About

This dataset is San Francisco's Financial Data of Spending and Revenue from 1999-2016 by department/organization group/fund categories. You can explore and download your own copy of the dataset through SF Open Data. This dataset consists of 549,215 rows and 22 columns. I focused mainly on the hierarchical columns of Organization Group, which consists of many Departments who manage Programs and gain revenue from various Fund Categories. The amount is entailed in the final column, which is a positive or negative number representing the revenue expenditures. This allows analysis of which types of departments use how much spending and how revenue/spending has been looking over the span of many years. Whether the amount is of Revenue/Spending is noted in the beginning of the dataset.

The multivariate line chart above shows the totals of revenue and spending from 1999-2016, giving a little background into the dataset before diving deeper. It is evident that until 2008, Revenue and Spending were intermittently greater than the other, respectively. After 2008, however, Revenue has shown itself to be greater than Spending until today. This may be a sign of the results of the 2008 Financial Crisis.

Processing

This data processing included a lot of filtering and sorting of the main dataset by focusing on only columns of interest, as mentioned above. I used both Tableau and Trifecta Wrangler to process this dataset. First, using Trifecta helped me clean up some column names and then Tableau allowed me to filter through certain sections, such as Revenue and Spending. This dataset includes 2017 data as well, but since the year is still ongoing, I decided to remove 2017 from my visualizations because it would result in somewhat of a lie factor, since the fiscal year is not yet complete.

Motivation

This dataset appealed to me because throughout my past three years at USF, I've always tried to find an overlap between my major, Finance, and minor, Computer Science. Selecting this dataset allowed me to do exactly that and analyze financials of the City of SF, while implementing visualizations using my technical skills.