Parcel and feature digitising
Boundaries and features digitised from imagery, scans or survey sources to an agreed tolerance.
GIS Mapping & Location Data Support
Spatial data, digitised to a tolerance
Parcel digitising, geocoding, map production and spatial QA — with the accuracy tolerance agreed before the work starts rather than assumed.
- Parcel and feature digitising
- Geocoding
- Spatial QA
What this covers
Spatial data errors hide well. A boundary digitised a few metres off looks correct in a table, plausible on a map at low zoom, and wrong at the moment somebody relies on it.
That is why tolerance is the first conversation. "Accurate" is not a specification; a stated tolerance is, and it determines both the cost and whether the output is fit for the use you have in mind.
StaffCloud staffs trained GIS analysts working to a written specification in your environment or ours, with QA weighted toward the features where an error would actually cause a problem.
What the team does
Geocoding and address matching
Addresses matched to coordinates with match confidence recorded, because a low-confidence geocode that looks like a high-confidence one is the classic failure.
Attribute and data entry
Feature attributes entered and maintained against your schema and conventions.
Map production
Cartographic output to your templates and standards, produced consistently rather than per-analyst.
Spatial QA
Topology, overlaps, gaps and attribute consistency checked — the errors that are invisible in a table and obvious in use.
Format conversion
Data delivered in the formats and projections your systems expect, with the projection handling documented rather than assumed.
How the engagement runs
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01
Specification and tolerance
Accuracy tolerance, conventions and source hierarchy agreed. This determines cost and fitness, so it comes first.
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02
Pilot area
A defined area worked and checked against your standard on real data.
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03
Production
Volume work to the specification with QA weighted by consequence.
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04
Delivery
Output in your formats and projections, with a QA report.
Why tolerance comes first
It is the difference between data that is fit for a use and data that is merely finished.
- A stated tolerance rather than an assumed one
- Geocode confidence recorded rather than implied
- Topology checked, not just attributes
Platforms and tools we work in
The pod works inside your stack. These are the systems this team uses day to day — if yours is not listed, we will learn it.
- QGISEditing and production
- ArcGISGIS production and analysis
- PostGISSpatial database work
- Google Earth ProReference and verification
- Python / GDALConversion, validation and bulk processing
Questions buyers ask
The things worth knowing before the first call.
Not sure this is the right scope?
Describe the workload in a sentence and we will point you at the right team — or tell you honestly if it is not something we should take on.
Ask usThat depends on your source material, and StaffCloud states the achievable ceiling for your sources before starting.
Digitising from high-resolution imagery and digitising from a scanned historical plan are different jobs with genuinely different limits. Quoting a single figure across both would mislead you on one of them, and you would find out at the point it mattered.
Yes, with access scoped to what the work needs.
Working inside your environment avoids format round-trips, which is where projection and precision problems tend to creep in unnoticed. Fewer conversions means fewer places for the data to quietly shift.
Yes, and it is worth being explicit about from the start.
Mismatched coordinate reference systems are one of the more common sources of spatial error and one of the most confusing to diagnose after the fact — everything looks plausible until it is overlaid on something else. Agreeing the CRS up front removes a whole category of rework.
Georeferencing and digitising from scans is common work.
The accuracy you can achieve is bounded by the source itself, and StaffCloud states that limit before starting rather than after delivery. That way the output is used for the purposes it can support, and nobody plans around a precision the original never had.
Through topology checks, overlap and gap detection, attribute validation and sampled visual review.
The review is weighted toward the features where an error would matter most, rather than spread evenly across the dataset. That concentrates the checking effort where the consequences are, which catches more of what actually counts for the same cost.
Yes, working to your cartographic templates.
Consistent map production is its own discipline and benefits from the same specification-first approach as the data work. Agreeing the template once means a series of maps looks like a series rather than a collection.