Exploring Urban Connectivity
Evaluation of Public Transport Accessibility and Green Spaces in San Francisco, California.
Introduction
Urbanization, a global phenomenon accompanying the growth and development of cities, brings both opportunities and challenges. Urban centers offer economic advancement, cultural enrichment, and social interaction, yet they also pose significant challenges to environmental sustainability and human well-being. Green spaces—comprising parks, gardens, green belts, and other vegetated areas—play a vital role in mitigating the adverse effects of urbanization. This project investigates the intricate relationship between public transport accessibility and connected green spaces in San Francisco, California, a city celebrated for its striking natural beauty amidst a dense urban landscape.
San Francisco, home to over 808,000 residents as of 2022, covers an area of approximately 121 square kilometers. The city's diverse demographic composition and varying socio-economic conditions make it an ideal case study for examining the complex interplay between transportation networks and green spaces. Historically, San Francisco has invested significantly in both its public transportation infrastructure and the development of green spaces, understanding their vital role in fostering a sustainable and livable urban environment. Nevertheless, the relentless pressures of urban growth demand a deeper examination of how these elements can be optimized to enhance residents' quality of life.
Public transportation accessibility is a cornerstone of urban mobility, influencing daily commutes, reducing traffic congestion, and decreasing environmental pollution. Simultaneously, access to green spaces contributes significantly to urban well-being, offering areas for recreation, relaxation, and community interaction. This project aims to conduct a comprehensive spatial analysis of San Francisco’s public transportation system and its connectivity to green spaces.
The accessibility of public transportation and green spaces is not uniform across San Francisco. Disparities in access can exacerbate socio-economic inequalities, with marginalized communities often facing limited availability of these amenities. This study aims to identify areas with inadequate access to public transportation and green spaces, offering insights into where targeted interventions are needed to promote equitable urban development that can guide urban planning efforts to create a more connected, sustainable, and inclusive city. The findings will contribute to the broader discourse on urban sustainability and well-being, offering valuable lessons for other cities facing similar challenges.
Research Objective
Despite San Francisco's significant investments in public transportation infrastructure and green space development, disparities in accessibility to these critical urban amenities persist. These inequalities can exacerbate socio-economic disparities, limiting opportunities for marginalized communities and impacting overall urban well-being. The lack of integrated planning and analysis of the spatial relationship between public transportation and green spaces further complicates efforts to create a connected and sustainable city. This study addresses the critical need to evaluate and understand the distribution and accessibility of public transportation and green spaces in San Francisco, identifying areas with inadequate access and providing insights for targeted interventions to foster equitable urban development.
Additionally, the current methods of evaluating public transportation accessibility are often manual, time-consuming, and may not accurately reflect the real-time changes in the transportation network. Therefore, there is a need for an automated, efficient, and accurate spatial analysis of public transportation accessibility in San Francisco, which can provide valuable insights for policy decisions and planning for improved public transportation services.
Analysis
Preprocessing for Transit Routes
The number of routes per neighborhood indicates the variety and reach of the transit services available to residents in San Francisco.
Preprocessing for Transit Stops
The number of stops per neighborhood indicates the variety and reach of the transit services available to residents in San Francisco.
- The stops were categorized according to the route types.
- This was used to calculate the accessibility index.
The analysis of buffer comparisons around transit stops revealed distinct differences in how each buffer size represented public transportation accessibility. The 0.2-mile buffer demonstrated limited spatial coverage, benefiting only those residing in the immediate vicinity of transit stops. While this buffer ensured that nearby residents could access public transport with minimal walking, it did not account for those living slightly farther away who also rely on public transit. Thus, this buffer size highlighted high accessibility areas but failed to capture the broader accessibility landscape.
The 0.5-mile buffer emerged as the most balanced approach, offering a realistic measure of public transport accessibility. It covered a significant area while maintaining a practical walking distance for most residents, thus providing a more accurate representation of accessibility without overstating ease of access. As a result, this buffer size was recommended for accurately depicting public transportation accessibility in San Francisco, balancing inclusivity with practicality.
In contrast, the 1-mile buffer provided extensive coverage, encompassing a much larger portion of the city. Although this buffer size included a broader range of residents, it likely overestimated the practical accessibility due to the unrealistic expectation that many would walk a mile to reach a transit stop. For many residents, especially those with mobility issues, such a distance is impractical, making this buffer size less effective in representing true accessibility.
The map highlights substantial gaps in the public transportation network, pinpointing neighborhoods with insufficient transit coverage. This visualization offered insights into areas that need improved infrastructure and resource allocation by identifying underserved areas. The map enables targeted enhancements to the public transport network, aiming to improve accessibility and service equity across the city.
Hotspot map showing clusters of high and low public transit stop availability.
This spatial statistical technique (hotspot analysis using the Getis-Ord Gi* statistic) assesses the degree of spatial clustering among transit stops, distinguishing between areas with a high density of stops (hotspots) and areas with a low density of stops (cold spots).
Preprocessing for Green Spaces
Using the Select by Attribute tool, parcels designated as parks and open spaces were isolated to ensure that only relevant land use types conducive to green spaces were included, thereby eliminating non-relevant land uses that could skew the results.
