文章来源：PNAS
作者：Jie Huang, David Levinson, Jiaoe Wang, Jiangping Zhou, and Zi-jia Wang
关键词：commuting pattern ,job dynamics ,housing dynamics ,mobility group ,smartcard data
简评
交通大数据和职住问题是我很感兴趣的两个方向，这篇文章利用北京7年间的地铁卡数据分析了通勤者的职住动态，将通勤者分为了四个mobility group — home mover，job hopper, job-and-residence switcher, and stayer, 研究了通勤者在旅行时间、房价与职住模式之间的权衡。 这是我第二遍读这篇文章，发现了研究框架、数据处理方法等方面都有很多可以学习的地方。
摘要
Residential locations, the jobs–housing relationship, and commuting patterns are key elements to understand urban spatial structure and how city dwellers live.Their successive(逐次） interaction（相互作用） is important for various fields including urban planning, transport, intraurban migration（市内人口迁移） studies, and social science.
However, understanding of the long-term trajectories of workplace and home location, and the resulting commuting patterns, is still limited due to lack of year-to-year data tracking individual behavior.
开头先简明扼要地阐述了研究意义（understand urban spatial structure and how city dwellers live）和当前研究的局限性（ lack of year-to-year data tracking individual behavior）
With a 7-y transit smartcard dataset, this paper traces individual trajectories of residences and workplaces.
研究结论
1.（45分钟拐点） Based on in-metro travel times before and after job and/or home moves, we find that 45 min is an inflection point where the behavioral preference changes. Commuters whose travel time exceeds the point prefer to shorten commutes via moves, while others with shorter commutes tend to increase travel time for better jobs and/or residences.
2. （四类通勤者）Moreover, we capture four mobility groups: home mover, job hopper, job-and-residence switcher, and stayer. This paper studies how these groups trade off travel time and housing expenditure with their job and housing patterns.
（1）Stayers with high job and housing stability tend to be home (apartment unit) owners subject to middle-to high income groups.——高收入群体
（2）Home movers work at places similar to stayers, while they may upgrade from tenancy to ownership.—— 收入上升群体
（3）Switchers increase commute time as well as housing expenditure via job and home moves, as they pay for better residences and work farther from home.——为了更高的居住条件（better residences)而选择换工作增加通勤时间
（4）Job hoppers mainly reside in the suburbs, suffer from long commutes, change jobs frequently, and are likely to be low-income migrants.——低收入群体
文章的大致框架通过摘要体现出来了：
这是一个关于职住地迁移的研究（也可以理解为通勤时间对职住地选择的影响），最后将通勤者分为了四类人群home mover, job hopper, job-and-residence switcher, and stayer， 分别也可以对应城市四类收入阶层的居民。
这篇文章利用的是地铁刷卡"大"数据,但是并不像一般研究大数据的文章一样试图对交通趋势进行预测，而是聚焦到了特征研究上。也许数据的限制是一个很重要的因素，毕竟7年都用同一张交通卡的人还是挺少的（像我就每年都在丢卡），而且这类人肯定也有一些共性的地方——本地人比例较高、高收入群体比例较少（随着收入的提高人们会偏向开车通勤，七年都没有转换通勤方式的人emmmm..？）、性格共性（比如比较偏执，这是师兄告诉我的哈哈哈哈）。
这样一个虽然是交通大数据，但是样本量并不大的研究都能发表到PNAS上，可见讲故事的方式还是十分重要的，我现在的研究也面临看似是大数据，但是通过筛选后的实际通勤群体并不多的问题（大概也就一百多个吧），这也是我第二遍精读这篇文章的原因。
正文开始啦！
P1 研究背景
Linking mobility patterns(移动模式，我理解为出行选择行为） to socioeconomic characteristics of city dwellers is important to economists, sociologists, geographers, and urban planners (1–4). Recent studies have explored the distribution of poverty and wealth, mobility rhythms of returners(离职生养小孩一段时间后复职的妇女) and explorers, human contact networks, demographic characteristics and neighborhood isolation phenomena from human mobility patterns by mobile phone call records, GPS data, transit smartcard data, and geocoded messages from social media (3, 5–8).
