docs+tooling: OSM POI export workflow + Shell stations data; graded DQ report

- scripts/export_osm_pois.py: reproducible OSM .pbf -> GeoJSON+CSV exporter
  (amenity/brand filter; pyosmium via uv, no system deps).
- docs/OSM_POI_EXPORT.md: runbook (extract -> export -> FleetNow layer) with
  reference counts (1,794 fuel stations; Shell=232).
- shell_stations.geojson/.csv: the Shell export of record (232 pts, kenya-260605).
- docs/reports/260608_fleet_registry_data_quality.*: rewritten as a graded
  (Red/Amber/Yellow) action plan with owners.
- .gitignore: ignore *.osm.pbf (331MB, reproducible). CLAUDE.md: index the new docs.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

.gitignore

@@ -13,6 +13,9 @@ __pycache__/
 
 # OS
 .DS_Store
+
+# OSM extracts (large, reproducible from download — see docs/OSM_POI_EXPORT.md)
+*.osm.pbf
 Thumbs.db
 
 # Backups

CLAUDE.md

@@ -105,7 +105,9 @@ docs/                       # Reference docs (connections, API, KPIs, project co
 docs/PLATFORM_OVERVIEW.html # Current-state platform reference (architecture, deploy, read-API,
                             #   full DB schema, Grafana panels) — open in a browser. Post n8n→fleetapi.
 docs/DWH_PIPELINE.md        # DWH pipeline operations runbook (setup, troubleshooting)
+docs/OSM_POI_EXPORT.md     # Runbook: OSM .pbf → POI GeoJSON → FleetNow map layer (Shell stations)
 docs/superpowers/          # Pitch specs and implementation plans (not deployed code)
+scripts/export_osm_pois.py # OSM .pbf → GeoJSON+CSV POI exporter (amenity/brand filter); see OSM_POI_EXPORT.md
 dwh/                        # DWH migrations for tracksolid_dwh@31.97.44.246:5888
                             #   260423_dwh_ddl_v1.sql — bronze/silver/gold schemas + roles
                             #   261001_dwh_control.sql — watermarks + run log

docs/OSM_POI_EXPORT.md

@@ -0,0 +1,50 @@
+# OSM POI Export → FleetNow map layer
+
+How we turn an OpenStreetMap extract into a toggleable **FleetNow overlay layer**
+(e.g. the 232 Shell fuel stations). Reproducible end-to-end.
+
+## 1. Get a Kenya extract
+
+Download a `.osm.pbf` (Geofabrik / BBBike), e.g. `kenya-260605.osm.pbf`.
+
+> **Do not commit the `.pbf`** — it's ~331 MB and is gitignored (`*.osm.pbf`).
+> It's fully reproducible from the download.
+
+## 2. Export the POIs
+
+No system tooling required — `pyosmium`'s prebuilt wheel is fetched by `uv`:
+
+```bash
+uv run --no-project --with osmium python scripts/export_osm_pois.py \
+  kenya-260605.osm.pbf --amenity fuel --brand Shell \
+  --out-geojson shell_stations.geojson --out-csv shell_stations.csv
+```
+
+- Gas stations are OSM **`amenity=fuel`**. Brand is the **`brand`** tag — but only
+  ~36% of Kenyan stations carry it (≈92% have a `name`), so when `--brand` is given
+  and a feature has no `brand` tag, the script falls back to matching `name`/`operator`.
+- Omit `--brand` to export **every** station of that amenity.
+- Nodes use their own coordinate; ways/areas use their node **centroid**.
+
+**Reference counts** (extract `kenya-260605`, captured 2026-06-08):
+
+| Metric | Value |
+|---|---|
+| Total fuel stations (`amenity=fuel`) | 1,794 (1,582 nodes + 212 areas) |
+| With a `brand` tag | 659 (36%) · with a `name` | 1,652 (92%) |
+| **Shell** | **232** (209 `brand=Shell` + 23 by name) |
+| Other top brands | Total 154 · Rubis 147 · TotalEnergies 50 · Kobil 10 |
+
+## 3. Add it to FleetNow as a layer
+
+Copy the GeoJSON into the FleetNow repo's `layers/` directory and register it —
+full details in the **fleetnow** repo `README.md` → *"Map overlay layers"*:
+
+```bash
+cp shell_stations.geojson ../fleetnow/layers/
+# then add one entry to the OVERLAYS array in fleetnow/index.html, commit + push.
+```
+
+FleetNow keeps its own served copy under `fleetnow/layers/`; the artifact here
+(`shell_stations.geojson` / `.csv`) is the export of record. Re-running step 2 on a
+newer extract and re-copying refreshes the layer.

docs/reports/260608_fleet_registry_data_quality.md

@@ -1,117 +1,75 @@
-# Fleet Registry — Data Quality Report
+# Fleet Registry — Outstanding Data-Quality Issues
 
-**Date:** 2026-06-08
-**Source:** `tracksolid.devices` (the single registry of record)
-**Prepared for:** Fireside leadership / fleet operations
-**Scope:** 181 tracking devices → **80 physical vehicles** after de-duplicating the GPS-tracker + dashcam pairs that share a number plate.
+**Date:** 2026-06-08 · **Source:** `tracksolid.devices` (verified live) · **Scope:** 181 devices → 80 vehicles
+
+**Urgency:** 🔴 **RED** = fix now (blocks ops / safety / visibility) · 🟠 **AMBER** = fix soon (accountability, classification, data trust) · 🟡 **YELLOW** = cleanup.
+
+> ## ⭐ Biggest single fix: run the driver/plate CSV import
+> Most items below are **missing source fields**. The prepared **`import_drivers_csv.py --apply`** has **not been run** (`vehicle_category` still 0/181, device count still 181). Running it — or entering the fields at source in the Tracksolid Pro portal — clears the bulk of this list at once.
 
