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Geospatial Data Processing For Mobility & Map Makers

Process billions of data points in seconds and unlock instant and continuous freshness of your data assets, for your and your customer’s benefit.

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Companies Accelerating Outcomes with Wherobots and Apache Sedona

Why mobility and map-making companies use Wherobots

Expensive operations
Inaccurate outputs
Complex joins
Siloed data
Fixed Infrastructure
Expensive operations
Industry Problem

Trajectory Processing Takes Hours or Days at Scale

Trajectory processing takes hours or days when analyzing billions of GPS points across millions of devices, delaying location intelligence products and fleet optimization insights

Wherobots solution

Process GPS Trajectories Up to 20x Faster with Distributed Spatial Processing

Quickly process trajectories up to 20x faster with Wherobots handling billions of GPS points in minutes using distributed spatial processing

20×
faster
Inaccurate outputs
Industry Problem

Raw GPS Traces Fail to Align Accurately to Road Networks

Raw GPS traces contain positioning errors and signal drift that prevent accurate map alignment. Without proper map matching, mobility data products produce incorrect routes, unreliable turn-by-turn data, and flawed fleet tracking — directly impacting the quality of location intelligence delivered to customers.

Wherobots solution

Achieve 99.8% Route Accuracy with Distributed Map Matching

Transform raw GPS traces into accurate routes with WherobotsAI’s distributed map matching processing millions of trajectories against road networks.

99.8%
Route Accuracу
Complex joins
Industry Problem

Complex Spatial Joins Across Millions of POIs Overwhelm Traditional Systems

Foot traffic analysis across millions of POIs requires complex spatial
joins that overwhelm traditional data systems and even modern warehouses

Wherobots solution

Run Spatial Joins Across Millions of POIs Simultaneously

Wherobots processes movement patterns across millions of points of interest simultaneously using distributed spatial joins — operations that overwhelm traditional data systems and modern warehouses. What once required complex infrastructure workarounds runs natively at scale on the Wherobots spatial lakehouse.

Processing efficiency
Billions
of POIs in seconds
Siloed data
Industry Problem

Multi-Source Mobility Data Stays Siloed When Platforms Can’t Integrate GPS, Raster, and Vector Data at Scale

Multi-source mobility data remains siloed when platforms can’t efficiently
integrate GPS traces, road networks, building polygons, telematics data, raster data, and geospatial regional context at scale in a single platform.

Wherobots solution

Unify GPS, Vector, and Raster Data in a Single Spatial Lakehouse

Wherobots unifies mobility data, including GPS traces, road networks, building polygons, telematics, and raster data — with native support for trajectories, vector data, and raster formats on a single lakehouse platform powered by Apache Sedona and Iceberg.

100%
Unified
Fixed Infrastructure
Industry Problem

Fixed Infrastructure Forces Mobility Teams to Over-Provision and Overpay

Traditional spatial infrastructure requires fixed compute provisioning, forcing mobility data teams to size for peak workloads and pay for idle capacity. As data volumes grow, scaling requires costly re-architecture rather than elastic expansion, creating a ceiling on what teams can process and a floor on what they must spend.

Wherobots solution

Reduce Infrastructure Costs by Up to 70% with Serverless Spatial Computing

Wherobots deploys as a serverless managed cloud service, scaling compute elastically with your workload. Mobility teams pay only for what they use, eliminating idle capacity costs and enabling up to 70% in infrastructure savings compared to fixed spatial computing environments.

70%
In common savings

“With Apache Sedona, we process millions of fleet-derived traffic signs, using scalable spatial joins and partitioning to automate map updates—enhancing Amazon Last Mile’s delivery networks for faster, more reliable routing.”

Arka Pratim Das
Sr. Manager, Software Development, Amazon Maps

Mobility Data Use Cases: Route Optimization, Fleet Management, and Location Intelligence

Schedule a demo to see how leading insurers process billions of location records and work with raster and vector data in the same platform.

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Mobility & Map-Making Resources: Geospatial Analysis, Map Matching, and Spatial Data Processing

Explore guides, webinars, and blog posts on geospatial data processing, map matching, and spatial analytics for mobility and map-making companies.

Frequently Asked Questions: Wherobots for Mobility Data Companies

How is Wherobots useful for mobility data companies?

Wherobots enables mobility data companies to process billions of geospatial data points in minutes using distributed spatial computing. It solves the challenge of computationally expensive spatial joins, allowing for rapid analysis at scale. This empowers companies to build scalable data products and gain enterprise-grade spatial insights for use cases like route optimization, fleet management, urban planning, and near real-time location services.

We use H3 to process our data, do we still need Wherobots?

Yes, Wherobots can still provide significant value even if you use H3. For example, while H3 handles spatial discretization, Wherobots handles the large-scale joins, aggregations, and multi-format data integration that H3 alone cannot perform. While H3 is excellent for discretizing spatial data, Wherobots, built on Apache Sedona, offers a comprehensive spatial analytics platform that excels at large-scale spatial operations, including complex spatial joins, queries, and analytics across diverse geometries, even when working with H3 indices. It complements H3 by providing the scalable infrastructure and advanced functions needed for deeper analysis and integration with your data lakehouse.

What types of mobility data (e.g., GPS, mobile device, IoT, vehicle location data) does Wherobots support?

Wherobots supports GPS traces, mobile device location data (including bidstream and app data), IoT sensor data from vehicles and infrastructure, and general vehicle telemetry. Wherobots is designed to support a wide variety of mobility data types, including but not limited to these formats and structures common in the mobility sector. Wherobots can ingest, process, and analyze diverse geospatial formats and structures common in the mobility sector.

What are the typical deployment options for Wherobots for mobility companies?

Wherobots supports GPS traces, mobile device location data (including bidstream and app data), IoT sensor data from vehicles and infrastructure, and general vehicle telemetry. Wherobots can ingest, process, and analyze diverse geospatial formats and structures common in the mobility sector.

How does Wherobots compare to other geospatial tools specifically for large-scale mobility datasets?

Wherobots differentiates itself by focusing on scalability and performance for spatial operations at billion-point scale, especially computationally intensive tasks like spatial joins and aggregations on billions of points. Built on Apache Sedona, it leverages distributed computing frameworks, making it significantly more efficient for big data mobility analytics compared to traditional GIS tools or less optimized geospatial libraries or cloud data warehouses. Unlike traditional GIS tools or cloud data warehouses, Wherobots handles computationally intensive spatial joins natively without requiring external workarounds or custom infrastructure.
Sphere

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in the lakehouse today.

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