We have used four datasets in our research project: civilian labor force in San Francisco by year, income inequality in San Francisco by year, unemployment rate by zip code and year and curb ramps distribution within SF city . Each data set contributes unique components to our research on economic inequality and population distribution in San Francisco and presents specific limitations.
The Civilian labor force in the San Francisco data set was retrieved from DataSFgov as a csv file and contains two key variables: Year (the year for which the data is recorded) and Civilian Labor Force (the number of individuals who are employed or actively seeking employment in San Francisco). The data on the civilian labor force allows us to understand the size and changes in the workforce, which can be an important indicator of economic growth or decline. In our project, this data set has given us insight into how previous events, such as the dot-com boom, 2018 financial crisis, and COVID reveal instability of the capitalist economy. Some limitations of this data set may reside in its process of collection. This data was collected from Current Population Survey (CPS), a monthly survey with a sample of 60,000 U.S. households conducted via telephone or in person interviews. Despite its large sample size, certain populations may not be represented by the survey and thus excluded from the data. For example, the large illegal immigrant populations within San Francisco are unlikely to receive surveys from the U.S. Census Bureau, where they are among the most vulnerable populations for exploitation. Additionally, because the samples were collected through verbal communication, non native English speakers may fail to convey information accurately, making it questionable whether the data properly describe this population.
Similarly, the income inequality data were obtained from the same source as the civilian labor force data. Income inequality data is another important measure of equity within a society and is central to Marxist theory of labor. It reveals economic disparities and social stratification within the city which can directly impact overall well-being of the residents. Because this data set was initially collected through the same method as the civilian labor force data, it shares the limitations with the previous data set that the sample population may not be inclusive enough to represent the entire U.S. population.
The third data set consists of data of the distribution of unemployment rate in San Francisco by zip code. It is retrieved from California’s Employment Development Department (EDD). Unemployment rate is also an important factor for a Marxist oriented analysis, as it reflects the overall health of an economy. It is also a sign of urban planning policy effectiveness within San Francisco. This data set has a limitation in its time span that it only includes the distribution of unemployment rate from 2019 to 2022, and we can only examine the impact of the pandemic on the local economy without earlier unemployment rate data to compare with. We also acknowledge that we might have some personal biases when examining this data set, as our methodology is instructed by a Marxist approach that critiques the uneven distribution of resources and exploitation of labor. In our research, we might have been focusing more on looking for inequalities within the map of San Francisco and potentially ignoring other factors that shape this data.
The curb ramps data from DataSF provides extensive information on the infrastructure of curb ramps within San Francisco. However, a critical analysis reveals potential gaps and limitations. While the dataset includes locations and statuses, it lacks detailed information of the accessibility compliance standards met by each ramp. Moreover, the data might not be updated frequently enough to reflect changes or repairs, leading to discrepancies. For a comprehensive understanding, integrating this dataset with real-time updates and accessibility compliance details would enhance its utility for urban planners and residents.