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PROGRESS IN GEOGRAPHY >

2018 , Vol. 37 >Issue 2: 276 - 286

DOI: https://doi.org/10.18306/dlkxjz.2018.02.010

Articles

Influence of ICT on housing and employment related migration space of residents in urban villages: A case study of five urban villages in Beijing

  • LIN Wensheng , 1 ,
  • FENG Jian 1, * ,
  • LI Ye 2
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  • 1. College of Urban and Environmental Science, Peking University, Beijing 100871, China
  • 2. China Academy of Urban Planning and Design, Beijing 100871, China;

Received date: 2017-07-27

  Request revised date: 2017-11-21

  Online published: 2018-02-28

Supported by

National Natural Science Foundation of China, No.41671157

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《地理科学进展》杂志 版权所有
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Abstract

In recent years, China's Internet-based information and communication technology (ICT) has experienced rapid development, influencing the economic activities, lifestyle, and other aspects of daily life of rural and urban residents. In this study we surveyed five typical urban villages in Beijing with questionnaire and WeChat interviews, and used an unordered multi-class logistic regression model to verify the influence of information sources on housing and employment related migration space of residents in urban villages. The results show that due to the informal nature of housing market in urban villages that prevents residents from gathering housing information from the Internet, information source has no significant effect on housing related migration distance of residents in urban villages; due to "employment anchor point," "distance attenuation law," and the informal economy, information source has no significant effect on short-distance employment related migration of residents in urban villages, but in a larger spatial scope, residents in urban villages can get employment information through recruitment websites, social networks, and other online sources. The rapid development of ICT enriches their information source, enlarging employment space choices. This study extended our understanding about the influence of ICT on daily activity space of urban residents and the relationship between technology and equality. It also enriches research on spatial behavior of residents.

Key words： ICT; housing related migration space; employment related migration space; informal economy; searching space; urban village in Beijing

Cite this article

LIN Wensheng , FENG Jian , LI Ye . Influence of ICT on housing and employment related migration space of residents in urban villages: A case study of five urban villages in Beijing[J]. PROGRESS IN GEOGRAPHY, 2018 , 37(2) : 276 -286 . DOI: 10.18306/dlkxjz.2018.02.010

1 引言

信息通讯技术(Information and Communication Technologies, ICT)是信息技术与通讯技术相融合而形成的新概念,既包括电视、电话、电报、收音机等传统技术手段,也包括互联网、移动电话、卫星通讯、传真等新兴技术手段(申悦等, 2011)。迈入21世纪,以互联网技术为核心的ICT发展迅速,对居民的经济活动、生活、娱乐、沟通以及出行等方方面面产生了深刻影响。中国自1994年正式接入国际互联网以来,以极快的速度实现了后发者赶超,成为仅次于美国的世界互联网大国。根据中国互联网网络信息中心2017年1月发布的数据,截至2016年底中国共有7.31亿网民,相当于欧洲人口总和。基于庞大的用户规模和相对封闭的市场环境,中国形成了种类丰富的网络应用,例如网络购物,网络社交,移动支付等,这些应用不仅便利了工作和生活,也在悄然改变日常的活动方式。Mokhtarian教授等(1997)指出,ICT主要影响居民的居住选择、就业选择和娱乐活动,其中前两者是ICT主要作用领域。居民的信息获取方式、生活方式和信息化水平受到ICT影响,越来越多的个人和家庭在城市间或城市内部进行居住、就业迁移(Nilles, 1991; Shen, 2000; Muhammad et al, 2008)。
居住空间和就业空间作为城市空间重要组成部分,居民的居住和就业迁移一直是地理学、城市经济学和社会学的研究重点。居住迁移是居民在城市内部的迁移,指居民对实际住房情况与住房需求或预期差异进行调整的过程(Brown et al, 1970)。住房价格、就业机会、交通状况、邻里环境以及家庭结构或生命周期被认为是驱动居民迁居的主要传统因素(Sanchez et al, 2001; Colwell et al, 2002; Krizek et al, 2002; Morrow-Jones et al, 2004; Montgomery et al, 2006)。相对于传统因子,ICT能改变居住迁移的方式及过程进而促进居住空间变化。远程办公者将比普通就业者更倾向于居住在远离城市中心的郊区,从而加剧城市功能扩散(Mokhtarian et al, 2004; Ory et al, 2006; Ettema, 2010);孙中亚(2014)认为,房地产中介网站等网络化渠道已经取代传统信息渠道如报纸杂志,成为城市居民主要的住房信息获取方式,居民倾向于选择信息基础设施完备的居住地,从而导致新城居民在居住地选择上更加分散;翟青等(2012)验证了更大家庭网络时长和更多手机上网流量的家庭具有更大程度的职住分离。在就业迁移的影响因子方面,Steinnes(1977)认为服务业人口的就业选择将以居住地为前提,Cooke(1978)认为不论何种产业,城市的郊区化都是就业跟随人口迁移的。国内地理学者对居民就业迁移的研究主要集中在农民工的城乡迁移,城市居民就业迁移的时空间特征(薛领等, 2007; 王德等, 2016),以及从城市职住平衡角度研究居住与就业空间的协同关系(张文忠等, 2003; 郑思齐等, 2005; 刘志林等, 2009; 孟斌, 2009; 周素红等, 2010; 余建辉等, 2014)。
每一次技术进步总会引起人们利用新技术缩小阶级差异、实现社会正义的美好期待。乐观派认为,信息时代的到来将对信息贫困户进行信息赋权,信息技术特有的去中心化特征将使所有群体接触信息通讯设备的机会扁平化,信息资源平民化,新技术将惠及所有人,阶层差异将在技术革新、市场力量和和国家政策的共同作用下逐渐缩小甚至消失(Norris, 2001)。但是,悲观者认为社会不公平、阶层差异的背后是人类的社会经济属性差异,只要这种差异存在一天,不平等将永远不会被消除。新技术提高了劳动生产率,纵向上相对于他们的前辈而言,弱势群体改善了生活条件;但从横向上对比,弱势群体与掌握资源的上层群体之间的贫富差距实际上更大了。美国贫民窟里的居民虽然能使用古代帝王不可想象的苹果手机,但近年来美国社会的基尼系数总体上却在不断上升(朱明熙等, 2014)。城中村居民作为城市弱势群体,受限于自身社会经济状况,在城市空间生产过程中处于不利地位,其时空行为受到的限制比普通城市居民更多(兰宗敏等, 2010)。在互联网特别是移动互联网时代,作为传统弱势群体的城中村居民能否借助新技术的力量“松绑”,拥有更大的居住和就业选择空间,还是受制于自身社会经济属性和其他客观因素,无法分享信息技术带来的福利?

