南京市住房困难人群职住关系及影响因素分析

doi:10.18306/dlkxjz.2019.12.006

摘要/Abstract

摘要：

居住和就业的空间关系,既是城市居民日常生活的空间反映,也是评判城市空间合理性的重要准则。新就业大学生(取得专科及以上学历且工作未满3 a,在南京无自有住房且租房居住)、外来务工人员(收入低于2016年南京市人均可支配收入4166元/月,在南京无自有住房且租房居住)和中低收入人群(南京户籍,家庭人均可支配收入低于3074元/月,人均住房面积不大于20 m ²)等住房困难人群,普遍面临住房机会不平等的问题,职住关系也呈现出特殊性。论文基于南京市这3类住房困难人群的873份问卷数据,采用两步聚类法对通勤流向模式进行划分,基于多项Logistic回归模型探究通勤时间和通勤方式的影响因素,并以南京鼓楼区为案例解析住房困难人群的职住关系。结果表明：新就业大学生通勤流向主要为内部通勤,通勤时间最短,职住关系受通勤流向和工作单位影响;外来务工人员通勤流向主要为侧向通勤,通勤方式机动化程度最低,职住关系受通勤流向、工作单位和性别显著影响;中低收入人群通勤流向主要为侧向通勤,职住分离程度最高,职住关系主要受到住房类型的显著影响。以鼓楼区为例,论文进一步发现新就业大学生以交通条件为导向、外来务工人员以工作地为导向和中低收入人群以居住地为导向的职住关系。

关键词: 职住关系, 住房困难人群, 多项Logistic回归, 南京

Abstract:

Job-housing relationship is not only a reflection of urban residents' daily activities, but also an important criterion for assessing the quality of urban spatial layout. Since the economic reform of China in 1978, the original system of jobs and housing allocation has undergone considerable changes. Along with economic development and industrial upgrading, suburbanization process of manufacturing industry has taken place. Because of the reform of housing system and the suburbanization of industries in China, urban residents suffer from the impact of spatial mismatch between residence and work. Newly-employed college graduates, migrant workers, and low- and middle-income people often face the problem of unequal housing opportunities as well as unique problems in job-housing relationship. In order to investigate the job-housing relationship of people with housing difficulties, this study took 873 questionnaires in Nanjing City and analyzed the job-housing characteristics of people with housing difficulties. A two-step clustering method was employed to examine commuting flow pattern. After clustering, multinomial Logistic regression models were applied to explore the impact factors of commuting time and method. Finally, Gulou District was used as a case to analyze the job-housing relationship of people with housing difficulties. The results show that newly-employed college graduates mainly commute within the inner city. Their commuting time is the shortest among the three groups of people with housing difficulties. Their job-housing relationship is affected by commuter flow and work unit. Migrant workers mainly commute outside the inner city. They have the lowest level of commuter motorization. Their job-housing relationship is significantly affected by commuter flow, work unit, and gender. Low- and middle-income people mainly commute outside the inner city, with the highest level of separation of work and residence. Their job-housing relationship is mainly affected by the type of housing. The case of Gulou District further shows that the main characteristics of the job-housing relationship of people with housing difficulties are that newly-employed college graduates prioritize traffic condition considerations, migrant workers prioritize consideration of location of workplace, and low- and middle-income people prioritize consideration of location of residence.

Key words: job-housing relationship, housing difficulties, multinomial Logistic regression, Nanjing City,China

吴翔华, 陈昕雨, 袁丰. 南京市住房困难人群职住关系及影响因素分析[J]. 地理科学进展, 2019, 38(12): 1890-1902.

WU Xianghua, CHEN Xinyu, YUAN Feng. Job-housing relationship of people with housing difficulties and influencing factors in Nanjing City[J]. PROGRESS IN GEOGRAPHY, 2019, 38(12): 1890-1902.