 ---
 
-## 1. Executive summary
+## 🔴 RED — fix now
 
-The fleet registry is **operationally usable but materially incomplete**, and the gaps concentrate in exactly the fields the business needs to run field-service KPIs: driver identity, driver contact, vehicle type, and device pairing.
+**R1. No driver phone — 175 / 181 (97%).** Dispatch/escalation can't contact a driver; only 6 are reachable. → Capture mobiles. **Owner:** Operations + Engineering (import).
 
-| Theme | Headline | Business impact |
-|---|---|---|
-| **Driver contact** | **97%** of records have **no driver phone** (175 / 181) | Dispatch and escalation can't reach the driver from our own systems |
-| **Driver identity** | **23%** have **no driver name** (41 / 181) | Trips, speeding and idle time can't be attributed to a person — no accountability |
-| **Vehicle type** | **22%** have **no model** (40 / 181) | We can't cleanly separate ticket-closing service cars from cranes / bikes |
-| **Device pairing** | **29%** of vehicles (23 / 80) are **missing a tracker or a camera** | Blind spots: 6 vehicles have **no GPS at all**; 16 have **no dashcam evidence** |
-| **Unidentified hardware** | **41** device rows carry **no plate**; 19 of those are fully blank | Hardware we are paying to track but cannot tie to a vehicle, driver or city |
+**R2. 6 vehicles have NO GPS tracker** (dashcam only → invisible on the live map, no trips/mileage): `KCN 496A · KCQ 215F · KCU 237Z · KDM 306S · KDN 759G · KCZ 199P`. → Field-check; install/repair or decommission. **Owner:** Field ops.
 
-**Root cause is upstream, not in our database.** Almost every gap is a field that was never entered in the **Tracksolid Pro portal** at provisioning time — our pipeline faithfully stores whatever the portal holds. This is a **data-entry discipline** problem at vehicle onboarding, plus an **incomplete driver/plate import** that is still pending (the 144-device CSV).
+**R3. 37 fully-unidentified devices being tracked** (no plate, model, or driver; "unknown" status). → Match serial/IMEI to a vehicle or decommission. **Owner:** Field ops + Engineering.
 
 ---
 
-## 2. The fleet, de-duplicated
+## 🟠 AMBER — fix soon
 
-After collapsing tracker+camera pairs to one row per number plate:
+**A1. No driver name — 41 / 181 (23%).** Trips/speeding/idle can't be attributed → no accountability. → Assign drivers at source. **Owner:** Operations. *(CSV covers most.)*
 
-| Segment | Vehicles | Detail |
-|---|---|---|
-| **Field service** (closes customer tickets) | ~62 | Probox ×57, Van, Vezel, Mazda, + a few UG/other cars |
-| **Specialist** (cranes, bikes, pick-ups — *not* immediate tickets) | ~17 | Crane ×3, Motorbike ×8, Pick-Up ×~6 |
-| **Ambiguous** (type conflicts — see §3) | 2 | KCY 080X, KCZ 223P |
-| **Total physical vehicles** | **80** | from 181 devices |
-| **Unassigned spare devices** (no plate) | 41 rows | cannot be counted as vehicles |
+**A2. 16 vehicles have a tracker but NO dashcam** — no incident/safety video (all 8 motorbikes, the Uganda units, several Proboxes). → Decide which need a camera; schedule installs. **Owner:** Field ops.
 
----
+**A3. No vehicle model — 40 / 181 (22%).** Forces guesswork on the field-service vs specialist split. → Set `vehicle_models` at source. **Owner:** Operations. *(CSV covers most.)*
 
-## 3. Findings by severity
+**A4. Duplicate plates corrupting the count:**
+- `KDS 453Y` entered twice — `KDS 453Y` (tracker) + `KDS 453 Y` (camera, stray space).
+- `KCC 199P` vs `KCZ 199P` — both pick-ups, both driver *Mbuvi Kioko*, one tracker + one camera → almost certainly **one vehicle under two plates**.
+→ Correct the plate in the portal, then re-sync. **Owner:** Operations.
 
