{
  "id": "1811.02644",
  "version": "v1",
  "published": "2018-10-25T16:46:38.000Z",
  "updated": "2018-10-25T16:46:38.000Z",
  "title": "DeepDPM: Dynamic Population Mapping via Deep Neural Network",
  "authors": [
    "Zefang Zong",
    "Jie Feng",
    "Kechun Liu",
    "Hongzhi Shi",
    "Yong Li"
  ],
  "comment": "AAAI2019",
  "categories": [
    "cs.CV"
  ],
  "abstract": "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 time-embedded 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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-10-25T16:46:38.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep neural network",
      "dynamic population mapping",
      "long short-term memory model",
      "super-resolution convolutional neural network",
      "generate dynamic population distributions"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}