图/表 8

图1

图2

表1

表2

图3

表3

表4

职住关系影响因素logistic回归结果"

自变量	所有样本				新就业大学生				外来务工人员				中低收入人群
	通勤时间	通勤方式^a			通勤时间	通勤方式			通勤时间	通勤方式			通勤时间	通勤方式
	通勤时间	A	B	A	通勤时间		B	A	通勤时间	B		A	通勤时间	B
常数
10 min内	0.21^***				0.17^***				0.14^***				0.57
10~30 min	2.19^***				2.98^**				1.74				4.90^**
30~60 min	19.47^***				28.42^***				23.13^***				40.70^***
人群类别(新就业大学生为参考)
外来务工人员	1.62^**	0.63^*	1.42
中低收入人群	2.07^***	1.13	2.69^**
性别(女性为参考)
男性	1.21	1.64^***	1.06		0.96	1.47	0.83		1.12	1.92^**	1.80		1.37	1.44	1.25
婚姻状况(未婚为参考)
已婚	0.80	1.09	1.24		0.58	0.76	3.49		0.65	0.84	0.26^**		1.17	1.84	5.62
离异或丧偶	0.39^**	0.65	0.54		-	-	-		0.29^*	0.63	0.19		1.03	1.30	1.81
工作单位(国有经济单位为参考)
合资及股份经济单位	0.86	0.89	0.83		1.52	1.01	0.90		0.38^**	0.22^**	0.25		2.12	4.61^**	2.63
个体或私营企业	0.61^**	0.44^***	0.20^***		0.94	0.42^**	0.10^***		0.49^*	0.29^**	0.14^**		1.26	1.52	1.11
其他	0.87	0.47^**	0.47		2.31	0.71	0.45		0.73	0.20^**	1.00		1.32	1.93	1.24
通勤流向(内部通勤为参考)
内向通勤	3.05^***	5.69^***	2.83^**		0.99	3.31^**	1.45		18.69^***	30.70^***	84.10^***		4.41^***	4.03^*	0.40
逆向通勤	2.75^***	0.57	3.04^**		1.91	0.67	8.62^***		18.46^***	0^b	20.62^**		1.55	0.46	0.56
侧向通勤	1.16	1.62^***	0.85		0.44^***	1.35	0.97		2.84^***	3.48^***	4.68^**		1.73^*	0.99	0.50
学历(大专为参考)
大专以下					0.14^***	0.58	0^b
本科					1.53	1.36	3.95
本科以上					1.461	1.40	3.98
住房类型(单位宿舍为参考)
商品房													0.83	0.85	0^b
保障房													2.63^**	1.54	0.55
拆迁安置房													1.96^*	1.56	0.66
租赁私人房屋													0.77	1.02	0.05
样本数量
Cox和Snell R²	0.066	0.137			0.132	0.170			0.197	0.286			0.129	0.206
Nagelkerke R²	0.073	0.162			0.148	0.205			0.217	0.339			0.141	0.240