2 信息网络化与搜寻空间理论

住房、就业信息搜寻是进行居住、就业迁移的第一步,也是实现居住和就业迁移的前提,租房者和待业者在实地探访空置房屋或投递简历之前一般都会先通过各种渠道搜索信息,选择合适的住房或工作岗位(Palm et al, 2002)。信息技术对居民居住和就业空间的影响是通过将住房和就业信息网络化,降低其搜寻成本,提高搜寻效益进而扩大搜寻空间实现的。地理学者用 “锚点理论”和“距离衰减规律”来解释居民住房和就业搜寻空间的变化。锚点理论(Anchor Point Theory)假设每个人到达新场所后必须先熟悉居住场所、商店和工作场所,以这3个场所为基点,活动扩展到这三点连线的周边区域(Golledge et al, 1978)。活动空间具有“距离衰减”规律,距离锚点越近,居民到这个空间活动的机会越多,随着距离增加到该空间活动机会减少(张文奎, 1990)。Huff等(1986)研究发现,家是居民居住和就业迁移过程中最重要的锚点,居民往往熟悉当前的居住、就业环境并习惯于搜索具有相似生活模式、交通成本、社会交往和公共服务的住房、工作岗位(Adams, 1969; Brown et al, 1971; Roseman, 1971),待出租房屋或工作机会距居民现居住、工作地越远,其信息被居民得知并访问的可能性越小(Clark et al, 1980; Baker, 1982; Curran et al, 1982),
在住房和就业信息被网络化之前,居民主要通过广告或传单、报纸、亲戚朋友介绍以及房产或就业中介获得住房和就业信息,搜寻空间有限(Brown et al, 1970; Clark et al, 1982; Palm et al, 2001)。将信息通讯技术运用到住房、就业信息市场后,居民能够通过网络渠道获得大量、全面、实时更新、低成本以及可反馈的住房和就业信息(Palm et al, 2001)。实证研究也表明,迁居者利用网络搜索住房信息减少了信息获取障碍,实现了更大的搜寻范围(Palm et al, 2001, 2002; Chen et al, 2012)。秦萧等(2016)研究了南京居民的迁居活动发现,新的信息渠道确实能增加城市居民的居住迁移距离。但对于其他社会群体特别是弱势群体而言,这种效应是否也存在,网络信息渠道能否促进居民的就业迁移距离,目前尚缺乏实证研究。因此文章假设ICT的使用能扩大城中村居民的居住和就业迁移距离。
本文利用问卷调查、微信访谈等方式调查了北京市5个典型城中村,先分析城中村居民的居住和就业迁移特征,再利用多元无序多分类logistic回归模型验证上述假设并探讨背后的形成机制,以加深ICT对居民特别是城中村居民时空间行为影响的理解。

3 研究区域与研究方法

3.1 研究区域与数据来源

参考《北京市2017年棚户区改造和环境整治任务》(引自首都之窗(2017)中的“城中村边角地”项目,利用百度卫星地图(地块细碎且不规则的很有可能是城中村)、街景功能和多次实地踏勘,本次调研最终确定5个典型村庄(图1))。考虑到城中村居民的就业特征,问卷发放在2017年2月25-26日和3月4-5日2个周末进行。城中村居民同质性较高,因此采用街头随机拦访再由其介绍其他合适访谈对象的滚雪球调查方式。本次调研共发放问卷450份,基本上按照每个村庄人口规模大小分配,回收问卷420份,回收率93.33%,其中有效问卷407份(表1),有效率96.90%。问卷内容包括个人信息、居住情况、就业情况、上网情况、休闲娱乐和活动日志6个部分。
Fig.1 Location of surveyed urban villages

图1 调研地点分布

Tab.1 Release of questionnaires

表1 问卷发放情况

调研地 问卷发放/份
西半壁店—史各庄村 104
圆明园—朱房社区 92
费家村 79
小武基村 70
东关街—沙岗村 62
总计 407
在发放问卷时作者也在现场对典型样本进行访谈,在调查受访者上网情况时特别询问了其对微信的使用情况,并留下微信号。407份有效问卷中,留下微信号的有117人,根据不同研究目的选择具有代表性的样本添加微信进行线上访谈。一般的访谈需要调查者多次回到调研地点寻找访谈对象,且访谈对象往往没有做过基本情况调查,而微信被访谈者同时也是纸质问卷受访者,这样既节省了时间精力,也使访谈内容更有深度。

3.2 模型及变量选择

学者们对居住迁移的影响因素进行过许多研究,家庭结构变化将导致居住迁移,性别的不同、个体年龄的增长、家庭规模的扩大、婚育状况、家庭收入和社会地位的变动是导致居住需求变化的重要原因(Rossi, 1955, Clark et al, 1982; Ermisch et al,1999; Li et al, 2001; Clark et al, 2003)。刘望保等(2007)总结了影响居住迁移的4个主要方面：①住房制度改革。在住房市场化之前,住房主要由单位分配,市场化后则通过市场解决;②住房供应和分配环境。居住在房改房、经济适用房、廉租房、自建房、商品房以及租赁住房的居民迁居的概率不同;③区域发展背景。城市空间结构、郊区化等都会影响居民的迁居行为;④个人因素。个体的职业和行业性质也将影响居民的居住迁移,那些资金雄厚、福利好的单位职工进行居住迁移的机会也更多,外来人口与本地人口之间也存在显著的迁居差异。目前对城市内部居民就业迁移影响因素的研究还很少,可以借鉴居住迁移的影响因子和余建辉等(2014)的研究。
根据已有研究和实际调研情况,为验证信息获取渠道对城中村居民居住和就业迁移空间的影响,以受访者的居住和就业迁移距离为因变量,控制人口学特征、社会经济属性,引入信息搜寻渠道变量,将其分为传统渠道(亲戚朋友介绍、广告传单、自己实地探查和其他)和网络渠道(表2)。有些变量为分类变量,需转换成哑变量并设置对照变量,连续型变量需进行标准化以消除量纲。因变量为连续性变量,利用多元线性回归模型分析时发现所有自变量都无法进入模型,说明自变量和因变量之间不存在线性关系,因此考虑非线性回归模型。借鉴已有研究成果(Potoglou et al, 2008; Tang et al, 2011),将因变量居住和就业迁移距离按0~5 km、5~10 km和10 km以上分为3个区间,10 km以上作为对照组,利用无序多分类logistic回归模型进行验证。
Tab.2 Model and variable selection

