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¶
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:
Understanding the Results¶
After running the analysis, you'll get:
- POI Data - Information about each library found
- Census Data - Demographics of areas within travel time
- CSV Files - Detailed data exported to
output/csv/
- 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
:
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¶
- Examples Directory - Complete working examples
- API Reference - Detailed function reference
- Command Line Guide - All CLI options
- User Guide - Understanding the features
Ready for more? Check out our tutorials for step-by-step guides!