Additionally, to evaluate accessibility, the Select Layer by Location tool was applied to determine the number of transit stops within a 0.2-mile buffer of green connections. This analysis aimed to elucidate the extent to which green spaces are served by public transportation. By creating a 0.2-mile buffer around all identified green connections in the city and selecting transit stops within this buffer, it was possible to assess the proximity of public transportation to these green spaces. The results revealed disparities in public transportation accessibility across different areas, particularly in neighborhoods with a higher concentration of low-income individuals who rely heavily on public transportation, highlighting significant implications for residents' ability to enjoy urban green spaces.
Population data detailing the number of residents living within half a mile or one mile of parks in San Francisco were obtained from the National Environmental Public Health Tracking Network.
A spatial join operation between the SF census tracts and the population data integrated spatial and demographic variables to analyze the distribution and accessibility of green spaces relative to population centers.
This spatial statistical technique (hotspot analysis using the Getis-Ord Gi* statistic) assesses the degree of spatial clustering of the number of people using the available green spaces, distinguishing between areas with high usage (hotspots) and areas with low usage (cold spots).
Calculating the Accessibility Index
The Accessibility Index developed in this study provides a guided measure of neighborhood accessibility, integrating both physical proximity to essential urban amenities and demographic factors.
By incorporating population density data, the analysis ensured that neighborhoods with higher population densities had a proportionately higher impact on the overall accessibility evaluation.
The spatial pattern of the accessibility index revealed that there are disparities in overall accessibility in San Francisco, highlighting areas with greater need.
However, some tracts, which had lower values of accessibility score, now have relatively higher values of weighted score.
This is a heat map of the Accessibility Index to highlight areas with varying levels of accessibility. It identified neighborhoods with low and high accessibility scores.
Some tracts, which had lower accessibility score, now have relatively higher values of weighted scores, indicating that neighborhoods with higher population densities had a proportionately higher impact on the overall accessibility evaluation.
Hotspot Analysis (Getis-Ord Gi*)
To identify statistically significant clusters of high and low accessibility, a Hotspot Analysis using the Getis-Ord Gi* statistic was conducted. This spatial statistical technique assesses the degree of spatial clustering among transit stops and green spaces, distinguishing between areas with a high density of stops (hotspots) and areas with a low density of stops (cold spots).
High positive z-scores indicated hotspots, areas with a significantly high concentration of transit stops or green spaces, suggesting good accessibility. Conversely, high negative z-scores indicated cold spots, areas with a significantly low concentration, highlighting potential gaps in accessibility.
To visualize the results, the Gi* scores were normalized using the natural breaks method. A graduated color ramp was applied to the scores, with red shades representing hotspots and blue shades indicating cold spots. This visual distinction helped intuitively convey areas of high and low public transport accessibility across San Francisco. The resulting hotspot map revealed significant spatial patterns: while certain neighborhoods exhibited good accessibility due to a high density of transit stops and green spaces, other areas were identified as cold spots, indicating substantial gaps in the public transport network and green space availability.
Results
The hotspot analysis underscored the need for targeted improvements in public transport infrastructure and green space distribution to enhance accessibility and promote equitable transit services across San Francisco. By highlighting areas of high and low accessibility, this analysis supports data-driven planning aimed at improving urban livability.
The results of the analysis were converted into a Pandas dataframe for flexible data manipulation. A custom user interface and toolbox were developed using Python's built-in functions, enabling easy access to results and facilitating new analyses. The findings provided quantitative measures of public transportation accessibility and land use distribution across San Francisco's neighborhoods. Spatial analysis identified areas with high or low accessibility to public transportation and green spaces through neighborhood-specific accessibility metrics and demographic overlays, aiding in the identification of disparities and planning for targeted urban interventions.
Conclusion
The study "Urban Oasis: Evaluation of Public Transport Accessibility and Green Spaces in San Francisco, California" provides an in-depth assessment of public transport and green space accessibility using GIS tools and Python programming. The research reveals significant disparities in public transport access across San Francisco, highlighting areas with insufficient service and underscoring the need for targeted interventions.
The analysis found that a 0.5-mile buffer offers the most accurate representation of public transport accessibility, balancing between underestimating and overestimating the reach of transit stops. This finding is crucial for urban planners to ensure public transport networks are effectively designed for widespread accessibility.
Incorporating green spaces into the evaluation, the study shows that many green areas are poorly connected to public transport, limiting residents' access to these amenities. Improving public transport links to green spaces can enhance urban livability, promoting physical activity, mental well-being, and sustainability.
Overall, the study's insights and recommendations provide a foundation for developing a more equitable and efficient public transport system in San Francisco. Addressing the identified gaps and enhancing connections to green spaces can significantly improve the city's accessibility and quality of life, paving the way for a more connected and sustainable urban environment.
Discussion
The analysis reveals significant disparities in public transportation accessibility across San Francisco, with some neighborhoods well-served while others are underserved. This imbalance affects low-income residents the most, limiting their access to jobs, education, and healthcare, and contributing to economic stagnation and increased reliance on private vehicles, which exacerbates traffic congestion and pollution.
Opportunities: The findings offer a foundation for targeted policy interventions to improve transit access in underserved areas and integrate green space proximity into public transport networks. The study also highlights the potential for advanced GIS tools in urban planning, providing a replicable framework for other cities facing similar challenges.
Limitations: The study's focus on spatial distribution without considering transit service quality or socio-economic barriers limits the depth of the analysis. The use of buffers for accessibility measurement may not account for all mobility constraints, and data quality issues could affect the findings.
