The impacts of information channels on moving space:A case study on Nanjing
Received date: 2016-03-20
Request revised date: 2016-07-26
Online published: 2016-10-26
Copyright
According to the theories of housing search space and anchor point, Internet channels (e.g., housing websites, real estate agents) can provide more housing information (e.g., prices, locations, styles, years, facilities) than traditional channels (e.g., newspaper, TV, advertisements, introduction from friends or relatives) to reduce limitations of previous houses where movers have higher flexibility to choose their new houses to extend search spaces in their moving processes. However, the previous studies usually focused on some factors of residential mobility including housing price, transport, household composition, lifestyle, service facility, neighbourhood environment, employment opportunity, rather than ICT (telecommuting and information channel). Meanwhile, compared with studies on relationships between telecommuting and residential mobility (telecommuters are more likely to make longer moving distance or move into suburban areas because of flexible ways of working, which can reduce limitations of workplace in the moving processes), few scholars have paid their attention to the impacts of information channels on residents' moving space. This paper collected data from the survey of residential mobility in Nanjing city. It mainly used 3D Kernel density analysis to simulate space-time characteristics of different information channel users, and also Multinomial Logistic Regression to demonstrate the impacts of information channel on moving space. Three consequences were found by the above analyses. Firstly, males, young persons, and high-educated and high-income individuals preferred to use Internet channels to collect housing information. Secondly, Internet channel has become the most popular tool for residential mobility to obtain housing information in recent years compared to usage of traditional channels in the earlier times. Finally, more importantly, the Internet channel users were likely to make longer moving distance than the traditional channel users because of more and detailed housing information obtained from websites and real estate agents. On the one hand, these findings can enrich the present studies in the impacts of ICT on moving space in which internet channels take a directive role in the expansion of moving space as well as telecommuting. On the other hand, they are useful to housing information management (e.g., sharing platform building of urban housing information for reducing the "digital divide" among different groups) and housing spatial planning for urban governments (e.g., optimization of urban housing space, especially the one in suburban areas).
Key words: Internet channels; traditional channels; moving space; Nanjing
QIN Xiao , ZHEN Feng . The impacts of information channels on moving space:A case study on Nanjing[J]. GEOGRAPHICAL RESEARCH, 2016 , 35(10) : 1846 -1856 . DOI: 10.11821/dlyj201610004
Fig. 1 The administrative division of Nanjing (2012) and the locations of questionnaires图1 2012年南京市行政区划和问卷发放点分布 |
Tab. 1 The percentages of different information channel usages in Nanjing city表1 南京城区居民不同类型信息渠道使用百分比 |
| 属性小类 | 传统渠道(%) | 网络渠道(%) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| 报纸、电视等 | 亲戚朋友介绍 | 广告、宣传单等 | 其他方式 | 小计 | 中介机构 | 房产信息网站 | 小计 | ||
| 男性 | 4 | 15 | 4 | 22 | 45 | 25 | 30 | 55 | |
| 女性 | 7 | 23 | 5 | 21 | 56 | 23 | 22 | 44 | |
| 20~29岁 | 4 | 19 | 4 | 21 | 47 | 25 | 27 | 53 | |
| 30~39岁 | 8 | 21 | 4 | 19 | 52 | 22 | 25 | 48 | |
| 40岁以上 | 10 | 10 | 10 | 41 | 71 | 18 | 11 | 29 | |
| 高中/中专及以下 | 11 | 32 | 4 | 15 | 62 | 25 | 13 | 38 | |
| 大专及本科 | 5 | 20 | 4 | 20 | 49 | 23 | 28 | 51 | |
| 研究生及以上 | 2 | 13 | 4 | 23 | 41 | 32 | 28 | 59 | |
| 3000元及以下 | 6 | 24 | 4 | 31 | 65 | 21 | 14 | 35 | |
| 3000~5000元 | 5 | 19 | 5 | 19 | 47 | 23 | 30 | 53 | |
| 5001~8000元 | 6 | 9 | 2 | 20 | 37 | 29 | 34 | 63 | |
| 8000元以上 | 9 | 14 | 2 | 22 | 47 | 26 | 28 | 53 | |
| 政府/事业单位人员 | 6 | 19 | 5 | 15 | 45 | 26 | 29 | 55 | |
| 企业办公人员 | 5 | 20 | 3 | 15 | 43 | 26 | 31 | 57 | |
| 自由职业者 | 6 | 21 | 8 | 15 | 50 | 31 | 19 | 50 | |
| 商业服务人员 | 4 | 17 | 4 | 30 | 54 | 22 | 24 | 46 | |
| 制造业工人 | 0 | 75 | 0 | 0 | 75 | 0 | 25 | 25 | |
| 外出务工人员 | 14 | 29 | 14 | 0 | 57 | 14 | 29 | 43 | |
| 其他 | 26 | 10 | 0 | 38 | 74 | 13 | 13 | 26 | |
Fig. 2 The Kernel density of time-distance of residential mobility in Nanjing city图2 南京城区居民迁居的时间—距离核密度分析 |
Tab. 2 The results of Multinomial Logistic Regression表2 多项式逻辑回归结果 |
| 自变量 | 迁居距离 | ||||
|---|---|---|---|---|---|
| 0~5 km(含5 km) | 5~10 km(含10 km) | ||||
| 系数 | 标准差(鲁棒检验) | 系数 | 标准差(鲁棒检验) | ||
| 信息渠道(参照项:传统渠道) | |||||
| 网络渠道 | -2.280*** | 0.859 | -1.689* | 0.900 | |
| 混合渠道 | -1.174 | 0.928 | -3.114** | 1.446 | |
| 远程办公水平(参照项:低水平) | |||||
| 高水平 | -1.271* | 0.701 | 0.045 | 0.868 | |
| 中水平 | -2.122** | 0.934 | -0.067 | 1.188 | |
| 主要通勤方式(参照项:私家车) | |||||
| 非机动车 | 0.912 | 1.056 | 0.483 | 1.213 | |
| 公共交通 | -0.514 | 0.809 | -0.324 | 0.827 | |
| 房价(2012年) | -0.049 | 0.296 | 0.109 | 0.293 | |
| 房屋拥有权(参照项:租住房屋) | |||||
| 自有房屋 | -1.661** | 0.741 | -0.316 | 0.878 | |
| 年龄 | -0.046 | 0.338 | 0.089 | 0.311 | |
| 性别(参照项:女性) | |||||
| 男性 | 0.590 | 0.633 | 0.035 | 0.760 | |
| 教育水平(参照项:低水平) | |||||
| 高水平 | 0.228 | 1.385 | -0.723 | 1.436 | |
| 截距 | 3.987** | 1.759 | 2.280 | 1.851 | |
| 样本数 | 94 | ||||
| -2倍对数似然值 | 154.439 | ||||
| 似然比 | 32.171* | ||||
注:模型因变量为迁居距离,参照类为>10 km;*表示显著性<0.1,**表示显著性<0.05,***表示显著性<0.01。 |
The authors have declared that no competing interests exist.
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