docs: tweak splitter doc indentation

docs/splitting-algorithm.md

@@ -7,9 +7,9 @@ polygon features such as buildings.
 
 !!! note
 
- For ease of understanding, I will replace the word 'feature'
- with 'building' in the following description. But the word
- 'building' could in theory be substituted by any feature type.
+        For ease of understanding, I will replace the word 'feature'
+        with 'building' in the following description. But the word
+        'building' could in theory be substituted by any feature type.
 
 ### 1. Split AOI By Linear Features
 
@@ -18,7 +18,7 @@ polygon features such as buildings.
 - To do this we:
   - Polygonize the linear features.
   - Centroid the features to make sure they only get counted in one
-    splitpolygon.
+        splitpolygon.
   - Clip by the AOI polygon.
 - We get the database table `polygonsnocount`.
 - Polygons with zero or too few features are merged into neighbours.
@@ -45,17 +45,17 @@ polygon features such as buildings.
   algorithm, to output X number of clusters.
 - X is calculated as:
 
- ```bash
- (T / A) + 1
- T - Total building count
- A - Average number of buildings desired per cluster
- ```
+        ```bash
+        (T / A) + 1
+        T - Total building count
+        A - Average number of buildings desired per cluster
+        ```
 
 !!! info
 
- K-Means will group buildings based on their spatial proximity, ideally
- grouping together buildings that are close together (reducing walking
- distance for mappers in the field).
+        K-Means will group buildings based on their spatial proximity, ideally
+        grouping together buildings that are close together (reducing walking
+        distance for mappers in the field).
 
 - We create a table `clusteredbuildings` where we have the original buildings,
   plus their assigned cluster ID from K-Means.
@@ -65,12 +65,12 @@ polygon features such as buildings.
 
 !!! tip
 
- Using K-Means, we should be aware:
+        Using K-Means, we should be aware:
 
-- Edge cases: sparse areas may create clusters with few buildings,
- and dense areas could result in many overlapping clusters.
-- Trial and error: the clustering quality depends on fine-tuning the
- average number of buildings per cluster.
+        - Edge cases: sparse areas may create clusters with few buildings,
+        and dense areas could result in many overlapping clusters.
+        - Trial and error: the clustering quality depends on fine-tuning the
+        average number of buildings per cluster.
 
 **Output**: Building dataset tagged with their containing polygon's ID,
 plus a cluster ID specific to the polygon.
@@ -81,24 +81,24 @@ plus a cluster ID specific to the polygon.
 
 !!! info
 
- We previously used a Voronoi based approach:
+        We previously used a Voronoi based approach:
 
- 1. Densify the buildings to reduce the impact of long edges
- (maximum edge 0.00004 degrees).
- 2. Dump the building polygons into points.
- 3. Create a Voronoi diagram (a technique to divide up the points within
- an area into polygons, where each final polygon contains the closest
- 'neighbour' points from the clusters in the previous step).
- This approach had some flaws, so we have attempted other approaches, below.
+        1. Densify the buildings to reduce the impact of long edges
+        (maximum edge 0.00004 degrees).
+        2. Dump the building polygons into points.
+        3. Create a Voronoi diagram (a technique to divide up the points within
+        an area into polygons, where each final polygon contains the closest
+        'neighbour' points from the clusters in the previous step).
+        This approach had some flaws, so we have attempted other approaches, below.
 
 - Divide up each cluster into polygons using convex hulls.
 - Here we essentially form small 'islands' of buildings.
 - Fixing polygon overlaps:
-  - We may have a few polygon overlaps, where a building could fall between
+      - We may have a few polygon overlaps, where a building could fall between
  two polygon areas.
-  - To solve this, we find all of the overlapping 'shards', and subtract
+      - To solve this, we find all of the overlapping 'shards', and subtract
    from the polygon area.
-  - We then union all de-overlapped hulls with their buildings (the
+      - We then union all de-overlapped hulls with their buildings (the
    de-overlapping will have left some feature polygons partially and
    maybe wholly outside of their home polygons, this should restore
    them without creating new overlaps unless the features themselves
@@ -119,23 +119,23 @@ that don't have jagged / complex edges.
   from the AOI.
 - The 'negative' space multipolygon can be filled using the
   'straight skeleton' algorithm:
-  - This is essentially a Voronoi algorithm, but for polygons
+      - This is essentially a Voronoi algorithm, but for polygons
    instead of points! (not exactly, but it's an analogy)
-  - The algorithm will work on the edges and corners of the 'hull'
+      - The algorithm will work on the edges and corners of the 'hull'
    polygons, to generate bounding 'filler' polygons between them.
-    - It will perfectly bisect between buildings or polygon areas,
+  - It will perfectly bisect between buildings or polygon areas,
    instead of creating wavy / zig-zag boundaries.
 - Finally, we identify the edge-sharing neighbor hull of each element
   of the polygonized skeleton, dissolve them into those neighbors.
 
 !!! info
 
-- Voronoi diagrams divide space based on distances to points or
- polygons, creating regions with perpendicular bisectors.
-- Straight skeletons shrink polygon edges inward at equal speed
- to create a network of lines (skeleton) and subdivided polygons.
- It’s more about preserving the shape of polygons rather than
- distance-based partitioning.
+        - Voronoi diagrams divide space based on distances to points or
+        polygons, creating regions with perpendicular bisectors.
+        - Straight skeletons shrink polygon edges inward at equal speed
+        to create a network of lines (skeleton) and subdivided polygons.
+        It’s more about preserving the shape of polygons rather than
+        distance-based partitioning.
 