-### 🔴 Critical — operational blind spots
-
-**C1. 6 vehicles have a dashcam but NO GPS tracker** — they are invisible on the live fleet map and contribute no trips/mileage.
-`KCN 496A · KCQ 215F · KCU 237Z · KDM 306S · KDN 759G · KCZ 199P`
-
-**C2. 16 vehicles have a GPS tracker but NO dashcam** — no video evidence for incidents, disputes, or safety review. Concentrated in the **specialist fleet** (all 8 motorbikes, the Uganda vehicles) plus several Proboxes.
-
-**C3. 41 device rows (23%) carry no number plate** — they cannot be tied to a vehicle, driver, or city. **19 of these are entirely blank** (no plate, no model, no driver, status "unknown") — hardware we track but cannot identify at all.
-
-### 🟠 High — accountability & classification gaps
-
-**H1. 97% of records have no driver phone** (175 / 181). Only 6 drivers are contactable from our data.
-
-**H2. 23% have no driver name** (41 / 181). Behaviour analytics (speeding, idle, harsh events) cannot be attributed.
-
-**H3. 22% have no vehicle model** (40 / 181) — this is the field that drives the field-service vs specialist split. Until it's populated, ~22% of the fleet can only be classified by guessing from the device name.
-
-**H4. Plate data-entry inconsistencies** corrupting the vehicle count:
-- **`KDS 453Y` is entered twice** — `KDS 453Y` (tracker) and `KDS 453 Y` (camera, stray space). One vehicle, counted as two.
-- **`KCC 199P` vs `KCZ 199P`** — both pick-ups, both driver *Mbuvi Kioko*, one tracker + one camera. Almost certainly **one vehicle mis-keyed under two plates**.
-
-**H5. Two plates disagree with themselves** — the tracker and dashcam on the *same plate* report different vehicle types:
+**A5. Two plates disagree with themselves (model conflict):**
 
 | Plate | Tracker says | Camera says | Driver |
 |---|---|---|---|
 | KCY 080X | Pick-Up | Probox | Lawrence Kijogi |
 | KCZ 223P | Pick-Up | Probox | Felix Muema |
 
-### 🟡 Medium — analytics reliability
+→ Confirm the real type; set both devices to match. **Owner:** Operations.
 
-**M1. `assigned_city` is unreliable.** 4 vehicles have their two devices assigned to *different cities* (e.g. KDC 490Q: Mombasa vs Nairobi; KCY 838X: Mombasa vs Voi). The field appears **derived from the Tracksolid account** the device sits in, not the vehicle's actual base — so **regional (Nairobi / Mombasa / Kampala) reporting is suspect**.
-
-**M2. Placeholder / non-person driver names** pollute driver-level analytics: `Garage` (×4), `UG` (×2), `Management_Mazda` (×2), `Parked` (×1). These are slots, not people.
-
-**M3. One driver, multiple plates** — 5 cases (`Garage`, `Gideon Kiprono`, `Kelvin Wambugu`, `Mbuvi Kioko`, `UG`). Some are the duplicate-plate issues above; the rest need confirmation of whether a driver genuinely rotates vehicles.
-
-### ⚪ Low — asset-register completeness (low operational impact today)
-
-**L1. `vehicle_brand` is 99% empty** (179 / 181) and **`vin` is 100% empty** (181 / 181). Not currently used in any KPI, but would be needed for a formal asset/insurance register.
+**A6. `assigned_city` unreliable → regional reporting suspect.** 4 vehicles show two cities (e.g. KDC 490Q: Mombasa/Nairobi; KCY 838X: Mombasa/Voi) — it's inherited from the account, not the vehicle. → Set per vehicle. **Owner:** Operations + Engineering.
 
 ---
 
-## 4. Completeness scorecard (device-level, 181 rows)
+## 🟡 YELLOW — cleanup
 
-| Field | Populated | Missing | % missing |
-|---|---|---|---|
-| Number plate | 140 | 41 | 23% |
-| Vehicle model | 141 | 40 | 22% |
-| Driver name | 140 | 41 | 23% |
-| Driver phone | 6 | 175 | **97%** |
-| SIM | 155 | 26 | 14% |
-| Assigned city | 152 | 29 | 16% |
-| Vehicle brand | 2 | 179 | **99%** |
-| VIN | 0 | 181 | **100%** |
+**Y1. Placeholder driver names** — `Garage` (×4), `UG` (×2), `Management_Mazda` (×2), `Parked` (×1). → Replace with real names or a clear "unassigned" convention. **Owner:** Operations.
+
+**Y2. Missing SIM (26) and cost-centre (31).** → Backfill from records/CSV. **Owner:** Operations + Engineering.
+
+**Y3. `vehicle_category` empty (0 / 181).** Low urgency — the map/KPIs derive the segment from `vehicle_models` automatically. → Optional; CSV fills it. **Owner:** Engineering.
 
