Which Streets are Complete? Mapping Pedestrian, Transit, and Cycling Infrastructure at Scale in San Francisco, CA
Bibliographic record
Abstract
Contemporary American streets are dominated by the automobile, both its movement and storage, which took root in the early 20th century and has been supported by transportation planners in the decades since. Pedestrian fatalities remain stubbornly high – even in cities with “Vision Zero” goals – and cars generate significant levels of both local air pollution and globally-relevant greenhouse gasses. Moreover, the default use of curbside lanes as on-street parking precludes a number of other street designs, such as wider sidewalks, protected bike lanes, transit bulbs and islands, parklets, rain gardens, and street trees. This dissertation probes these long-standing and consequential street layouts with methods that are both highly granular (accurate to the block level), and at the same time, comprehensive to an entire municipality. San Francisco, California, a dense, high-income, and nominally “progressive” American city serves as the case for three separate analyses: an in-person census of roughly 3,000 municipal bus stops, and review of satellite-imagery of nearly 6,400 intersections for the presence or absence of marked crosswalks, and the provision of on-street parking and its obstruction of a growing bike-lane network. Chapter 1 demonstrates that crosswalk provision varies dramatically across San Francisco, from Census tracts with 100% of intersections exhibiting marked crosswalks, to others with less than 10% coverage. Chapter 2 entails the largest bus-stop census of its kind, covering the entire SFMTA system. This approach finds that one third of all bus stops are blocked by on-street parking, and two thirds lack seating of any kind, as well as spatial clusters of stop amenities. Indeed, both high-quality bus stops and marked crosswalks are present to a far greater extent in the city’s northern half, unexplained fully by transit service, density, or pedestrian volumes. Lastly, Chapter 3 identifies fifty miles of angled parking in San Francisco, a format that roughly doubles on-street parking capacity, and decreases the opportunity for high-quality bicycle lanes. These studies provide both salient datasets to guide San Francisco’s street improvements (of which some progress at City Hall has already occurred), and serve as proof of concepts for planners and researchers elsewhere to probe their own blocks granularly and at scale. Indeed, this dissertation demonstrates new frontiers in applied urban analytics, and broadens and challenges the academic literature on transportation equity. It does so by expanding the types of street elements to be spatialized across neighborhoods, and demonstrates how the provision of seemingly-mundane features such as bus stops, crosswalks, and on-street parking can mirror (and exacerbate) other long-running investment disparities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".