Indicator ST.1.a Proportion of households without a motor vehicle

ST.1.a Proportion of households without a motor vehicle

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ST.1.a Proportion of households without a motor vehicle

Data Source

Data on vehicles available and households from U.S. Census 2000, Geolytics software. Census variables used: ‘Occupied housing units with at least one vehicle available' (CAR0) and ‘Total households' (NUMHHS0).

Vehicle Availability Forecasts for the San Francisco Bay Area, 1990-2030; Metropolitan Transportation Commission, November 2005. Accessed online at http://www.mtc.ca.gov/maps_and_data/datamart/forecast/index.htm on August 10th, 2006.

Map data is presented at the level of the census tract. The map also includes planning neighborhood names, in the vicinity of their corresponding census tracts.

Table data is presented by planning neighborhood. Planning neighborhoods are larger geographic areas then census tracts. SF DPH used ArcGIS software and a 'centroids within' methodology to convert census tracts to geographic mean center points. We then assigned census tracts to planning neighborhoods based on the spatial location of those geographic mean center points and calculated the planning neighborhood totals for the table.

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

http://www.thehdmt.org/etc/Geographic_Units_of_Analysis.September_2009.pdf

http://www.thehdmt.org/data_map_methods.php

Explanation and Limitations

Household vehicle availability and number of vehicles available per household are indicators of the expense invested in automobiles and indirect indicators of the use of automobiles for travel. The data in the above chart illustrates the average number of vehicles per household by super district (or quadrants, as defined by the Metropolitan Transportation Commission or MTC) in San Francisco over time.

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).

According to the MTC, "San Francisco is projected to have the most zero vehicle households of Bay Area counties, increasing from 90.8 thousand households in 2000 to 115.5 thousand households by the year 2030. This is a 27.2 percent increase in the number of San Francisco zero-vehicle households. The zero-vehicle share of households is projected to increase from 27.5 percent of households in 2000 to 29.0 percent of households by the year 2030." (2005 Vehicle Ownership Forecasts for the San Francisco Bay Area -- 1990-2030. Accessed online on October 30, 2006: http://www.mtc.ca.gov/maps_and_data/datamart/stats/vmt.htm)

Lower income communities tend to rely more heavily on public transportation than higher income communities. Car ownership is dependent upon numerous variables including income, cost of car, insurance and maintenance, distance regularly traveled, availability of public transportation, bike lines, and walking paths, perceived and actual safety, weather conditions, pedestrian safety, traffic patterns, hours of public transit operation, and availability of parking, availability of public transit travel stipends/incentives provided by work or for low income families.

Why is this a Community Health Indicator?

Car ownership is directly related to driving behaviors. Vehicle driving, in turn, is directly proportional to air pollution and greenhouse gas emissions. Air pollutants, including ozone and particulate matter are causal factors for cardiovascular mortality and respiratory disease and illness. Areas with high levels of motor vehicle driving also tend to have higher motor vehicle collision and injury rates.a

Driving time independently predicts 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.b

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

  2. Frank LD, Andresen MA, Schmid TL. Obesity relationships with community design, physical activity, and time spent in cars. Am J Prev Med. 2004;27(2):87-96.