Tracking the dynamics and spread of COVID-19 is critical to mounting an effective response to the pandemic. In the absence of randomized representative serological surveys, many SARS-CoV-2 serosurveillance studies have relied on convenience sampling to estimate cumulative incidence. One common approach is to recruit at frequently visited community locations ("venue-based" sampling), but the sources of bias and uncertainty associated with this strategy are still poorly understood. Here, we used data from a venue-based community serosurveillance study, GPS-estimated foot traffic data, and data on confirmed COVID-19 cases to report an estimate of cumulative incidence in Somerville, Massachusetts, and a methodological strategy to quantify and reduce uncertainty in serology-based cumulative incidence estimates obtained via convenience sampling. The mismatch between the geographic distribution of participants' home locations (the "participant catchment distribution") and the geographic distribution of infections is an important determinant of uncertainty in venue-based and other convenience sampling strategies. We found that uncertainty in cumulative incidence estimates can vary by a factor of two depending how well the participant catchment distribution matches the known or expected geographic distribution of prior infections. GPS-estimated business foot traffic data provides an important proxy measure for the participant catchment area and can be used to select venue locations that minimize uncertainty in cumulative incidence.