表4

图4

参考文献 46

[1]	程鹏, 唐子来 . 2017. 上海中心城区的职住空间匹配及其演化特征研究[J]. 城市规划学刊, ( 3):62-69.
	[ Cheng P, Tang Z L . 2017. A Study on the spatial match of jobs-housing balance and its change over time in the central city of Shanghai. Urban Planning Forum, ( 3):62-69. ]
[2]	党云晓, 董冠鹏, 余建辉 , 等. 2015. 北京土地利用混合度对居民职住分离的影响[J]. 地理学报, 70(6):919-930. doi: 10.11821/dlxb201506006
	[ Dang Y X, Dong G P, Yu J H , et al. 2015. Impact of land-use mixed degree on resident's home-work separation in Beijing. Acta Geographica Sinica, 70(6):919-930. ] doi: 10.11821/dlxb201506006
[3]	冯健, 钟奕纯 . 2018. 北京社会空间重构(2000—2010)[J]. 地理学报, 73(4):711-737. doi: 10.11821/dlxb201804010
	[ Feng J, Zhong Y C . 2018. Restructuring of social space in Beijing from 2000 to 2010. Acta Geographica Sinica, 73(4):711-737. ] doi: 10.11821/dlxb201804010
[4]	符婷婷, 张艳, 柴彦威 . 2018. 大城市郊区居民通勤模式对健康的影响研究: 以北京天通苑为例[J]. 地理科学进展, 37(4):547-555. doi: 10.18306/dlkxjz.2018.04.010
	[ Fu T T, Zhang Y, Chai Y W . 2018. Implications of commuting pattern for suburban residents' health in large Chinese cities: Evidences from Tiantongyuan in Beijing. Progress in Geography, 37(4):547-555. ] doi: 10.18306/dlkxjz.2018.04.010
[5]	何嘉明, 周素红, 谢雪梅 . 2017. 女性主义地理学视角下的广州女性居民日常出行目的及影响因素[J]. 地理研究, 36(6):1053-1064. doi: 10.11821/dlyj201706005
	[ He J M, Zhou S H, Xie X M . 2017. Female residents' daily travel purpose and its influencing factors from the perspective of feminism: A case study in Guangzhou, China. Geographical Research, 36(6):1053-1064. ] doi: 10.11821/dlyj201706005
[6]	霍华德 E . 2010. 明日的田园城市 [M]. 金纪元, 译. 北京: 商务印书馆: 80.
	[ Howard E. 2010. Garden cities of tomorrow. Translated by Jin J Y. Beijing, China: The Commercial Press: 80. ]
[7]	康达西, 殷洁 . 2018. 城市中心区流动人口聚居空间考察: 以南京市红庙小区为例[J]. 城市问题, ( 2):90-97.
	[ Kang D X, Yin J . 2018. Investigation on the living space of floating population in urban central district: A case of Hongmiao Community in Nanjing. Urban Problems, ( 2):90-97. ]
[8]	李梦玄, 周义, 胡培 . 2013. 保障房社区居民居住-就业空间失配福利损失研究[J]. 城市发展研究, 20(10):63-68.
	[ Li M X, Zhou Y, Hu P . 2013. Welfare loss study of the job-housing spatial mismatch of residents in ensuring housing communities. Urban Studies, 20(10):63-68. ]
[9]	申犁帆, 张纯, 李赫 , 等. 2018. 大城市通勤方式与职住失衡的相互关系[J]. 地理科学进展, 37(9):1277-1290. doi: 10.18306/dlkxjz.2018.09.010
	[ Shen L F, Zhang C, Li H , et al . 2018. Interaction between commuting modes and job-housing imbalance in metropolis: An empirical study by Bayesian-tobit analysis in Beijing. Progress in Geography, 37(9):1277-1290. ] doi: 10.18306/dlkxjz.2018.09.010
[10]	宋伟轩, 马雨竹, 李晓丽 , 等. 2018. 南京城市住宅小区房价增长模式与效应[J]. 地理学报, 73(10):1880-1895. doi: 10.11821/dlxb201810005
	[ Song W X, Ma Y Z, Li X L , et al. 2018. Housing price growth in different residences in urban Nanjing: Spatiotemporal pattern and social spatial effect. Acta Geographica Sinica, 73(10):1880-1895. ] doi: 10.11821/dlxb201810005
[11]	孙斌栋, 但波 . 2015. 上海城市建成环境对居民通勤方式选择的影响[J]. 地理学报, 70(10):1664-1674. doi: 10.11821/dlxb201510010
	[ Sun B D, Dan B . 2015. Impact of urban built environment on residential choice of commuting mode in Shanghai. Acta Geographica Sinica, 70(10):1664-1674. ] doi: 10.11821/dlxb201510010
[12]	孙晨, 甄峰, 常恩予 , 等. 2016. 基于招聘网数据的南京市新增就业空间分布[J]. 经济地理, 36(6):83-90.
	[ Sun C, Zhen F, Chang E Y , et al. 2016. Spatial distribution of new employment demand in Nanjing based on the data from Zhaopin.com. Economic Geography, 36(6):83-90. ]
[13]	塔娜, 柴彦威 . 2017. 基于收入群体差异的北京典型郊区低收入居民的行为空间困境[J]. 地理学报, 72(10):1776-1786. doi: 10.11821/dlxb201710004
	[ Ta N, Chai Y W . 2017. Spatial dilemma of suburban low-income residents: An analysis of behavior space among different income groups. Acta Geographica Sinica, 72(10):1776-1786. ] doi: 10.11821/dlxb201710004
[14]	谭洁, 赵杨 . 2013. 基于行业就业吸纳能力的我国大学生就业问题研究[J]. 教育与经济, ( 3):25-32.
