To understand what factors encourage and discourage SFBC member participation, “R” is used for correlation and regression coefficient analysis for this research. For data collection, ArcGIS is used mainly for extracting geographical data. The below table shows the data table, which is used for the regression coefficient analysis.
There are six variables other than percentage of SFBC membership. These include: 1) all bike networks miles (per sq.mi), 2) bike route miles (per sq.mi), 3) bike lane/path miles (per sq.mi), 4) average slope grade, 5) number of bicycle racks (per sq.mi), and 6) median annual household income. Each value in the variables is divided by each San Francisco’s planning district.
All bike networks and number of bike racks show a strong correlation with the SFBC membership ratios. Taking a look at their p-values and 95 percent Confidence Intervals, these are statistically significant. Although it was expected that bike lane/path shows strong correlation with the SFBC membership ratios compared to the bike route, this analysis didn’t show statistically significant results because of its high p-value. Interestingly, average slopes didn’t show any correlations. And there was also no correlation with median annual household income.
Finally, the analysis visualizes the data as a summary. To visualize, a “plot” function is used as well as adding a regression line with a “abline” function.
Ethnographic research was designed in two ways—observation and participation. Observation was conducted at the intersection of Arleta Avenue, San Bruno Avenue and Bayshore Boulevard in Visitation Valley, and multiple locations on San Bruno Avenue between Bacon Street and Silver Street.
Participation had two parts—taking a bus and riding a bicycle from the observed neighborhoods to Chinatown and downtown, which are the common destinations for the target audiences in Southeast San Francisco.
Although there is a bike lane on Bayshore Boulevard, a very few bicyclists used the bike lane, instead riding on sidewalks. This seems very natural behavior for bicyclists, because of the traffic volume of Bayshore Boulevard. Especially, the automobiles joining to Bayshore Boulevard from Arleta Avenue often drove into the bike lane on Bayshore Boulevard.
This was extremely dangerous for bicyclists—regular bicycle commuters may know this dangerous intersection from their daily experience.
The research conducted participation—taking the 8X Bayshore Express with daily passengers. The author took this line from the bus stop at San Bruno Avenue and Mansell Street, which is about a half way from the intersection of Bayshore Boulevard, Arleta Avenue and San Bruno Avenue, and San Bruno Avenue and Silver Street.
While the bus drove along San Bruno Avenue, it filled very quickly with passengers—the majority Asian seniors (50 percent to 60 percent). As a result, about 20 percent to 30 percent of Asian seniors had no seats. Not surprisingly, the great number of passenger debussed near Chinatown.
The research also uses the participation of bike rides from Bayview to downtown San Francisco. This was done by participating in the Bike Bayview, organized by Chris Waddling—the District 10 member of the San Francisco County Transportation Authority (SFCTA) Citizens Advisory Committee, on January 18, 2014.
There were about 10 participants in the bike ride. The main conclusion from this participation was a possibility of biking from the Bayview to the downtown because of relatively flat route and easy traffic.
A survey was conducted to understand the target audiences’ transportation behaviors, barriers to biking, familiarity with community issues, and preference of outreach methods. The survey was also intended to examine their ability to understand graphs and pictograms because the Creative Work plans to use them for communication purposes.
There were a total of 103 survey respondents. 87 survey respondents identified their nationality as Chinese. This survey analysis excludes survey respondents whose nationalities are not Chinese. The below graphs shows demographic information of the Chinese respondents.
As hypothesized in the research, very few respondents (1%) indicated bicycling as their main mode of transportation. Their mode split is 61 percent for public transit, 20 percent for car and 18 percent for foot. The average monthly transportation cost was $62.
Only 15 percent of the respondents currently own a bicycle. In contrast to this lower figure of current bicycle ownership, 62 percent owned a bike in the past and 56 percent want to own bicycle, which shows their familiarity with bicycling and the potential to promote biking among them.
However, the survey found motor vehicle traffic, safety concerns, topographic difficulty, lack of understanding of the traffic laws and a shortage of storage space as the top five barriers to biking from the people answered this section (N=61). These barriers likely discourage Chinese immigrants from owning and using bicycles. Figure 27 shows the summary of transportation behavior among the Chinese survey respondents.
As hypothesized in this research, the survey also found very few Chinese immigrants (3 survey respondents) who were classified as high-income. Although this result affects the accuracy of the analysis, it shows the tendency that as income levels become higher, monthly transportation costs also becomes higher.
Use of public transit, which is an inexpensive means of transportation (excluding foot and bicycle), decreases as income increase. It proves people with low-income need affordable means of transportation.
Lastly, the survey analysis compares three types of Chinese survey respondents— all respondents, low-income respondents, and respondents with language barriers, to understand how their familiarity with community issues and computer usage are different. This analysis is also intended to determine the best media to reach out those target populations. The respondents with lowincome have annual household incomes of less than $30,000, using the classification used earlier.
Regarding, the respondents with language barriers are defined by the question to ask English proficiency in the survey. In this analysis, people who answered “novice” or “beginner” (total 35 respondents) are considered people with language barriers.