Descriptive Title: Traffic density (average daily miles of vehicle travel per square kilometer)

Geographic Unit of Analysis: Street segment

Percent of households living in each traffic density* quintile (2010)
NeighborhoodLowLow-Medium Medium-HighHighHighest
Bayview/Hunter's Point 77% 2% 3% 7% 12%
Bernal Heights 33% 28% 5% 19% 15%
Castro/Upper Market 51% 49% 0% 0% 0%
Chinatown 9% 91% 0% 0% 0%
Excelsior 67% 4% 4% 10% 15%
Financial District/South Beach 9% 54% 3% 34% 0%
Glen Park
Golden Gate Park 0% 100% 0% 0% 0%
Haight Ashbury 35% 65% 0% 0% 0%
Hayes Valley
Inner Richmond 55% 36% 1% 8% 0%
Inner Sunset 47% 51% 0% 2% 0%
Lakeshore 54% 31% 0% 15% 0%
Lincoln Park
Lone Mountain/USF
Marina 39% 58% 0% 3% 0%
McLaren Park
Mission 46% 38% 3% 9% 5%
Mission Bay
Nob Hill 5% 78% 0% 16% 0%
Noe Valley 75% 25% 0% 0% 0%
North Beach 86% 14% 0% 0% 0%
Outer Mission 24% 24% 8% 29% 15%
Outer Richmond 93% 6% 0% 2% 0%
Pacific Heights 55% 45% 0% 0% 0%
Potrero Hill 27% 9% 5% 31% 28%
Presidio 75% 2% 5% 18% 0%
Presidio Heights 68% 25% 0% 7% 0%
Russian Hill 40% 51% 0% 10% 0%
San Francisco 44% 40% 2% 10% 3%
Seacliff 100% 0% 0% 0% 0%
South of Market 0% 37% 17% 34% 12%
Treasure Island 91% 0% 1% 4% 5%
Twin Peaks 76% 24% 0% 0% 0%
Visitacion Valley 72% 6% 4% 12% 6%
West of Twin Peaks 75% 24% 0% 1% 0%
Western Addition 20% 60% 0% 20% 0%

Why Is This An Indicator Of Health and Sustainability?

Traffic density is a general proxy for adverse environmental exposures and health hazards of traffic.  Epidemiological research supports consistent statistical associations among traffic proximity and several adverse respiratory health outcomes, including impairment of lung function and asthma incidence and symptoms in children; these associations remain significant after adjustment for economic position.a  Chronic exposure to road traffic noise is associated with several adverse health outcomes, including interference with thoughts and feelings, deficits in cognitive functioning, lowered school performance, sleep disturbance, and ischemic heart disease.b  The intensity of vehicle air pollution emissions, traffic noise, and safety hazards are all strongly predicted by the density and proximity of vehicles in an area.c-e 

Land use and transportation planning decisions shape the amount of traffic on our roadways, where it is concentrated, and whether traffic is concentrated near residents or other sensitive uses serving more vulnerable populations including seniors, children, people with pre-existing health conditions, or low-income communities who are often confronted with other social and environmental stressors which cumulatively contribute to disproportionate adverse health effects, including: poor housing quality; stressful work environments; and limited economic resources to meet basic needs for housing, food, transportation, and health care.

Interpretation and Geographic Equity Analysis

Neighborhoods with households in areas with the highest traffic densities are Potrero Hill, the South of Market, and the Outer Mission (based on both the high and highest traffic density categories) – all communities with residents living proximate to the 280 and 101 freeways running through San Francisco which strongly influence the traffic density ,metric.  Bernal Heights, the Financial District, Ocean View, Mission Bay, and the Excelsior –also proximate to those freeways - also have relatively higher residential exposure to high traffic densities.  These communities also often experience higher local street traffic volumes along key corridors as vehicles travel on local streets to and from the freeway network.  


The proportion of households per neighborhood within each Traffic Density quintile category was based on a Kernel Density analysis conducted using ESRI’s ArcMap 10. The 24-hour daily vehicle volume per street segment for 2010 was provided by the San Francisco County Transportation Authority from their travel forecasting model, SF CHAMP.  Estimated Traffic Density was calculated as a smooth surface over San Francisco using the ArcGIS 10 Kernel Density tool and a 100-meter grid size (with the default search radius of 450 meters).  This method calculates the density of traffic on roadways in the neighborhood of each 100-meter cell.  A smoothly curved surface is fitted over each street, with its value greatest on the street and diminishing as distance increases from the street (line) reaching zero at the search radius. The surface is defined so the volume under the surface equals the product of street length and the 24-hour vehicle count metric described above. The density at each 100-meter grid cell is calculated by adding the values of all the surfaces where they overlay the grid cell center. For more information regarding this method see: Kernel Density works

Once created, the final Traffic Density raster image was then classified into quintiles from Low to Highest. Quintiles split the ordered Traffic Density data into five groups, each containing equal counts of observations.  For mapping purposes, the ‘Low’ category was split into two groups, ‘Lowest’ and ‘Low,’ to allow for the visualization of the location of the absolute lowest Traffic Density values (see map above).  For the Neighborhood Tables, however, the 5 original Traffic Density quintile categories (which collapse the “lowest” and the “low” categories depicted on the map into the first quintile) were joined to the nearest household and then used to calculate the proportion of households in each Traffic Density category per neighborhood. For each neighborhood, the household count was summed per the Traffic Density category and divided by the total household count to assess the proportion of households within each Traffic Density category.


Data is from the SFCTA’s travel forecasting model, SF CHAMP.  While the model is internationally regarded as a sophisticated travel forecasting approach which provides the best available estimates, its outputs are not precise predictions. This indicator only reflects absolute household counts and does not capture socio-demographic differences in the distribution of Traffic Density in San Francisco.

Data Source

Street segment level daily vehicle traffic data estimates for 2010 provided by the San Francisco County Transportation Authority from their travel forecasting model, SF CHAMP.

Map and table prepared by City and County of San Francisco, Department of Public Health, Environmental Health Section using ArcGIS software.

Detailed information regarding census data, geographic units of analysis, their definitions, and their boundaries can be found at the following links:

Interactive boundaries map

  1. HEI (Health Effects Institute). 2009. Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects. Special Report #17. Available at:

  2. Miedema HM, Vos H. 1998. Exposure-response relationships for transportation noise. Journal of the Acoustical Society of America 104: 3432-3445.

  3. Elvik R. The non-linearity of risk and the promotion of environmentally sustainable transport. Accident Analysis & Prevention. 2009;41:849-855.

  4. AERMOD Modeling System. 2011.  Washington, DC: U.S. Environmental Protection Agency.  Available at:

  5. Seto EY, Holt A, Rivard T, Bhatia R. 2007. Spatial distribution of traffic induced noise exposures in a US city: an analytic tool for assessing the health impacts of urban planning decisions. International Journal of Health Geography 6: 24.