Best Practices For Exporting Features With Full Attribute Tables

When exporting geographic features from one data format or geodatabase to another, a common frustration is the unintentional loss or truncation of attribute data associated with those features. Detailed tabular information containing measurements, descriptions, categorizations, or other metadata may suddenly find columns missing or values blanked out upon import to a new geospatial dataset.

This data loss occurs because not all geospatial file formats or geodatabases support the full retention of feature attribute data when running export or conversion processes. Complex attribute tables may get simplified or stripped down to basic geometric properties during certain exports. This can lead to time-consuming reconstruction of lost data, or worse, flawed analyses and decisions based on now incomplete dataset.

The Problem: Losing Attribute Information During Exports

Many basic export workflows, like using the Layer To KML tool in ArcMap to convert a feature class with rich attributes to Google Earth format, will discard all columns in the attribute table aside from basic identifiers. Similar unexpected data loss can happen when converting datasets between formats like file geodatabases, shapefiles, CSV files, and so on.

Certain destinations may only support a limited number of attribute columns, so exporting may try to fit a table with 50 diverse fields into a format that only allows 10 attributes. Decisions have to be made on which columns to eliminate. Oftentimes, the export process will simply truncate extra fields instead of handling this intelligently.

In other cases, attributes may be incorrectly mapped between source and output, splitting a single column into multiple parts, failing to maintain relationships between fields, or suffering encoding issues that corrupt attribute values. Subtle issues like these can be very difficult to catch with a quick visual check, but substantially alter the structure and integrity of the attribute data.

Best Practices for Retaining Full Attribute Tables

Thankfully, it is possible to circumvent attribute data loss during exports through careful choice of environments, tools, and settings modifications. Follow these best practices when exporting features to new datasets:

Export Using Feature Classes Rather than Layer Files

First and foremost, build your export workflows from the underlying feature classes or feature layers themselves, not from ArcMap .lyr files or ArcGIS Pro layer files pointing to these sources. Layer files do not always fully capture or transfer attribute information attached to features, since their primary purpose is controlling display properties rather than carrying data.

Connecting directly to geodatabase feature classes as inputs and outputs guarantees that all available schema properties are accessed, analyzed, and (if supported) transferred by the export tool or function instead of relying on intermediary layer configurations.

Choose File Formats That Support Full Attributes

Be selective regarding output formats when exporting features. Certain geospatial file types have inherent limitations on attribute table complexity and size. Shapefiles, for example, only allow attribute names up to 10-characters and cannot contain special characters.

Here are good options for export formats that provide full fidelity for attribute data storage and transfer:

File Geodatabase

Esri’s File Geodatabase format is specifically designed to maintain full internal integrity and consistency for attribute tables and relationships even during repetitive export and import processes. Copying feature classes between File Geodatabases will transfer complete tables. Just watch for general size limits of around 1TB per database.

Shapefiles (With Precautions)

While shapefiles traditionally suffer from column limits (254 fields) and other restrictions, modern export processes can automatically split attribute data across multiple linked file tables as needed, avoiding cuts to your data. Carefully review settings and save outputs before assuming a shapefile export succeeded fully.

Configure Output Geometry and Fields Carefully

When using a tool or wizard for exporting features to a new output dataset, pay close attention to parameters that control geometry and field mapping behavior. You may have options to preserve all fields, force a certain number of fields, or explicitly define included and excluded fields.

Leaving these settings to default values or ignoring them can lead to inadvertent stripping down of attributes. Define settings like output spatial reference, geometry type, field maps, or field lists explicitly in the export tool interface prior to running it.

Check Exported Dataset for Lost Attributes

After the export process concludes, thoroughly inspect the output feature class, preferably using a low-level interrogator tool that displays the technical schema structure of the database. Verify that field datatypes, sizes, and value contents match expectations, with no missing fields, clipped values, or other obvious gaps.

Do not rely solely on a visual check of symbolized features in ArcMap or the field list interface of ArcCatalog. Errors like truncated text fields are easy to miss in standard views, so take the time to deeply check integrity at the attribute table back-end via schema tools or data viewers.

Example Python Script for Exporting with Full Attributes

This Python script exports a source feature class from a file geodatabase into an output shapefile, handling field mapping and translations automatically to retain complete attribute structure:

import arcpy

# Paths to input data
inputFC = r"C:\Project\InputGDB.gdb\ProjectData"  

# Output paths
outputFolder = r"C:\Project\OutputData"
outputName = "ProjectOutput"  

# List fields in the input FC
inputFields = arcpy.ListFields(inputFC) 
fieldList = []
for field in inputFields:
# Execute CopyFeatures GP tool    
arcpy.CopyFeatures_management(inputFC, outputFolder + "\\" + outputName + ".shp") 
# Get count of records in the new shapefile
result = arcpy.GetCount_management(outputFolder  + "\\" + outputName + ".shp")
count = int(result[0])
print("Copied " + str(count) + " features with " + str(len(fieldList)) + " fields (" + str(fieldList)[1:-1] + ") to shapefile")

The script uses ArcPy to first enumerate fields present in the input feature class via ListFields(), then explicitly copies features to a defined output shapefile path using CopyFeatures(). Finally record and field counts confirm successful export of the full data contents.

Verifying Exports Contain Expected Attribute Data

What proactive checks can help identify cases where exports did suffer from unintended attribute loss during processing? Beyond just visually scanning the output for missing pages or sections, consider these practices:

Compare Field Counts Between Source and Output

Check record count parity, but more importantly compare the number of attribute fields present in the source dataset versus the exported result. All source fields should transfer over unless explicitly excluded via configuration. Any reduction indicates something stripped columns unexpectedly.

Spot Check Field Contents Row By Row

Even if overall field counts match perfectly between old and new datasets, that does not guarantee perfect preservation of values during the export. It’s always smart practice to compare specific row samples side by side in source and output tables to verify mappings held up for real records.

Review Field Datatypes and Sizes

Maybe all source fields transferred over, but were they truncated or converted to less suitable datatypes in the process? Carefully review output geodatabase schema and compare definitions to source expectations. Did text fields maintain proper lengths? Did date fields shift to less precise values unexpectedly?

Pay extra attention to fields that utilized the “String” datatype in the source database, as exports may choke on translating these varying length strings depending on output format.

Troubleshooting Missing or Truncated Attributes

If an export finishes but suffers from obvious field or data loss relative to the source, what steps help track down the cause? Some issues to investigate:

Doublecheck Output Format Limitations

Review technical specifications on column counts, string field length limits, reserved characters, and other constraints which might have forced truncations upon export. For example, shapefiles cannot contain field names longer than 10 characters–a frequent trip up.

Use Intermediate Geodatabases

Try exporting from the problem source format into an intermediate file geodatabase first, then transferring the data into the final problem output format from there. Since file geodatabases robustly retain schema, this tests whether issue lies in initial export handling.

Explicitly Set Field Mappings

Rather than relying on default import/export behavior, explicitly define source and target field relationships via field mapping parameters. This bypasses assumptions or automatic truncations that may be getting triggered silently otherwise.

scripts allow fine-grained control over field mappings to force inclusion of otherwise dropped source columns if possible.

Conclusion: Following Best Practices Prevents Data Loss

Losing attribute fidelity during export processes can introduce serious hidden gaps or corruptions within geospatial datasets. But following field-tested techniques around selective file formats, careful output configuration, schema verification checks, and custom export scripts allows robust retention of attributes.

Take the time to understand and troubleshoot export workflows early, avoiding rushed assumptions that can lock in missing metadata problems. Implementing export validation checks and append processes instead of one-step overwrite operations also provides backup options in case of issues down the line.

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