表2 模型及变量选择

居住迁移 就业迁移
因变量 居住迁移距离：百度地图上一次居住地与现居住地最短路径距离 就业迁移距离：百度地图上一次就业地与现就业地最短路径距离
解释变量 信息渠道 信息渠道
控制变量 性别 性别
年龄 年龄
户籍 户籍
受教育程度 受教育程度
个人月收入 个人月收入
行业 行业
家庭规模 企业规模
婚育状况 婚育状况
居住形式 居住形式
迁居原因 —
模型 多元无序logistic回归模型

4 信息渠道对城中村居民居住和就业迁移的影响

4.1 居住和就业迁移特征

剔除本次调查中未发生居住、就业迁移活动即只有一个居住或就业地点、不愿意透露或遗忘迁移前具体地址的样本,共获得有效居住迁移样本198份,就业迁移样本156份。城中村居民的居住和工作地点主要分布在四环以外的城市外围地带,呈现出“边缘化”特征(图2)。从迁移前居住和工作地点的分布来看,迁移来源地分布在现居住地或工作地附近,比如靠近史各庄—东半壁村的沙河街道,靠近圆明园—朱房社区的清河街道,靠近费家村的孙家河、金盏地区,以就近迁移为主,证实了城中村居民居住和就业迁移活动的空间依赖性,符合“锚点理论”和“距离衰减规律”。
Fig.2 Housing and employment related migration of surveyed residents

图2 受访居民居住和就业迁移

假设如果受访者现在的居住、就业地比迁移前的居住、就业地更靠近城市中心,则居住或就业向城市中心(以天安门广场为基准)迁移,反之则向城市外围迁移,如果两次居住、就业地点距离不超过1 km或与城市中心的距离相当且两地居住、就业环境相差不大,则认为其居住、就业环境没有变化,各自计算3种迁移类型的比例：有54.04%的居民居住地向城市外围迁移,17.68%的居民居住环境变化不大,28.28%的居民居住地向城市中心迁移;从就业迁移来看,有44.74%的居民就业地向城市外围迁移,29.82%的居民就业环境相对变化不大,还有25.44%的居民就业地向城市中心迁移。城中村居民的居住和就业迁移呈现明显的“离心化”特征,这与北京市近年来开展的非首都功能疏解活动有关。2014年以来,北京加大了对非首都核心功能产业的疏解,天皓服装城、金五星百货批发城、动物园和大红门服装批发市场相继搬离北京,失去中心城区就业岗位的城中村居民不得不到城市外围寻找工作机会(黄子懿, 2016)。同时,北京市政府双管齐下,大规模拆除城中村,城中村居民的居住地也被迫向城市外围转移,从这个角度看,北京市疏解非首都核心功能产业人口的政策取得了立竿见影的效果。

4.2 不同信息渠道使用者特征

4.2.1 不同信息渠道使用情况
城中村居民的住房信息来源主要以传统渠道为主,网络渠道比例较低(表3)。主要是自己实地探查(44.95%)和老乡亲戚朋友介绍(39.90%),仅有(9.60%)个受访样本通过58同城、微信朋友圈、QQ等网络渠道进行房屋信息搜索,没有居民通过报纸电视、中介机构寻找新房源。同样,基于血缘—地缘关系网络的亲戚朋友或者老乡介绍仍然是城中村居民获取就业信息的首要来源(43.59%),其次是自己实地探查(25.64%),这部分受访者主要是个体从业者,需要自己实地考察市场环境,有将近1/5(19.23%)的居民通过网络渠道来获取就业信息,比获取住房信息的比例要高,渠道也较丰富,既有58同城、赶集网、智联招聘等专业招聘网站,也有QQ群组、微信、朋友圈等社交网络,还有“工作通”这类针对中低端就业者的专门招聘软件。
Tab.3 Main housing and employment information sources of residents in urban villages

表3 城中村居民主要住房和就业信息来源

住房信息来源 就业信息来源
人数/人 比例/% 人数/人 比例/%
老乡亲戚朋友介绍 79 39.90 68 43.59
报纸电视 0 0.00 7 4.49
广告、宣传单 6 3.03 4 2.56
中介机构 0 0.00 0 0.00
自己实地探查 89 44.95 40 25.64
网上查询 19 9.60 30 19.23
其他 5 2.52 7 4.49

注：其他就业信息来源包括参加招聘会、亲戚的公司、公司内部调动等

将居民住房、就业信息获取渠道分为传统渠道和网络渠道2类,不同属性居民具有不同的信息获取特征：男性居民更熟悉网络工具,通过网络寻找新房源、新工作的比例是女性居民的2倍(表4);年轻居民使用网络搜索房源、就业信息的比例远高于其他年龄段居民;利用网络住房、就业信息渠道的本地户籍(12.50%和25.00%)居民要高于外地户籍居民(9.47%和19.09%);受教育程度为大学及以上的居民利用网络住房、就业信息渠道的比例高于初中及以下、高中(中专及技校)的居民;从行业属性看,从事第三产业特别是高端服务业的居民更擅长使用网络渠道(14.14%和29.33%);对城中村居民而言,收入越高意味着利用网络搜索住房和就业信息的可能性就越高。总而言之,年龄越小、受教育程度和收入水平越高,从事第三产业的本地户籍男性居民越有可能通过网络渠道找到新住房、新工作。
Tab.4 Housing and employment information sources of residents of different demographic and socioeconomic characteristics in urban villages

