Quantifying and visualizing jobs-housing balance with big data: A case study of Shanghai
Quantifying and visualizing jobs-housing balance with big data: A case study of Shanghai
复制标题
大数据量化可视化职住平衡：以上海为例
DOI：
10.1016/j.cities.2017.03.004
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发表时间：
2017-06
期刊：
影响因子：
6.7
通讯作者：
Zhang Tianran
中科院分区：
文献类型：
--
作者：
Zhang Ping;Zhou Jiangping;Zhang Tianran
Existing jobs-housing balance studies have relied heavily if not solely on small data. Via a case study of Shanghai, this study shows how cellular network data can be processed to derive useful information, job and housing locations of commuters in particular, for those studies. Based on cellular network data, this article quantifies and visualizes Shanghai's jobs-housing balance with a much larger sample (n = 6.3 million), finer spatial resolution and greater geographic coverage than ever before. It identifies and geocodes the local commuters by Base Transceiver Station (BTS), which has on average a service area of 0.16 km2. After detecting jobs and housing by BTS, it aggregates them by subareas of particular interest (e.g., traffic analysis zones, inner city, suburbs and exurbs) to local planners and decision-makers. It also visualizes the traffic flows associated with the actual (Tact), theoretical minimum (Tmin) and maximum (Tmax) commutes. It shows that Shanghai's commuting pattern is far from the extremes (indicated by Tmaxand Tmintraffic flows) and Shanghai's relative balance of jobs with respect to housing is decent (3.2 km) despite its huge population (24 million) and land area sizes (6800 km2). The cumulative distribution of the Tactand Tminflows vary more significantly when the commuting distance is less than 6 km. In theory, there is high concentration of both jobs and housing within a 6-kilometer radius across different locales of the city. This potentially allows over 95% of all the local workers to find a job within 6 km of his/her residence or vice versa. In reality, a much lower percentage (71%) of workers can enjoy such a benefit. This can imply that there is qualitative mismatch between jobs and housing.
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DOI：
10.11821/dlxb201310002
发表时间：
2013
期刊：
--
影响因子：
--
作者：
Jiangping Zhou;Xiaojian Chen;Wei Huang;Pengqiu Yu;Chun Zhang
通讯作者：
Jiangping Zhou;Xiaojian Chen;Wei Huang;Pengqiu Yu;Chun Zhang
影响因子：
27.5
作者：
Batty M
通讯作者：
Batty M
DOI：
10.1068/a374
发表时间：
2006-06
期刊：
Environment and Planning A
影响因子：
--
作者：
N. Morrison;S. Monk
通讯作者：
N. Morrison;S. Monk
影响因子：
6.4
作者：
Michael A. Niedzielski;M. Horner;N. Xiao
通讯作者：
Michael A. Niedzielski;M. Horner;N. Xiao
影响因子：
6.7
作者：
Saint John Walker
通讯作者：
Saint John Walker