	[ Tan J, Zhao Y . 2013. Research on the employment of college students in China: Based on the trade's employment absorbing capacity. Education & Economy, ( 3):25-32. ]
[15]	田金玲, 王德, 谢栋灿 , 等. 2017. 上海市典型就业区的通勤特征分析与模式总结: 张江、金桥和陆家嘴的案例比较[J]. 地理研究, 36(1):134-148. doi: 10.11821/dlyj201701011
	[ Tian J L, Wang D, Xie D C , et al. 2017. Identifying the commuting features and patterns of typical employment areas in Shanghai using cellphone signaling data: A case study in Zhangjiang, Jinqiao and Lujiazui. Geographical Research, 36(1):134-148. ] doi: 10.11821/dlyj201701011
[16]	王慧, 吴晓, 强欢欢 . 2014. 南京市主城区就业空间布局初探[J]. 经济地理, 34(6):115-123.
	[ Wang H, Wu X, Qiang H H . 2014. A preliminary analysis of spatial distribution of employment spacial in Nanjing main city. Economic Geography, 34(6):115-123. ]
[17]	魏海涛, 赵晖, 肖天聪 . 2017. 北京市职住分离及其影响因素分析[J]. 城市发展研究, 24(4):43-51.
	[ Wei H T, Zhao H, Xiao T C . 2017. A study on the characteristics and influencing factors of spatial mismatch between housing and employment in Beijing. Urban Studies, 24(4):43-51. ]
[18]	吴瑞君, 朱宝树, 古荭欢 . 2017. 上海市就业人口的职住分离和结构分异[J]. 中国人口科学, ( 3):101-115.
	[ Wu R J, Zhu B S, Gu H H . 2017. Spatial mismatch of job-housing and the structure variation for employed population in Shanghai. Chinese Journal of Population Science, ( 3):101-115. ]
[19]	徐菊芬, 胡月, 朱杰 . 2014. 新就业大学生的住房供需现状及特征研究: 基于南京市的调研[J]. 现代城市研究, ( 10):95-99.
	[ Xu J F, Hu Y, Zhu J . 2014. The present situation and characteristic on housing demand for entrant graduate: Based on the investigation in Nanjing City. Modern Urban Research, ( 10):95-99. ]
[20]	张济婷, 周素红 . 2018. 转型期广州市居民职住模式的群体差异及其影响因素[J]. 地理研究, 37(3):62-69.
	[ Zhang J T, Zhou S H . 2018. The diversity of different groups' job-housing patterns and their impact factors under the background of institutional transformation: A case study of Guangzhou, China. Geographical Research, 37(3):62-69. ]
[21]	张艳, 刘志林 . 2018. 市场转型背景下北京市中低收入居民的住房机会与职住分离研究[J]. 地理科学, 38(1):11-19. doi: 10.13249/j.cnki.sgs.2018.01.002
	[ Zhang Y, Liu Z L . 2018. Access to housings and home-work separation of moderate to low-income residents in Beijing under the market-oriented transition. Scientia Geographica Sinica, 38(1):11-19. ] doi: 10.13249/j.cnki.sgs.2018.01.002
[22]	郑承智, 张旺锋, 武炳炎 , 等. 2017. 北京市外来人口集聚型城中村流动人口职住分离研究[J]. 地理科学进展, 36(4):416-425. doi: 10.18306/dlkxjz.2017.04.003
	[ Zheng C Z, Zhang W F, Wu B Y , et al. 2017. Job-housing mismatch of floating population in urban villages of Beijing. Progress in Geography, 36(4):416-425. ] doi: 10.18306/dlkxjz.2017.04.003
[23]	钟炜菁, 王德, 谢栋灿 , 等. 2017. 上海市人口分布与空间活动的动态特征研究: 基于手机信令数据的探索[J]. 地理研究, 36(5):972-984. doi: 10.11821/dlyj201705013
	[ Zhong W J, Wang D, Xie D C , et al. 2017. Dynamic characteristics of Shanghai's population distribution using cell phone signaling data. Geographical Research, 36(5):972-984. ] doi: 10.11821/dlyj201705013
[24]	周艳, 李妍羲, 黄悦莹 , 等. 2017. 基于社交媒体数据的城市人群分类与活动特征分析[J]. 地球信息科学学报, 19(9):1238-1244. doi: 10.3724/SP.J.1047.2017.01238
	[ Zhou Y, Li Y X, Huang Y Y , et al. 2017. Analysis of classification methods and activity characteristics of urban population based on social media data. Journal of Geo-information Science, 19(9):1238-1244. ] doi: 10.3724/SP.J.1047.2017.01238
[25]	左为, 吴晓, 强欢欢 . 2017. 性别视角下外来务工人员就业空间的分异探析: 以南京市主城区为实证[J]. 城市规划, 41(8):54-64.
	[ Zuo W, Wu X, Qiang H H . 2017. Analysis on the differentiation of employment space of migrant workers from the perspective of gender: An empirical study on the main city of Nanjing. City Planning Review, 41(8):54-64. ]
[26]	Chiu T, Fang D P, Chen J, et al. 2001. A robust and scalable clustering algorithm for mixed type attributes in large database environment [C]// KDD-2001: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, USA:263-268.
[27]	Fan Y L, Allen R, Sun T S . 2014. Spatial mismatch in Beijing, China: Implications of job accessibility for Chinese low-wage workers[J]. Habitat International, 44:202-210. doi: 10.1016/j.habitatint.2014.06.002
