2022 Volume 10 Issue 2 Pages 111-130
This study examined how to generate demand for predictive user flow models, which enable designers to anticipate human activity in public spaces. Data were collected via observations, interviews, and photo analysis to assess the status quo and answer study questions. These data serve two purposes: first, to calibrate an accurate user activity pattern, based on actual data for many months, to examine the relationship between human activities and space, and second, because the data is longitudinal, to test how accurate our forecast is. If the city knows where changes in activity patterns occur and where those changes affect the physical dimension of public space, it can prioritize investments in better public areas and ask developers to contribute to better public spaces rather than broader roadways.
