Update on Project Activities
This week, we completed another draft of the survey and received feedback from Jonathan from the City of Salinas. We switched the survey from an online platform to a written platform for the accessibility of the volunteers who will perform the survey. Our draft thus far is a one page, cleanly laid out attempt at encompassing all the necessary components for the audit, such as type of housing, roof, walling, etc. We will be speaking to Jonathan this week to go over the feedback he sent on the survey and to define our next steps moving forward with the training module. What We Learned and Observed From the feedback we have received thus far from Jonathan, we learned that to improve the survey we need to expand many of its current features. For example, we can move some text on the current survey to a cover page, and we can expand on the term "multi-family" for multifamily units. Deciding how to make the survey more concise while still containing all it's implications has been quite a challenge. Since we want the results of the survey to remain valid no matter who is conducting it, we need to be very careful with our language and how we define certain features. We will be speaking to Jonathan this weekend to discuss the issue of clarity versus brevity. Critical Analysis/Moving Forward Moving forward, we aim to put the finishing touches on the survey and consequently complete the training module. We are still processing the GIS data that was provided last week to decide how those variables can be included in the context of the survey. One of our biggest priorities is making sure the community groups can resonate with the survey and module, given they were created by an outside group. In the time we have left, we will be taking feedback from these various groups to ensure that it's a survey for them. Additionally, in our discussion with Jonathan we will analyze more critically the issues of sustainability and transportation that the results of this survey will entail. For example, as a consequence of evidence of overcrowding, how can the city sustainably provide more public services, high density housing, and parking space without compromising the environmental viability of the area? Update on Project Activities
This week, we examined data on the Transbasesf.org and Vision Zero websites and discussed specific improvements to the current LTS metric (see existing map). We are also in the process of researching other factors that should matter for a biking metric. What We Observed and Learned This week, we found more information on how to improve LTS for San Francisco regarding slope, pavement quality, intersection comfort, car ownership, and income level. Slope, which is not in the current LTS, plays a large part in comfort because hills cause an increase in the exertion of energy required, whether it be a car driving up the hill or a person walking or biking. Low pavement quality can also make biking stressful, which we experienced firsthand at Townsend Street. Intersection safety is another aspect that could improve the comfortability of bikers as well as drivers, because the space between bikers, cars, and pedestrians is essential in order to reduce collisions. The original LTS metric developed by the Mineta Transportation Institute does evaluate intersections based on width, signalization, traffic speed, and right turn conflicts, but the SFMTA did not include those components. The number of people who have a car is another important aspect in regards to LTS because it determines how many cars could possibly be on the road, which feeds directly into the improvement of biker safety. If less cars are on the streets, the more comfortable bikers are when commuting. Moreover, lower car ownership rates could indicate higher demand for better bicycling infrastructure. According to the 2014 American Community Survey (ACS), bicycle commuting rates are higher for households with fewer or no cars, as well as for low income groups. However, income levels could pose complications when used in bikeability metrics, as discussed below. Moreover, trends in San Francisco could differ from the national statistics. In order to help improve the effectiveness of LTS, a new method to address each of these complications will be necessary. Critical Analysis/Moving Forward As we continue to work on finalizing our project deliverable, we are concentrating on which changes we want to add to the current Level of Traffic Stress. This will allow us to move forward with the development of our own map that displays the different levels of traffic stress for District 6 and District 11 (chosen because they have relatively poor bicycling conditions). As of now, we have found that LTS fails to acknowledge the hilliness and condition of streets in San Francisco. We intend to improve the current LTS that the San Francisco Municipal Transportation Agency and Bicycle Coalition currently use by adding slope and pavement quality as major consideration that factor into the different levels. A level of one signifies areas that are safe to bike for general cyclists whereas a level of four depicts streets where only the “fearless” bikers would ride through. For our map and newly improved LTS, however, we plan to add letters to the different levels from A-D, where A (represented by dotted, thin, or unhighlighted lines) signifies areas with minimal slopes or new pavement and D represents areas with extreme slope levels or potholes. This allows us to keep the SFMTA’s existing methodology intact even as we