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Classifying Urban Fabrics into Mobile Call Activity with Supervised Machine Learning

机译：通过监督机器学习将城市面料分类为移动电话活动

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Strong spatial relationships exist between call volume and human activities. In this paper, we show machine learning models can classify base station coverage areas into classes of mobile call profile based on areas geographic and demographic properties. We propose the novel approach of taking clustered Call Detail Records (CDR) based time series as ground truth, and passing heterogeneous features as inputs. These features were land use and points of interest retrieved from OpenStreetMap database, and demographic data provided Facebook Research. Together, they allowed the creation of a representation of urban fabric. CDR clusters were then characterized by these features through SHAP model interpretation. Three models were tested in the region of Dakar with CDR coming from D4D-Senegal Challenge. Results exceeded the accuracy of systematic most common class classification and the accuracy of models trained only on population dataset. Additionally, models generalization capacity were evaluated in Thies, with results equalling those of the baseline.
机译：呼叫卷和人类活动之间存在强大的空间关系。在本文中，我们显示机器学习模型可以将基站覆盖区域分类为基于区域地理和人口统计属性的移动呼叫配置文件的类别。我们提出了将基于集群呼叫详细记录（CDR）的时间序列作为地面真理提出的新方法，并将异构功能传递为输入。这些功能是从OpenStreetMap数据库中检索的土地利用和感兴趣点，并且提供了人口统计数据提供了Facebook的研究。他们一起创建了城市面料的代表。然后通过Shap模型解释表征CDR簇的特征在于这些特征。在达喀尔的地区进行了三种模型，CDR来自D4D-塞内加尔挑战。结果超出了系统最常见的阶级分类的准确性，并且仅在人口数据集上培训的模型的准确性。此外，模型泛化容量在附近进行了评估，结果等于基线的结果。

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