 ---
 
-## 5. Recommended actions
+## Action plan
 
-| # | Action | Owner | Effort | Fixes |
-|---|---|---|---|---|
-| 1 | **Run the pending driver/plate CSV import** (144 devices with names + plates already prepared) | Engineering | Low — one command | H1, H2, H3 (large chunk) |
-| 2 | **Mandate model + driver + phone at vehicle onboarding** in the Tracksolid Pro portal; make them required fields in the process | Operations | Process change | Root cause of most gaps |
-| 3 | **Resolve the 4 specific record issues** in §3 (KDS 453Y dup, KCC/KCZ 199P, KCY 080X, KCZ 223P) | Operations to confirm, Engineering to correct | Low | H4, H5 |
-| 4 | **Audit the 22 vehicles missing a tracker or camera** (§3 C1/C2) — install missing hardware or confirm intentional | Field ops | Medium | C1, C2 |
-| 5 | **Identify the 19 fully-blank devices** — match serial → vehicle, or decommission | Field ops | Medium | C3 |
-| 6 | **Stop trusting `assigned_city` for regional reporting** until it's set per-vehicle rather than inherited from the account | Engineering + Ops | Medium | M1 |
-| 7 | Replace placeholder driver names with real assignments or a clear "unassigned" convention | Operations | Low | M2, M3 |
+| # | Tier | Issue | Action | Owner | Cleared by CSV import? |
+|---|---|---|---|---|---|
+| R1 | 🔴 | No driver phone (97%) | Capture mobiles | Ops + Eng | ✅ mostly |
+| R2 | 🔴 | 6 vehicles, no GPS tracker | Field-check / install | Field ops | — |
+| R3 | 🔴 | 37 unidentified devices | Identify or decommission | Field ops + Eng | ✅ partly |
+| A1 | 🟠 | No driver name (23%) | Assign drivers | Ops | ✅ mostly |
+| A2 | 🟠 | 16 vehicles, no dashcam | Schedule installs | Field ops | — |
+| A3 | 🟠 | No vehicle model (22%) | Set model at source | Ops | ✅ mostly |
+| A4 | 🟠 | Duplicate plates (KDS 453Y; KCC/KCZ 199P) | Fix plate, re-sync | Ops | — |
+| A5 | 🟠 | Model conflicts (KCY 080X, KCZ 223P) | Confirm type | Ops | partial |
+| A6 | 🟠 | Unreliable assigned_city | Set per vehicle | Ops + Eng | ✅ yes |
+| Y1 | 🟡 | Placeholder driver names | Replace | Ops | partial |
+| Y2 | 🟡 | Missing SIM (26) / cost-centre (31) | Backfill | Ops + Eng | ✅ mostly |
+| Y3 | 🟡 | vehicle_category empty | Optional populate | Eng | ✅ yes |
 
-**Quick wins:** Actions 1 and 3 are low-effort and would clear the majority of the High-severity gaps immediately.
-
----
-
-## 6. Method & reproducibility
-
-All figures derived from a single read-only scan of `tracksolid.devices`. De-duplication keys on a normalised plate (`UPPER(REPLACE(vehicle_number,' ',''))`) so spacing variants collapse. Tracker = device types `GT06E / X3 / AT4`; camera = `JC400P`. The scan script and the proposed `tracksolid.v_vehicles` de-duplicated view are tracked in the repository; re-running the scan after each remediation step will show progress against this baseline.
+**Suggested order:** (1) run/validate the **CSV import** — clears R1, R3(part), A1, A3, A6, Y2, Y3; (2) field-ops sweep for **R2 + A2** (tracker/camera hardware); (3) Operations fixes **A4/A5 plates + Y1** in the portal; (4) re-run the scan to confirm.

scripts/export_osm_pois.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""Export OSM POIs (e.g. fuel stations) from a .osm.pbf to GeoJSON + CSV.
+
+These exports feed FleetNow's toggleable map-overlay layers (see
+docs/OSM_POI_EXPORT.md and the fleetnow repo's README "Map overlay layers").
+
+No system tooling needed — run via uv so pyosmium's prebuilt wheel is fetched:
+
+    uv run --no-project --with osmium python scripts/export_osm_pois.py \
+        kenya-260605.osm.pbf --amenity fuel --brand Shell \
+        --out-geojson shell_stations.geojson --out-csv shell_stations.csv
+
+Notes:
+- Gas stations are OSM ``amenity=fuel``. Brand lives in the ``brand`` tag, but
+  only ~36% of Kenyan stations carry it, so when ``--brand`` is given and a
+  feature has no ``brand`` tag we fall back to matching ``name``/``operator``.
+- Omit ``--brand`` to export every feature of that amenity.
+- Nodes use their own coordinate; ways/areas use the centroid of their nodes
+  (so ``locations=True`` is required on apply_file).
+"""
+import argparse, json, csv
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("pbf", help="input .osm.pbf")
+    ap.add_argument("--amenity", default="fuel", help="OSM amenity value (default: fuel)")
+    ap.add_argument("--brand", default=None, help="case-insensitive brand/name match; omit for all")
+    ap.add_argument("--out-geojson", default="pois.geojson")
+    ap.add_argument("--out-csv", default="pois.csv")
+    args = ap.parse_args()
+
+    import osmium
+
+    brand_lc = args.brand.lower() if args.brand else None
+
+    def match(t):
+        if t.get("amenity") != args.amenity:
+            return False
+        if brand_lc is None:
+            return True
+        b = (t.get("brand") or "").lower()
+        if b:
+            return brand_lc in b
+        return brand_lc in (t.get("name") or "").lower() or brand_lc in (t.get("operator") or "").lower()
+
+    feats = []
+
+    def add(lon, lat, t, kind, oid):
+        feats.append({
+            "type": "Feature",
+            "properties": {
+                "name": t.get("name"), "brand": t.get("brand"),
+                "operator": t.get("operator"), "osm_type": kind, "osm_id": oid,
+            },
+            "geometry": {"type": "Point", "coordinates": [round(lon, 6), round(lat, 6)]},
+        })
+
+    class H(osmium.SimpleHandler):
+        def node(self, o):
+            if match(o.tags):
+                add(o.location.lon, o.location.lat, o.tags, "node", o.id)
+        def way(self, o):
+            if match(o.tags):
+                xs = []; ys = []
+                for n in o.nodes:
+                    try:
+                        if n.location.valid():
+                            xs.append(n.location.lon); ys.append(n.location.lat)
+                    except Exception:
+                        pass
+                if xs:
+                    add(sum(xs) / len(xs), sum(ys) / len(ys), o.tags, "way", o.id)
+
+    H().apply_file(args.pbf, locations=True)
+
+    json.dump({"type": "FeatureCollection", "features": feats}, open(args.out_geojson, "w"))
+    with open(args.out_csv, "w", newline="") as f:
+        w = csv.writer(f); w.writerow(["name", "lat", "lon", "brand", "operator", "osm_type", "osm_id"])
+        for ft in feats:
+            p = ft["properties"]; lon, lat = ft["geometry"]["coordinates"]
+            w.writerow([p["name"], lat, lon, p["brand"], p["operator"], p["osm_type"], p["osm_id"]])
+
+    print(f"exported {len(feats)} features -> {args.out_geojson}, {args.out_csv}")
+
+
+if __name__ == "__main__":
+    main()