In the era of big data, studies have uncovered individual patterns and scaling laws and pose the prospect of predicting human mobility (9–11). Of course, one advantage of big data is volume, but big data rarely include socioeconomic attributes directly and the availability is usually of a short duration. In contrast, household surveys (relatively small data in comparison) provide more socioeconomic attributes and travel information. 将大数据和household surveys进行了优劣势对比 Investigating human mobility, including travel behavior and the journey to work, has traditionally relied on household surveys (12, 13). Still, some limitations exist in the surveys such as the data resolution of travel trajectories and time use. 对household surveys的劣势进行了进一步分析——出行轨迹和时间数据的准确度不够
第一段的背景阐述和Charisma关于CDR数据Demographic Prediction Model的那篇有异曲同工之妙啊！本来不想逐段精读的，但是看完这段就感觉戳到自己痛处了—交通大数据往往缺少社会人口统计数据。
P2 文献综述
Mobility patterns can reflect human movement at various spatial scales（空间尺度） so that they can be used to critique and address(应对） increasing social challenges. 这句话我还需要好好体会一下
（1） migration Recently, many researchers haveinvestigated patterns of international or intercity（城市间的） migration (14–16), while fewer have explored intraurban migration or residential mobility (17). 研究数量较小 In the field of residential mobility（住宅流动性）, empiricalstudies often harness the life course framework（生命周期框架） (18), whiletheoretical models describe housing choice with the trade-offbetween commuting cost and housing expenditures (19). Indeed,the jobs–housing relationship, job and housing tenures, andtheir dynamics affect daily commutes and travel behavior andvice versa (2, 20, 21). 事实上，职住关系，工作和住房所有权及其动态会影响通勤者的日常通勤和旅行行为 However, few studies have assessed thejob and housing dynamics with a longitudinal analysis at theindividual level.
研究对象逐渐清晰——通过纵向分析评估工作和住房动态
P3
这个研究的研究对象是地铁出行黏性用户 Transit station choice can be a proxy(代理） to capture patterns of individual mobility in a city (22). With the help of smartcard data, we probe（探测） consecutive（连续不断的） trajectories of workplaces and residences over 7 y in Beijing to understand urban dwellers’ job and housing dynamics. We identify the most preferred station near each traveler’s workplace and residence (i.e., the work and home stations) according to individual commuting regularity (23). 根据个人通勤规律，我们确定了靠近每个通勤者工作场所和住所的最偏好站点（即工作站和家庭站）As transit use is a major part of commutes in megacities（大城市）, regular public transport commuters present higher temporal（暂时的） regularity than nonregular commuters (Fig. 1A). 由于运输用途是大城市通勤的主要部分，因此有规律的公交通勤者比没有规律的公交通勤者体现出了更强的规律性／特征。
简单的数据描述 From 2011 to 2017, the annual proportion of regular commuters rose from 23.74% to 31.40%, and their trip records account for over 80% of transit trips. We observed that 5,001 regular commuters retained their smartcard for seven consecutive years. The sampling process is shown in Fig. 1B. After assessing the spatiotemporal（时空的） regularity of trips, we find 4,248 sample commuters whose workplaces and residences can be identified successively. The sample size is more than equivalent（等价） to a travel survey.
图A不就是数据量不够时间长度来凑！我学到了哈哈哈哈哈哈
Each sample commuter generates at least four trips per week, and they generated more than 271,000 transit records over 7 year. With this dataset, we conduct an empirical study of job and housing dynamics at the intraurban scale.
第三段的主要目的就是为了解释为什么研究对象选取的是 regular commuters（判定方法：每周通勤4天及以上）, 通过图表分析对黏性用户和非黏性用户的通勤特征进行了对比，表明了前者比后者的通勤规律性更明显，从而能更好地进行通勤特征分析。虽然进行了这样的筛选后，样本量和传统的household survey样本量相差无几。
这也是我现在面临的困局：sample size和高质量数据之间的权衡。如果我试图用样本数据代表全体，保留全部通勤样本，数据量确实会比较大，但是数据质量并不高，研究结果可能不具有说服力；但是筛选掉非黏性用户，样本量瞬间变得很小，用这样的数据跑模型，估计结果的可靠性又....
Results
P4
This paper tracks trip records by a unique smartcard ID. Smart-cards, if they are retained, are likely to be held by the samecard owner. We use the method in ref. 23 to identify the homeand work station of regular commuters with 1-wk trip records byyears (SI Appendix, section R1). With 7-y trajectories of work-places and residences estimated, four mobility groups can becaptured (Fig. 2A) so that we can answer the first question:
P5
Who Are They? Commuters whose home locations and work-places remained constant are “stayers” (st) (16.38%), which isthe group with stability. Commuters who relocated residencesat least once but their workplaces were constant are “homemovers” (hm) (11.09%). Commuters who changed workplacesbut retained a constant residence are regarded as “job hoppers”(jh) (11.18%). “Job and residence switchers” (sw) (61.35%)changed both jobs and homes during the period studied. Onaverage, switchers changed jobs 2.65 times and home locations2.51 times over 7 y, while home movers and job hoppers averagedfewer than two moves each.
