Points of Interest and Urban Flows in Fort-de-France City Centre from Open Data

Context

Fort-de-France (Martinique) has almost no local open data. The city’s own datasets were last updated in 2019, and there are no public pedestrian counters, open transit ridership figures or open road counts at city-centre scale. The project asks how far national open data and continuously archived feeds can go in describing the city centre as a system of points of interest (shops, administrations, schools, health, culture, entry gates) and flows (residents, commuters, pupils, visitors, cruise passengers, vehicles, buses) over a ten-year horizon (2016–2026).

No single source answers that question. The business register knows where establishments are but not who visits them. The census knows where people sleep but not where they spend the day. Transit feeds show the buses but not their passengers, and cruise schedules show the ships but not the people ashore. The approach is therefore to cross many heterogeneous sources on a common spatial and temporal frame, so that each one fills a blind spot of the others, and to check the result against the few measured signals that do exist.

Approach

The method comes from an earlier knowledge-base project of mine. Raw data is immutable and addressed by content. Every database is disposable and rebuilt from it. Every value keeps its provenance, and a set of invariants is checked on each rebuild.

flowchart TB
    A[Source catalogue
sources.yaml] --> B[Immutable raw store
sha256 + JSONL manifest]
    C[Scheduled collectors
Meta every 14 days · GTFS-RT 60 s] --> B
    B --> D[Versioned extractors
normalised Parquet]
    D --> E[DuckDB spatial warehouse
rebuilt, never edited]
    E --> F[QA invariants I1–I6]
    E --> G[Statistics
requetes/*.sql]
    E --> H[Desktop application
map + time selector]
    G --> I[Calibration against
measured signals]

Methods

• Acquisition. Each download is hashed (SHA-256) and logged in a JSONL manifest, and acquisition is idempotent. National files larger than 1 GB (Sirene business register) are read as remote HTTP-range extracts filtered to the municipality. The filtering query is recorded as part of the provenance.
• Ephemeral sources. Meta Movement Distribution keeps only 90 days online, so a scheduled task archives it every 14 days. A bus collector polls GTFS-RT vehicle positions every 60 s and predictions every 10 min, and zips each day with a SHA-256 checksum.
• Spatial frame. Data is stored in WGS84 and computed in EPSG:5490 on the INSEE 200 m grid. Points of interest are classified by one typology (NAF codes, OSM tags, stops), with family, weight and an hourly rhythm over seven time slots.
• Models. Hourly presence per grid cell combines residents (Filosofi 2021, distributed dasymetrically to buildings), employees, pupils and tourists. A Huff model estimates catchment. Cruise passengers onshore are modelled from ship schedules and capacities. An island-wide gravity model with BPR assignment covers visitors and through-traffic.
• Quality. Six invariants are checked, from provenance to the absence of personal data. Unconfirmed parameters go in a decision log.

Data sources

Crossing the sources is the core of the project. Business registers (Sirene, BODACC, URSSAF) give the stock and turnover of activities, but say nothing about the people around them. The census and tax data (RP, Filosofi) place residents, their income and housing vacancy, but only at night. Commuting files and the national travel survey (MOBPRO, MOBSCO, EMP) say who travels, why and when. Transit, accident and air-quality feeds (GTFS-RT, BAAC, Madininair) are the only measured traces of movement. Cruise schedules and ship capacities bring a population that no census counts. The geographic frames (IGN, OpenStreetMap, the INSEE 200 m grid) are what make all of these comparable cell by cell.

The catalogue lists 120 sources: 51 integrated, 32 to integrate, 7 to verify, 13 available on request only, 5 paid, 1 by partnership and 11 not retained. The tables below list every integrated source and summarise the others by theme.

Businesses, jobs & amenities

Population, income & housing

Mobility & transport

Tourism & cruise

Context: weather, air, calendar, safety

Geography & base maps

Results

The project started in October 2026 and is at an early, uncalibrated stage (application v0.6.0). All city-centre figures depend on a provisional perimeter (see below). Each result below states which sources it crosses: none of them could be produced from a single dataset.

• Catalogue. 120 sources are catalogued, of which 51 are integrated.
• Warehouse (first build). 117,161 entities, about 25,000 measures and 1,278 indicators, with all 6 invariants passing.
• Retail decline vs residents (measured, registers × census × tax data). Active personal-goods establishments (Sirene) fell from 498 (2016) to 363 (2026), a drop of 27 %, and the city centre’s share of the municipality in this sector fell from 75.5 % to 64.7 %. Over the same period, population in the city-centre IRIS (census) fell from 13,444 (2016) to 12,898 (2022), and housing vacancy rose from 24.9 % to 27.6 %. 47 % of city-centre households are below the poverty line, against 30 % for the municipality (Filosofi 2021). Read together, the three sources show a centre losing shops, residents and purchasing power at the same time.
• Day/night rhythm (modelled, six sources crossed). About 3,700 people are present at night and 11,200 in the morning, a ratio of 3.0. Night-time residents come from the Filosofi 200 m grid placed in BD TOPO buildings; daytime workers from Sirene headcount bands; pupils from the education directory; tourists from classified accommodation capacity. The morning slot is adjusted on the hourly NO₂ profile of traffic stations.
• Observed bus speed vs model (GTFS-RT × road graph × travel surveys). Over one full day, buses ran at 12.7 km/h at 05:00, falling to 7–8 km/h during the day and 10.6 km/h at 19:00. The assignment model, built from BD TOPO roads, MOBPRO commuting, EMP trip profiles and BPE amenities, predicts a 20 % slowdown at peak hours, whereas the buses show 45 %. The measured feed exposes what the model misses.
• Cruise check (schedules × ship capacities × reported counts). Onshore passengers are modelled from the CruiseMapper call schedule and ship capacities. On the one documented cruise day (24 April 2026), the estimates came within about 1–2 % of the passenger counts reported in the press (3,075 vs 3,112, and 1,995 vs 1,969). The fill rate used in the model was set on that same day, so this is a consistency check, not an independent validation.

Limitations & next steps

• D1: the city-centre perimeter is provisional. It is a rectangle around the historic core plus a 1 km extended zone, used until an official perimeter is provided. Every city-centre figure depends on it.
• No pedestrian, ridership or road-count data is open. Hourly rhythms, attractiveness weights and parking assumptions are still uncalibrated. The measured controls so far are hourly NO₂ at traffic stations and observed bus speeds.
• The 2016–2026 business stock is rebuilt from creation and closure dates only.
• Next steps: build up several months of GTFS-RT data for measured congestion, request survey and counting data, and calibrate presence on field counts.

Stack

Python · DuckDB (spatial, httpfs) · Parquet · SQL · GTFS / GTFS-RT · pytest · ruff · Electron · React · TypeScript · ECharts · Leaflet · GitHub Actions