shell_stations.csv

@@ -0,0 +1,233 @@
+name,lat,lon,brand,operator,brand_source,osm_type,osm_id
+Shell Kabuagi,-1.316594,36.718419,Shell,,brand_tag,node,30088444
+Shell,-1.316337,36.814074,Shell,,brand_tag,node,30092268
+Shell,-1.292004,36.784895,Shell,,brand_tag,node,30210342
+Shell,-1.292514,36.821731,Shell,,brand_tag,node,30215041
+Shell,-1.264944,36.811142,Shell,,brand_tag,node,30402252
+Shell Bellevue,-1.321222,36.841704,Shell,,brand_tag,node,30695844
+Shell,-1.259212,36.818551,,,name,node,295829498
+Shell,-3.400363,38.549144,Shell,,brand_tag,node,344711386
+Shell,-4.064245,39.671241,Shell,,brand_tag,node,344969183
+Shell,-4.04045,39.678266,Shell,,brand_tag,node,344970554
+Shell,-4.035198,39.684952,Shell,,brand_tag,node,344971084
+Shell Links Road Filling Station,-4.051857,39.688222,Shell,,brand_tag,node,346562706
+Shell,-0.71861,36.432908,Shell,,brand_tag,node,415582755
+Shell,-1.307743,36.803669,Shell,,brand_tag,node,490741522
+Shell Petrol Station,-1.288024,36.741743,Shell,,brand_tag,node,558923029
+Shell,-1.310587,36.817074,Shell,,brand_tag,node,582803761
+Shell,-1.305716,36.827261,Shell,,brand_tag,node,582803763
+Shell,-1.520877,37.267142,Shell,,brand_tag,node,582803840
+Shell Makupa,-4.041392,39.656827,Shell,Shell,brand_tag,node,612947282
+Shell Changamwe,-4.027279,39.62732,Shell,Shell,brand_tag,node,612947302
+Shell,-1.249864,36.861223,Shell,,brand_tag,node,703562830
+Shell,-1.246981,36.871498,Shell,,brand_tag,node,703562919
+Shell - Ruiru,-1.149099,36.958483,Shell,,brand_tag,node,711491743
+Shell,-1.293773,36.886453,Shell,Shell,brand_tag,node,712868923
+Shell,-1.256982,36.922912,Shell,,brand_tag,node,734051667
+Shell,-1.230839,36.80343,Shell,Shell,brand_tag,node,812840504
+Shell,-1.232602,36.875076,Shell,,brand_tag,node,900302335
+Shell Avenue Service Station,-1.285422,36.818863,Shell,,brand_tag,node,911026324
+Shell Adams Arcade,-1.300601,36.779709,Shell,,brand_tag,node,1045731889
+Shell,-1.267364,36.81745,Shell,,brand_tag,node,1147234418
+Shell,-4.085069,39.654867,Shell,Shell,brand_tag,node,1320763086
+Shell,0.181801,34.295817,Shell,Indivdual,brand_tag,node,1419926800
+Shell,-1.265026,36.740926,Shell,,brand_tag,node,1506606959
+Shell,-0.284215,36.068872,Shell,,brand_tag,node,1636429762
+Shell,-1.31465,36.859873,Shell,Shell/BP,brand_tag,node,1678061764
+Shell,-1.090556,35.871981,Shell,,brand_tag,node,1690703696
+Shell,0.03559,37.655216,Shell,,brand_tag,node,1814694405
+Shell,0.05734,37.642474,Shell,,brand_tag,node,1814694430
+Shell,0.016696,37.077472,Shell,,brand_tag,node,1830086395
+Shell,-0.363566,35.293414,Shell,,brand_tag,node,1838955746
+Shell,0.634907,34.280633,Shell,,brand_tag,node,1856237414
+Shell Bidii,-1.279267,36.848606,Shell,,brand_tag,node,1997615249
+Shell,-2.693933,38.166679,Shell,,brand_tag,node,2137912798
+Shell Baraka,-1.259949,36.785711,Shell,,brand_tag,node,2218541216
+Shell,-4.047937,39.662737,Shell,,brand_tag,node,2377336397
+Shell,2.32971,37.988219,Shell,,brand_tag,node,2462468244
+Shell,-1.281187,36.816595,Shell,,brand_tag,node,2533881587
+Shell,-1.446789,36.968782,Shell,Shell,brand_tag,node,2653484553
+Shell Roysambu,-1.217787,36.890669,Shell,,brand_tag,node,2953872784
+Shell petrol station,-0.424642,36.952432,,2NK,name,node,3078763165
+Shell petrol station,-0.421785,36.952518,,,name,node,3078776472
+Shell,-0.334861,37.646147,Shell,,brand_tag,node,3243527416
+Isiolo Service Station,0.3519,37.582829,Shell,,brand_tag,node,3247766463
+Shell,-0.500952,36.314893,Shell,,brand_tag,node,3421681816
+Bajoo Shell Services,1.749142,40.057779,Shell,,brand_tag,node,3683457938
+Buna Filling Station,2.786999,39.509127,Shell,,brand_tag,node,3695322660
