Heat related illnesses are one of the leading causes of weather-related mortality in the United States, and heat extremes continue to increase in frequency and duration. Public health interventions include population mobility, including travel to central cooling centers or wellness checks on vulnerable populations. Using anonymized cellphone data from Safegraph’s neighborhood patterns dataset and gridded temperature data from gridMET, we explored the mobility-temperature relationship in the San Francisco Bay Area at fine spatial and temporal scale. We leveraged spatial variability in median income and temporal variability in COVID-19 related policies across two summers (2020-2021) to analyze their influence on the mobility-temperature relationship. We completed quantile regressions for a dataset stratified by income and year. We found that mobility increased at a higher rate with higher temperatures in 2020 than 2021. However, in 2021, the relationship reversed for several wealthier income groups, where mobility decreased with higher temperatures. We then augmented the analysis and calculated a panel regression with fixed effects to characterize the mobility-temperature relationship while controlling for temporal and spatial variability. This analysis suggested that all areas exhibited lower mobility with higher summer temperatures. However, similar to the results of the quantile regression, the rate of decrease in mobility in response to high temperature was significantly greater among the wealthiest census block groups compared with the least wealthy. Given the fundamental difference in the mobility response to temperature across income groups, our results are relevant for heat mitigation efforts in highly populated regions in current and future climate conditions.