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Quick Start Guide

Get up and running with SocialMapper in minutes! This guide will walk you through your first analysis.

Prerequisites

  • Python 3.11 or higher installed
  • Internet connection for downloading data
  • (Optional) Census API key for enhanced data

Installation

pip install socialmapper

Your First Analysis

Let's analyze library accessibility in a community:

1. Basic Command Line Usage

socialmapper analyze --state "North Carolina" --county "Wake County" \
  --place-type "library" --travel-time 15

2. Python Script

Create a file my_first_analysis.py:

from socialmapper import run_socialmapper

# Analyze library accessibility
results = run_socialmapper(
    state="North Carolina",
    county="Wake County",
    place_type="library",
    travel_time=15,
    census_variables=["total_population", "median_household_income"],
    export_csv=True
)

# Display results
print(f"Found {len(results['poi_data'])} libraries")
print(f"Analyzed {len(results['census_data'])} census block groups")

Run it: 

python my_first_analysis.py

Understanding the Results

After running the analysis, you'll get:

  1. POI Data - Information about each library found
  2. Census Data - Demographics of areas within travel time
  3. CSV Files - Detailed data exported to output/csv/
  4. Maps (optional) - Visualizations in output/maps/

Next Steps

Try Different POI Types

# Schools
results = run_socialmapper(
    state="California",
    county="Los Angeles County",
    place_type="school",
    travel_time=10
)

# Healthcare facilities
results = run_socialmapper(
    state="Texas",
    county="Harris County",
    place_type="hospital",
    travel_time=20
)

Use Custom Locations

Create a CSV file my_locations.csv: 

name,latitude,longitude
Community Center,35.7796,-78.6382
City Park,35.7821,-78.6589

Then analyze: 

results = run_socialmapper(
    custom_coords_path="my_locations.csv",
    travel_time=15,
    census_variables=["total_population"]
)

Add More Census Variables

# Detailed demographic analysis
census_vars = [
    "total_population",
    "median_age",
    "median_household_income",
    "percent_poverty",
    "percent_without_vehicle"
]

results = run_socialmapper(
    state="New York",
    county="New York County",
    place_type="park",
    travel_time=10,
    census_variables=census_vars
)

Common Patterns

Batch Analysis

# Analyze multiple POI types
poi_types = ['library', 'school', 'hospital', 'park']

for poi_type in poi_types:
    print(f"\nAnalyzing {poi_type}s...")
    results = run_socialmapper(
        state="Washington",
        county="King County",
        place_type=poi_type,
        travel_time=15
    )
    print(f"Found {len(results['poi_data'])} {poi_type}s")

Different Travel Times

# Compare accessibility at different time intervals
for minutes in [5, 10, 15, 20, 30]:
    results = run_socialmapper(
        state="Colorado",
        county="Denver County",
        place_type="grocery_store",
        travel_time=minutes
    )
    total_pop = sum(r['total_population'] for r in results['census_data'])
    print(f"{minutes} minutes: {total_pop:,} people")

Troubleshooting

No Results Found?

  • Check spelling of state/county names
  • Try a different POI type
  • Ensure internet connection is active

Slow Performance?

  • First runs build caches (normal)
  • Reduce number of census variables
  • Use smaller geographic areas

Memory Issues?

  • Process one county at a time
  • Limit census variables
  • Close other applications

Learn More

Ready for more? Check out our tutorials for step-by-step guides!