+Shell,-1.395443,36.939718,Shell,,brand_tag,node,3729203207
+Shell,-1.10888,36.641976,Shell,,brand_tag,node,3808532767
+Shell,-1.299065,36.763624,Shell,,brand_tag,node,4210622090
+Shell,-1.245784,36.662797,Shell,,brand_tag,node,4324873067
+Shell,0.337182,37.578977,Shell,,brand_tag,node,4461211795
+Shell,-3.405215,38.362976,Shell,,brand_tag,node,4475364889
+Shell,-0.454161,39.645824,Shell,,brand_tag,node,4685351383
+Shell,-0.625556,34.75581,Shell,,brand_tag,node,4720565411
+Shell,-0.697715,36.427394,Shell,,brand_tag,node,4914118412
+"Shell Petrol Station, Muguga",-1.061481,37.157952,,,name,node,4946660224
+"Shell New Thika Rd, Ruiru",-1.160387,36.958055,Shell,,brand_tag,node,4947103423
+Shell,-1.226429,36.663491,Shell,,brand_tag,node,4972148621
+Shell,-1.229222,36.840393,Shell,,brand_tag,node,5119552023
+Shell,-1.264064,36.838234,Shell,,brand_tag,node,5119573528
+Shell,-0.977475,37.095977,Shell,,brand_tag,node,5126219526
+Shell,-0.929697,37.160111,Shell,,brand_tag,node,5126247329
+Shell,-1.011913,36.903025,Shell,,brand_tag,node,5162725223
+Shell Petrol Station Kajiado,-1.835716,36.799667,Shell,Shell,brand_tag,node,5179571022
+Shell,-1.180887,37.440173,Shell,,brand_tag,node,5181243643
+Shell,-1.280736,36.827944,Shell,,brand_tag,node,5217418921
+Shell,-1.171399,36.828308,Shell,,brand_tag,node,5327749961
+Shell,-1.262778,36.801048,Shell,,brand_tag,node,5340449107
+Shell,0.540157,35.296916,Shell,,brand_tag,node,5392739324
+Shell,0.448948,35.967056,,,name,node,5403105422
+Shell,-1.292007,36.84313,Shell,,brand_tag,node,5496066065
+Shell,-1.295157,36.854441,Shell,,brand_tag,node,5496079228
+Shell Kasarani Petrol Station,-1.218327,36.895867,,,name,node,5526332569
+Shell,-1.30385,36.856284,Shell,,brand_tag,node,5539579719
+Shell,-1.195935,36.751887,Shell,,brand_tag,node,5540791550
+Shell,-1.3074,36.842468,Shell,,brand_tag,node,5569591300
+Shell Banana,-1.175701,36.759722,Shell,,brand_tag,node,5581701614
+Shell Kiserian,-1.432004,36.686175,Shell,Vivo,brand_tag,node,5588931919
+Mwingi Shell Petrol Station,-0.937425,38.044514,Shell,Shell,brand_tag,node,5768445294
+Mart Petrol Station,0.494496,35.746217,Shell,,brand_tag,node,5781632986
+Shell,-1.263474,36.981924,Shell,,brand_tag,node,5887135685
+Shell,-4.060892,39.662713,Shell,,brand_tag,node,6054282205
+Shell,-4.043209,39.663984,Shell,,brand_tag,node,6054858991
+Shell,-0.984791,36.584131,Shell,,brand_tag,node,6054861952
+Shell,-1.089833,35.882185,Shell,,brand_tag,node,6054877097
+Shell,-0.769528,36.501316,Shell,,brand_tag,node,6054980226
+Shell,-1.057588,36.776239,,,name,node,6063644317
+Shell Filling station,-4.084926,39.654747,Shell,,brand_tag,node,6072960948
+Shell,-1.456045,37.004306,Shell,,brand_tag,node,6086991186
+Shell,-0.780234,34.948265,Shell,,brand_tag,node,6105930796
+Shell,1.01741,35.002629,Shell,,brand_tag,node,6106202680
+Shell,-0.929519,37.159777,Shell,,brand_tag,node,6140765579
+Shell,-0.074245,37.670579,Shell,,brand_tag,node,6144610331
+Shell,0.524964,35.251383,Shell,,brand_tag,node,6147580921
+Shell,0.489345,35.269825,Shell,,brand_tag,node,6149443773
+Shell Petrol Station,0.465501,35.297389,,,name,node,6149634448
+Shell,-1.248207,36.876,Shell,,brand_tag,node,6198796724
+Shell,-1.043083,37.066139,Shell,,brand_tag,node,6207691351
+Shell,-1.042001,37.071543,Shell,,brand_tag,node,6207707073
+Shell,-1.037044,37.071925,Shell,,brand_tag,node,6209964953
+Shell,-0.841126,37.137857,,,name,node,6212995383
+Shell,-0.485965,37.138349,Shell,,brand_tag,node,6213405879
+Shell,-1.347761,36.662815,Shell,,brand_tag,node,6216321668
+Shell Petrol Station,-1.416823,36.686915,,,name,node,6218487553
+Shell,-1.396852,36.755199,Shell,,brand_tag,node,6218698870
