Descriptive Title: A relative measure of the number of transit routes within one mile, weighted by frequency and distance.

Geographic Unit of Analysis: Intersection (point) and street segment

Average Transit Resource Score and resident and workers proximity to streets with high transit ridership (2010)
NeighborhoodAverage scoreProportion of residential population near high transit ridership streetsProportion of jobs near high transit ridership streets
Bayview/Hunter's Point 14 2% 6%
Bernal Heights 29 9% 57%
Castro/Upper Market 37 38% 93%
Chinatown 90 100% 100%
Excelsior 23 4% 29%
Financial District/South Beach 72 72% 99%
Glen Park
Golden Gate Park 22 0% 3%
Haight Ashbury 35 1% 13%
Hayes Valley
Inner Richmond 26 8% 50%
Inner Sunset 24 8% 95%
Lakeshore 8 6% 59%
Lincoln Park
Lone Mountain/USF
Marina 33 0% 7%
McLaren Park
Mission 38 35% 61%
Mission Bay
Nob Hill 89 100% 97%
Noe Valley 29 0% 6%
North Beach 50 36% 47%
Outer Mission 26 20% 72%
Outer Richmond 19 3% 29%
Pacific Heights 48 10% 68%
Potrero Hill 18 0% 0%
Presidio 9 0% 0%
Presidio Heights 26 7% 20%
Russian Hill 59 26% 61%
San Francisco 25 21% 76%
Seacliff 14 0% 0%
South of Market 58 42% 84%
Treasure Island 1 0% 4%
Twin Peaks 22 0% 10%
Visitacion Valley 16 0% 0%
West of Twin Peaks 20 9% 49%
Western Addition 50 29% 90%

Why Is This An Indicator Of Health and Sustainability?

Public transportation systems can provide affordable, safe and equitable access to work, home, education, food, health services, and social activities. In addition to providing a link between people and the services listed, public transportation usage, particularly as an alternative to driving, also provides health benefits such as increasing physical activity, reduced pollution and greenhouse gas emissions, reduced fatalities and injuries and greater social cohesion.a  

For normal trips, it has been estimated that only 10% of Americans will walk one-half mile.  A study in King County, WA demonstrated that for every quarter mile increase in distance to transit, the likelihood of using transit fell 16%.b  Frequency of public transit service is also an important predictor of whether people use and rely on transit as an alternative to driving alone for daily trips.  Residents of communities with access to good public transportation systems tend to drive 20 to 40 percent fewer annual miles than they would if they lived in a more vehicle oriented community.c Shifts to transit from driving also support increases in physical activity through walking and biking trips to get to transit, and its related benefits to mental health.d  Twenty-nine percent of people using transit to get to work meet their daily requirements for physical activity from walking to work.e  Health benefits of physical activity include a reduced risk of premature mortality and reduced risks of coronary heart disease, hypertension, colon cancer, and diabetes mellitus.f 

Interpretation and Geographic Equity Analysis

For this indicator we provide two metrics of transit resources. The Transit Resource Score is a measure of the density, proximity, and frequency of all transit routes near any point in San Francisco, while Proximity to High Transit Ridership Streets illustrates the locations where transit riders most frequently get on and off of transit. Transit ridership is generally higher in places with denser and more frequent transit service and where population and job density is higher. However, there may also be high transit ridership in areas with poorer transit resources but a significant job center (i.e. a university or hospital) or a place where a lower number of residents own cars.

The Transit Resource Score shows that the northeastern part of the City generally has the highest scores in the City. The neighborhoods with the highest transit resource scores are Chinatown, Nob Hill, Downtown/Civic Center, Financial District, and Russian Hill. The neighborhoods with the lowest scores are Treasure Island, Lakeshore, Presidio, Bayview, and Seacliff. Because of the high density of transit resources in the downtown core compared to the rest of the city, the scores are not normally distributed. Because of this, a score of 35 or higher would still fall within the top quintile of Transit Resource Scores. The quintile distribution of the scores is as follows: Q1-≤ 12, Q2-13-18, Q3-19-24, Q4-25-34, Q5-35-100.

The greatest concentration of San Francisco streets with high daily transit ridership (defined by more than 12,000 riders per day within ¼ mile) is in the northeast, downtown area of San Francisco, and near large regional transit stations along the BART route, as seen in blue on the map.  21% of San Francisco’s residents live in close proximity to streets with high transit ridership.  Neighborhoods with the highest residential population proportion within proximity of high transit ridership streets are Chinatown (100%), Downtown/Civic Center (100%), Financial District (72%), and Nob Hill (100%).  76% of jobs in San Francisco are located in close proximity of streets with high transit ridership.  Jobs are primarily concentrated in the Financial District, South of Market, and Downtown/Civic Center (29%, 17%, and 9%, of all jobs respectively – data not shown) all with high proportions of their worker population in close proximity to the high daily transit ridership streets.  


