[2H2] [General Session] 3. Data Mining

[2H2-05] Learning sense of locality?

labelling local area by machine learning

daisuke moriwaki¹, 〇isshu munemasa², toshikazu fukami¹ (1. CyberAgent, Inc., 2. Meiji University)

Keywords:area clustering, open data, spatial information, survey data, machine learning

To improve the performance of location-based advertising, a model of ``sense of locality'' is estimated, where the output variable is the ``label'' of each location and inputs are geographical and demographic information associated with the location. As the input variables are all taken from the Internet, the output is unique dataset that we collects from people who know well the location. The model is estimated with three methods and XGBoost outperforms over logistic regression and SVM. The results show fairly predictive power with f1 score of 0.66.