About

Assets
The project utilized four different datasets sourced from the U.S. Census Bureau, the U.S. Bureau of Labor Statistics, the San Francisco Government, and DataSF. These datasets provide: the annual income inequality measure in San Francisco, reflecting the disparity in income distribution among residents; the monthly count of the labor force, including those employed and actively seeking employment in San Francisco; the unemployment rate by zip code over the years, revealing geographic disparities in employment; and the location and distribution of curb ramps, analyzing urban accessibility. By integrating these datasets, we were able to conduct a comprehensive analysis of income inequality, labor dynamics, unemployment rates, and urban inclusivity in San Francisco.

Services

The project adopted various data science-related techniques and programs to prepare, process, and visualize data, most of which were based in Python. For data cleaning and processing, we used NumPy and Pandas, which enabled us to efficiently handle large datasets, perform numerical operations, manage arrays, and utilize powerful data manipulation functions to easily handle and transform data. For visualization, we used Matplotlib and Seaborn to create a wide range of plots and generate statistical graphics, helping to intuitively understand data patterns and insights. Additionally, we utilized the Geopandas library to draw unemployment distribution by zipcode and the Folium library to create interactive HTML heatmaps, enhancing our spatial analysis capabilities. By integrating these tools, we were able to create comprehensive and insightful visualizations that supported our analysis of income inequality, labor dynamics, unemployment rates, and urban accessibility in San Francisco.

In order to preserve the raw data and see the trend more clearly, we didn't perform intensive data cleaning. In the regression model, we combined two datasets about income inequality and the civilian labor force. For the unemployment rate by zip code and curb ramps in the San Francisco dataset, we only dropped N/A data. Specifically, for the curb ramps dataset, we only dropped data where latitude and longitude are N/A, but ignored cases where there were other N/A values. This approach allowed us to preserve as much data as possible, making our visualization more reliable.

Interface

The project used Google Sites to share and display all information and findings on a webpage. This user-friendly platform helped us seamlessly integrate text, images, and visualizations, providing the public with data and corresponding background information. The smooth and stable access is appealing to a broad audience. Users can easily navigate through our research and gain comprehensive insights and visual representations of income inequality, labor dynamics, unemployment rates, and urban accessibility in San Francisco, thereby enhancing their understanding of urban planning.

Acknowledgments

Thank you, Dr. Scott Caddy and Anooj Kansara, our Professor and GSI, for giving great advice to advance our project, and thank you, Rosa Norton and Hannah Ellis, DigHum100 course GSI and other staff for teaching and informing us of all the many materials and theories that helped develop this project.

Data Sources

• Income Inequality in San Francisco: Income Inequality in San Francisco County, CA [2020RATIO006075], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/2020RATIO006075, Accessed Jun 17,2024.

Description: This dataset provides an annual measure of income inequality in San Francisco, reflecting the disparity in income distribution among residents.

• Civilian Labor Force in San Francisco: U.S. Bureau of Labor Statistics, Civilian Labor Force in San Francisco County/City, CA [CASANF0LFN], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CASANF0LFN, Accessed Jun 18,2024

Description: This dataset provides the monthly count of individuals in the labor force, which includes those employed and actively seeking employment in San Francisco.

• Unemployment rate in San Francisco over years by zip code: "San Francisco Unemployment by ZIP Code." San Francisco Government, https://www.sf.gov/data/san-francisco-unemployment-zip-code, Accessed Jun 19, 2024

Description: This dataset provides the unemployment rate of San Francisco from 2019 to 2022 by zip code.

• Curb ramp locations and attribute data: "Curb Ramps." DataSF, City and County of San Francisco, https://data.sfgov.org/City-Infrastructure/Curb-Ramps/ch9w-7kih/about_data. Accessed 25 June 2024.

Description: This dataset provides information of the position and distribution of curb ramps in San Francisco.