DeepDPM: Dynamic Population Mapping via Deep Neural Network


  • Zefang Zong Tsinghua University
  • Jie Feng Tsinghua University
  • Kechun Liu Tsinghua University
  • Hongzhi Shi Tsinghua University
  • Yong Li Tsinghua University



Dynamic high resolution data on human population distribution is of great importance for a wide spectrum of activities and real-life applications, but is too difficult and expensive to obtain directly. Therefore, generating fine-scaled population distributions from coarse population data is of great significance. However, there are three major challenges: 1) the complexity in spatial relations between high and low resolution population; 2) the dependence of population distributions on other external information; 3) the difficulty in retrieving temporal distribution patterns. In this paper, we first propose the idea to generate dynamic population distributions in full-time series, then we design dynamic population mapping via deep neural network(DeepDPM), a model that describes both spatial and temporal patterns using coarse data and point of interest information. In DeepDPM, we utilize super-resolution convolutional neural network(SRCNN) based model to directly map coarse data into higher resolution data, and a timeembedded long short-term memory model to effectively capture the periodicity nature to smooth the finer-scaled results from the previous static SRCNN model. We perform extensive experiments on a real-life mobile dataset collected from Shanghai. Our results demonstrate that DeepDPM outperforms previous state-of-the-art methods and a suite of frequent data-mining approaches. Moreover, DeepDPM breaks through the limitation from previous works in time dimension so that dynamic predictions in all-day time slots can be obtained.




How to Cite

Zong, Z., Feng, J., Liu, K., Shi, H., & Li, Y. (2019). DeepDPM: Dynamic Population Mapping via Deep Neural Network. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1294-1301.



AAAI Technical Track: Applications