Descriptive Title: Average daily distance travelled in private autos by residents (miles)

Geographic Unit of Analysis: Transportation District (San Francisco County Transportation Authority)

Average daily distance travelled in private autos by residents (miles)
District Miles travelled in autos
San Francisco 11.6
Downtown 2.6
SoMa 5.7
N.Beach/ Chinatown 4.5
Western Market 7.7
Mission/ Potrero 8.3
Noe/ Glen/ Bernal 13.9
Marina/ N.Heights 9.0
Richmond 13.3
Bayshore 17.0
Outer Mission 16.9
Hill Districts 16.8
Sunset 16.2

Why Is This An Indicator Of Health and Sustainability?

Land use and transportation planning defines the distances people travel to access jobs, schools, goods, services, and recreation.  As distances between destinations increase so do the miles driven in motor vehicles, along with the associated hazards from air and water pollutants, noise, and vehicle collisions.  Nationally, for people aged 5 to 34, traffic injuries are the leading cause of death with over 30,000 people killed each year.a  Driving longer distances and spending more time on the road increases the risk of being killed or injured in a traffic collision.b  Air pollutants, including ozone and particulate matter, are causal factors for cardiovascular mortality and respiratory disease and illness.c  Traffic-related noise triggers community annoyance and sleep disturbanced and is associated with hypertension and heart disease.e  Driving time has been found to independently predict obesity risk.  Driving takes time away from other positive activities for physical and mental health, such as exercise, community involvement or time with family. In the Bay Area, transportation contributes over one-third of greenhouse gas emissions.f  Climate change in turn threatens to have global and catastrophic effects on health through: increased frequency, intensity and length of heat waves, floods, droughts, windstorms and wildfire, leading to increased mortality, illness and mental health impacts; increased exposures to ground-level ozone and aeroallergens, exacerbating cardiovascular and pulmonary illness; and shifts towards warmer temperatures, leading to increased risk of food- and waterborne infectious diseases.g  Neighborhoods with higher densities of development and a mix of different land uses support reduced trip lengths, increased mode choice, and decrease the need for vehicle ownership.h

Interpretation and Geographic Equity Analysis

Residents in N. Beach/Chinatown and Downtown San Francisco’s Transportation Districts are estimated to travel by auto less than one-third of the daily distance travelled per capita as residents in the southern half of San Francisco who travel an estimated 16-17 miles daily in private autos.  In part, this is due to the comparatively wider availability and use of public transportation and high concentration of housing, goods and services, and jobs in northeastern San Francisco – which also facilitates travel via non-auto modes. Distance travelled by auto is notably also impacted by regional and local planning decisions regarding the locations of jobs relative to housing, including the types of jobs created and their salary levels, and the cost of proximate housing. Distance travelled by car is further impacted by transportation planning decisions, including whether employment centers and neighborhoods are served by public transit, bike routes, or have safe pedestrian connections.  While residents of the northwestern (Marina/N.Heights, the Richmond) and northeastern districts (Western Market, SoMa, Mission/Potrero) closer to Downtown San Francisco have relatively low daily distances travelled by auto, there are opportunities to further decrease reliance on motor vehicles and their associated hazards through coordinated land use and transportation planning that support non-auto modes including walking, biking, and transit and their associated health benefits.  

Methods

Data on the distance per capita traveled in autos by San Francisco Transportation District was provided by the San Francisco County Transportation Authority from their travel forecasting model, SF CHAMP, version 4.3.0 data for 2011, then mapped to San Francisco’s 12 Transportation Districts.  

Limitations

Data is from the SFCTA’s travel forecasting model, SF CHAMP 4.3.0 data.  While the model is internationally regarded as a sophisticated travel forecasting approach which provides the best available estimates, its outputs are not precise predictions and the district-level estimates presented in the above map and table have not been validated. 

This indicator does not currently capture socio-demographic differences in daily distance travelled in autos for residents.

Data Source

Transportation District-level data provided by the San Francisco County Transportation Authority from their travel forecasting model, SF CHAMP 4.3.0.

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

Map and table data presented at the level of the transportation district.

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

Interactive boundaries map

http://sfindicatorproject.org/resources/data_map_methods

  1. CDC. 2010. WISQARS (Web-based Injury Statistics Query and Reporting System). Atlanta, GA: US Department of Health and Human Services.

  2. Lourens PF, Vissers JA, Jessurun M. 1999. Annual mileage, driving violations, and accident involvement in relation to drivers' sex, age, and level of education. Accident Analysis & Prevention. 31(5):593-7.

  3. PolicyLink, Prevention Institute, the Convergence Partnership. Healthy, Equitable Transportation Policy.  2009. Ed. Shireen Malekafzali. Available at: http://www.convergencepartnership.org/atf/cf/%7B245a9b44-6ded-4abd-a392-ae583809e350%7D/HEALTHTRANS_FULLBOOK_FINAL.PDF.

  4. Seto EYW, 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). Available at: http://www.ij-healthgeographics.com/content/6/1/24. Accessed November 17, 2008.

  5. Miedema HME, Vos H. 1998.  Exposure Response for Transportation. J Acoust Soc Am. 1998;104:3432–3445. 

  6. Bay Area Air Quality Management District. 2010. Source Inventory of Bay Area Greenhouse Gas Emissions.  Available at:  http://www.mtc.ca.gov/planning/climate/Bay_Area_Greenhouse_Gas_Emissions_2-10.pdf

  7. Environmental Defense Fund, National Association of City and County Health Officers, George Mason University. 2007. Are We Ready?  Preparing for the Public Health Challenges of Climate Change.  Available at: http://www.edf.org/documents/7846_AreWeReady_April2008.pdf.

  8. Ewing R, Frank L, Kreutzer R. 2006. Understanding the Relationship between Public Health and the Built Environment: A Report to the LEED-ND Core Committee.