03153nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000700153072001600160072002300176072001500199100007600214245015500290250001800445264007200463300006400535336002600599337002600625338003600651347002400687490003700711505006000748520151500808650002102323650001402344650002102358650002102379650001402400650003002414710003402444773002602478776003602504776003602540776003602576830003702612856004602649978-981-97-8185-0DE-He21320251030114924.0cr nn 008mamaa250114s2024    si |    s    |||| 0|eng d  a97898197818507 a10.1007/978-981-97-8185-02doi 4aGF 7aRGC2bicssc 7aSOC0150002bisacsh 7aRGC2thema1 aZhou, Xingang.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aJobs-Housing Balance and Self-Containment Using Cellphone Big Datah[electronic resource] :bCase Studies in Shenzhen and Shanghai /cby Xingang Zhou.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXIX, 91 p. 38 illus., 23 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUrban Sustainability,x2731-64910 aIntroduction -- Literature Review -- Research Design...  aThis book addresses the analysis of self-containment of employment (SCE), which measures journey-to-work trips among the percentage of workers who work locally. High SCE encourages the use of non-motorized transport and reduces transport-related energy consumption. In this book, mobile phone location data is employed to assess journey-to-work trips and explore spatial variations in SCE at multiple geographic scales. It finds that SCE is significantly higher in the suburbs than that in the central urban areas and tends to decrease as the spatial analysis unit shifts from the macro to the micro scale. The relationship between Jobs–housing balance is found to be more important in self-containment of employment for secondary-sector workers compared with that for tertiary-sector workers. Secondary-sector workers tend to reside near their workplaces because of relatively balanced jobs and housing, whereas tertiary-sector workers tend to reside farther away from their workplaces to save housing cost. A mixed-use index (MUI) in terms of employment is examined. The interconnections between MUI and SCE are examined in both industrial and commercial areas, to gauge the effect of the industrial-residential mix or commercial-residential mix on SCE. This book will enhance readers’ understanding of the spatial variations in SCE at multiple scales. In addition, its investigation of the effect of mixed use on SCE will shed new light on the relationship between land use and journey-to-work patterns. 0aHuman geography. 0aBig data. 0aQuality of life.14aHuman Geography.24aBig Data.24aQuality of Life Research.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981978184308iPrinted edition:z978981978186708iPrinted edition:z9789819781874 0aUrban Sustainability,x2731-649140uhttps://doi.org/10.1007/978-981-97-8185-0