From 2011 to 2017, Beijing experienced rapid economic development and urban transformation (25). Under this background,stayers seem to find satisfactory locations to live and work, aswell as acceptable commuting routes, distance, and time. Withthe categories above, we propose the first hypothesis:
Hypothesis 1. Stayers have shorter commutes than other nonstayer groups, including job hoppers, home movers, and job andresidence switchers.
Stayer的通勤时间比改变职住地的通勤者更短
Table 1 corroborates（证实） the hypothesis that average travel time ofstayers was less than that of nonstayers, as measured before thenonstayers’ moves. All models show statistical significance. Furthermore, correlations of average travel time for home movers,switchers, and job hoppers are studied. The travel time of jobhoppers tends to exceed that of switchers and home movers.To sum up, we find the relationship
. In thispaper, t denotes the average travel time in the subway system, which is estimated from the boarding and alighting times of tripsbetween home and work stations.
P.S 没搞懂这个t检验是怎么做的
Meanwhile, the tendency in Fig. 2B supports the conclusiononce again. The average travel time of stayers remains significantly lower than that of other groups. Stayers’ travel timeremains around 36 min, while job hoppers’ time is volatile(反复无常的）.Moreover, the travel time of regular commuters steadily growsfrom 36.87 min to 40.20 min. Home movers and switchersfollow this trend. This phenomenon indicates that congestionarises in the subway system, often manifested as the commuter being unable to board the first (or second) train thatarrives due to crowding, breakdown of the timetable, construction delay, and/or transfer delay. The travel distance ofnoncommuting trips, and their number, probably increaseswith network expansion, which helps explain subway crowdingand delays.几乎所有类型通勤者的平均通勤时间都变长了，这也证明了地铁日渐拥堵。很有趣的解释啊
With suburbanization（郊区化） and subway network expansion, theincrease in commuting time corroborates several studies basedon travel surveys (26, 27). These studies suggest that employment decentralization（分散） results in a slight increase of commuting time (28). However, this paper provides empiricalevidence that contrasts with that of US studies on the “colocation” or “rational locator” hypotheses (29–31). These studiesargue that the stability of automobile commuting time emergesfrom the process when people periodically change their workplace and/or residence. They suggest that transit commutingtimes result from a different spatial process than automobilecommuting times.这些研究认为，汽车通勤时间的稳定性来自于人们定期改变其工作场所和/或居住地的过程。他们认为，中转通勤时间来自与汽车通勤时间不同的空间过程。
今天暂时先看到这里吧（下次再捡起来可能就要等我做完毕设了），接下来的内容都是对这四类人群的职住特征进行对比分析的。我得回去吧自己的研究好好理一下了。
我先跳到结论部分
Discussion
This paper investigates job and housing dynamics by assembling（组合） and analyzing longitudinal（纵向） transit smartcard data in Beijing. 我的研究是横向的 Theresearch framework identifies stayers, home movers, job hoppers, and job and residence switchers. It illustrates the resultingcommuting pattern by groups and quantifies trade-offs betweentravel time and housing expenditure.
This paper demonstrates that longitudinal transit smartcarddata allow scholars to track and examine individual commuters’workplace and residential location choices, which sheds morelight on the forces underpinning（巩固） urban spatial structure (44).Meanwhile, it unravels four groups’ residential mobility andcommuting patterns. Spatial mismatch（错配） was found at the subgroup level, which suggests that group characterization shouldbe considered in housing studies, transport demand management, and urban planning. 这也是我很感兴趣的方向！！Finally, it identifies a 45-min inflection point where the travel behavioral preference changes. Itimplies that commuters in metropolises like Beijing may havea tolerable limit of in-metro time by transit. This finding isuseful in transit network design, and transport planners shouldimprove the accessibility where commuters suffer from the in-metro commute time over 45 min. For example, direct transit services could be introduced between workplaces and residences where there is a concentration of regular commutersidentified.
Still, several limitations need to be mentioned.
One limitationis that we focus on the study of rail transit users, which reflectsover 20% of commuters in Beijing (24). We did not observejob and housing dynamics for commuters by other modes, e.g.,private cars, taxis, or buses.
With various datasets (e.g., GPSdata, mobile records), we may capture job and housing dynamics and travel behavior for other social groups. With more spatialdata and/or social background (e.g., income level at the suburban level), we may be able to provide more specific profiles andpredict where people move. （可以把这个补到我的研究背景里）
