Abstract：“Job” and “housing” are the most basic functions of the city, and the job-housing relationship reflect the order and efficiency of the city. Car dependence, traffic congestion, long commute are torturing many metropolises. To deal with those, different countermeasures have been proposed. Among them, the jobs-housing balance has been considered as one of the most effective. Existing jobs-housing balance studies have relied heavily if not solely on small data. This article argues that Baidu Heat Map and Baidu POI big data can be used to effectively facilitate the jobs- housing balance studies, making them transcend the constraints such as detection of latency and limited geographic/temporal coverage posed by small data. Via a case study of Lanzhou, it shows how Baidu Heat Map and Baidu POI can be processed to derive useful information to advance the aforementioned studies. The results showed that: 1) Within 1 working days, between workplace and residence, population flow have obvious space-time displacement phenomena. 2) On the urban areascale, the job-housing relationship is in a balance state in Lanzhou, the job-housing balance typeccount for 99.40%, residential leading type and employment oriented type only ccount for 0.12% and 0.48% respectively. 3) On the scale of the subdistrict office, there is a significant difference in job-housing relationship, and most subdistrict offices are the Job-housing Balance type. The ssubdistrict offices in the urban core area are mainly "employment orientedtype", while the subdistrict offices in the middle urban areas show "job-housing balance type"; The subdistrict offices in the urban fringe show "residential leadingtype".
王录仓, 常飞. 基于多源数据的兰州市主城区城市职住空间关系研究[J]. 人文地理, 2020, 35(3): 65-75.
WANG Lu-cang, CHANG Fei. THE STUDY OF JOB-HOUSING RELATIONSHIP OF CITYBASED ON MULTISOURCE BIG DATA-TAKING CENTRAL URBAN AREA OF LANZHOU AS AN EXAMPLEMAIN CITY AREA. HUMAN GEOGRAPHY, 2020, 35(3): 65-75.
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