Smart-cities mobility data procurement has matured. City agencies, metropolitan planning organizations (MPOs), and the consultancies who support them have moved past the early-2020s posture of 'buy a mobility feed and see what it tells us.' The 2026 procurement brief is specific: origin-destination flows at census-block-group granularity, dwell patterns anchored to POIs that match the agency's land-use typology, equity overlays that let the planner read the data against the populations the plan is supposed to serve, and a consent-scoped privacy posture that survives a public-records review. GSDSI's smart-cities and urban-planning solution page frames the product side; this piece is about what agencies actually put the data to work for.
The single most procured mobility product by city and regional planning agencies in 2026 is origin-destination flows at census-block-group granularity. The use pattern is specific: transit-network planners run OD to pressure-test proposed bus-route realignments, MPOs run it to validate travel-demand models against observed behavior, and active-transportation planners run it to identify where bike-infrastructure investment will actually move mode share. The minimum usable specification is block-group-level origin, block-group-level destination, time-of-day distribution, and a stable aggregation floor (most agencies operate to a k-anonymity floor of 10 or higher, meaning no cell is published with fewer than 10 underlying devices).
GSDSI's global-mobility-location-data product publishes OD at that granularity with an explicit aggregation floor. The US Census Bureau's LEHD Origin-Destination Employment Statistics program is the reference dataset for commuting-flow triangulation — mature agencies use LEHD for employment-based commuting and a mobility-data OD for non-work travel, rather than treating either as the complete picture.
The second-most procured product is dwell data — how long devices spend at a given location — anchored to a POI layer that actually matches the agency's land-use typology. A mobility feed that returns 'dwell at this lat-long' without a POI join is a geospatial engineering task the agency has to fund; a feed that returns 'dwell at Park X, Library Y, Civic Center Z' with a POI layer matching the agency's own GIS tables drops in and runs. GSDSI's POI and geofencing product is specifically the POI-typology join that makes dwell data useful for an agency — the polygon library, not the lat-long point.
Use patterns: parks-and-rec departments run dwell to pressure-test space-activation programs; public-health agencies run dwell at civic centers and testing sites; the public-works office runs dwell at libraries and senior centers to rationalize operating hours against observed usage. USDOT's Bureau of Transportation Statistics publishes the national reference framing for this category of analysis; agency-level work layers the mobility data on top.
The fastest-growing procurement line in smart-cities data is the equity overlay — mobility data intersected with the American Community Survey (race, income, language, disability), with environmental-justice layers (CalEnviroScreen in California, the state-equivalents elsewhere), and with the agency's own capital-planning geography. Every capital-improvement plan and transit-service change now goes through an equity screen, and the agency cannot complete that screen without mobility data broken out against the equity overlay.
The procurement implications are concrete: the mobility data has to ship at a granularity that supports overlay at block-group or finer, the aggregation floor has to be consistent across cuts (so that small cells don't disappear when you overlay to a sparse equity cohort), and the delivery has to include the overlay-join documentation so that the agency's equity analyst can produce an auditable output. A companion piece on how CRE investors use origin-destination data covers the private-sector analog of the same OD-plus-overlay pattern.
Smart-cities mobility procurement in 2026 runs under privacy scoping that a private-sector buyer does not carry in the same way. Public-records laws mean that documentation the agency holds is, by default, disclosable; the mobility vendor has to ship under a posture that allows the agency to publish its analysis without publishing the raw device-level data. Consent-scoped provenance is one leg; aggregation-floor enforcement is the other. The 2024 FTC enforcement actions against location-data providers established the regulatory baseline for what a public-sector buyer needs from a vendor — and a companion piece on FTC location-data enforcement for data buyers walks through the enforcement record in detail. Vendors who cannot produce the consent documentation lose the agency RFP at the first-round review.
The agency procurement brief that survives legal, IT, and capital review in 2026 specifies, at minimum: