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On the determinants of Uber accessibility and its spatial distribution: Evidence from Uber in Philadelphia
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2020-02-11 , DOI: 10.1002/widm.1362 Sina Shokoohyar 1 , Anae Sobhani 2 , Saeed R. Ramezanpour Nargesi 3
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2020-02-11 , DOI: 10.1002/widm.1362 Sina Shokoohyar 1 , Anae Sobhani 2 , Saeed R. Ramezanpour Nargesi 3
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This study investigates the impact of socioeconomics and demographic factors (e.g., population density, minority rate, age, gender, education, wealth, and crime) and transportation infrastructure (e.g., walk score, transit score, and bike score) on the accessibility of Uber in the city of Philadelphia. K‐means clustering is applied for initial data exploration. Based on the spatial model selection diagnostic tests, we developed maximum likelihood spatial lag models with queen contiguity spatial weight matrix. The results show that Uber accessibility is not balanced in different neighborhoods in Philadelphia. Uber is more accessible in denser areas with the high male population, better public transportation access and less access to amenities in the walkable distances. Moreover, we observed that Uber is more accessible in areas with a high crime rate. This observation shows that Uber has made it easier to get out of high crime rate areas. Finally, contribution in the literature on accessibility in ride‐sourcing networks is discussed. Findings are additionally used to provide managerial implications to mitigate discrimination in ride‐sourcing platforms.
中文翻译：
关于优步可达性及其空间分布的决定因素：来自费城优步的证据
这项研究调查了社会经济学和人口因素（例如人口密度，少数族裔比率，年龄，性别，教育，财富和犯罪）和交通基础设施（例如步行得分，运输得分和自行车得分）对无障碍环境的影响。 Uber在费城。ķ均值聚类适用于初始数据探索。基于空间模型选择诊断测试，我们开发了具有女王连续性空间权重矩阵的最大似然空间滞后模型。结果表明，在费城的不同社区中，Uber的可及性并不均衡。在人口众多，人口稠密，公共交通便利且步行距离之内的设施较少的地区，Uber的交通更加便利。此外，我们观察到，在犯罪率较高的地区，Uber更容易接近。该观察结果表明，Uber使走出高犯罪率地区变得更加容易。最后，讨论了关于乘车来源网络可访问性的文献。
更新日期：2020-02-11
中文翻译：
关于优步可达性及其空间分布的决定因素：来自费城优步的证据
这项研究调查了社会经济学和人口因素（例如人口密度，少数族裔比率，年龄，性别，教育，财富和犯罪）和交通基础设施（例如步行得分，运输得分和自行车得分）对无障碍环境的影响。 Uber在费城。ķ均值聚类适用于初始数据探索。基于空间模型选择诊断测试，我们开发了具有女王连续性空间权重矩阵的最大似然空间滞后模型。结果表明，在费城的不同社区中，Uber的可及性并不均衡。在人口众多，人口稠密，公共交通便利且步行距离之内的设施较少的地区，Uber的交通更加便利。此外，我们观察到，在犯罪率较高的地区，Uber更容易接近。该观察结果表明，Uber使走出高犯罪率地区变得更加容易。最后，讨论了关于乘车来源网络可访问性的文献。
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