This case study explains how Tipping Point Community used data science to uncover systemic inequities in parking fines and fees affecting low-income residents in San Francisco. The nonprofit partnered with the city’s Financial Justice Project to analyze more than seven million parking citation and tow records, along with additional datasets related to geography, business activity, and enforcement zones. Using TIBCO Data Science, the team cleansed inconsistent records, merged disparate datasets, and uncovered patterns showing that individuals with older vehicles—often tied to lower incomes—were disproportionately burdened by late fees. These insights directly informed policy changes that forgave late fees for residents living below 200% of the federal poverty line, eliminating hundreds of t

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