 **Output**: Split task area polygons.
 
@@ -149,9 +149,9 @@ to these task polygons, to assign them to each task area.
 - The final problem here is aligning the polygon areas back with the
   linear features, as they may have shifted slightly during all the
   processing!
-  - For example the task boundaries should ideally align in the
+      - For example the task boundaries should ideally align in the
    center of a highway polylin.
-  - Using a window function, we can essentially run the same steps
+      - Using a window function, we can essentially run the same steps
    as above, but for each specific cluster area, instead of the
    whole AOI, reducing the drift from the linear features.
 
@@ -174,7 +174,7 @@ Input from Ivan Gayton @ 18/12/2024
 
 - Allow polyline input from sources other than OSM.
 - From OSM:
-  - Polylines: default all, but user configurable (major vs minor highways, etc).
+      - Polylines: default all, but user configurable (major vs minor highways, etc).
   - Polygons: filter tags for traffic circles, water bodies, etc, then split into
     polylines.
 
@@ -190,25 +190,25 @@ In both cases, we likely only need the geometry, no tags.
 - **Polylines**:
   - Geometries, plus tags.
   - Convert relevant polygons such as traffic circles / water bodies into
-    polylines.
+        polylines.
   - Split roads at all intersections, so that every polyline constitutes an
-    edge in a graph.
+        edge in a graph.
 
 - **Polygons**:
   - Geometries, plus tags.
   - Convert multipolygons (like OSM buildings with holes) into simple polygons for
-  the purpose of splitting (maybe we want the multipolygons to send to the data
-  collection app later, but for splitting we definitely don't want holes).
+    the purpose of splitting (maybe we want the multipolygons to send to the data
+    collection app later, but for splitting we definitely don't want holes).
   - Do some checking/cleaning for invalid geometries.
 
 ### Output Datasets
 
 We need the following datasets of geometries, but probably not any tags
 associated:
 
-- AOI
-- Splitlines
-- Features
+    - AOI
+    - Splitlines
+    - Features
 
 The original features should probably be retained for later use in the
 actual data collection (e.g. conflation), but for splitting purposes we
@@ -233,48 +233,50 @@ bleeding-edge version of PostGIS and SFCGAL.
 
 The easiest way is via Docker (single command):
 
- ```bash
- docker run --name aoi-splitting-db --detach \
- -p 5432:5432 -v ./db_data:/var/lib/postgresql/data/  \
- -e POSTGRES_USER=hotosm -e POSTGRES_PASSWORD=hotosm -e POSTGRES_DB=splitter \
- docker.io/postgis/postgis:17-master \
- && sleep 5 \
- && docker exec aoi-splitting-db psql -d splitter -U hotosm -c \
- 'CREATE EXTENSION IF NOT EXISTS postgis_sfcgal WITH SCHEMA public;'
- ```
+        ```bash
+        docker run --name aoi-splitting-db --detach \
+          -p 5432:5432 -v ./db_data:/var/lib/postgresql/data/  \
+          -e POSTGRES_USER=hotosm \
+          -e POSTGRES_PASSWORD=hotosm \
+          -e POSTGRES_DB=splitter \
+          docker.io/postgis/postgis:17-master \
+        && sleep 5 \
+        && docker exec aoi-splitting-db psql -d splitter -U hotosm -c \
+          'CREATE EXTENSION IF NOT EXISTS postgis_sfcgal WITH SCHEMA public;'
+        ```
 
 The instance will be available:
 
-- Host: `localhost`
-- Port: `5432`
-- Database: `splitter`
-- User `hotosm`
-- Password `hotosm`
+    - Host: `localhost`
+    - Port: `5432`
+    - Database: `splitter`
+    - User `hotosm`
+    - Password `hotosm`
 
 !!! NOTE
 
- Changing the port on the left side in the command `8888:5432`,
- will make Postgres available on a different port for you.
+        Changing the port on the left side in the command `8888:5432`,
+        will make Postgres available on a different port for you.
 
 ### Importing OSM Data
 
 Get the raw-data-api Lua OSM import script:
 
- ```bash
- curl -LO https://raw.githubusercontent.com/hotosm/osm-rawdata/refs/heads/main/osm_rawdata/import/raw.lua
- ```
+        ```bash
+        curl -LO https://raw.githubusercontent.com/hotosm/osm-rawdata/refs/heads/main/osm_rawdata/import/raw.lua
+        ```
 
 Download some data from GeoFabrik:
 
- <https://download.geofabrik.de>
+         <https://download.geofabrik.de>
 
 Import into Postgres:
 
- ```bash
- osm2pgsql --create -H localhost -U hotosm -P 5432 -d splitter \
- -W --extra-attributes --output=flex --style ./raw.lua \
- /your-geofabrik-file.osm.pbf
- ```
+        ```bash
+        osm2pgsql --create -H localhost -U hotosm -P 5432 -d splitter \
+          -W --extra-attributes --output=flex --style ./raw.lua \
+          /your-geofabrik-file.osm.pbf
+        ```
 
 ### QGIS DB Manager