[28]	Gao J L, Chen W, Yuan F . 2017. Spatial restructuring and the logic of industrial land redevelopment in urban China: I. Theoretical considerations[J]. Land Use Policy, 68:604-613. doi: 10.1016/j.landusepol.2017.07.021
[29]	Gao J L, Yuan F . 2017. Economic transition, firm dynamics, and restructuring of manufacturing spaces in urban China: Empirical evidence from Nanjing[J]. Professional Geographer, 69(3):504-519. doi: 10.1080/00330124.2016.1268059
[30]	Haddad E A, Barufi A M B . 2017. From rivers to roads: Spatial mismatch and inequality of opportunity in urban labor markets of a megacity[J]. Habitat International, 68:3-14. doi: 10.1016/j.habitatint.2017.03.016
[31]	Han H, Yang C F, Wang E , et al. 2015. Evolution of jobs-housing spatial relationship in Beijing metropolitan area: A job accessibility perspective[J]. Chinese Geographical Science, 25(3):375-388. doi: 10.1007/s11769-015-0738-1
[32]	Horner M W . 2009. Analyzing the robustness of jobs-housing metrics due to travel time uncertainty [R]. Paper presented at the annual meeting, Transportation Research Board. Washington DC, USA.
[33]	Jan K B, Richard W M . 1997. Spatial mismatch: An equilibrium analysis[J]. Regional Science and Urban Economics, 27(6):693-714. doi: 10.1016/S0166-0462(97)00004-5
[34]	Loo B P Y, Chow A S Y . 2011. Jobs-housing balance in an era of population decentralization: An analytical framework and a case study [J]. Social Science Electronic Publishing, 19(4):552-562.
[35]	Miller J S . 2010. Feasibility of using jobs/housing balance in Virginia statewide planning [R/OL]. Virginia Transportation Research Council. 2010-08[ 2018- 12- 20]. .
[36]	Modarres A . 2017. Commuting, energy consumption, and the challenge of sustainable urban development[J]. Current Opinion in Environmental Sustainability, 25:1-7. pmid: 12293542
[37]	Niedzielski M A, O'Kelly M E, Boschmann E E . 2015. Synthesizing spatial interaction data for social science research: Validation and an investigation of spatial mismatch in Wichita, Kansas[J]. Computers Environment & Urban Systems, 54:204-218. doi: 10.3791/60425 pmid: 31840663
[38]	Raphael B-E . 2006. Inter-regional labor market equilibrium: Another pattern of spatial mismatch[J]. Annals of Regional Science, 40(2):393-405. doi: 10.1007/s00168-006-0059-5
[39]	Sultana S, Weber J . 2010. Journey-to-work patterns in the age of sprawl: Evidence from two midsize southern metropolitan areas[J]. Professional Geographer, 59(2):193-208. doi: 10.1111/j.1467-9272.2007.00607.x
[40]	Wang E, Song J P, Xu T . 2011. From "spatial bond" to "spatial mismatch": An assessment of changing jobs-housing relationship in Beijing[J]. Habitat International, 35(2):398-409. doi: 10.1016/j.habitatint.2010.11.008
[41]	Yuan F, Gao J L, Wang L , et al. 2017. Co-location of manufacturing and producer services in Nanjing, China[J]. Cities, 63:81-91. doi: 10.1016/j.cities.2016.12.021
[42]	Yuan F, Gao J L, Wu J W . 2016. Nanjing: An ancient city rising in transitional China[J]. Cities, 50:82-92. doi: 10.1016/j.cities.2015.08.015
[43]	Zenou Y . 2013. Spatial versus social mismatch[J]. Journal of Urban Economics, 74:113-132. doi: 10.1016/j.neuroimage.2012.06.068 pmid: 22776450
[44]	Zhang P, Zhou J P, Zhang T R . 2017. Quantifying and visualizing jobs-housing balance with big data: A case study of Shanghai[J]. Cities, 66:10-22. doi: 10.1016/j.cities.2017.03.004
[45]	Zhou S H, Liu Y, Kwan M P . 2016. Spatial mismatch in post-reform urban China: A case study of a relocated state-owned enterprise in Guangzhou[J]. Habitat International, 58:1-11. doi: 10.1016/j.habitatint.2016.08.003
[46]	Zhou X G, Yeh A G O, Yue Y . 2018. Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data[J]. Journal of Transport Geography, 68:102-108. doi: 10.1016/j.jtrangeo.2017.12.006

