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).

本研究配套的数据与软件，用于探究10个城市共享单车数据中逾2亿次出行场景下自然屏障所发挥的作用。该数据集整合了气象数据、坡度数据及日出日落数据，并依据受访者自我报告的性别（男/女）开展分组分析，采用广义可加模型（Generalized Additive Models，GAMs）检验不同性别群体间是否存在显著差异。