San Francisco Neighborhood Livability and Safety Index
2024
Source, license and coverage
Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.
- License
- Not documented — confirm reuse terms with the seller
- Source / creator
- Supplier-cited: US Census Bureau ACS
- Collection method
- uploaded
- Coverage / snapshot
- 2024
- Data last updated
- Not documented
- Update schedule
- Not documented
Source attribution is supplier-reported. The source link identifies publisher documentation; it does not independently verify this uploaded extract.
Sample structure score: 100 / 100
This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.
Assessed 10 sample records (CSV) on 2026-10-09. All records in the provided sample were checked.
| Check | Points | Evidence |
|---|---|---|
| Populated cells | 50 / 50 | 150 of 150 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated. |
| Consistent value types | 30 / 30 | 150 of 150 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth. |
| Consistent record shape | 20 / 20 | 10 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys. |
Field-level findings and improvements
Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.
| Field | Missing cells | Most common type | Other populated types |
|---|---|---|---|
| neighborhood | 0 / 10 | string | 0 / 10 |
| population_acs_2022 | 0 / 10 | number | 0 / 10 |
| median_household_income_usd | 0 / 10 | number | 0 / 10 |
| poverty_rate_pct | 0 / 10 | number | 0 / 10 |
| median_rent_1br_usd | 0 / 10 | number | 0 / 10 |
| rent_burden_pct | 0 / 10 | number | 0 / 10 |
| total_crime_incidents_2024 | 0 / 10 | number | 0 / 10 |
| violent_crime_incidents_2024 | 0 / 10 | number | 0 / 10 |
| property_crime_incidents_2024 | 0 / 10 | number | 0 / 10 |
| crime_rate_per_1k | 0 / 10 | number | 0 / 10 |
| violent_crime_rate_per_1k | 0 / 10 | number | 0 / 10 |
| property_crime_rate_per_1k | 0 / 10 | number | 0 / 10 |
| walk_score | 0 / 10 | number | 0 / 10 |
| transit_score | 0 / 10 | number | 0 / 10 |
| bike_score | 0 / 10 | number | 0 / 10 |
About this data
Composite neighborhood-level dataset for 38 San Francisco residential areas combining crime reports, Census income and poverty data, walkability scores, and rental prices. Includes population, median household income, poverty rate, median 1-bedroom rent, rent burden, crime counts and per-capita rates, and walk/transit/bike scores.
Retrieve with your agent or Python
Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.
Download the Python examplepython3 retrieve-dataset.py 83cb1a66-13c2-459d-b132-7eae4aa5aeeb --output dataset.bin
Full supplier documentation
Schema
| Name | Type | Description |
|---|---|---|
| neighborhood | string | |
| population_acs_2022 | string | |
| median_household_income_usd | string | |
| poverty_rate_pct | string | |
| median_rent_1br_usd | string | |
| rent_burden_pct | string | |
| total_crime_incidents_2024 | string | |
| violent_crime_incidents_2024 | string | |
| property_crime_incidents_2024 | string | |
| crime_rate_per_1k | string | |
| violent_crime_rate_per_1k | string | |
| property_crime_rate_per_1k | string | |
| walk_score | string | |
| transit_score | string | |
| bike_score | string |
Sample Data
Preview a sample of the data before downloading.
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For AI Agents
# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
"mcpServers": {
"databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
}
}
# 2. Your agent can then call:
search_datasets({ query: "San Francisco Neighborhood Liv" })
// Found: 83cb1a66-13c2-459d-b132-7eae4aa5aeeb
get_download_url({ dataset_id: "83cb1a66-13c2-459d-b132-7eae4aa5aeeb" }) // free — sign in with MCP OAuth first# Free dataset — sign in or use your account API key: curl https://api.databazaar.io/datasets/83cb1a66-13c2-459d-b132-7eae4aa5aeeb/download-url -H "Authorization: Bearer $DATABAZAAR_API_KEY"