S2: Commercial Buildings along Arterial Roads — Toulouse

Beginner CLI + Map

Capabilities: filter

Use case

An urban planning team wants to identify Toulouse commercial buildings within 50 m of an arterial road (IGN importance 2, 3 or 4 — national, departmental and main urban arteries) to map the retail and service offer along high-traffic corridors.

The pipeline chains three steps (each one produces a layer that shows up on the map):

filter_routes_arterials — keeps only road segments with importance in ['2','3','4']. Visible network layer.
filter_near_arterials — on the batiments layer, spatial intersects against filter_routes_arterials with buffer_distance: 50 (Lambert93): a true 50 m metric ring around the arterials, kept buildings intersect it.
filter_commercial — attribute filter: usage_1 or usage_2 == Commercial et services (BD TOPO exposes two usage columns: a shop can be encoded as primary or secondary use).

IGN BD TOPO V3 Data

Layer	Content	Features	Source
`batiments`	IGN Toulouse buildings (usage, height, floors, dwellings)	~31,000	data.geopf.fr — BDTOPO_V3:batiment
`routes`	IGN Toulouse road segments (importance 1-5, nature, nom_voie, width)	~6,000	data.geopf.fr — BDTOPO_V3:troncon_de_route

Importance distribution (Toulouse): 1 (motorways): ~70 · 2 (national): ~250 · 3 (departmental): 987 · 4 (main urban): 410 · 5 (local/communal streets): 3,497 · 6 (paths/accesses): 1,112.

bash

python examples/prepare_playground_data.py --city toulouse
gispulse info examples/datasets/toulouse_bdtopo.gpkg

Pipeline (3 steps)

routes  ──► filter_routes_arterials                 # step 1: arterial road network
              (importance in ['2','3','4'])         #          → ~1,600 segments (visible layer)
                │
                ▼ (used as ref_layer)
batiments ──► filter_near_arterials                 # step 2: buildings along the network
              (intersects ref_layer, buffer 50 m,   #          → spatial cohort (visible layer)
               Lambert93)
                │
                ▼
            filter_commercial                       # step 3: commercial
              (usage_1 == 'Commercial et services'  #          → final cohort (visible layer)
               or usage_2 == 'Commercial et services')

Each step exposes its result as a GeoJSON layer, so the interactive map can visualize both the filtered road network and the building cohorts at every stage.

Why a separate step for the roads?

With an inline ref_filter (previous version), the retained sub-network was never materialized as a layer — you saw the building results but not WHICH segments drove the calculation. The explicit filter_routes_arterials step produces the arterials as an intermediate artifact, making the diagnostic auditable.

Metric CRS

IGN data is stored in EPSG:4326 (WGS84). The 50 m buffer is computed in EPSG:2154 (Lambert93) to guarantee real-world 50 m on the ground. Without crs_meters, the default EPSG:3857 (Web Mercator) would give ~36 m at Toulouse's latitude.

Two usage columns

BD TOPO encodes a primary usage (usage_1) and a secondary usage (usage_2). A mixed-use block (shops + housing) may be tagged Résidentiel / Commercial et services or the reverse. The or pandas expression catches both cases.

Rules

json

{
  "version": 2,
  "name": "toulouse_commercial_buildings_near_arterials",
  "ref_layers": {
    "routes": "routes",
    "batiments": "batiments"
  },
  "steps": [
    {
      "id": "filter_routes_arterials",
      "type": "capability",
      "capability": "filter",
      "params": {
        "expression": "importance in ['2', '3', '4']"
      },
      "input": "routes"
    },
    {
      "id": "filter_near_arterials",
      "type": "capability",
      "capability": "filter",
      "params": {
        "spatial_predicate": "intersects",
        "ref_layer": "filter_routes_arterials",
        "buffer_distance": 50,
        "crs_meters": "EPSG:2154"
      },
      "input": "batiments"
    },
    {
      "id": "filter_commercial",
      "type": "capability",
      "capability": "filter",
      "params": {
        "expression": "usage_1 == 'Commercial et services' or usage_2 == 'Commercial et services'"
      },
      "input": "filter_near_arterials"
    }
  ]
}

Download

scenario-2-rules.json

Execution

bash

gispulse run examples/datasets/toulouse_bdtopo.gpkg \
  --layer batiments \
  --rules playground/scenario-2-rules.json \
  -o output/toulouse_commerces_near_arterials.gpkg \
  --ref-source routes:examples/datasets/toulouse_bdtopo.gpkg:routes

Expected Result

Output schema

Column	Type	Source	Description
`geometry`	MultiPolygon	source	Building geometry
`usage_1`	string	source	Primary usage
`usage_2`	string	source	Secondary usage (may be null)
`hauteur`	float	source	Height (m)
`nombre_de_logements`	int	source	Dwelling count

On Toulouse, the pipeline keeps commercial-use buildings sitting along the arterial network (national + departmental + main urban roads). Order of magnitude: 1,000–2,000 buildings tagged purely or mixed Commercial et services, concentrated on transit corridors (av. de Grande-Bretagne, av. de Toulouse, route de Narbonne, etc.).

Full interactive playground

Live 3-step pipeline (requires the demo backend):

Step by step:

filter_routes_arterials (blue) — importance 2-4 sub-network (~1,600 segments), shown as a layer
filter_near_arterials (orange) — buildings intersecting the 50 m buffer around that sub-network (Lambert93)
filter_commercial (purple) — buildings whose usage_1 or usage_2 equals Commercial et services

Interactions:

Building popup: usage_1, usage_2, hauteur, nombre_de_logements
Street popup: nom_voie, importance, largeur_de_chaussee — useful on the step-1 layer to inspect the BD TOPO category

Try it live

Live DemoCheck filter + ref_filter

GET/capabilities

Live DemoDataset toulouse_bdtopo available

GET/datasets

S2: Commercial Buildings along Arterial Roads — Toulouse ​

Use case ​

IGN BD TOPO V3 Data ​

Pipeline (3 steps) ​

Rules ​

Execution ​

Expected Result ​

Full interactive playground ​

Try it live ​

Further reading ​