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Unpacking urban scaling and socio-spatial inequalities in mobility: Evidence from England
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Qili Gao (高琦丽), Zhong Chen, Wang Yiknag
Income estimation based on human mobility patterns and machine learning models
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Qili Gao (高琦丽), Zhong Chen, Yue Yang, Cao Rui, Zhang Bowen
Exploring How Street-level Images Help Enhance Remote Sensing-based Local Climate Zone Mapping
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Liao Cai, Cao Rui, Qili Gao (高琦丽), Cao Jinzhou, Luo Nianxue
Untangling the association between urban mobility and urban elements
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Cao Jinzhou, Tu Wei, Cao Rui, Qili Gao (高琦丽), Chen Guanzhou, Li Qingquan
Understanding internal migration in the UK before and during the COVID-19 pandemic using twitter data
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Wang Yikang, Zhong Chen, Qili Gao (高琦丽), Cabrera-Arnau Carmen
An extended node-place model for comparative studies of transit-oriented development
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Yang Yongxin, Zhong Chen, Qili Gao (高琦丽)
Revealing transport inequality from an activity space perspective: A study based on human mobility data
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Qili Gao (高琦丽), Yue Yang, Zhong Chen, Cao Jinzhou, Tu Wei, Li Qing-Quan
Towards a new paradigm for segregation measurement in an age of big data
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Li Qingquan, Yue Yang, Qili Gao (高琦丽), Zhong Chen, Barros Joana
Delineating urban functional zones using mobile phone data: A case study of cross-boundary integration in Shenzhen-Dongguan-Huizhou area
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Zhang Bowen, Zhong Chen, Qili Gao (高琦丽), Shabrina Zahratu, Tu Wei
融入城市空间因素的通勤异质性多层解析
居民通勤特征受到个体社会经济属性与城市空间等多层面因素影响常表现出异质性。然而现有的通勤影响因素建模多考虑个体属性及区域建成环境，但对于多中心组团城市而言，综合组团的职住因素能更全面地解析通勤特征的异质性，同时能对空间因素的作用范围有所甄别。基于此，本文以具有多中心组团结构的深圳市为例，分析不同的识别组团及群体的通勤异质性，顾及个体层面的个人与家庭社会经济特征、交通小区层面的建成环境特征及组团层面的职住特征，建立居住端通勤时间与距离的三层HLM模型，综合解析空间多层次及群体因素对城市通勤特征的影响机制。证明了考虑空间多层次及群体因素对通勤异质性进行嵌套性影响因素建模的必要性和可行性，结合模型参数结果表明，个体差异是通勤异质性最主要来源，交通小区层的混合土地利用以及组团层的职住更匹配、租房更便捷有助于缩短平均通勤时间与距离。
梁宇豪, Qili Gao (高琦丽), 郭莉, 乐阳