Population grid aggregated for a metro study areaSparseDense
Overview
Most spatial problems are data problems first
Analysts lose more time to broken geometry than to analysis. Self-intersecting polygons, mismatched projections, boundaries that do not tile, road networks that look continuous but are not connected at the junctions: none of it is visible on a screenshot, and all of it changes the answer.
We prepare geospatial data so that the first thing you do with it is analysis, not repair. Geometry is validated, topology is checked, projections are stated explicitly and coordinate precision is appropriate to the source.
Where a national mapping agency or an open dataset already publishes what you need, we say so and help you use it. Paying for data that already exists in the public domain is the most common avoidable cost in this field.
Sample output
The same data, mapped
A static preview rather than an embedded map SDK, so the page stays fast. Interactive maps are built on request as part of a visualization project.
Population grid aggregated for a metro study areaSparseDense
Scope
Datasets we prepare
Layers are delivered individually or as a coherent, joined package.
Administrative boundaries
Countries, regions, municipalities, census units and postcode areas, cleaned so they tile without gaps or overlaps.
Custom zones
Trade areas, delivery zones, sales territories and catchments built to your definition.
Road networks
Routable geometry with classification, one-way flags and validated junction connectivity.
Point layers
POIs, assets, stops or events as validated point features with attributes.
Building and parcel footprints
Polygon layers with area, and height or storey counts where the source provides them.
Land use and land cover
Classified polygons for context, constraint mapping and suitability analysis.
Population and demographic grids
Statistical values disaggregated onto grids or apportioned to your custom zones.
Isochrones and catchments
Drive-time, transit-time and walking-time polygons generated on a road network.
Address datasets
Geocoded address points with match quality and confidence recorded per record.
Raster products
Density surfaces, interpolations and derived grids at a stated resolution.
Output formats
Delivered the way your stack expects
GeoJSON
EPSG:4326, right-hand-rule compliant, suitable for web mapping and APIs.
Shapefile
Delivered with .prj, .cpg and a field name mapping, since shapefiles truncate long names.
GeoPackage
Single-file, multi-layer delivery with preserved field types and no name limits.
PostGIS
Direct load with spatial indexes and geometry constraints already applied.
CSV with WKT
For tools that expect tabular input but still need geometry.
Vector tiles
MBTiles or PMTiles for fast rendering of large layers in a browser.
Sample project data. Every layer states its coordinate reference system, its source type and its vintage, because mixing those silently is how spatial analysis goes wrong.
Sample project · Layer manifest for a metro data package
Layer
Geometry
Features
CRS
Source type
Updated
admin_boundaries
Polygon
412
EPSG:4326
Open government
2026-06
road_network
LineString
128,940
EPSG:4326
Open mapping
2026-07
poi_points
Point
23,517
EPSG:4326
Collected
2026-08
population_grid
Polygon
9,864
EPSG:3857
Statistical agency
2026-01
drive_time_15min
Polygon
48
EPSG:4326
Derived
2026-08
Delivered work
This service on a real project
Sample projects built on this service, with the numbers they produced and what each one settled.
Heatmap AnalysisGIS AnalysisMap Visualization
Urban Heatmap Data Analysis
Turning a scattered point dataset into a normalised density surface that separates real activity clusters from population artefacts.
Input records
48,600
Records corrected
9.1%
Hotspots confirmed
2 of 5
What it showed
Central had by far the largest raw count and by far the largest visual hotspot, but ranked third on activity per resident. The original map had been describing population distribution, not activity.
Harbourside, which barely registered on the unnormalised map, was the strongest genuine hotspot at 99% confidence. It has a small residential population and a high concentration of activity, precisely the pattern raw-count heatmaps hide.
Building a comparable category census across five metro areas, and the standardisation work that made the comparison valid.
Raw records
94,200
Unique POIs
71,480
Category labels
14 → 1
What it showed
Ranked by absolute count, Metro A led by a wide margin. Ranked per capita, it placed third, and the two markets the team had considered marginal turned out to be the most densely served.
Chain share varied from 17.9% to 51.4% across markets that industry commentary had treated as broadly similar. That spread became the central finding of the research rather than a footnote.
Describe the study area, the layers and the tools the data has to work in.
Step 02
We define the data scope
We identify sources, licences, projections and the schema each layer will use.
Step 03
We collect and process the data
Layers are acquired, reprojected, clipped to the study area and given consistent field names.
Step 04
We validate the dataset
Geometry validity, topology, connectivity and attribute completeness are all checked.
Step 05
We deliver the final result
A documented package with a layer manifest, CRS notes and licence attributions.
Quoted on feature counts, the number of layers, source complexity and validation depth. Tell us the layers and coverage you need and you get a fixed price against a written scope.
FAQ
Questions we get asked
What is geospatial data?
Any data with a location attached: a point, a line, a polygon or a grid cell tied to coordinates on the Earth. What makes it geospatial rather than tabular is that distance, containment and adjacency between records carry meaning.
Which coordinate systems do you work in?
We deliver in EPSG:4326 by default and reproject to whatever your workflow needs, including national grids and local projected systems. Any measurement of distance or area is done in an appropriate projected system, never in degrees.
Can you work with open data such as OpenStreetMap?
Yes, and we recommend it where it fits. We extract, clean and validate it for your study area, and we include the required attribution. Where open data has known gaps for your use case, we say so and propose how to fill them.
Can you build custom boundaries?
Yes. Trade areas, delivery zones, sales territories and catchments can be built from drive times, postcode groupings, population targets or rules you define, and delivered as clean, non-overlapping polygons.
Do you handle large datasets?
Yes. National road networks and multi-million-feature layers are handled with tiled processing and delivered as GeoPackage, PostGIS or vector tiles rather than shapefiles, which are not suited to that size.
Can you fix a dataset we already have?
Often the fastest option. We run a validity and topology audit, report what is wrong, and repair geometry, projections, field types and joins in place so your existing analysis keeps working.
Tell us the geography, the fields and the cadence you need for geospatial data. You get a scoped plan, a sample and a fixed price before any work starts.