表4 不同属性城中村居民住房和就业信息渠道

住房信息渠道/% 就业信息渠道/%
传统渠道 网络渠道 传统渠道 网络渠道
男性 87.74 12.26 76.06 23.94
女性 93.48 6.52 88.37 11.63
18岁以下 100.00 0.00 - -
18~44岁 86.86 13.14 75.56 24.44
45~60岁 98.08 1.92 100.00 0.00
60岁及以上 100.00 0.00 100.00 0.00
本地户籍 87.50 12.50 75.00 25.00
外地户籍 90.53 9.47 80.91 19.09
初中及以下 97.00 3.00 93.88 6.12
高中(中专及技校) 91.49 8.51 95.65 4.35
大学及以上 76.47 23.53 57.14 42.86
没有工作 97.22 2.78 100.00 0.00
个体 94.12 5.88 100.00 0.00
第二产业 93.10 6.90 100.00 0.00
第三产业 85.86 14.14 70.67 29.33
低收入 100.00 0.00 100.00 0.00
中低收入 88.37 11.63 89.53 10.47
中高收入 93.33 6.67 54.55 45.45
高收入 81.25 18.75 38.46 61.54
4.2.2 不同信息渠道使用者居住、就业迁移时空间特征
城市扩张和公共政策会影响居民的居住和迁移活动,在城市快速扩张期,居民的居住和就业迁移活动将更加频繁,人口疏解政策也将不可避免地导致外来人口向城市外围区域移动,因此必须控制不同信息渠道使用者的居住和就业迁移发生时间大体一致。在居住迁移发生时间上,网络渠道使用者除近3个月迁居活动发生比例高于传统渠道外,其他各个时间段内与传统渠道使用者相比分布比较一致(图3),可将198个样本全部纳入回归模型;
Fig.3 Timing of housing and employment related migration of residents in urban villages

图3 城中村居民居住、就业迁移发生时间

使用网络渠道的居民就业迁移都发生在近3年内,而19.87%传统渠道居民的就业迁移活动发生在过去3年及以上,实际上北京真正大规模疏解非首都核心功能产业人口正是从2014年开始的(黄子懿, 2016)。为控制城市产业疏解政策对就业迁移距离的影响,剔除了31个就业迁移活动发生在2014年以前的传统渠道样本,最后将125个样本纳入回归模型。
从迁移距离看,传统渠道使用者平均居住迁移距离为14.15 km(图3),网络渠道使用者为12.05 km;网络渠道居民平均就业迁移距离为17.2 km,传统渠道居民为14.16 km。网络渠道的使用似乎并不能帮助城中村居民拥有更大的居住迁移空间,但却能使其实现更大的就业迁移距离,下文运用无序多分类logistic回归模型定量验证这一判断。

4.3 模型验证

就业迁移模型中,年龄与受教育程度、婚育状况、信息渠道相关系数在0.6以上,为避免多重共线性,剔除年龄变量。2个模型均以10 km及以上为对照组,居住迁移模型似然比检验值为335.138,显著性检验P值为0.001<0.01;就业迁移模型似然比检验值为125.28,显著性检验P值为0.006<0.01,拟合效果较好,模型变量能够被模型所解释(表5)。从模型结果可以看出：
Tab.5 Housing and employment related migration regression result of residents in urban villages

表5 城中村居民居住和就业迁移回归结果

居住迁移 就业迁移
0~5 km 5~10 km 0~5 km 5~10 km
系数β Exp(β) 系数β Exp(β) 系数β Exp(β) 系数β Exp(β)
截距 6.236*** 510.811 5.726*** 306.739 6.770*** 871.312 7.380*** 1603.589
性别(女性#) -1.603*** 0.201 -0.805* 0.447 0.500 1.650 -0.040 0.960
年龄 -0.178 0.837 -0.410 0.664 - - - -
户籍(外地户口#) 1.028 2.797 -1.256 0.285 -11.200 0.000 -0.480 0.620
受教育程度(大专及以上#)
初中及以下 -1.162 0.313 -0.193 0.824 -0.870 0.420 -0.970 0.380
高中、中专及技校 -1.207* 0.299 -1.060* 0.347 -0.170 0.850 0.770 2.150
个人月收入 -1.441** 0.237 -1.573*** 0.207 -3.270* 0.040 -2.020* 0.130
行业(第三产业#)
没有工作 0.051 1.052 -0.803 0.448 14.420 2.860 -0.050 0.950
个体 1.312* 3.715 1.219* 3.384 1.980** 7.220 0.810 2.250
第二产业 -0.114 0.892 0.151 1.163 -0.900 0.410 -0.240 0.790
家庭规模 -0.483* 0.617 -0.329 0.720 - - - -
企业规模/人 - - - - 1.480 2.760 0.40 1.270
婚育状况(已婚已育#)
未婚 -1.004* 0.366 -0.959 0.383 -0.070 0.930 0.010 1.010
已婚未育 -1.342** 0.261 -0.952 0.386 -1.460 0.230 -1.490* 0.230
居住形式(自有住房#) -15.660*** 1.58E-07 -14.57*** 4.69E-07 -16.4*** 7.74E-08 -16.07*** 1.04E-07
迁居原因(其他#)
拆迁迁居 0.983* 2.672 0.746 2.109 - - - -
工作迁居 -1.635*** 0.195 -0.465 0.628 - - - -
信息渠道(传统渠道#) -0.359 0.698 -0.189 0.827 -4.770 0.010 -1.840** 0.160
样本数量 198 125
-2倍对数似然值 335.138 125.300
P值 0.001 0.010
考克斯—斯奈尔 0.289 0.440
内戈尔科 0.332 0.500
麦克法登 0.168 0.280

