Smart Cities & Urban Planning Mobility Analytics

Since 2018

Privacy-safe enterprise data

VOSB & SDVOSB

U.S. Veteran-Owned, Service-Disabled

SCC Transfers

EU/UK/CH per privacy policy

GDPR · CCPA · CPRA

Consent-first data sourcing

Overview

GSDSI (Global Source Data Solutions Inc.) equips city planners, transportation authorities, and government agencies with privacy-safe mobility intelligence derived from consented, device-level GPS signals. Our mobility data powers infrastructure planning, transit optimization, and urban development: delivered in aggregated, privacy-compliant formats with custom-geography support for municipal boundaries, census tracts, or planner-defined analysis zones.

Buying guide

Use this guide when you’re comparing vendors or documenting diligence. It’s built to be linkable (and citeable) without overlapping the product pages.

Location intelligence buying guide

Request API key + JSON sample

If you’re evaluating implementation, we can share sample payloads, join keys, and delivery options (S3/SFTP/Snowflake) for your use case.

Request API key + JSON sample Developer hub

Use Cases

Transportation flow optimization
Infrastructure investment planning
Pandemic response & public safety
Tourism & economic development analysis

Key Benefits

Aggregated mobility intelligence
Historical trend analysis
Custom geography support
Privacy-safe aggregation methods

How Mobility Data Powers Urban Intelligence

GSDSI gives city planners and government agencies aggregated mobility intelligence derived from GPS-enabled devices. Origin-destination matrices reveal how populations move between residential areas, commercial districts, transit hubs, and recreational zones throughout the day. Transportation mode detection distinguishes between pedestrian, vehicular, and public transit movements, helping prioritize targeted infrastructure investments. All data is delivered in privacy-safe aggregated formats with no individual-level tracking, making it suitable for public sector procurement and FOIA-compliant applications. Custom geography support lets planners define analysis zones that match municipal boundaries, census tracts, or custom planning districts.

Why Data-Driven Planning Outperforms Traditional Methods

Traditional urban planning relies on periodic surveys, census data updated every ten years, and manual traffic counts that capture only snapshots of activity. GSDSI's continuous mobility intelligence provides daily-updated movement data with historical baselines going back five or more years. This lets planners detect trends, measure intervention impacts, and forecast future demand with far greater accuracy. Cities have used our data to optimize bus route placement by identifying underserved corridors, justify infrastructure investments with quantified pedestrian and vehicular flow data, and model pandemic-era behavioral shifts to adapt zoning and commercial development plans. The outcome is evidence-based urban planning that responds to how communities actually function, not how they were assumed to function decades ago.

Recommended Data Products

Points of Interest (POI) & Geofencing

Global place intelligence with 23M+ U.S. POIs with coverage in 150+ countries: polygon boundaries, brand hierarchy, NAICS classification, and geofencing for foot traffic analytics and attribution.

Global Mobility/Location Data

Device-level GPS signals and aggregated mobility patterns, with a daily US and Canada feed plus country-specific coverage.

Implementation checklist

Teams that operationalize this solution move faster by defining join keys, match-rate targets, and governance up front. If you need audit-ready provenance and exclusions, start with our methodology page.

Define the endpoint: activation, measurement, research, or risk model.
Set match-rate targets for your audience and geography.
Pick delivery: SFTP/S3/Snowflake/API; Parquet/CSV/JSON.
Governance: exclusions, retention, opt-out/deletion SLAs, permitted uses.

Request a Quote Sourcing methodology Talk to solutions engineering

Frequently asked questions about Smart Cities & Urban Planning

What data does GSDSI offer for urban planning and smart cities?

POI inventory with polygon geofences, daily MAID-level location events, origin-destination pairs at the census-tract or H3-hex level, dwell-time distributions, and modeled home/work/frequent-location anchors. All data is privacy-safe and panel-based, no individual identifiers are exposed in standard deliverables.

Who typically licenses this for civic use cases?

Municipal planning departments, MPOs, transit authorities, university research labs, civic-tech consultancies, and federal grant-funded planning initiatives. Federal buyers are typically routed through GSDSI's OSINT / CAI offering for procurement compliance.

How is the data delivered for analytics workflows?

Aggregated origin-destination matrices, visit counts by POI, dwell-time histograms, and spatial-grid (H3) tile densities, typically Parquet on S3 or Snowflake direct-share. Raw MAID-level data is available for buyers with the engineering capacity to handle it.

Is the data privacy-safe for civic publication?

Standard aggregated deliverables are designed for civic publication: minimum-aggregation thresholds prevent re-identification, and methodology documentation accompanies each delivery. Raw MAID data is licensed under more restrictive terms, check the deliverable shape before publishing.

What's a typical first project?

Origin-destination analysis for a transit corridor or planned development, foot-traffic baselining for a downtown revitalization study, or visit-share-by-POI analysis to inform zoning decisions. Most projects are scoped at 4-8 weeks with a defined geographic boundary.