Names
222 records have an empty NAME value. Review by road class and intended use before deciding whether action is needed. No replacement names were invented.
GIS Data Quality & Workflow Automation
Check your GIS data.
Know what needs attention.
InfraSpatial Solutions automates the checks your team repeats when GIS data is updated or delivered. We identify records that need review and provide a clear report showing what was flagged, why, and where to find it.
See a working example ↓Public-data demonstration · Fredericksburg area, Virginia
U.S. Census Bureau road features. No client data or simulated defects.
U.S. Census Bureau road features. No client data or simulated defects.
Less time searching.
A clear starting point
for review.
The problem
Each new GIS data delivery can mean checking hundreds or thousands of records again. Staff need to find missing information, repeated identifiers and records that do not meet the requirements of the job.
What we do
We agree the checks with your team, build a repeatable script or tool, and test it against representative data. Each run identifies the records that match those rules and explains why they need attention.
What your team receives
A reusable checking tool, a record-by-record review report, and instructions for running the checks on the next delivery. Your team reviews the findings and decides what to correct or accept.
A working example: reviewing road data.
Suppose a team needs a road layer with names for its map. Before using a new dataset, it needs to find the features without names and decide whether those features belong in that layer.
We ran the check on 544 public Census road features. It identified 222 records without a name and produced a review list linked to their locations. The map below lets you inspect those actual records.
This demonstrates the checking and reporting process. An unnamed road may be valid; no source errors were assumed and no records were automatically corrected.
From public source to reviewable output.
- 01
Capture
Save the source response, query extent and file fingerprint.
- 02
Check
Inspect identifiers, names, classification presence and basic line geometry.
- 03
Review
Locate flagged records and decide whether each is suitable for the intended use.
- 04
Deliver
Export the review queue and retain the script and source snapshot.
Steps 1, 2 and 4 were executed for this demonstration. Human review and source corrections have not been completed.
Executed Python check / saved public-data snapshot
See which records need review.
Choose a review group or road class. Select a record to see its original attributes and location.
544features checked
222name-review candidates
322records with no flags under these rules
Loading saved results…
Fredericksburg areaNorth ↑
Context roadsFiltered recordsSelected record
Source geometry in the query area; map view clipped to the study box. This is not a routing or navigation map.
The browser explores results computed by the downloadable Python script. It does not run ArcGIS or change the source data.
What this example found.
Identifiers & classification
No missing or repeated OID values and no empty MTFCC values were found in this extract. Classification codes are retained as supplied; this is not a full domain-compliance check.
Basic geometry
No empty paths, invalid coordinate ranges or zero-length lines were found by the script. Network connectivity, positional accuracy and road topology were not tested.
Inspect the source. Reproduce the result.
Save the source snapshot, source record and Python script into one folder, then run python check_roads.py. The script verifies the source fingerprint and writes results.json and review.csv. It uses Python’s standard library.
To reproduce the run, the required inputs are source.json, provenance.json and check_roads.py; the CSV is the existing output for comparison.
Source, scope and reproducibility
The query selects features intersecting a bounding box, not the administrative city boundary. Features retain their original, unclipped geometry in the saved response. Identifiers locate records in this snapshot; stability across future Census releases is not assumed.
Census TIGERweb source layer · Census classification definitions
Which GIS checks does your team repeat?
InfraSpatial Solutions can turn an agreed set of data rules into a repeatable workflow, with clear exception reports and documented handover.
Discuss a workflow