Geospatial & Location Intelligence
GPS, addresses, and spatial analytics that drive growth decisions
You have stores, customers, and addresses. The hard question is simple: where should you grow next? We turn GPS traces and location data into a clear, ranked answer, not another confusing map in a slide deck.
Geospatial use cases
From raw GPS and addresses to prioritized growth recommendations, not static maps in a deck.
GPS & mobility trace analysis
Ingest and analyze GPS tracks, trip records, and device pings: pattern detection, dwell time, route efficiency, and behavioral segmentation at scale with proper spatial indexing.
Address geocoding & standardization
Normalize messy addresses, resolve duplicates, and geocode at production quality, with confidence scoring, fallback hierarchies, and audit trails for downstream spatial joins.
Trade area & market penetration
See where each store's customers actually come from, not a random circle on a map. Measure how much local demand you already capture vs. what's still on the table.
Site selection & expansion scoring
Rank candidate locations: Is there enough demand? Are competitors already there? Would a new site steal from an existing one? A scorecard before you sign a lease.
Find underserved pockets
Cluster customer and transaction data by area to find neighborhoods or ZIPs with strong demand where you're barely present. Priorities for sales, marketing, or new locations.
Clean address & GPS pipelines
Messy addresses break everything downstream. We standardize, geocode, and monitor match quality so location analysis is built on data you can trust.
Case study · Anonymized
Which markets to open next: a ranked list, not another spreadsheet
We fixed the address data, figured out where each store's customers actually come from, and built a weekly ranked list of markets with strong demand where the brand was still under-served. Growth and real estate stopped debating conflicting decks; they opened one dashboard.
Read full case study →6 weeks
To a live expansion dashboard
6 weeks from kickoff to production dashboard
Where location intelligence moves the needle
Geospatial expertise applies across industries. We pair spatial depth with data science consulting and production ML when prediction layers on top of location features.
- Retail & consumer brands: store footprint, trade areas, and regional product mix
- PropTech & real estate: acquisition targeting, rent comps, and portfolio mapping
- Logistics & field services: route density, service coverage, and depot placement
- Telecom & utilities: coverage maps, outage geography, and commercial account clustering
- B2B SaaS with location data: customer geo-segmentation and expansion planning
- Mobility & fleet: GPS utilization, territory alignment, and operational efficiency
Growth questions we answer
Where should we open next?
A ranked list of markets: strong local demand, low share, low risk of cannibalizing your existing stores.
Where are we leaving money on the table?
Stores surrounded by demand you're not capturing. Fix with sales, marketing, or a new location.
Is this new site a good idea?
A plain scorecard before you sign: demand, competition, and distance to your nearest store.
Why teams choose PrismBase for geospatial
- Deep hands-on experience with GPS data, addresses, and spatial joins, not GIS tourists
- Analytics tied to business decisions: where to expand, what to launch, where to send sales
- Production pipelines your team can run: PostGIS, warehouse spatial SQL, monitored ETL
- Honest scoping: we redirect when a spreadsheet and a map layer suffice
- Same principal from discovery through deployment under the Production Standard
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Frequently asked questions
What geospatial data do you work with?
GPS traces and trip data, street addresses and POIs, lat/lng coordinates, polygons (trade areas, census, parcels), and spatial joins against customer, transaction, and operational datasets. We build the pipelines that make messy location data analysis-ready.
How does geospatial analytics support product growth?
Location intelligence answers where demand concentrates, where you're under-penetrated, which markets to enter next, and how new sites or campaigns affect existing coverage. We translate GPS and address data into prioritized growth recommendations, not just maps.
What tools and stacks do you use?
PostGIS, GeoPandas, H3/geohash grids, spatial SQL in Snowflake/BigQuery/Postgres, geocoding APIs with fallback logic, and visualization layers for stakeholder dashboards. Stack follows your infrastructure. We ship deployable pipelines, not notebook one-offs.
Can you fix our geocoding and address quality problems?
Yes. Address standardization, duplicate resolution, and geocode confidence scoring are common entry points. Clean spatial dimensions unlock trade-area analysis, clustering, and ML features that were blocked by bad location data.
How do engagements start?
Discovery call to understand your location data sources and growth questions, then a scoped SOW with KPI targets, or a diagnostic phase if data quality needs assessment before modeling.
Have GPS or address data and growth questions?
Tell us what location data you have, what decisions it should inform, and your timeline. We'll be direct about fit and scope a path to production analytics.
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