Natural barriers facing female cyclists and how to overcome them: a cross national examination of bikesharing schemes

Data and software for examining the role of natural barriers in over 200 million trips from bikesharing data from 10 cities. The data is integrated with weather data, gradient data, and sunrise/sunset data and examined by gender (male / female self-reported) to check for significant differences between gender using Generalized Additive Models (GAMs).本数据集及配套软件，用于分析10座城市共享单车出行数据中逾2亿次行程的自然障碍影响作用。该数据集整合了气象数据、坡度数据与日出/日落数据，并按自我报告的性别（男/女）分组，通过广义可加模型（Generalized Additive Models, GAMs）检验不同性别间是否存在显著差异。