add our own elements. Additionally, we will include a key that shows examples of this metric (for example, Townsend is 2AD because it is LTS 2, flat, and has poor pavement). District 11 is much hillier than District 6, so it will interesting to compare how our metric works in those two vastly different topographies. We plan to display intersection comfort as color-coded points. However, we recognize that points vastly oversimplify the complex range of possible movements, such as right/left turns and crossings, each of which is unique from a transportation planner’s perspective. Interestingly, the San Francisco Department of Public Health had already shown intersection comfort as points in its BEQI metric. We decided to display two equity concerns - car ownership and income levels - as Census tracts rather scoring specific segments and intersections. We did not feel comfortable doing the latter because it could require us to make judgement calls about how the City should allocate resources based on demographics. One the other hand, a concern with displaying raw demographic data is that we must make a connect that data to bikeability. One possibility is to display the frequency of various LTS streets in each tract, or to display car ownership rates and income levels near each segment. We also hope to develop pie or bar charts of car ownership and income levels for each District, to establish a better overall picture. We are still considering how to most effectively communicate this information through our map and literature review, but we are all excited to develop the final product. Update on Project Activities
Our major task this week was continuing our survey efforts. Over the weekend we surveyed businesses at Town and Country, and during the week we were finally able to make a few trips down to Redwood City. In both cases, surveying has still been slow— we collected about 5 responses in person and digital. Despite frequent survey trips by group members, we have been unable to collect more than 3 responses per trip. An approach we hope to try this weekend involves scheduling a different time for people to fill out the survey, if they are busy when we first approach them because folks seem to be more interested in filling out the survey other than work times. One of the hopes we had was that we could distribute the survey electronically through organizations such as Commute.org and SFMTA. This week, we were able to get in contact with both groups. Unfortunately, it appears that they will not be able to publicize and share the survey broadly until after the quarter. We are incredibly grateful that the they have started distributing the survey and have gotten a few online responses because of their efforts, but not at a very high frequency. This, along with the fact that few people fill out the online survey if given the brochure, perhaps points to the difficulty of both accessing the online survey, and also the length of the survey in general. Finally, we began planning out and outlining our final presentation and report, as well as compiling our project deliverables. The deliverables we have completed thus far are as follows: completed online and in-person survey, tested and revised survey methodology, informational survey brochure, project website. We still have to complete the expandable report, that will be adjusted to reflect results once we have more survey responses. For the now, the report will be based on our literature review and the survey responses we have received thus far. What We Observed and Learned As we increased our surveying efforts, we noticed several interesting trends among those we approached to take our survey. Overall, though people are quick to offer their insights and share their personal experiences commuting within the Bay Area, the motivation and capacity to complete the physical survey during work hours has proven to be indeed made it difficult for people to take the survey. We have noticed that the most likely places to gain responses are from places whose lines of business particularly emphasizes customer service tailored to individual patrons, such as retail stores. Conversely, the least likely places to gain responses are from places whose lines of business require simultaneous attention to a number of different tasks or limits the amount of direct service shown to the patron, such as in restaurants. Additionally, we have also noted that higher possibilities of gaining responses may be linked to poor weather conditions, slow business during the middle of the work day (even if business is slow, during the early morning, people seem to be more likely to pass on survey taking), and the presence of other coworkers that can cover them. It is important to note that out of the total of 15 responses we have collected thus far, all were collected in person by a team member; in other words, despite our wide distribution of informational brochures and flyers, as is most people are not inclined to take the survey on their own. This trend is particularly problematic as the conduction of in-person surveys requires a great deal of time investment and number of volunteers. In our experience thus far, gaining one survey response via in-person surveying roughly requires one hour of surveying. Therefore, our current total of 15 responses required at least 15 hours of surveying a several different locations. We’ve noted some patterns in our own survey methodology. For example, we usually do not read the first couple of information pages during field surveying. Instead, we