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Tutorial

Offline Geocoding on Midway2

Step-by-step guide for setting up and running offline geocoding using ArcPy and ArcGIS Server on Midway2

geocoding midway2 arcgis offline

Offline Geocoding on Midway2

Note: This guide is specifically for the Midway2 high-performance computing environment at the University of Chicago.

Overview

This guide provides instructions for setting up and running offline geocoding using ArcPy and ArcGIS Server on the Midway2 environment. This is particularly useful for processing large batches of addresses without relying on external geocoding services.

Prerequisites

  • Active Midway2 account
  • Access to RCC resources
  • Basic familiarity with Linux command line
  • ArcGIS Server installation files (available to UChicago affiliates)

Installation

1. Obtain ArcGIS Server Files

Download the ArcGIS Server package from the UChicago Box link provided in your welcome email or contact RCC support.

2. Prepare the Environment

# Navigate to your home directory
cd ~

# Unpack the ArcGIS installation package
tar xvzf ArcGIS_for_Server_Linux_xxxx_xxxxxx.tar.gz

# Increase the maximum number of open files (required by ArcGIS)
ulimit -n 65535

3. Run the Installation

# Navigate to the setup directory
cd ~/ArcGISServer/Linux

# Make the setup file executable
chmod +x Setup

# Run the installer
./Setup

4. Configure Environment Variables

Add the following to your ~/.bashrc or ~/.bash_profile:

# ArcGIS Server
export AGSSERVER=/path/to/arcgis/server
export PATH=$AGSSERVER/tools:$PATH

# Python environment
export PYTHONPATH=$AGSSERVER/arcpy:$PYTHONPATH

Setting Up for Geocoding

1. Prepare Your Address Data

Ensure your address data is in a compatible format (CSV, Excel, or database table) with properly formatted address fields.

2. Create a Python Script

Create a new Python script (e.g., geocode.py) with the following template:

import arcpy
from arcpy import env
import os

# Set workspace
env.workspace = "/path/to/your/workspace"

# Set up the geocoding service
arcpy.AddToolbox("C:/path/to/geocoding/service.ags")

# Input address table
in_table = "addresses.csv"

# Output feature class
out_feature_class = "geocoded_addresses.shp"

# Geocode addresses
arcpy.GeocodeAddresses_geocoding(
    in_table,
    "Street Address VISIBLE NONE;City VISIBLE NONE;State VISIBLE NONE;ZIP VISIBLE NONE",
    out_feature_class,
    "",
    ""
)

print("Geocoding completed successfully!")

3. Submit Job to Midway2

Use the following SLURM script to submit your geocoding job:

#!/bin/bash
#SBATCH --job-name=geocoding
#SBATCH --output=geocoding_%j.out
#SBATCH --time=04:00:00
#SBATCH --partition=broadwl
#SBATCH --ntasks=1
#SBATCH --mem=16G

# Load required modules
module load python/3.8

# Run the geocoding script
python geocode.py

Best Practices

Performance Optimization

  • Process addresses in batches
  • Use appropriate coordinate systems
  • Monitor resource usage

Error Handling

  • Implement try-except blocks
  • Log errors to a file
  • Validate input data

Data Management

  • Clean addresses before geocoding
  • Store results in a geodatabase
  • Document processing steps

Troubleshooting

Common Issues

  1. Permission Denied

    • Ensure you have execute permissions on all directories
    • Check file ownership

  2. Memory Errors

    • Reduce batch size
    • Request more memory in your SLURM script

  3. Geocoding Failures

    • Verify address formatting
    • Check for special characters
    • Validate against reference data

Support

Getting Help

Documentation

Example Workflow

1. Prepare Data

# Create a directory for your project
mkdir -p ~/geocoding_project/data
cd ~/geocoding_project

# Copy your address data
cp /path/to/your/addresses.csv data/

2. Create Configuration File

Create a config.json file:

{
  "input_file": "data/addresses.csv",
  "output_file": "output/geocoded.shp",
  "batch_size": 1000,
  "crs": "EPSG:4326"
}

3. Run the Geocoding

# Submit the job
sbatch run_geocoding.sh

# Check job status
squeue -u $USER

Advanced Topics

Parallel Processing

For large datasets, consider using parallel processing:

import multiprocessing

def process_batch(batch):
    # Your geocoding logic here
    pass

if __name__ == '__main__':
    # Split data into batches
    batches = split_into_batches(addresses, batch_size=1000)
    
    # Process in parallel
    with multiprocessing.Pool(processes=4) as pool:
        results = pool.map(process_batch, batches)

Quality Assessment

  • Calculate match scores
  • Visualize geocoding results
  • Compare with reference data

Maintenance

Updating Reference Data

  1. Download latest TIGER/Line files
  2. Update your geocoding service
  3. Validate with test addresses

Monitoring

  • Set up job monitoring
  • Track success/failure rates
  • Monitor system resources

Security Considerations

  • Keep sensitive data secure
  • Follow UChicago data policies
  • Use secure file transfers
  • Regularly update software

Additional Resources

Contact

For additional support:

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