变量	样本数	百分比/%	通勤时间/min	变量	样本数	百分比/%	通勤时间/min
人群类别				工作单位
新就业大学生	340	38.9	39.0	国有经济单位	128	14.7	45.8
外来务工人员	280	32.1	40.7	合资及股份经济单位	275	31.5	41.5
中低收入人群	253	29.0	44.1	个体或私营企业	411	47.1	38.8
性别				其他	59	6.8	42.8
男	515	59.0	42.2	通勤流向
女	358	41.0	39.3	内部通勤	377	43.2	39.0
婚姻状况				内向通勤	94	10.8	52.7
未婚	394	45.1	40.2	逆向通勤	29	3.3	49.0
已婚	455	52.1	41.9	侧向通勤	373	42.7	39.5
离异或丧偶	24	2.8	37.1

通勤流向模式	住房困难人群
	总体		新就业大学生		外来务工人员		中低收入居民
	样本量	比例/%	样本量	比例/%	样本量	比例/%	样本量	比例/%
内部通勤(主城区—主城区)	377	43.2	194	57.1	95	33.9	88	34.8
内向通勤(远城区—主城区)	94	10.8	39	11.5	30	10.7	25	9.9
逆向通勤(主城区—远城区)	29	3.3	15	4.4	4	1.4	10	4.0
侧向通勤(远城区—远城区)	373	42.7	92	27.1	151	53.9	130	51.4