产业与地形因素影响下的山地城市职住空间关系——以重庆市主城九区为例

doi:10.13284/j.cnki.rddl.003466

摘要/Abstract

摘要：

选择重庆主城九区作为典型案例，基于百度慧眼识别的就业人口与居住人口数据，通过测算就业－居住偏离度指数分析其人口分布特征与职住空间关系，划分就业主导区、基本匹配区与居住主导区。并基于全国第四次经济普查数据，在对产业进行综合因子分析基础上，定量化识别产业综合因子和地形因子对重庆主城九区职住空间匹配性影响。结果表明：1）重庆市主城区的就业与居住人口空间分布趋势具有一致性特征，整体上呈现“中间高、四周低”的特征；2）重庆市主城区的职住空间基本平衡，街道尺度上职住偏离特征不显著，但城市多组团的中心地区职住空间匹配特征稍有差异，呈现主中心（解放碑）职住平衡度低、4个副中心职住平衡度高的格局；3）空间回归模型表明，综合性服务产业因子和生产性服务产业因子强化了职住分离程度，社会性服务产业因子、制造业因子和地形起伏度降低了职住空间差异。总体上，产业布局与地形地貌是重庆主城区职住空间关系的重要影响因素，二者与政府规划、交通条件和居民生活相互补充、共同作用，塑造了重庆主城现今协调发展的就业－居住空间格局。

关键词: 山地城市, 职住空间关系, 就业居住偏离度, 产业布局, 地形地貌, 空间回归模型, 重庆市

Abstract:

The coordinated development of jobs and housing spaces is of great importance for the rational allocation of urban functions and residents' quality of life. We focus on the measurement of the spatial relationship between working and living space in a mountainous city and the impact of industrial and topographic factors, with the main city of Chongqing as the study area. Using the employed population and residential population as identified by Baidu Huiyan, this paper analyzes the relationship between jobs and housing spaces in the central city of Chongqing by calculating the degree of job-housing deviation, dividing the study area into three groups: the Employment-Dominant Area (EDA), the Balanced Area (BA), and the Residential-Dominant Area (RDA). Factor analysis and window analysis are then applied to explore the impact of industrial and topographic factors on job-housing space based on the Fourth National Economic Census. We find that, first, the spatial distributions of employment and the residential population in the central city of Chongqing are basically the same, showing the characteristics of "high in the middle and low around" and a polycentric spatial structure; there is spatial overlap between the areas with the densest employment and residential population, such as Jiefangbei, Qixinggang, and Nanping. Second, the job-housing space in Chongqing is basically balanced; the deviation of work and residence in most suburban districts is not obvious. The sub-districts with the highest Standard Deviation (SD) are mainly concentrated in the employment-dominant areas within the outer ring, while a few are distributed in the residential-dominant areas. The multi-center structure shows a low job-housing balance in the main center (Jiefangbei) and a high job-housing balance in the four sub-centers. Third, industrial factors and relief amplitude have a great impact on job-housing balance in Chongqing's main urban area. Using SD as the dependent variable can better explain the influencing factors. The integrated service industry factor and the production service industry factor exacerbate the separation of jobs and residences because of their ability to attract the employment population. In the meantime, the social service industry factor, manufacturing factor, and relief amplitude reduce the difference between jobs and residences. In addition, urban spatial structure, traffic patterns, government planning, and residents' attributes are important factors shaping the job-housing space in the Chongqing main area.

Key words: mountainous city, job-housing space, job-housing deviation degree, industrial layout, typography, spatial regression models, Chongqing

中图分类号:

TU984.113

雷玙冰, 林耿, 杨忍, 王英. 产业与地形因素影响下的山地城市职住空间关系——以重庆市主城九区为例[J]. 热带地理, 2022, 42(4): 616-628.

Yubing Lei, Geng Lin, Ren Yang, Ying Wang. The Spatial Relationship between Employed and Residential Populations in a Mountainous City: A Case Study of the Chongqing Main Area[J]. Tropical Geography, 2022, 42(4): 616-628.

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行业	主因子1	主因子2	主因子3	主因子4
X₁	-0.032	0.021	-0.007	0.906
X₂	0.176	0.141	0.567	0.061
X₃	0.374	0.054	0.693	0.250
X₄	0.837	0.282	0.050	0.035
X₅	0.277	0.437	0.187	0.396
X₆	0.707	0.315	0.178	0.058
X₇	0.343	0.829	0.010	0.020
X₈	0.077	0.822	0.035	-0.033
X₉	0.749	0.246	0.149	0.283
X₁₀	0.786	0.398	0.292	0.070
X₁₁	0.451	0.775	0.272	0.109
X₁₂	0.129	0.831	0.245	0.061
X₁₃	0.787	0.140	0.325	-0.016
X₁₄	0.571	-0.049	0.368	0.016
X₁₅	0.652	0.070	0.318	-0.201
X₁₆	0.851	0.279	0.206	0.012
X₁₇	0.215	0.200	0.691	-0.180

变量	就业－居住偏离度（Z）			偏离度标准差（SD）
	模型1	模型2	模型3	模型4	模型5	模型6
	OLS	SLM	SEM	OLS	SLM	SEM
FAC₁	0.002	0.005	0.001	-0.031	-0.037	0.053
FAC₂	0.125^***	0.122^***	0.126^***	0.116^***	0.116^***	0.121^***
FAC₃	-0.075^*	-0.076^*	-0.064^*	-0.062^*	-0.058^*	-0.048^*
FAC₄	-0.048	-0.048	-0.059^*	-0.063^*	-0.061^**	-0.068^**
RDLS	-0.346	-0.249	-0.280	-0.881^**	-0.739^**	-0.800^**
Constant	1.171^***	0.849^***	1.147^***	0.596^***	0.486^***	0.576^***
Rho	—	0.281	—	—	0.218	—
Lambda	—	—	0.320	—	—	0.275
R²	0.175	0.223	0.224	0.195	0.218	0.229
Adjusted R²	0.149	—	—	0.170	—	—
AIC	249.44	244.511	243.264	195.100	193.951	190.771
SC	267.965	266.124	261.789	213.625	215.564	209.297
Log Likelihood	-118.72	-115.256	-115.632	-91.550	-89.975	-89.386

检验统计量	Z		SD
检验统计量	统计量	P值	统计量	P值
Moran's I (error)	2.617	0.009	2.201	0.028
LM-lag	5.940	0.015	2.475	0.116
RLM-lag	1.018	0.313	0.085	0.771
LM-error	5.050	0.025	3.401	0.045
RLM-error	0.129	0.720	1.010	0.315
Lagrange Multiplier (SARMA)	6.068	0.048	3.485	0.175

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