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Documentation Index

Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt

Use this file to discover all available pages before exploring further.

The following content is a read-only preview of an executable Jupyter notebook.To run this notebook interactively:
  1. Go to Wherobots Cloud.
  2. Start a runtime.
  3. Open the notebook.
  4. In the Jupyter Launcher:
    1. Click File > Open Path.
    2. Paste the following path to access this notebook: examples/Reading_and_Writing_Data/Loading_Common_Spatial_File_Types.ipynb
    3. Click Enter.

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📖 Introduction

In this notebook, we will demonstrate how to load geospatial data into Wherobots using the following formats:
  1. GeoParquet
  2. GeoJSON and Shapefiles
  3. Raster Data (GeoTIFF)
  4. Overture Maps Data
  5. Data from S3
Each section will walk through the necessary steps with annotated code and provide links to relevant Wherobots documentation.

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🗂 Step 1: Loading GeoParquet Files

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What you’ll learn:

  • How to load GeoParquet files into a DataFrame.
  • Perform basic spatial queries.
# Import necessary libraries
from sedona.spark import *
from pyspark.sql import SparkSession

# Initialize Sedona and Spark session
config = SparkSession.builder \
    .appName("Dataset Loader") \
    .getOrCreate()
sedona = SedonaContext.create(config)

# Load GeoParquet data
gdf = sedona.read.format("geoparquet").load("s3://wherobots-examples/data/mini/es_cn.parquet")

gdf.printSchema()
📄 Documentation Reference: Loading GeoParquet

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🌍 Step 2: Loading GeoJSON and Shapefiles

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What you’ll learn:

  • How to ingest GeoJSON and Shapefiles.
# Load GeoJSON file
geojson_df = sedona.read.format("geojson").load("s3://wherobots-examples/data/mini/2015_Tree_Census.geojson")

geojson_df.printSchema()

import pyspark.sql.functions as f

df = sedona.read.format("geojson").load("s3://wherobots-examples/data/mini/2015_Tree_Census.geojson") \
    .withColumn("address", f.expr("properties['address']")) \
    .withColumn("spc_common", f.expr("properties['spc_common']")) \
    .drop("properties").drop("type")

df.printSchema()

# Load Shapefile
shapefile_df = sedona.read.format("shapefile").load("s3://wherobots-examples/data/mini/HurricaneSandy/geo_export_2ca210ed-d8b2-4fe6-81eb-53cc96311073.shp")

# Inspect and perform a query
shapefile_df.printSchema()
📄 Documentation Reference: Ingesting GeoJSON

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🖼️ Step 3: Loading Raster Data (GeoTIFF)

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What you’ll learn:

  • How to load raster datasets and inspect metadata.
# Load a GeoTIFF raster file
raster_df = sedona.read.format("binaryFile").load("s3://wherobots-examples/data/mini/NYC_3ft_Landcover.tif")

# Convert binary content to a raster object
raster_df = raster_df.selectExpr("RS_FromGeoTiff(content) as raster")
📄 Documentation Reference: Loading Raster Data

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🗺️ Step 4: Loading Overture Maps Data

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What you’ll learn:

  • Load and query datasets provided by Overture Maps.
# Load Overture Maps building dataset
buildings_df = sedona.read.format("iceberg").load("wherobots_open_data.overture_maps_foundation.buildings_building")

# Filter based on geometry (example: within a bounding box)
bbox_wkt = '''POLYGON((-122.5 37.0, -122.5 37.5, -121.5 37.5, -121.5 37.0, -122.5 37.0))'''
buildings_filtered = buildings_df.where(ST_Intersects("geometry", f.expr(f'''ST_GeomFromText('{bbox_wkt}')''')))

# Show results
buildings_filtered.show()

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🔮 Next Steps

In this notebook, we demonstrated how to:
  1. Load GeoParquet, GeoJSON, Shapefiles, and raster data into Wherobots.
  2. Query spatial data using basic spatial operations.
  3. Integrate datasets directly from S3 and Overture Maps.

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What’s next?

  • Explore spatial transformations like buffering or intersecting geometries.
  • Perform spatial joins for more advanced analytics.
  • Visualize query results with SedonaKepler or SedonaPyDeck.
For further details, check out the Wherobots Documentation.