融入就业者感知的职住关系影响因素差异性研究——以乌鲁木齐市为例

doi:10.12118/j.issn.1000-6060.2025.825

摘要/Abstract

摘要：

城市职住关系直接影响就业者的通勤成本与生活质量，是衡量城市高质量发展的重要标志。深入分析职住关系的内在影响机制，有助于为城市空间结构优化和居民福祉改善提供科学决策依据。以乌鲁木齐市为例，基于2024年问卷调查数据与百度热力图数据，采用就业-居住偏离指数来划分城市职住关系的类型，运用多元logistics回归分析方法和空间滞后模型，探究不同类型职住关系区域的影响机制差异。结果表明：（1）城市职住关系可划分为职住平衡、居住主导失衡和就业主导失衡3类，乌鲁木齐市中心城区以职住平衡区为主体，就业主导失衡区多于居住主导失衡区，这种分布格局与产业集聚和交通布局相关。（2）个体属性、客观建成环境和主观感知对职住关系均具有影响。个体属性中出行方式、学历与职业是共性因素，而年龄、家庭年收入和家庭成员数量是就业失衡区的关键因素，性别与职业对居住失衡区的影响显著，户籍与年龄对职住平衡区的影响较大；客观建成环境影响街道职住功能的偏向，其中医疗设施与BRT站点推动就业功能，学校推动居住功能；主观感知显著影响职住平衡区就业者的通勤行为，而2类失衡区的就业者则通过自身行为调整缓解了职住失衡带来的负面影响。

关键词: 职住平衡, 通勤时间, 多元logistics回归, 乌鲁木齐市

Abstract:

The urban jobs-housing relationship, which directly impacts commuters’ costs and quality of life, is a crucial indicator of high-quality urban development. In-depth analysis of its underlying mechanisms provides a scientific basis for the optimization of urban spatial structure and enhancing resident welfare. Taking Urumqi of Xinjiang, China, as a case study, drawing on data from a 2024 questionnaire survey data and on Baidu heat maps, this study employs the jobs-housing deviation index to classify types of jobs-housing relationships. Using multinomial logistic regression and spatial lag models, it investigates the differential influencing mechanisms across area types. The findings show that: (1) Urban jobs-housing relationships are categorized into three types: jobs-housing balance, housing-led imbalance, and employment-led imbalance. The central urban area of Urumqi is predominantly characterized by a jobs-housing balance, with areas having employment-led imbalance outnumbering those having housing-led imbalance. The spatial distribution pattern is closely correlated with industrial agglomeration and transportation layouts. (2) Individual characteristics, the objective built environment, and subjective perceptions all had significant influence on the jobs-housing relationship. Regarding individual characteristics, travel mode, educational attainment, and occupation type are common influencing factors. In particular, age, household annual income, and number of household members are critical factors in employment-led imbalance areas; gender and occupation impact housing-led imbalance areas; while household registration and age predominantly affect jobs-housing balance areas. The objective built environment influences the functional bias of the streets: medical facilities and BRT stations promote employment functions, while schools foster residential functions. Subjective perceptions significantly affect the commuting behavior of workers in balanced areas, while workers in both types of imbalanced areas mitigate the negative effects of spatial mismatch through behavioral adjustments.

Key words: jobs-housing balance, commuting time, multinomial logistics regression, Urumqi City

周耘天, 杜宏茹, 冉娜·哈孜汉, 郭佳丽. 融入就业者感知的职住关系影响因素差异性研究——以乌鲁木齐市为例[J]. 干旱区地理, 2026, 49(9): 1901-1913.

ZHOU Yuntian, DU Hongru, Ranna HAZIHAN, GUO Jiali. Differential analysis of influencing factors in jobs-housing relationships incorporating workers’ perception: A case of Urumqi City[J]. Arid Land Geography, 2026, 49(9): 1901-1913.

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数据类型	数据处理
百度热力图	2024年5—6月以1 h为最小单位的研究区内工作日热力图数据。在ArcGIS Pro平台上，通过核密度分析，获取各时段的热力图栅格数据
百度POI	2024年研究区内商业、餐饮、交通、游憩、教育、医疗6类设施POI数据，共85210个点，用于分析客观建成环境对职住平衡的影响
高德路网	2024年研究区内涵盖主干道、次干道、支路等各级道路网络数据。运用ArcGIS Pro网络分析工具提取道路交叉口节点，以街道为单元计算交叉口密度，衡量路网连通性对职住空间分布的影响
调查问卷	2024年城市居民社会满意度调查中18~60岁之间的就业者进行问卷统计。调查时间为2024年5—8月，由各街道办事处组织开展。经数据清洗与编码筛选，得到有效问卷25234份，有效率为81.3%。对问卷中主观感知类变量进行信度效度检验，Cronbach’s α系数为0.818，KMO值为0.783，Bartlett球形检验显著（P<0.01），信度效度良好，可用于后续分析

模型	-2对数似然值	似然比检验
模型	-2对数似然值	卡方	自由度	显著性
就业主导失衡型街道	5687.421	822.711	120	0.000
职住平衡型街道	34435.466	4274.519	120	0.000
居住主导失衡型街道	7113.865	892.208	120	0.000

因子	变量	系数
公共服务设施	医疗设施密度	0.014^***
	商业零售设施密度	0.001^***
	学校密度	-0.124^***
	餐饮服务设施密度	-0.001
交通可达性	道路交叉口密度	-0.007
	公交站点密度	0.031
	BRT站点密度	0.175^***

模型	-2对数似然值	似然比检验
模型	-2对数似然值	卡方	自由度	显著性
就业主导失衡型街道	1458.912	241.922	56	0.000
职住平衡型街道	3435.711	1470.769	56	0.000
居住主导失衡型街道	1733.541	283.585	56	0.000

因子	变量	15~29 min	30~44 min	45~59 min	大于60 min
您认为城市就业者的通勤距离远吗?	很近	-0.498^***	-0.635^***	-0.463^***	-0.523^***
	比较近	-0.181^***	-0.223^***	-0.286^***	-0.315^***
	比较远	0.033	0.345^***	0.850^***	1.016^***
	很远	-0.126	0.114	0.717^***	1.492^***
您认为城市平时道路通畅吗?	非常拥堵	0.146	0.281^**	0.477^***	0.675^***
	比较拥堵	0.180^**	0.400^***	0.310^***	0.429^***
	比较通畅	0.116	-0.058	-0.042	-0.190
	很通畅	-0.142	-0.173	-0.434^***	-0.485^***
您认为公共交通站点设置是否方便?	很不方便	0.162	0.168	0.010	0.457^**
	不太方便	0.228^**	0.062	0.141	0.192
	比较方便	0.108	0.062	0.000	-0.140
	很方便	-0.038	-0.003	-0.192	-0.136
您认为当前街区的商业类型?	较差，种类单一，无法满足生活购物需求	-0.101	-0.133	0.021	-0.126
您认为当前街区的商业类型?	较多，种类齐全，完全满足生活购物需求	-0.164^***	-0.138^***	-0.190^***	-0.162^**