注：*表示p<0.10,**表示p<0.05,***表示p<0.01;#表示参照变量,10 km以上为因变量对照组。

(1) 住房信息获取渠道对城中村居民的居住迁移距离并没有显著影响。与10 km及以上相比,不论是0~5 km还是5~10 km,网络渠道居民与传统渠道居民在居住迁移距离上并没有显著差异。网络渠道在2个距离内的系数符号为负,说明网络渠道有可能增加城中村居民的居住迁移距离,只是在统计学上还不显著。城中村地区房屋面积小、租金低,房屋出租相对容易,房东没有动机和能力(城中村的房东多为本地中老年人)将住房信息发布到网上或进行大规模广告宣传活动,一般以小广告或简易传单形式将租房信息张贴在门前及沿街建筑上;专业的房产中介机构也没有兴趣收集城中村的住房信息并将其上网,从源头上切断了城中村住房信息网络化的可能性。我们在国内最大的租房信息网站“安居客”上搜索5个调研地附近的租房信息,发现招租信息几乎为零。同时城中村居民通常以血缘—地缘为纽带集中居住,比如小武基村以从事建筑拆除行业的四川仪陇县籍居民和从事室内装修的江苏泰州籍居民为主,通过亲戚老乡寻找新房源不仅节省时间精力,也有利于同乡之间的交流联系,不少受访者表示居住地选择的主要目的是“老乡多,方便打听活路”。本次调查中有53.84%的受访者将“接近工作地点”作为选择新住所的主要原因,城中村居民的居住地选择有明显“人随业走”的特征。
(2) 网路渠道的使用确实能够帮助城中村居民实现更大的就业迁移距离。相对于传统渠道居民,网络渠道使用者就业迁移距离为5~10 km的可能性是10 km及以上的0.16倍,选择10 km及以上的可能性是5~10 km的6.29倍(1/0.16),说明网络渠道居民就业迁移距离比传统居民显著更大。来自四川绵阳的李师傅住在小武基村,今年49岁,从事水电工作,刚到北京没多久,前几天通过手机软件“工作通”找到了一份石景山的工作,虽然需要来回奔波,好在工资比较高;住在费家村的周先生今年28岁,从事厨师工作,刚从望京一家中学食堂换到雍和宫附近做素斋大厨,他所有的工作都是通过QQ群找到的。
我们注意到在0~5 km距离内,2种渠道居民的就业迁移距离并没有显著差异,但随着距离不断扩大不同渠道的使用开始显著影响就业迁移距离。根据“锚点”理论(Huff, 1986),就业者熟悉原工作地的工作环境,附近也聚集了大量同类企业,就业机会丰富,可以通过私人网络或实地探查转变就业岗位,而不需要借助网络工具。住在朱房社区的韩师傅原来在科实大厦五楼的火锅店当厨师,后来火锅店倒闭,因为与其他餐馆的厨师相熟,很快就转到四楼的快餐店上班。而且许多城中村居民多从事中低端的服务业或非正规行业,这些岗位同质性高,具有“遍在性”,受访者缺乏长距离迁移的动机。
(3) 家庭结构能影响城中村居民的居住和就业迁移。家庭规模小,未婚、已婚未育家庭和男性居民有更大的居住和就业迁移空间;没有自有住房的外地户籍居民居住和就业迁移距离显著地比拥有自有住房的本地居民大;受教育程度和收入水平较低的居民,其居住、就业迁移距离也较大,可能是因为城中村居民受教育程度较低,缺乏核心专业技能,收入较低,工作和居住流动性大;相对而言,个体从业者居住、就业相对稳定,居住和迁移距离在10 km以内的可能性更大。一般来说个体经营者的居住、工作和生活空间高度重合,改变经营场所不仅意味着要支付包括店面装修、进货渠道、顾客等在内的沉没成本,开辟新市场也需要冒相当大的风险,而且个体经营行业也具有“遍在性”,不同区域经营条件相差不大,个体经营者通常不愿意轻易改变场所;城中村居民的居住地选择具有明显的就业导向特征,工作变动导致搬迁的居民居住迁移距离比其他居民更大,而因拆迁搬迁的居民一般会选择就近居住。

4.4 ICT对城中村居民居住和就业迁移的影响机制

在信息技术还未大规模运用到居民日常生活场景中时,城中村居民主要通过亲戚朋友老乡介绍、自己实地探查和传统媒体如报纸电视、广告传单等方式来获取住房和就业信息;受“居住、就业锚点”和“距离衰减定律”影响,城中村居民的搜寻空间呈倒U型曲线,居住地和就业地周边的住房或工作岗位被搜寻到的可能性最大,距离中心点越远,被搜寻到的可能性越小。在互联网时代,信息通讯技术对城中村居民住房和就业搜寻空间的影响存在差异,且与城中村发达的非正规经济有关。改革开放以来,北京经历了快速的城市化过程,吸引全国各地人才涌入,给北京带来了巨大的就业压力。正规就业部门只能吸纳部分劳动力,那些具有受教育程度较低、没有专业技能的劳动力往往只能在非正规部门就业,而这些部门大多分布在城中村区域。就业的非正规化决定了城中村居民的收入不会太高,支付能力不足,对价格敏感,只能消费较低档的产品或服务,反过来进一步导致了城中村非正规经济部门的发展。除了就业需求,外来人口还有庞大的住房需求,但正规住房市场只能优先满足本地城市居民的需求,而且城中村居民的低支付能力也导致其无法消费正规城市住房。北京快速的城市化催生了一大批“城中村”和失去土地又缺乏就业技能的原住民,他们只能依靠“瓦片经济”,加上城市管理不到位,城中村非正规住房市场便得到蓬勃发展(图4)。
Fig.4 Theoretical framework of influence of information and communication technologies (ICT) on housing and employment related migration of residents in urban villages

图4 ICT对城中村居民居住和就业迁移影响的理论框架

城中村住房市场的非正规化从源头上切断了城中村居民从网上获取住房信息的渠道,加上城中村居民选择新住所具有明显的“人随业走”和以地缘—血缘为纽带集聚的特征,因此信息技术无法扩大城中村居民的居住选择空间。信息技术对城中村居民就业选择空间的影响分以下2种情况：近距离就业迁移和长距离就业迁移。同样受“就业锚点”和“距离衰减定律”影响,城中村居民倾向于在现工作地附近寻找工作机会,加上有相当比重(16.22%)的居民在非正规经济部门(未在工商部门登记)就业,这些部门高度本地化且具有“遍在性”特征,就业信息也无法网络化,因此网络信息渠道对城中村居民近距离就业迁移并没有显著影响;但在更大空间范围内,城中村居民能通过招聘网站、社交网络等网络渠道获取就业信息,信息通讯技术的发展丰富了他们的信息获取渠道,扩大了就业的选择空间。

