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 aggregated urban
data at coarse, low resolution geographic units. The goal is to increase the
usability and realize the values as much as possible of highly aggregated urban
data. To address the issue of simplicity of some traditional disaggregation
methods -- 1) we experimented with numerous neural-based models that are
capable of modeling intricate non-linear relationships among features. Neural
methods can also leverage both spatial and temporal information concurrently.
We showed that all neural methods perform better than traditional
disaggregation methods. Incorporating the temporal information further enhances
the results. 2) We proposed a training approach for disaggregation task,
Chain-of-Training (COT), that can be incorporated into any of the
training-based models. COT adds transitional disaggregation steps by
incorporating intermediate geographic dimensions, which enhances the
predictions at low geographic level and boosts the results at higher levels. 3)
We adapted the idea of reconstruction (REC) from super-resolution domain in our
disaggregation case -- after disaggregating from low to high geographic level,
we then re-aggregate back to the low level from our generated high level
values. Both strategies improved disaggregation results on three datasets and
two cities we tested on.

通过将粗糙、低分辨率地理单元的聚合城市数据细分，我们尝试合成细粒度、高分辨率的城市数据，并提出了三种改进方法以提高细分结果。