Achieving real-time and spatially fine-grained population estimation in a metropolitan city is extremely valuable for a variety of applications. Based on mobile accessing logs, we exploit context-aware city segmentation and dynamic population estimation model to solve this problem. Extensive evaluations and analysis reveal that our system reduces the population estimation error by 22.5% and show several important observations of urban mobility as well as one application enabled by our system.
If you are interested in our results, you can download data like city functional regions and predicted population distribution here. The data, includes 396 dowtown regions' boundary/ function/ predict population, is stored as MAT file like cell and matrix. The description for function label id is in the README file. Our geographic coordinates are based on Baidu Map.
It's our honor to open these data and code. Please cite our paper if you use our data or code.
We open our STEP1 code here. The code conclude two parts:  Get assigned city Road network image from Baidu Map.  Based on the morphology operation, segment the city into regions by the road network. You can also contact with us by Email to get more code and data.Phantomjs and put phantomjs to the workspace.
Xu, Fengli, et al. "Context-aware Real-time Population Estimation for Metropolis." Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2016.
Wang, Huandong, et al. "Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment." Proceedings of the 2015 ACM Conference on Internet Measurement Conference. ACM, 2015.