A major metropolitan city council faced a critical challenge: understanding tourist movement patterns to allocate infrastructure investment effectively. Traditional survey methods provided limited insight, and manual GIS analysis consumed weeks of staff time for each report.
The city had access to massive datasets—over 50 million monthly GPS pings from anonymized mobile devices—but lacked the capacity to process them. Their existing workflow involved:
This latency meant that decisions on where to place public transit stops, signage, and sanitation facilities were often based on data that was months old.
We deployed a custom Spatial Reasoning Agent designed to ingest, process, and interpret geospatial data autonomously.
Connects directly to raw telemetry S3 buckets.
Performs spatial joins against city GIS layers (Roads, Zoning).
Generates PDF reports and Heatmap layers.
The system uses a clustering algorithm to identify persistent high-density zones and overlays this with current infrastructure capacity to highlight gaps.
The impact was immediate. The agent reduced the analysis cycle from 3 weeks to 2 hours.
This case proves that for urban planning, the future isn't just about collecting more data—it's about building agents that can see the patterns within it.
From retail site selection to urban planning, our agents decode location data.
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