5 结论与讨论

本文利用问卷调查、微信访谈等方式调查了北京5个典型城中村,发现北京城中村居民的居住和就业迁移以基于边缘空间的就近迁移为主,具有空间依赖性,并有从城市中心向城市外围“撤退”的趋势,这与北京市加强疏解非首都核心功能产业人口的政策有关。城中村居民寻找新房源、新工作时,以基于血缘—地缘的亲戚老乡朋友介绍,自己实地探查和广告传单为主,网络渠道使用比例较低而年龄较小、受教育程度和收入水平越高,且从事第三产业的本地户籍男性居民越有可能通过网络渠道找到新住房、新工作。为验证信息渠道对城中村居民居住和就业迁移距离的影响,本文利用无序多分类logistic回归模型进行验证,结果表明：
(1) 城中村居民的受教育程度和收入水平越低,其居住和就业迁移距离越大;家庭结构也将影响其居住和就业迁移,家庭规模小,未婚或已婚未育和男性居民有更大的居住和就业迁移空间;缺少自有住房的外地户籍居民居住和就业迁移距离明显大于拥有自有住房的本地居民。
(2) 非正规化的城中村住房市场从源头上切断了城中村居民从网络获取住房信息的渠道,加上城中村居民选择新住所时往往以就业为导向、以地缘—血缘为纽带集聚,因此信息技术无法扩大城中村内居民的居住迁移距离;“就业锚点”和“距离衰减定律”决定了城中村居民倾向在现工作地附近寻找工作机会,而且有相当比重居民在非正规经济部门就业,因此网络信息渠道对城中村居民的近距离就业迁移没有显著影响;但在更大空间范围内,城中村居民能够利用各种网络渠道获取就业信息,信息技术的发展扩大了他们的就业迁移空间。信息技术的进步丰富了城中村居民的信息索搜索渠道,特别是大部分年轻城中村居民能够通过互联网实现工作升迁,这在一定程度上说明新技术的发展确实能够减少弱势群体的时空制约,扩大他们的就业选择空间,有助于缓解当前的城乡二元分割状况。
本文分析了北京城中村居民的居住和就业迁移特征,定量验证了ICT对居民居住和就业迁移空间的影响,加深了关于ICT对城市居民日常活动空间影响、技术进步与社会平等关系的理解,丰富了居民时空间行为研究,有利于城市政策制定者评估人口和产业政策。但本文仅研究了城中村居民的居住和就业迁移活动,研究群体较小,无法全面掌握ICT对普通城市居民的影响;未来应扩大研究群体,研究ICT对城市居民交通出行、休闲娱乐、日常购物等活动的影响。也可从城中村居民的居住和就业迁移路径切入,结合个案访谈,更详细地剖析北京人口疏解政策对城市弱势群体的影响。

The authors have declared that no competing interests exist.

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DOI

[Liu W B, Yan X P.2007. Comparison of influencing factors for residential mobility between different household register types in transitional urban China: A case study of Guangzhou[J]. Geographical Research, 26(5): 1055-1066.]

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DOI

[Liu Z L, Zhang Y, Chai Y W.2009. Home-work separation in the context of institutional and spatial transformation in urban China: Evidence from Beijing household survey data[J]. Urban Studies, 16(9): 110-117.]

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DOI

[Qin X, Zhen F.2016. The impacts of information channels on moving space: A case study on Nanjing[J]. Geographical Research, 35(10): 1846-1856.]

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DOI

[Shen Y, Chai Y W, Wang D G.2011. Reviews on impacts of information and communication technologies on human spatial-temporal behavior[J]. Progress in Geography, 30(6): 643-651.]

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DOI

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DOI

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[33]
Huff J O.1986. Geographic regularities in residential search behavior[J]. Annals of the Association of American Geographers, 76(2): 208-227.Abstract Households exhibit strong spatial regularities in their residential search behavior; moreover these regularities arise from two distinct but interrelated sources: the spatial variation in the number of vacancies meeting particular housing needs and spatial biases in the household's search strategies. Three models of the search process are evaluated against observed search behavior for a sample of prospective home buyers in the San Fernando Valley of Los Angeles; in each instance, analysis of observed search behavior supports the hypothesized relationship between the search pattern and the locational variables influencing it. A constrained choice set model calls attention to the relationship between the spatial variation in the number of vacancies in the household's possibility set and the pattern of vacancies visited by the household. The area-based search model focuses on the locational persistence in the household's search behavior. The key to the anchor points model is that locational preference declines with increasing distance from critical anchor points in the household's activity space. The locations of the prior residence and the workplace (or workplaces for dual worker households) are shown to have a significant influence on the resulting pattern of vacancies seen by the household.

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[34]
Krizek K, Waddell P.2002. Analysis of lifestyle choices: Neighborhood type, travel patterns, and activity participation[J]. Transportation Research Record: Journal of the Transportation Research Board, (1807): 119-128.Activity-based travel modeling has begun to make significant progress toward a more behavioral framework for simulating household travel behavior. A significant challenge remains in the need to address the interaction of daily activity and travel patterns with longer-term household choices of vehicle ownership, residential location, and employment location. The choices often depend on one another and jointly define the lifestyle of the household. These choices are likely to evolve over the course of the life cycle as households are formed; as children are born, raised, and ultimately depart to form their own households; and as retirement and old age change patterns of residence, work, and travel. A framework is developed for analyzing household choices relating to three dimensions of lifestyle: travel patterns (including vehicle ownership), activity participation, and residential location (neighborhood type). With cluster analysis on data from the Puget Sound Transportation Panel, nine classifications of lifestyle are uncovered. These clusters demonstrate empirically how decisions of residential location reinforce and affect daily decisions related to travel patterns and activity participation. The applicability of these lifestyle clusters for land use transportation planning is discussed.

[35]
Li S M, Siu Y M.2001. Residential mobility and urban restructuring under market transition: A study of Guangzhou, China[J]. The Professional Geographer, 53(2): 219-229.Residential mobility can be conceptualized as an outcome of a choice process exercised under complex institutional and personal constraints. China's rather unique pattern of housing market segmentation under market transition impinges directly on residential location and relocation. Drawing upon data from a sample survey, this paper analyzes the pattern of residential moves resulting from commodity housing construction in a major Chinese city, Guangzhou. Most moves are of short distance, although the general direction is towards the urban periphery. Danweis and the municipal housing bureau, rather than the market per se, are the primary driving forces behind suburbanization in China today.

DOI

[36]
Mokhtarian P L, Collantes G O, Gertz C.2004. Telecommuting, residential location, and commute-distance traveled: Evidence from state of California employees[J]. Environment and Planning A, 36(10): 1877-1897.

