Analysis was done in desktop GIS but cannot be reproduced, shared, or updated when inputs change.
Spatial Analytics & Location Intelligence Services
Advanced Spatial Analytics, Location Intelligence & Predictive Spatial Modeling
Enterprise spatial analytics services and location intelligence solutions. Uncover geographic patterns, build predictive spatial models, optimize territory networks, and drive location-aware decisions.
// The buying trigger
You probably need this when…
Spatial questions are being answered with tabular or attribute-only methods that ignore proximity, adjacency, connectivity, or terrain.
A consultant delivered a map but not the method, parameters, or validation — so the results cannot be challenged or extended.
Your team needs automated, repeatable geoprocessing to run on a schedule rather than manually each month.
// Concrete scope
What the engagement delivers
Every item is tied to an acceptance owner and evidence. Final scope is confirmed after data, security, and integration review.
Spatial analysis design document with method, parameters, assumptions, and validation plan
Documented, reproducible geoprocessing workflow (Python/GDAL/QGIS model or Jupyter notebook)
Output geodatasets with full metadata, coordinate reference documentation, and QA report
Map outputs in required format (print-ready cartography, web layers, or dashboard integration)
Results interpretation brief written for the intended decision-making audience
Automated pipeline for recurring analysis (optional, scoped separately)
// Delivery process
From evidence to operational handover
- 01
Define the spatial question
Translate the business or research question into a testable spatial hypothesis with defined inputs, outputs, and success criteria.
- 02
Data audit and preparation
Assess coverage, resolution, coordinate systems, topology, and fitness for the intended analysis.
- 03
Analysis design
Select and document methods — buffer, overlay, network, terrain, interpolation, spatial regression, or custom — with parameter justification.
- 04
Execute and validate
Run analysis, test edge cases, validate against independent sources or known outcomes, and document uncertainty.
- 05
Communicate and transfer
Deliver results in the format your audience needs and transfer the workflow so your team can reproduce or extend it.
// Proof before promises
How value is verified
No vanity accuracy number. Evidence is chosen around the operational decision and agreed before delivery starts.
- ✓Reproducible workflow that a second analyst can run and get the same result from the same inputs
- ✓Sensitivity test showing how outputs change with reasonable parameter variations
- ✓Validation against independent data, historical evidence, or expert review where applicable
// Buyer questions
Frequently asked questions
What types of spatial analysis do you cover?+
Site suitability and multi-criteria evaluation, network and routing analysis, catchment and watershed delineation, viewshed and terrain modelling, proximity and buffer analysis, spatial interpolation (IDW, Kriging), point pattern analysis, land-use change detection, and spatial statistics.
Can you automate analysis that we currently run manually in QGIS or ArcGIS?+
Yes. We convert manual desktop workflows into scripted Python, GDAL, or PyQGIS processes that can be scheduled, version-controlled, and run without a GUI.
Do you provide the source code and data, or just a report?+
Both. You receive all output data, the documented methodology, and the processing scripts so your team owns and can extend the work.
What spatial analytical methods do you implement?+
Our methods include spatial regression, spatial lag/error modeling, spatial autocorrelation (Moran I, Getis-Ord Gi*), kernel density estimation, H3/S2 spatial discrete global grid indexing, and multi-criteria spatial decision analysis (MCDA).
How do you ensure spatial analysis is defensible for regulatory or legal review?+
We document complete data lineage, coordinate transformations, confidence intervals, edge-effect treatments, and sensitivity analyses in reproducible Jupyter notebooks and versioned analytical code.
Can your team deliver one-off analytical studies or continuous automated services?+
Both. We execute targeted 2-to-4 week analytical deep dives (such as our published urban thermal inequality study) or operationalize analytical logic into recurring, automated data products.
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Bring us the problem, not a perfect brief.
Three fields start the conversation. An engineer will help determine fit, data readiness, and the smallest useful next step.
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