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.

翻译：开放数据的粒度水平常常与其所能带来的益处相冲突。较低粒度的数据能够保护个人隐私，但在一定程度上削弱了开放数据促进透明度和协助研究的承诺。类似地，在城市环境中，高层级地理单元的聚合城市数据可能掩盖了城市动态在较低地理层面可能存在的差异。本研究旨在通过分解粗糙、低分辨率地理单元上的聚合城市数据，合成细粒度、高分辨率的城市数据，目标是提高高度聚合城市数据的可用性并尽可能实现其价值。为解决传统分解方法过于简单的问题：1）我们试验了多种能够建模特征间复杂非线性关系的神经模型。神经方法还能同时利用空间和时间信息。我们证明所有神经方法均优于传统分解方法，融入时间信息进一步增强了结果。2）我们提出了一种适用于分解任务的训练方法——链式训练（COT），该方法可集成到任何基于训练的模型中。COT通过引入中间地理维度添加过渡分解步骤，从而增强低地理层面的预测并提升高层级结果。3）我们将超分辨率领域的重建（REC）思想应用于我们的分解场景——从低地理层面分解到高地理层面后，再根据生成的高层值重新聚合回低层。这两种策略在我们测试的三个数据集和两个城市中都改进了分解结果。