GISEYE Value Converter: Tips to Clean and Standardize Your GIS Data

How to Use GISEYE Value Converter — A Step-by-Step Guide

What GISEYE Value Converter Does

GISEYE Value Converter maps, standardizes, and transforms attribute values in GIS datasets so your layers share consistent, usable fields across projects and systems.

When to Use It

• Converting inconsistent attribute values (e.g., “Rd”, “Road”, “RD”) into a standard form
• Mapping legacy codes to new classification schemes
• Preparing data for joins, analysis, or export to other systems

Prerequisites

• GIS dataset (shapefile, GeoJSON, GeoPackage, etc.) with attribute fields
• Target mapping rules (source value → desired target value)
• GISEYE Value Converter installed or accessible in your GIS environment

Step 1 — Inspect the Source Field

1. Open your dataset in your GIS application.
2. Identify the field to standardize and review unique values.
3. Export a list of unique values if the application supports it (helps with mapping).

Step 2 — Define Mapping Rules

1. Create a two-column mapping table: Source value → Target value.
2. Include catch-alls for unexpected values (e.g., “Other” or leave blank to flag).
3. Save mapping as CSV or as the format required by GISEYE Value Converter.

Step 3 — Load Data into GISEYE Value Converter

1. Launch GISEYE Value Converter.
2. Select your input dataset and the field to convert.
3. Import the mapping table you prepared.

Step 4 — Configure Conversion Options

• Match type: Choose exact match or case-insensitive/partial match as needed.
• Null handling: Decide whether nulls should be left, mapped, or flagged.
• Preview changes: Use preview mode to see a sample of transformed values before applying.

Step 5 — Run Conversion

1. Execute the conversion on a test subset first (e.g., 1–5% of records).
2. Review results and logs for unmapped values or errors.
3. If satisfactory, run the conversion on the full dataset.

Step 6 — Validate Output

1. Compare unique values in the output field to your mapping table.
2. Check for unexpected duplicates, blanks, or unmapped items.
3. Use spatial or attribute queries to ensure conversions preserved geometry and relationships.

Step 7 — Save and Document Changes

1. Save the converted dataset as a new file to preserve the original.
2. Record the mapping file, conversion date (February 8, 2026), and any notes about match rules used.
3. Share documentation with team members or include in metadata.

Troubleshooting Tips

• Unmapped values: Re-run mapping after adding missing source values.
• Partial matches failing: Switch to substring or regex matching if supported.
• Performance issues: Convert by chunks or run on a machine with more memory/CPU.

Best Practices

• Keep a canonical value list centrally stored for reuse.
• Use version-controlled mapping files (CSV) so changes are auditable.
• Always run conversions on a copy and validate with queries.

Example Mapping (CSV)

Code
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source,target Rd,Road rd,Road St,Street Ave,Avenue UNKNOWN,Other 

Follow these steps to standardize attribute values quickly and reliably with GISEYE Value Converter.