To calculate the Transit Resource Scores, the distance from each residential intersection (intersections within 100 meters of residential lots) to each transit route stop (multiple route stops can be found at one location when multiple transit routes share stop or station) within 1 mile of the intersection was calculated. A distance of < 0.25 miles was given a score of 1, while distances between 0.25-0.49 miles were given a score of 0.9 and distances between 0.5-1.0 miles were given a score of 0.75.

Route stops were gathered by using General Transit Feed Specification (GTFS) data from Muni, Bart, Caltrain, the Bay Ferries, and AC Transit. The frequency that each route ran through each stop during a seven day week was attached to each route stop. Route and stop data for samTrans and Golden Gate Transit was taken from the Metropolitan Transportation Commission Transit Geodatabase (2008) and seven day route frequency was manually attached. 

For each intersection the distance scores were multiplied by the seven day frequency for each route stop within 1 mile of the intersection. All of the products of the seven day route frequency and of the distance score were then summed for all of the route stops within 1 mile of the intersection. This number was normalized to a score of 100 to derive the final intersection Transit Resource Score. Intersection scores were then interpolated over the surface of San Francisco using inverse distance weighting. Neighborhood averages for the Transit Resource Score were calculated using zonal statistics.

To determine which street segments had high daily transit ridership, daily transit ridership (boardings and alightings) was calculated for each Muni bus, Muni Metro, BART, Caltrain, Transbay Terminal, and Ferry Terminal stop. Quarter mile buffers were applied to each street segment, and then daily ridership was calculated by summing the total ridership for all stops within the buffer.  

Using ESRI ArcMap 10.0 software, residential population proportions per neighborhood in close proximity of high daily transit ridership streets (denoted as more than 12,000 riders per day) were calculated by first summing population per neighborhood using estimated residential lot population.  Total residential population of lots that lie within census blocks (see Note) that “touch” high transit ridership streets were then summed per neighborhood.  The census blocks that “touch” high transit ridership streets are those that either share a border or have a corner that touches an edge of a high ridership street (i.e., using ArcMap 10.0, census blocks were selected that intersected within 10 feet of a high transit ridership street).  The neighborhood population of residents in close proximity to high transit ridership streets was then divided by the total neighborhood population to estimate the proportion of neighborhood residents living within close proximity of high daily transit ridership streets.  

Similarly, the 2010 LEHD job counts per census block (see data sources) were first summed per neighborhood, then by census blocks that touch (using methods as described above) high daily transit ridership streets in each neighborhood, and those results were then divided to assess the proportion of jobs within close proximity to high daily transit ridership streets per neighborhood. Note:  Census blocks are the smallest geographic unit of analysis used by the United States Census Bureau and are defined by boundaries of streets, roads, or creeks and represent a city block.


These indicators do not capture other factors that impact on transit access and use, including cost, time and distance and number of transfers to destination, reliability, accessibility for people of different abilities and ages, perceived and actual safety, weather, availability of bicycle racks on transit and at key destinations, parking availability and cost at key destinations, hours of operation, and availability of travel stipends/incentives/discounts provided by work or to low income families. 

Data Source

Routes, stops, and route frequencies for Muni, BART, Caltrain, and all SF Bay ferry operators (2012) gathered through GTFS feeds:; stops and routes for Golden Gate Transit, WestCat, and samTrans retrieved from MTC Bay Area Transit Geodatabase (2008) and route frequencies retrieved through transit operator websites (2012).

Daily transit ridership data provided by the San Francisco Municipal Transportation Agency (2010); daily ridership categories calculated for San Francisco’s WalkFirst Project (more information available at: 

Job counts per census block were provided by the U.S. Census Bureau Longitudinal Employer-Household Dynamics Survey (LEHD), 2010. Available at:

Table data is presented by planning neighborhood.

Map and table prepared by City and County of San Francisco, Department of Public Health, Environmental Health Section using ArcGIS 10.0 software, February 2012.

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. American Public Health Association. 2012. Public Transportation: A link to better health and equity. Accessed April 2012 at 

  2. 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.

  3. American Public Health Association. 2012. Public Transportation: A link to better health and equity. Accessed April 2012 at

  4. Litman T. Public Transportation and Health (Chapter 3). In: Healthy, Equitable Transportation Policy: Recommendations and Research. PolicyLink, Prevention Institute, Convergence Partnership. Ed. Shireen Malekafzali. 2009. Accessed online September 2009.

  5. Besser LM, Dannenberg AL. Walking to public transit: steps to help meet physical activity recommendations. Am J Prev Med. 2005;29(4):273-80.

  6. Task Force on Community Preventive Services. Increasing Physical Activity: A Report on Recommendations of the Task Force on Community Preventive Services. Morbidity and Mortality Weekly Report. October 26, 2001.