+PETRO Shelly,-4.085731,39.667207,,,name,node,6222293185
+Shell Maanzoni,-1.516527,37.107005,Shell,,brand_tag,node,6226894926
+Shell,-1.532784,37.131763,Shell,,brand_tag,node,6229374240
+Shell Kitengela,-1.50442,36.954102,,,name,node,6229505975
+shell,-1.735489,37.198837,Shell,,brand_tag,node,6229624406
+Shell,-2.081484,37.477328,Shell,,brand_tag,node,6232739137
+Shell,-3.973832,39.547043,Shell,,brand_tag,node,6235829860
+Shell,-4.007792,39.60037,Shell,,brand_tag,node,6235830590
+Shell,0.580828,34.557734,Shell,,brand_tag,node,6236062090
+Shell,-1.365272,38.012171,Shell,,brand_tag,node,6236068237
+Shell Filling Station,0.035,36.364412,,,name,node,6241664256
+Shell Chaka,-0.361651,36.999992,Shell,,brand_tag,node,6244921195
+Shell,0.005805,37.072986,Shell,,brand_tag,node,6246803075
+Shell,-1.260591,36.710164,Shell,,brand_tag,node,6251561800
+Shell service station Fedha,-1.316073,36.895595,Shell,,brand_tag,node,6259798932
+Shell Service Station - Kayole,-1.2829,36.90304,Shell,,brand_tag,node,6259810690
+Shell Mumias Road,-1.286405,36.88066,Shell,,brand_tag,node,6259860371
+Shell,3.094471,35.614012,Shell,,brand_tag,node,6263511612
+Shell,0.509984,35.293509,Shell,Shell Petrol Station,brand_tag,node,6327543347
+Shell,-0.144338,34.8022,Shell,,brand_tag,node,6633204913
+Shell,-0.779415,36.426665,Shell,,brand_tag,node,6644488886
+Shell,-3.578128,39.87102,Shell,,brand_tag,node,6716512885
+Shell,-3.214362,40.117773,Shell,,brand_tag,node,6793044096
+Shell,-1.273195,36.911201,Shell,,brand_tag,node,6819692388
+Shell Petrol Station,-1.055007,37.111221,Shell,,brand_tag,node,6851086245
+Shell,-0.542158,37.454875,Shell,,brand_tag,node,6860253187
+Shell,0.06024,37.636552,Shell,,brand_tag,node,6882011852
+Shell,-0.71496,37.261577,Shell,,brand_tag,node,6895782975
+shell- Bonje,-3.991998,39.562412,Shell,,brand_tag,node,6938700260
+Shell,0.453207,34.130811,Shell,,brand_tag,node,6945529552
+Shell,-0.130428,34.794978,Shell,,brand_tag,node,6969176798
+Shell,-3.211576,40.121388,Shell,,brand_tag,node,7166621203
+Shell,-3.39087,38.581827,Shell,,brand_tag,node,7166621204
+shell makupa service station,-4.040825,39.656857,Shell,,brand_tag,node,7172031316
+Shell,-0.322371,36.15036,Shell,,brand_tag,node,7187879248
+shell,-0.074322,34.682137,Shell,,brand_tag,node,7227297576
+Shell,0.28749,34.756277,Shell,,brand_tag,node,7239126633
+Shell,-1.323004,36.706266,Shell,,brand_tag,node,7305927852
+Shell- Syokimau,-1.357374,36.907769,Shell,,brand_tag,node,7507287888
+Shell,-1.210901,36.875751,Shell,Shell,brand_tag,node,7643261185
+Shell,-0.973363,37.095865,Shell,,brand_tag,node,7872591802
+Shell,-0.366585,35.284074,Shell,,brand_tag,node,7893551877
+Shell,0.87809,35.120238,Shell,,brand_tag,node,7934573084
+Shell,-1.089807,35.882577,Shell,,brand_tag,node,7988261701
+Shell,-0.541871,37.455485,Shell,,brand_tag,node,8039934769
+Shell,-1.358313,38.007156,Shell,,brand_tag,node,8047307634
+Shell,-3.92977,39.53407,Shell,,brand_tag,node,8050570392
+Shell,-0.264808,36.377656,,,name,node,8182514303
+Shell Ruai,-1.276187,37.016097,Shell,,brand_tag,node,8202067662
+Shell,-1.170384,36.91699,Shell,,brand_tag,node,8231893217
+Shell - Eastern By-pass - Ruiru,-1.166976,36.964879,Shell,,brand_tag,node,8247882582
+Shell,-1.281858,36.962424,Shell,,brand_tag,node,8293614674
+Shell,-1.280476,36.690323,Shell,,brand_tag,node,8318718279
+Shell,-1.281238,36.634916,Shell,,brand_tag,node,8321307638
+Shell,-1.267623,36.609868,Shell,,brand_tag,node,8321307639
+Shell,-1.234142,36.989696,Shell,,brand_tag,node,8338714141
+Shell,-1.267493,37.315826,Shell,,brand_tag,node,8338714151
+Shell,-1.256284,36.879297,Shell,,brand_tag,node,8345317201
