Urban Spatiotemporal Data Synthesis via Neural Disaggregation. (arXiv:2306.07292v1 [cs.LG])

The level of granularity of open data often conflicts the benefits it can
provide. Less granular data can protect individual privacy, but to certain
degrees, sabotage the promise of open data to promote transparency and assist
research. Similar in the urban setting, aggregated urban data at high-level
geographic units can mask out the underline particularities of city dynamics
that may vary at lower areal levels. In this work, we aim to synthesize
fine-grained, high resolution urban data, by breaking down …

benefits data high privacy protect research sabotage transparency