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However, how the effects change with the analysis scales are still noteworthy. Therefore, a multiscale exploratory study was conducted in Chengdu, China, by using the stepwise regression selection and three spatial regression models. The main findings are summarized as follows. First, as the grid size increases, the number of built environment factors that have significant effects on trip intensity decrease continuously. Second, the effects of population density and road density are always positive from the 500 m grid to the 3000 m grid. As the analysis scale increases, the effect of proximity to public transportation shifts from inhibitory to facilitation, while the positive effect of land-use mix becomes stronger. Land-use type has both positive and negative effects and shows different characteristics at different scales. Third, the effects of built environment factors on online car-hailing trip intensity show different spatial variability characteristics at different scales. The effect of population density gradually decreases from north to south. The effect of road network density shows circling and wave patterns, with the former at relatively fine scales and the latter at relatively coarse scales. The spatial variation in the effect of land-use mix can only be observed more significantly at a relatively coarse scale. The effect of bus stop density is only obvious at the relatively fine and medium scales and shows a wave-like pattern and a circle-like pattern. The effect of various land-use types shows different spatial patterns at different scales, including wave-like pattern, circle-like pattern, and multi-core-like pattern. The spatial variation in the effects of various land-use factors gradually decrease with the increase in the analysis scale.<\/jats:p>","DOI":"10.3390\/ijerph19095325","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T13:40:57Z","timestamp":1651066857000},"page":"5325","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["How Does the Urban Built Environment Affect Online Car-Hailing Ridership Intensity among Different Scales?"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3311-9062","authenticated-orcid":false,"given":"Guanwei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"},{"name":"Institute of Land Resources and Coastal Zone, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-3571","authenticated-orcid":false,"given":"Zhitao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhen","family":"Shang","sequence":"additional","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzhuang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"},{"name":"Institute of Land Resources and Coastal Zone, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.tranpol.2018.06.009","article-title":"Guiding vacant taxi drivers to demand locations by taxi-calling signals: A sequential binary logistic regression modeling approach and policy implications","volume":"76","author":"Szeto","year":"2019","journal-title":"Transp. 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