Cartographic Quality Control and Metadata

Why Cartographic Quality Control Matters

A map that looks professional but contains a wrong datum, a mislabeled feature, or an inconsistent scale bar can cause serious real-world harm — from a surveying crew staking a boundary in the wrong location to a drone pilot flying in restricted airspace because a no-fly zone was omitted. Cartographic quality control (QC) is the systematic process of checking every element of a finished map against an established standard before the product is delivered or published. In GIS and surveying work, QC is not optional; it is a professional obligation.

The QC Checklist Approach

A structured QC checklist forces you to inspect each map element in a deliberate order rather than relying on a general impression. The checklist used in this lesson covers seven categories:

Projection and datum — Confirm the map projection is appropriate for the area and purpose (e.g., a State Plane coordinate system for a county survey, not a global Web Mercator projection that distorts area). Verify that the stated datum matches the data source — mixing NAD 83 data with a map labeled NAD 27 introduces positional errors measured in tens of meters.

Symbol accuracy — Check that every symbol matches the legend. A filled circle used for two different feature types, or a line weight applied inconsistently, misleads the reader.

Label correctness — Verify spelling, capitalization style, font size hierarchy, and that labels are placed according to standard cartographic convention (e.g., water body names follow the shoreline curvature; road names run parallel to the road).

Scale appropriateness — The map's scale bar must match the actual representative fraction. If you zoom a layout without updating the scale bar, the bar becomes wrong. Also confirm the level of detail shown is appropriate for the stated scale — showing parcel-level detail on a statewide overview map is a mismatch.

Neatline integrity — The neatline is the border that frames the map body. Check that it is complete, that no features bleed outside it unintentionally, and that it is geometrically clean (right-angle corners, consistent line weight).

Marginalia completeness — Marginalia are all elements outside the neatline: title, subtitle, north arrow, scale bar, legend, data source statement, coordinate grid or graticule labels, author/organization, and date. Every finished map must have all required marginalia present and accurate.

Data quality visual check — Look for obvious topology errors visible at the map scale: slivers, gaps between polygons that should share a boundary, or roads that do not connect at intersections.

Maintaining a Defect Log

For each error you find, record it in a defect log with four fields: (1) the checklist category, (2) a brief description of the error, (3) the location on the map (page coordinates or layer name), and (4) the corrective action taken. After correcting all defects, re-run the full checklist to confirm that every item now passes. The defect log becomes part of the project file and demonstrates due diligence.

FGDC Metadata: What It Is and Why It Is Required

The Federal Geographic Data Committee (FGDC) Content Standard for Digital Geospatial Metadata defines a common set of fields that must accompany any geospatial data product produced for or shared with U.S. federal agencies, and it is widely adopted by state and local governments and private firms. Metadata answers the fundamental questions: What is this dataset? Who made it? When? In what coordinate system? How accurate is it? How do I contact someone about it?

ArcGIS Pro includes a built-in Metadata editor that can produce FGDC-compliant records. You access it through the Catalog pane by right-clicking a layer or map document and selecting Edit Metadata.

Key FGDC Sections You Will Complete

Identification Information — Title, abstract, purpose, time period of content, and status.

Spatial Reference Information — The horizontal coordinate system (datum, projection, and parameters) and, if applicable, the vertical datum. This section must precisely match the projection and datum confirmed during QC.

Data Quality Information — Positional accuracy report, attribute accuracy report, completeness report, and lineage (the history of how the data were collected and processed).

Lineage — List every source dataset used, its publication date, and the processing steps applied, in chronological order. For a drone-collected survey product, lineage would include the flight date, sensor model, ground control point datum and accuracy, and the photogrammetry software used.

Contact Information — The name, organization, address, phone, and email of the person responsible for the dataset.

Safety and Professional Notes

Data integrity is a safety issue in this field. Survey plats, engineering basemaps, and drone flight planning layers all carry legal and physical consequences when wrong. Before submitting any map product:

• Never alter a datum identifier without verifying the underlying coordinate values have actually been transformed.
• Keep an unedited backup of the original flawed file before making corrections — this is your audit trail.
• If you discover a datum or projection error in a file that has already been delivered to a client, report it immediately to your supervisor. Concealing a known map error is an ethical and potential legal violation.

In the lab, standard electrical PPE applies when operating workstations, and ergonomic practices (proper chair height, monitor distance, regular breaks) reduce repetitive-strain risk during long editing sessions.