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Automate JXLStoKML: Scripts and Workflows for Bulk Conversions

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Convert JXLS to KML: A Quick Guide for Geodata Conversion

Converting geospatial data stored in JXLS-formatted Excel files to KML (Keyhole Markup Language) enables visualization in mapping apps like Google Earth and many GIS tools. This guide walks through a straightforward, reliable workflow: extract coordinates from a JXLS spreadsheet, transform them into the KML structure, and validate the resulting file.

What you need

  • A JXLS spreadsheet containing geographic coordinates (latitude, longitude) and optional attributes (name, description, style).
  • A text editor or code environment (Python recommended).
  • Python 3 and these packages: pandas, simplekml (install with pip install pandas simplekml).

Step 1 — Inspect and prepare the JXLS file

  • Open the spreadsheet and confirm column headers: at minimum have latitude and longitude. Common extra fields: name, description, altitude, style.
  • Ensure coordinates are numeric and in decimal degrees. Remove rows with missing or invalid coordinates.
  • Save a copy as XLSX if needed.

Step 2 — Load the spreadsheet (Python example)

Use pandas to read the sheet. Save this as convert_jxls_tokml.py and run with Python.

python
import pandas as pd from simplekml import Kml 
# Load spreadsheet (adjust filename and sheet name as needed)
df = pd.read_excel(“input_jxls.xlsx”, sheetname=0)

# Basic cleanup: drop rows without coords
df = df.dropna(subset=[‘latitude’, ‘longitude’])

Step 3 — Create KML and add placemarks

Use simplekml to convert rows to placemarks. Include optional name/description if present.

python
kml = Kml()
for , row in df.iterrows():
lat = float(row[‘latitude’])
    lon = float(row[‘longitude’])
    name = str(row.get(‘name’, “)) if ‘name’ in row else None
    desc = str(row.get(‘description’, ”)) if ‘description’ in row else None

    p = kml.newpoint(name=name, description=desc, coords=[(lon, lat)])
    # Optional: set altitude or style if present
    if ‘altitude’ in row and not pd.isna(row[‘altitude’]):
        p.altitudemode = ‘absolute’
        p.coordinates = [(lon, lat, float(row[‘altitude’]))]

Step 4 — Save the KML

python
kml.save(“output.kml”)

Step 5 — Validate and view

  • Open output.kml in Google Earth or a KML-aware GIS. Verify placemarks appear in correct locations.
  • If coordinates seem flipped or incorrect, confirm column mapping (latitude vs longitude) and sign conventions.

Tips and variations

  • Batch conversion: loop through multiple files or sheets and append placemarks to the same KML.
  • Styles: use simplekml to set icons, colors, and balloon content for richer display.
  • Polylines/polygons: if your JXLS contains ordered coordinate sequences, convert them to LineString or Polygon objects instead of points.
  • Large datasets: consider writing a KMZ (compressed KML) or using tiling/cluster strategies for performance.

Troubleshooting

  • Missing coordinates: log and skip rows with invalid lat/lon.
  • Non-decimal coordinates: convert DMS (degrees-minutes-seconds) to decimal degrees before conversion.
  • Encoding issues in descriptions: ensure strings

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