Descriptive Title:

Proportion of households with a motor vehicle

Geographic Unit of Analysis: Census tract

Percent of households with a motor vehicle (2009-2013)
Neighborhood%90% MOE
Bayview/Hunter's Point 80% 4%
Bernal Heights 84% 5%
Castro/Upper Market 74% 4%
Chinatown 20% 3%
Excelsior 89% 4%
Financial District/South Beach 66% 6%
Glen Park 88% 7%
Golden Gate Park 100% 48%
Haight Ashbury 69% 5%
Hayes Valley 55% 5%
Inner Richmond 71% 5%
Inner Sunset 82% 4%
Japantown 53% 13%
Lakeshore 75% 6%
Lincoln Park 85% 31%
Lone Mountain/USF 72% 6%
Marina 79% 4%
McLaren Park 51% 20%
Mission 60% 3%
Mission Bay 73% 7%
Nob Hill 36% 3%
Noe Valley 86% 4%
North Beach 59% 5%
Oceanview/Merced/Ingleside 90% 6%
Outer Mission 88% 7%
Outer Richmond 79% 3%
Pacific Heights 70% 4%
Portola 87% 7%
Potrero Hill 88% 5%
Presidio 95% 13%
Presidio Heights 76% 7%
Russian Hill 58% 5%
San Francisco 70% 1%
Seacliff 94% 11%
South of Market 41% 4%
Sunset/Parkside 88% 3%
Tenderloin 17% 2%
Treasure Island 80% 14%
Twin Peaks 92% 8%
Visitacion Valley 80% 6%
West of Twin Peaks 94% 4%
Western Addition 53% 4%

Why Is This An Indicator Of Health and Sustainability?

Car ownership is often indicative of the degree of transportation mode choice, shaped by factors including land use, the transportation system, and individual characteristics.  Transportation mode choice can have effects community health through pathways including air quality, noise, physical activity, and traffic injuries.a  Air pollutants, including ozone and particulate matter, are causal factors for cardiovascular mortality and respiratory disease and illness.  Traffic-related noise triggers community annoyance and sleep disturbanceb and is associated with hypertension and heart disease.c  Driving time has been found to independently predict obesity risk.  A study on the driving habits of over 10,000 Atlanta residents found that each additional hour spent in the car was associated with a 6% increase in the likelihood of being obese.d  Additionally, areas with high levels of motor vehicle driving tend to have higher motor vehicle collisions and injury rates.e

Car ownership is dependent upon many factors including individual and household income, cost of car, insurance and maintenance, distance regularly traveled, accessibility of public transportation, presence of bike routes and walking paths, perceived and actual safety from crime and traffic hazards, weather conditions, traffic patterns, availability of parking, availability of public transit travel stipends/incentives.  Neighborhoods with higher densities of development and a mix of different land uses reduce trip length, increase mode choice (i.e., opportunities to walk, bike or take public transit), and decrease the need for vehicle ownership and travel by private vehicle.  Projects in these types of communities with designed with restricted residential parking, parking pricing strategies and a variety of transportation demand management programs can reduce negative health impacts associated with dependence on motor vehicles.f,g

Interpretation and Geographic Equity Analysis

The proportion of households with a motor vehicle is presented by census tract on the map and by neighborhood in the table (above).  A notably lower proportion of households in the northeast area of San Francisco have access to a motor vehicle.  Neighborhoods with the lowest private vehicle access include: Tenderloin (17%), Chinatown (20%), Nob Hill (36%), and South of Market (41%).  These communities also have relatively higher rates of households with low incomes, concentrated employment centers and residential density, access to neighborhood goods and services, both local and regional public transportation, and a diverse mix of land uses.

Neighborhoods with the highest rates of vehicle access are: Presidio (95%), West of Twin Peaks (94%), Seacliff (94%), Twin Peaks (92%), and Oceanview/Merced/Ingleside (90%). These neighborhoods have characteristics that are the converse of those in the northeast - including lower residential and job densities, and limited regional transit access. 

Methods

A household can be any individual or group of individuals sharing a housing unit, including families related by blood or marriage. (A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied as a separate living quarters from other individuals in the building.)

The total number of households with a car (see data sources) was divided by the total number of occupied households (see data sources) and the margins of error were recalculated using the ACS users handbook. For more information on the margin of error or ACS guidance, please visit: http://www.census.gov/acs/www/guidance_for_data_users/handbooks/

The ACS is a sample survey, and thus, data are estimates rather than counts. Estimates have accompanying margins of error that indicate the span of values that the true value could fall within.  Margins of error should be subtracted from and added to the estimate to determine the range of possible values. If the margin of error is too big relative to the value, data are not shown because they are statistically unstable.  A coefficient of variation of 30% was used to determine statistical instability.

Limitations

Although there are many public health risks associated with dependence on motor vehicles and their associated hazards, people are also at a disadvantage if they lack accessibility to transport to connect with goods and services, social, education and work-related activities.  In some cases, public transportation does not adequately connect households to destinations at the hours needed, or at all.  While some households choose not to have a car because they are able to access basic needs with other transportation modes, others are reliant on motor vehicles in the absence of public transit that meets their transportation needs - or in the absence of a motor vehicle are unable to or severely limited in their ability to access fundamental goods and services due to factors including lower income, age (e.g., seniors) or disability.

Data Source

American Community Survey (ACS), 5-year Estimates, 2009-2013. 

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

Map data is presented at the level of the census tract

Table data is presented by analysis neighborhood

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. 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.

  2. 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.

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

  4. 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.

  5. 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

  6. Litman T, Steele R. 2012. Land Use Impacts on Transport.  Victoria Transport Policy Institute. Available at: http://www.vtpi.org/landtravel.pdf.