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[37]
Mokhtarian P L, Salomon I.1997. Modeling the desire to telecommute: The importance of attitudinal factors in behavioral models[J]. Transportation Research Part A: Policy and Practice, 31(1): 35-50.This paper begins to operationalize a previously published conceptual model of the individual decision to telecommute. Using survey data from 628 employees of the City of San Diego, hypothesized drives to telecommute and constraints on/facilitators of telecommuting are measured. A binary logit model of the preference to telecommute from home is estimated, having a 2 of 0.68. The explanatory variables include attitudinal and factual information. Factor analysis is performed on two groups of attitudinal questions, identifying a total of 17 (oblique) factors which can be classified as drives and constraints. Additional measures are created from other data in the survey, usually objective sociodemographic characteristics. Variables representing at least four of the five hypothesized drives (work, family, independence/leisure, and travel) are significant in the final model. Variables from four of the ten groups of constraints (Job suitability, social/professional and household interaction concerns, and a perceived benefit of commuting) are significant, primarily representing internal rather than external constraints. The results clearly demonstrate the importance of attitudinal measures over sociodemographic ones, as the same demographic characteristics (such as the presence of children, commute time) will have different effects on preference for different people.

DOI

[38]
Montgomery M, Curtis C.2006. Housing mobility and location choice: A review of the literature[R]. Impacts of Transit Led Development in a New Rail Corridor, Working Paper No.2: 4-32.

[39]
Morrow-Jones H A, Irwin E G, Roe B.2004. Consumer preference for neotraditional neighborhood characteristics[J]. Housing Policy Debate, 15(1): 171-202.ABSTRACT Much research on residential mobility relies on examining people's choices within the context of what is available in a local housing market. However, it is difficult to determine the demand for alternative housing or neighborhood types that may not be available or are available only in limited quantities. Hence, the market may not accurately reveal consumer preferences for such alternatives.We estimate a discrete choice model of neighborhood choice by using data from a choice ased conjoint analysis survey that allows us to vary characteristics experimentally. The model is used to determine consumer preferences for neotraditional neighborhood design features, including neighborhood layout, housing density, surrounding open space, and commuting time, while holding other characteristics, including school quality and neighborhood safety, constant. The results indicate that the neotraditional design with higher density is less preferred on average, but that niche marketing, additional open space, or other amenities can overcome its negative effects.

DOI

[40]
Muhammad S, Ottens H F L, de Jong T.2008. Modelling the impact of telecommuting on future urbanisation in the Netherlands[J]. Tijdschrift voor Economische en Sociale Geografie, 99(2): 160-177.ABSTRACT Information and communication technologies (ICTs) have evolved to such a level that they can facilitate people's access to opportunities in virtual space (through telecommuting, teleshopping, e-learning, and so forth) along with accessing them in physical space. A hybrid space is therefore emerging, which will have consequences for people's spatial behaviour. In particular telecommuting is expected to change residential preferences and affect future urbanisation patterns. To explore this assertion, residential land-use allocation in the 2000 to 2030 period is projected for the Netherlands using a specially designed set of linked models for two scenarios (physical space and hybrid space). Results indicate that urban decentralisation and deconcentration are likely to accelerate because of increasing telecommuting. Attractive regions to live in at medium distances from large cities will in particular be confronted with new urban pressure of a sprawling nature. Urban policies have to be reconsidered to cope with these new spatial development trends.

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[41]
Nilles J M.1991. Telecommuting and urban sprawl: Mitigator or inciter[J]. Transportation, 18(4): 411-432.There is some evidence to the effect that as cities become increasingly congested new housing starts occur at greater distances from urban centers while jobs tend to remain center-concentrated or develop in other suburbs. In either case, mean commute distances tend to increase. Telecommuting is seen as a means of increasing the jobs-housing balance in urban and suburban areas by enhancing the ability to move work to, or closer to, the workers' residences rather than requiring workers to commute to work daily. This has the immediate side effect of decreasing automobile congestion and associated energy consumption and air pollution. There is a possible longer term adverse impact of telecommuting resulting from its ability to decrease constraints on household location, thereby enhancing the rate of spread of suburbia. This paper reviews evidence concerning the possible effects of telecommuting on urban sprawl, as derived from a two-year test of telecommuting in California, and describes two scenarios of urban form development made more feasible by telecommuting.

DOI

[42]
Norris P.2001. Digital divide: Civic engagement, information poverty, and the Internet worldwide[M]. Cambridge, UK: Cambridge University Press: 56-61.

[43]
Ory D T, Mokhtarian P L.2006. Which came first, the telecommuting or the residential relocation? An empirical analysis of causality[J]. Urban Geography, 27(7): 590-609.Researchers have questioned whether the ability to telecommute is encouraging workers to relocate to more desirable residences farther from work, and in doing so, exacerbate sprawl and increase their net vehicle-miles traveled. The research presented here directly asks, is telecommuting a "friend or foe" of travel-reducing policies? Given that telecommuters tend to have longer commutes than non-telecommuters, is the ability to telecommute prompting workers to move farther away from work? Or rather, does the ability to telecommute allow those who for other reasons have already chosen, or would in any case choose, to live in more distant locations to commute less frequently? These questions are addressed using data collected from more than 200 State of California workers, including current, former, and non-telecommuters. The survey inquired retrospectively about their residential relocations, as well as their telecommuting engagements, over a 10-year period. The results indicate that, as expected, residential moves that are temporally associated with telecommuting episodes tend to increase commute time and length compared to other moves lthough the differences are not statistically significant. Analyzing the temporal order of telecommuting engagement and residential relocation, the data show that those who are telecommuting and then move actually tend to relocate closer to their workplace, whereas those who begin telecommuting following a residential relocation tended to have moved much farther from their workplace. Analysis of the stated importance of telecommuting to specific residential relocations did not show a convincing effect toward more distant moves. Thus, the evidence more strongly supports the positive view of telecommuting, that it is ameliorating the negative transportation impacts of moves that occur for other reasons.

