热带地理 ›› 2022, Vol. 42 ›› Issue (4): 616-628.doi: 10.13284/j.cnki.rddl.003466
收稿日期:
2021-12-28
修回日期:
2022-01-22
出版日期:
2022-04-05
发布日期:
2022-04-25
通讯作者:
林耿
E-mail:leiyubing288@163.com;lingeng00@163.com
作者简介:
雷玙冰(1996—),女,广西南宁人,硕士研究生,主要研究方向为城市地理学,(E-mail)leiyubing288@163.com;
基金资助:
Yubing Lei1(), Geng Lin1,2(
), Ren Yang1, Ying Wang3
Received:
2021-12-28
Revised:
2022-01-22
Online:
2022-04-05
Published:
2022-04-25
Contact:
Geng Lin
E-mail:leiyubing288@163.com;lingeng00@163.com
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。
中图分类号:
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.
表1
主因子载荷矩阵
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 |
表2
回归模型计量结果
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 |
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