+Shell,-1.425451,36.958715,Shell,,brand_tag,node,8371880141
+Shell,-0.78364,36.871819,,,name,node,8483709617
+Shell,-1.28234,37.104729,Shell,,brand_tag,node,8862700293
+Shell,-1.231358,36.924224,Shell,,brand_tag,node,9004506167
+Shell Mirema,-1.213808,36.892432,Shell,,brand_tag,node,9014259343
+Shell,-1.459477,37.25047,Shell,,brand_tag,node,9028867156
+Shell,0.027305,36.365863,Shell,,brand_tag,node,9048281880
+Shell,-0.20372,35.843587,Shell,,brand_tag,node,9053460332
+Shell,-1.229246,36.84025,Shell,,brand_tag,node,10080456017
+Shell,-3.936283,39.744896,,,name,node,10255650224
+Shell Petrol Station,-1.286734,36.740804,,,name,node,10971302670
+Shell -Bombolulu,-4.025633,39.697377,Shell,,brand_tag,node,11500364389
+Shell,-1.313698,36.720914,Shell,,brand_tag,node,11711795482
+Shell,-1.274671,36.799912,Shell,,brand_tag,node,12163148139
+Shell,-1.307959,36.781587,Shell,,brand_tag,node,12165032865
+Shell,0.28095,34.744859,Shell,,brand_tag,node,12489868997
+Shell Syokimau,-1.378091,36.927818,,,name,node,12600151901
+Shell,-1.262862,36.90678,,,name,node,12886546509
+Shell Gas Station,-0.779732,36.427412,,,name,node,13088820396
+Shell,-1.230649,34.482215,Shell,,brand_tag,node,13446964574
+Shell,-0.900865,34.53639,Shell,,brand_tag,node,13446964578
+Shell,0.078482,34.720706,Shell,,brand_tag,node,13447042942
+Shell,0.600804,35.163178,Shell,,brand_tag,node,13528448115
+shell,-0.143442,34.801363,,,name,node,13714210044
+shell,-0.077947,34.727069,Shell,,brand_tag,node,13720608494
+Shell,-1.282701,36.824838,Shell,,brand_tag,way,123365084
+Shell,-1.2799,36.82273,Shell,,brand_tag,way,125107079
+Shell,-1.289727,36.811096,Shell,,brand_tag,way,125336232
+Shell,-1.287955,36.838738,Shell,,brand_tag,way,126936993
+Shell,-1.272506,36.83183,Shell,,brand_tag,way,126995763
+Shell Service Station - Jogoo Road,-1.295715,36.860244,Shell,,brand_tag,way,129323643
+Shell,-1.268949,36.817225,Shell,,brand_tag,way,133548150
+Shell,-1.310807,36.817766,Shell,,brand_tag,way,144188726
+Shell- Lavington,-1.280811,36.769286,Shell,Shell,brand_tag,way,226322138
+Shell,-1.322412,36.708022,Shell,,brand_tag,way,226714111
+Shell,-1.263021,36.802726,Shell,,brand_tag,way,238857325
+Shell,-1.30349,36.828502,Shell,,brand_tag,way,366568545
+Shell,-0.09443,34.76444,Shell,,brand_tag,way,390104534
+Shell,-0.282248,36.09429,Shell,,brand_tag,way,483211211
+Shell,-0.285481,36.075122,Shell,,brand_tag,way,488459901
+Shell Kitengela ex-Engen,-1.491085,36.954337,Shell,,brand_tag,way,536796727
+Shell Voi,-3.400372,38.549126,Shell,Shell,brand_tag,way,593734920
+Shell,-0.865417,36.566545,,,name,way,642076596
+Shell,-1.275817,36.834388,Shell,,brand_tag,way,686179946
+Shell,-0.536827,37.452256,Shell,,brand_tag,way,732602668
+Shell Petrol Station - Greenspan,-1.288893,36.902407,Shell,,brand_tag,way,977833647
+Shell petrol station (feroze),-1.26303,36.906097,Shell,,brand_tag,way,1055608664
+Shell,-0.268103,34.971231,Shell,,brand_tag,way,1069555494
+Shell Hurlingham,-1.295405,36.799606,Shell,,brand_tag,way,1088482284
+Shell Service Station - Embakassi,-1.317926,36.917619,Shell,,brand_tag,way,1107378445
+Shell,-1.328958,36.683097,Shell,,brand_tag,way,1107729725
+Shell,-2.831336,37.524927,Shell,,brand_tag,way,1110216588
+Shell Kahawa Sukari,-1.188788,36.931934,Shell,,brand_tag,way,1135425767
+Shell,-0.074421,34.691528,Shell,,brand_tag,way,1222533725
+Shell Service Station - Likoni Road,-1.303854,36.856227,Shell,,brand_tag,way,1273152921
+Shell,-1.835509,36.799548,Shell,,brand_tag,way,1290701573
+Shell,-2.538055,36.800975,Shell,,brand_tag,way,1413265354
+Shell Fuel Station - Tala,-1.26749,37.315706,,,name,way,1434009884
+Shell,-1.266132,36.98704,Shell,Shell,brand_tag,way,1497383075