give a concise expedited version of the explanation, because we are usually pressed for time. The time limitation is something that we have to take into consideration because we are trying to survey folks at their workplace during working hours. In terms of the last part of the survey considering the conversation/widening alternatives, we want to reconsider what we are trying to find out because that has been the part that takes the most effort. It has also prompted the most questions during surveying in-person. Critical Analysis/Moving Forward In moving forward, we will continue to brainstorm and test how we can increase efficiency while conducting field surveys and increase motivation to fill out the online form. Together, we have thought of two ways to do so: ask folks if there is a more convenient time to come back or to incentivize workers through an Amazon random drawing. We will begin testing the former option, and only if there is time and we deem it to be a viable option, we will try the incentivizing option. This is however not ideal because not only is it not as scalable, but also Chris pointed out that some folks may try to game the system through filling out more surveys. Therefore, we will test the re-scheduling option first. Folks are passionate about the issue, but most are not filling out the online form. Questions that we have considered are: Is the form too long? Does it slip out of their mind when they get off of work? We have to put ourselves in these people’s shoes in order to understand what would motivate them to answer the survey. These folks have a lot on their minds, so we would rather increase efficiency during field survey days to record their insights while we are able to converse with them about these transportation issues and the upcoming Express Lanes project. In our next meeting with Chris and Adina, we will bring up these low-efficiency concerns and discuss what their capacity is once we pass off this project to them to consider which option is the best for getting the most survey responses. Other than disseminating the online form through organizations and making efficient field surveying, one option is to conduct short interviews instead and to have them code the recordings. This would lessen field survey efforts but increase the burden on those analyzing the recordings. Field survey efficiency, motivation to fill out the form, and analysis efforts are the factors that we are keeping into account as we move into considering the scalability of the project. We will continue to test various options to ensure that the transition from Stanford team to our community partners is smooth and that they are able to carry out the surveying methodology in the coming months in order to produce the final report later in the year. Update on Project Activities
We are finally putting our animal data into ArcGIS online! We managed to divide up our raw data into different species and converted the sheets into CSV files (which are the only files that ArcGIS online will take). We then uploaded all of the files to our shared map. Now that we’ve done this, we can freely manipulate the data in the platform. We’ve sent out a message to the good people at the Stanford Geospatial Center in the hopes of setting up a bigger, potentially more productive meeting. On top of adding the data we are creating notes on the map such as area identifiers to portray the areas that are county parks/land versus private land. What We Observed and Learned This week has been a big learning week in Excel and ArcGIS. We continued our journey in manipulating Excel data, using a combination of filters and formulas to narrow down then separate our camera data. We then faced compatibility issues between Google Sheets and ArcGIS, since sheets isn’t in a format that can be uploaded to ArcGIS. In order to transfer the data, we needed to download the animal data in the form of CSV documents, then re-upload them into ArcGIS one species at a time. This took a bit of tedious work, but the results are promising! Since we’ve uploaded all of the species data to ArcGIS now, we’re starting to learn our way around the tools and features. By mapping out the county land we have figured out that a lot of our camera trapping data is not on/in county land. Thus the conclusions reached by the images and amalgamation of data might not directly correlate to what species and quantities lie in the parks. Critical Analysis / Moving Forward Moving forward, we have begun planning out our final paper and consolidating and completing research. The last push for reaching unreached contacts and community members is also happening this week. We don’t feel that we can write up the paper without adequate time to consider the respective voices properly, so we are investing a large portion of our energy into making those connections as fast as we can. If we cannot find the sources from outside campus (such as from the Muwekma tribe, which has yet to get back to us), we will try to find sources on campus that provide a similar viewpoint (as in the case of the Muwekma Ohlone tribe, there are organisations on campus that have contacts/information). Work has mostly been on the data analysis and processing side of the project this week, and so we have not been thinking on the critical level. This is fine, as we have had to put our heads down and do the necessary work for our deliverable. We will pick up the big picture thinking again as we do our research for our final paper. |
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