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

Thu, 01 Oct 2026 00:00:00 +0000

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

Source	What it contributes	Period / frequency	Status
Sirene business register — INSEE	Establishments, activity code (NAF), creation and closure dates, headcount bands	Full history; stock rebuilt at any date 2016–2026	Integrated
BODACC — DILA	Business turnover and failures (sales of goodwill, insolvency proceedings)	2016–2026, daily	Integrated
Permanent amenities database (BPE), geolocated — INSEE	Shops, services, health, administrations, education, sport, tourism	2024 and 2025 editions, annual	Integrated
Establishments and salaried headcount by municipality × activity — URSSAF / Acoss	Private salaried employment by activity	2006–2025, annual	Integrated
Education directory and pupil numbers — MENJ / DEPP	Schools, location, pupils per school year, staff	Pupil numbers 2016–2025	Integrated
FINESS — DREES / ARS	Health and social facilities, pharmacies	Extract of May 2026, bimonthly	Integrated
Overture Maps places — Overture Maps Foundation	Points of interest with a confidence score (comparison layer)	Frozen extract of release 2026-09-23, monthly	Integrated
OpenStreetMap (Overpass) — OpenStreetMap contributors	Points of interest, shops, streets, pedestrian paths	Current state	Integrated
Historic monuments (Mérimée / POP) — Ministry of Culture	Heritage attractors (cathedral, Schœlcher library, Fort Saint-Louis)	Current	Integrated
23 more — e.g. Flores localised employment (INSEE), Foursquare Open Source Places, LOCOMVAC vacant commercial premises (DGFiP), Google Places			Catalogued

Population, income & housing

Source	What it contributes	Period / frequency	Status
Census, IRIS-level files (RP) — INSEE	Residents, age, occupations, dwellings (vacancy, car ownership), workforce	RP 2016 → RP 2022, annual	Integrated
Filosofi by IRIS — INSEE	Disposable and declared income, poverty	2016–2021, annual	Integrated
Filosofi 200 m grid files — INSEE	Tax households, individuals, poverty, social housing and age per 200 m cell	2015, 2017, 2019, 2021	Integrated
Filosofi 200 m grid, Géoplateforme WFS layer — INSEE	Grid cell geometries	2015 layer	Integrated
IRCOM income tax by municipality — DGFiP	Tax households, declared income	2016–2024, annual	Integrated
Cafdata — CNAF	Recipients of minimum income, housing and family benefits	2020–2024 (2022 missing), annual	Integrated
Priority neighbourhoods (QPV) — ANCT / INSEE	Priority urban policy areas, which include the city centre	2024 boundaries	Integrated
LOVAC — Cerema / DGALN	Short-term and structural (≥ 2 years) vacancy of private housing	2020–2026 editions, annual	Integrated
RPLS social housing — SDES	Social dwellings (address, surface, rent, vacancy)	2023 and 2025	Integrated
Sitadel building permits — SDES	Dwellings and premises authorised, started, demolished	2013–2026, monthly	Integrated
Geolocated DVF — DGFiP / Etalab	Property transactions (price, surface, commercial premises)	2021–2025, rolling 5 years	Integrated
8 more — e.g. census detail files and legal populations (INSEE), DV3F and land files (Cerema / DGFiP), WorldPop / GHSL gridded population			Catalogued

Mobility & transport

Source	What it contributes	Period / frequency	Status
Commuting flows (MOBPRO) — INSEE	Home → work flows between municipalities	2016–2022, annual	Integrated
School commuting (MOBSCO) — INSEE	Home → study flows between municipalities	2016–2022, annual	Integrated
National travel survey 2018–2019 (EMP) — SDES	Trip purposes, departure and return times, activity durations, distances	2018–2019	Integrated
GTFS, urban network Centre zone (Mozaïk, BRT lines) — Martinique Transport	Scheduled transit supply	Archives since June 2023	Integrated
GTFS-RT, continuous collection — Martinique Transport	Observed bus speeds, buses in service, declared occupancy	Since 7 October 2026; 60 s (positions), 10 min (predictions)	Integrated
GTFS, ferry shuttles — Martinique Transport	Shuttles landing on the waterfront	Versions since August 2024	Integrated
Road injury accidents (BAAC) — ONISR	Geolocated accidents (pedestrians, vehicles), with time	2016–2024 loaded	Integrated
Movement Distribution — Meta Data for Good / HDX	Share of users at home, within 10 km, 10–100 km, beyond 100 km	Daily; 90-day window, archived every 14 days	Integrated
Movement Range Maps — Meta Data for Good / HDX	Change in movement, share of users staying put	March 2020 → May 2022, daily (discontinued)	Integrated
BD TOPO main road network — IGN	Island road graph for traffic assignment	Current	Integrated
BD TOPO road sections — IGN	Carriageway width, lanes and direction, used to measure kerbside parking	Current	Integrated
OpenStreetMap car parks — OpenStreetMap contributors	Car parks, type, declared capacity	Current	Integrated
18 more — e.g. ridership and ticket validations (Martinique Transport, on request), road counts (on request), Orange Flux Vision mobile data (paid), Waze for Cities / Strava Metro (partnership), a pedestrian counting campaign (to be created)			Catalogued