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[44]
Palm R, Danis M A.2001. Residential mobility: The impacts of web-based information on the search process and spatial housing choice patterns[J]. Urban Geography, 22(7): 641-655.In the past few years, a vastly enriched information source on housing vacancies has become available through real estate industry-sponsored sites on the Internet. This rich information source has the potential to influence spatial patterns and processes in the search for housing by eliminating information barriers in the search process. Thirty years ago, geographers documented the constraints to the search process caused by limited or biased information fields. The question addressed by this research was whether this new information availability would change the search patterns documented in the 1970s. In order to investigate this question recent movers in Wake County, North Carolina, were surveyed in early 2000, and the characteristics of Web users were compared with nonusers. The major finding of this research is that to date, the Internet has had little impact on search patterns, except that those using the Internet tend to visit a larger number of houses personally than those who do not use the World Wide Web as an information source. [Key words: residential mobility, housing search, Internet.]

DOI

[45]
Palm R, Danis M.2002. The internet and home purchase[J]. Tijdschrift voor Economische en Sociale Geografie, 93(5): 537-547.In the pre–internet era, information was a key determinant of the geographic nature of intra–urban moves. Information was a far more limited commodity, and therefore subject to management and even manipulation. Information brokers, particularly real estate agents, could order and limit the kinds of information to which prospective buyers gained access, with the potential of strongly biasing search space. In the past few years, a vastly enriched information source on housing vacancies has become available through real estate industry–sponsored sites on the internet. This rich information source has the potential to influence spatial patterns and processes in the search for housing by eliminating information barriers in the search process. Three hypotheses were tested with data from Wake County (Rayleigh), North Carolina. First, those with less prior familiarity with the destination area would make more frequent use of the internet. Second, internet users would visit fewer houses personally because they would have more prior information about the market, eliminating the need for some personal home visits. Third, internet users, armed with greater information about the house, neighbourhood, and prices of other recent sales, would pay less for the same housing/neighbourhood bundle. Recent movers in Wake County, North Carolina, were surveyed in early 2000, and the characteristics of web users were compared with nonusers. The major finding of this research is that to date, the internet has had little impact on search patterns, except that those using the internet tend to visit a larger number of houses personally than those who do not use the world wide web as an information source.

DOI

[46]
Potoglou D, Susilo Y.2008. Comparison of vehicle-ownership models[J]. Transportation Research Record: Journal of the Transportation Research Board, (2076): 97-105.Empirical studies on household car ownership have used two types of discrete choice modeling structures: ordered and unordered. In ordered response structures, such as the ordered logit and ordered ...

DOI

[47]
Roseman C C.1971. Migration as a spatial and temporal process[J]. Annals of the Association of American Geographers, 61(3): 589-598.ABSTRACT Within a framework of total human movement, migrations can be divided into two categories: 1) those which involve complete spatial displacement of the daily/weekly reciprocal movement patterns of the migrant (total displacement migrations), and 2) those which involve displacement of only part of the everyday reciprocal movements of migrants (partial displacement migrations). These two types of migration are related to different information-gathering processes and play different roles in the assimilation process of the migrant at his destination. They are also important as a basis for the identification of relationships between temporal and spatial dimensions of migration.

DOI

[48]
Rossi P H.1955. Why families move: A study in the social psychology of urban residential mobility[M]. New York: The Free Press, 14-35.

[49]
Sanchez T W, Dawkins C J.2001. Distinguishing city and suburban movers: Evidence from the American housing survey[J]. Housing Policy Debate, 12(3): 607-631.A significant amount of research has concentrated on the process of urban decentralization. Resulting patterns of urban development have far-reaching effects on land use, transportation, regional fiscal structure, public services and facilities, economic development, and social equity. Because planning processes are being developed to attempt to revitalize the urban core, it is important to know which households may be deciding to relocate to the central cities and why. A discriminant analysis is used to explore the similarities and differences among movers to central cities and suburban locations drawn from metropolitan samples of the 1989 through 1991 American Housing Surveys. The analysis compares the reasons for relocation, demographic differences, and metropolitan characteristics between central-city-to-suburb movers and suburb-to-central-city movers. The results indicate that these two groups are very similar in some respects and that some metropolitan-area characteristics may play a role in urban residential decentralization patterns.

DOI

[50]
Shen Q.2000. New telecommunications and residential location flexibility[J]. Environment and Planning A, 32(8): 1445-1463.Information and communications technologies are now used by a rapidly increasing number of people as means of spatial interaction. This has two major effects on transportation: (1) telecommunications partially substitute for travel to the workplace and to some other preexisting destinations; and (2) often indirectly, telecommunications stimulate new activities and generate extra travel. In this paper, I first develop an analytical framework for understanding changes in residential location flexibility that result from the direct travel substitution effect. I then apply this analytical tool to an exploration of the emerging urban spatial structure in the information age; alternative scenarios of telecommunications usage are simulated in a hypothetical metropolitan setting, and insightful results are obtained. The simulation is followed by a discussion of the role of urban planning in shaping the future pattern of residential growth. In an effort to stimulate further thinking and research, I point out in the conclusion that the analytical approach should be extended by taking into account the travel generation effect of telecommunications, and could be incorporated into land-use models.

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[51]
Steinnes D N.1977. Causality and intraurban iocation[J]. Journal of Urban Economics, 4(1): 69-79.Most intraurban location models in urban economics are based on a a priori assumption that employment is exogenous and a determinant of residential location. In this paper this assumption is relaxed or tested by estimating a dynamic model using pooled cross-sectional time-series data; the results suggest that causality runs from residence to employment, not vice versa.

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[52]
Tang W, Mokhtarian P L, Handy S L.2011. The impact of the residential built environment on work at home adoption and frequency: An example from northern California[J]. The Journal of Transport and Land Use, 4(3): 3-22.Considerable research related to telecommuting and home-based work has been conducted in the last two decades. The contribution of this study is to examine the effect of residential neighborhood built environment (BE) factors on working at home. Using data from a survey of eight neighborhoods in Northern California, we develop binary logit models of work-at-home (WAH) adoption, and multinomial logit models of WAH frequency category. Potential explanatory variables include sociodemographic traits, neighborhood preferences and perceptions, objective neighborhood characteristics, and travel attitudes and behavior. The results clearly demonstrate the contribution of built environment variables to WAH choices, in addition to previously-identified influences such as sociodemographic predictors and commute time. The findings suggest that land use and transportation strategies that are desirable from some perspectives will tend to weaken the motivation to work at home, and conversely, some factors that seem to increase the motivation to work at home are widely viewed as less sustainable. Accordingly, this research points to the complexity of trying to find the right balance among demand management strategies that sometimes act in competition rather than in synergy.

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