Himachal region
Visualize forests using green stripes / semi-transparent
OSM forest layer superimposed over a Google Satellite map showing a match in location of actual forests (sat map)
Settlements and Forests
When overlaying settlements over this forest layer in QGIS we can roughly see that villages are located in clear (main valleys) regions while many dwellings are inside / near forest regions and hamlets somewhere in between (valley slopes). See Images 1+2 below.
Using the “intersection” processing function we can find out exactly how many settlements are located in the OSM mapped forest regions. Run “Intersection” on all settlement types and count (right click and “Show Feature Count”) to confirm our above understanding on distribution of settlements:
village – 1877 total / 260 in forest (13%)
hamlets – 9972 total / 2515 in forest (25%)
dwellings – 3202 total / 1367 in forest (42%)Distribution of villages, hamlets and dwellings with respect to forest regions as downloaded from OSM
Villages (red) mostly located in valleys along roads, hamlets on valley slopes and dwellings in more isolated forest regions (OpenTopo base map)
Identifying dwellings inside forest regions using QGIS “Intersection” geometric function
Villages located in the forest (orange)
Hamlets located in forest (purple)
Dwellings located in forest (pink)
Settlements and Districts
Now let’s take a closer look at the distribution of hamlets across Himalayan districts (refer chapter 9). Run the “Count Points in Polygon” processing function on “Districts” and “Hamlet” layers. Bring up the “Open Attribute Table” on the newly created “Count” layer – the attribute “NUMPOINTS” identifies the number of hamlets in each district. See Images 1+2 below.
Bring up “Symbology” and chose a red color spectrum graduated visualization on the “NUMPOINTS” attribute with 10 equal interval classes. Districts are now colored as per density of hamlets (dark – more hamlets, light – fewer hamlets). See Images 3+4 below.Counting the number of hamlets in each Himalayan district
Number of hamlets in each of Himalayan districts
Visualize districts as a graduated red color ramp as per number of hamlets in each district
Himalayan districts shaded as per number of hamlets
Settlements and lakes
Let’s identify which is the nearest hamlet to each alpine lake in Himachal. Run the “Distance to nearest hub (line to hub)” processing function on “lake centroids” (refer module 10B) and “hamlet” layers. For each of the 684 alpine lakes QGIS will determine the nearest hamlet out of 9972 total hamlets. Refer Images 1+2+3 below.
Bring up the “Open Attribute Table” for the newly created “Hub_distance” layer to see – for each lake – the name and distance (in meters) to nearest hamlet. See Image 4 below.Lake centroids (blue) and hamlets (yellow) across the Himachal region
Identifying the nearest hamlet to each lake in Himachal
Each alpine lake has a line to its nearest hamlet
Name (Hubname) and distance to nearest hamlet for each alpine lake (name)
