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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/Analyzing_Data/PMTiles-railroad.ipynb
    3. Click Enter.
PMTiles rendered in Esri This notebook demonstrates how to generate a PMTiles file from the U.S. Census Bureau’s TIGER railroad dataset using Wherobots. This notebook is part of a hands-on project that shows you how to generate and visualize PMTiles. It consists of three parts:
  1. Blog Post – introduces and showcases this capability.
  2. Jupyter Notebook (this file) – step-by-step code for generating the PMTiles file.
  3. Web Visualization Repo – tile server and client code using the Esri JavaScript SDK.
In this notebook, you will:
  • Download and prepare the TIGER railroad shapefile and store it in Wherobots Managed Storage.
  • Filter nationwide data to a specific region, Texas, using Spatial SQL with Sedona.
  • Generate a PMTiles file using the Wherobots vtiles library.
  • Visualize the resulting map tiles directly within the notebook.

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Cost to generate PMTiles for Texas

  • Time taken: 1m 18s
  • Cost: $0.16
  • Runtime size: Tiny
import os
import requests
import zipfile
import io
import boto3
import wkls
from wherobots import vtiles
from urllib.parse import urlparse
from sedona.spark import *
from pyspark.sql.functions import *

​
Download the railroad dataset from TIGER

This piece of code is a helper function that downloads the zipped folder, extracts it, and uploads it to your Managed Storage (S3 bucket). If the TIGER dataset’s FTP server is down, we have mirrored the data in our public S3 bucket: s3://wherobots-examples/data/pmtiles-blog/tl_2024_us_rails/ 
def parse_s3_uri(s3_uri):
    """
    Parses an S3 URI (e.g., 's3://bucket-name/folder/path')
    and returns the bucket name and the path.
    
    Args:
        s3_uri (str): The S3 URI string.
        
    Returns:
        tuple: A tuple containing (bucket_name, folder_path).
    """
    parsed_uri = urlparse(s3_uri)
    if parsed_uri.scheme != 's3':
        raise ValueError("Invalid S3 URI. Must start with 's3://'")
    return parsed_uri.netloc, parsed_uri.path.lstrip('/')

def download_and_upload_to_s3(zip_url, s3_uri):
    """
    Downloads a zip file from a URL using requests, extracts its contents,
    and uploads each file to an S3 bucket specified by an S3 URI.

    Args:
        zip_url (str): The URL of the zip file to download.
        s3_uri (str): The S3 URI (e.g., 's3://bucket-name/folder/path')
                      where extracted files will be uploaded.
    """
    try:
        # Ignore the InsecureRequestWarning when verify=False
        requests.packages.urllib3.disable_warnings(requests.packages.urllib3.exceptions.InsecureRequestWarning)

        # 1. Parse the S3 URI
        s3_bucket, s3_path_prefix = parse_s3_uri(s3_uri)

        # 2. Download the zip file into memory, ignoring SSL certificate errors
        print("Downloading zip file...")
        response = requests.get(zip_url, verify=False)
        response.raise_for_status()
        
        # 3. Extract and upload each file to S3
        zip_buffer = io.BytesIO(response.content)
        s3_client = boto3.client('s3')
        with zipfile.ZipFile(zip_buffer, 'r') as zip_file:
            file_list = zip_file.namelist()
            print(f"Found {len(file_list)} files in the zip.")
            for filename in zip_file.namelist():
                if not filename.endswith('/'):
                    with zip_file.open(filename, 'r') as file_in_zip:
                        file_buffer = io.BytesIO(file_in_zip.read())

                        s3_key = f"{s3_path_prefix}/{filename}".lstrip('/')

                        # Upload the file from memory to S3
                        print(f"Uploading {s3_key} to {s3_bucket}...")
                        s3_client.upload_fileobj(file_buffer, s3_bucket, s3_key)
            
            print("All files extracted and uploaded to S3 successfully!")
                        
    except requests.exceptions.RequestException as e:
        print(f"HTTP Request failed: {e}")
    except zipfile.BadZipFile:
        print("The downloaded file is not a valid zip file.")
    except ValueError as e:
        print(f"Input error: {e}")
    except Exception as e:
        print(f"An error occurred: {e}")

zip_url = 'https://www2.census.gov/geo/tiger/TIGER2024/RAILS/tl_2024_us_rails.zip'
base_s3_uri = f'{os.getenv("USER_S3_PATH")}PMTiles-example'
s3_destination_uri = f'{base_s3_uri}/data'

download_and_upload_to_s3(zip_url, s3_destination_uri)

​
Getting WherobotsDB started

This gives you access to WherobotsDB and PMTiles generator 
config = SedonaContext.builder().getOrCreate()

sedona = SedonaContext.create(config)

​
Read in the files that we downloaded

df_rail = sedona.read.format("shapeFile").load(s3_destination_uri)

​
Filter by Texas boundary

Feel free to alter this to some other US state or remove it entirely to get the same experience of the blog. The code to generate PMTiles on the entire dataset: 
df_rail = df_rail.withColumn("layer", lit("railroads"))
Click here to learn how to select another state using the wkls library. 
texas_wkt = wkls.us.tx.wkt()

df_rail = df_rail \
                .where(f"ST_Intersects(geometry, ST_GeomFromWKT('{texas_wkt}'))")\
                .withColumn("layer", lit("railroads"))

df_rail.printSchema()

​
FYI about the data

MTFCC stands for MAF/TIGER Feature Class Code and is a code that is assigned by the U.S. Census Bureau to classify and describe geographic objects or features, such as roads, rivers, and railroad tracks. The MTFCC code R1011 means a Railroad Feature (Main, Spur, or Yard). LINEARID is a Linear Feature Identifier, a unique ID number used in U.S. Census Bureau TIGER (Topologically Integrated Geographic Encoding and Referencing) data to associate a street or feature name with its location, such as an edge or address range in the spatial data. 
df_rail.show()

df_rail.select("LINEARID").distinct().count() == df_rail.count()

​
Generating the PMTiles

A single line of code generates the PMTiles file from the processed DataFrame and saves it directly to your S3 bucket. 
df_rail.count()

s3_full_path = f"{base_s3_uri}/pmtiles/railroads.pmtiles"

vtiles.generate_pmtiles(df_rail, s3_full_path)
Alternatively, you can load the PMTiles to Wherobots hosted PMTiles viewer to visualize it. 
vtiles.show_pmtiles(s3_full_path)