Tourism & cruise

Source	What it contributes	Period / frequency	Status
Cruise call schedule, port of Fort-de-France — CruiseMapper	Ship-by-ship calls with arrival and departure times	March 2026 → March 2029; archived monthly, as past months disappear	Integrated
Ship pages — CruiseMapper	Passenger capacity, crew, tonnage of ships in port	Current	Integrated
Cruise season reviews — Martinique Tourism Authority (CMT), via the press	Passengers and calls per season, cruise terminals	Seasons 2024–25 and 2025–26	Integrated
PortWatch — IMF / HDX	Daily port calls from AIS	2019–2026, daily	Integrated
Monthly commercial air traffic — DGAC	Arrivals at Martinique Aimé Césaire airport (tourist seasonality)	1990–2025, monthly	Integrated
Classified tourist accommodation — Atout France	Classified hotels and residences, rooms and capacity	Current stock	Integrated
Tourist accommodation capacity — INSEE	Hotels by rating, campsites, residences, holiday villages	2015–2026, annual	Integrated
7 more — e.g. DATAtourisme (ADN Tourisme), INSEE hotel occupancy survey, Inside Airbnb / AirDNA, port authority call data			Catalogued

Context: weather, air, calendar, safety

Source	What it contributes	Period / frequency	Status
Climatological data — Météo-France	Rain, temperature, wind (Fort-de-France Desaix and Le Lamentin stations)	2010–2026, hourly / daily	Integrated
Air quality — Madininair	NO₂ as a traffic proxy, plus PM10, PM2.5, O₃ and H₂S from sargassum	Hourly; monthly NO₂ 2016–2026	Integrated
Recorded crime by municipality — SSMSI	Recorded offences	2016–2025, annual	Integrated
Public holidays in Martinique — DINUM	Calendar, including 22 May	2006–2031	Integrated
School calendar — MENJ	School holidays, including Carnival	2017–18 → 2026–27	Integrated
Pageviews — Wikimedia Foundation	Online interest in Fort-de-France, Carnival and heritage	2016–2026, monthly	Integrated
4 more — e.g. NASA Black Marble night lights, Google Trends / GDELT, an events agenda (to be compiled)			Catalogued

Geography & base maps

Source	What it contributes	Period / frequency	Status
Study perimeters — project	City centre and extended centre (provisional, see D1)	Version 0.1	Integrated
Administrative boundaries API (geo.api.gouv.fr) — DINUM / IGN / INSEE	Municipal boundary and legal population	Current	Integrated
IRIS boundaries — IGN / INSEE	Sub-municipal census units	Current layer	Integrated
BD TOPO — IGN	Buildings (height, use), roads, facilities	Quarterly editions	Integrated
BD TOPO residential buildings, extended centre — IGN	Use, height and footprint used to place households in buildings	Current edition	Integrated
OpenStreetMap vector base map — OpenStreetMap contributors	Streets, water, parks, coastline drawn in the application	Current	Integrated
9 more — e.g. Etalab cadastre, National Address Base, BD ORTHO aerial imagery (IGN), local urban plan (Géoportail de l’Urbanisme), natural risk maps (Géorisques)			Catalogued

The “Cœur de ville” application

The warehouse is explored through a desktop application (Electron, React, ECharts, Leaflet on OpenStreetMap, DuckDB reading a copy of the warehouse). It is organised around a few questions — who is there, when, what attracts, does parking cope, who depends on public transport, is the centre gaining or losing — with a map always in the same place, a shared day × time-slot selector, and every figure labelled with its source and status.

Synthesis — the centre at a glance: day/night ratio, parking pressure, retail 2016 → 2026, poverty, the next big cruise day, presence by 200 m cell and the daily rhythm of residents, workers, pupils and tourists.

Flows and moments of life — presence by cell for any day and time slot, playable as an animation over the week, with the stacked daily curve per population group.

Points of interest — establishments by family (business registry and OpenStreetMap), the most attractive cells at the selected moment, and the share of establishments open hour by hour.

Street-level zoom on one block — the facade of the street number by number, active and closed establishments since 2016, and the sector’s trend against the whole centre.

Evolution 2016–2026 — attractiveness per cell with a year slider, openings and closures, tax households and other signals crossed on the same grid.

Parking — supply rebuilt street by street and theoretical occupancy per cell and time slot; every hypothesis (space length, lane width, modal share…) is a slider and the result is recomputed in SQL.

Parking pressure — Tuesday, 9–12 h: cars of residents and workers per public space within 300 m on foot, cell by cell. The week table shows theoretical occupancy above 1 on weekday mornings, while evenings and weekends stay well below.

Cruise calls and events — the yearly calendar of calls with public holidays and school holidays, the ships of a given day and an estimate of passengers ashore hour by hour.

Sources and hypotheses — the catalogue behind every number (filterable by status), the modelling parameters with their justification, and the archived acquisitions.

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

Duckdb on Boris Dupoux