城市道路与公共交通网络中心性对住宅租赁价格的影响研究——以北京市为例

doi:10.18306/dlkxjz.2019.12.001

Abstract

Abstract:

Higher housing demand leads to housing price increasing rapidly, with lower housing affordability. Under these circumstances, renting a place has progressively become an alternative way for residents of cities. Urban transportation is one of the most significant influencing factors for housing rent, which needs to be examined at greater depth. This study used complex network analysis to explore how transportation centrality impacts housing rent. The conclusions are: transportation network shows significant impacts on housing rent, with higher impacts of public transport network than street network. Closeness in public transport has the highest impact on housing rent. Each centrality aspect influences housing rent differently, and relative accessibility in public transport network and transfer capacity in street network have the greatest impacts on housing rent. This article discussed two modes of urban transportation and their spatial characteristics. By refining the impacts of different transportation modes by network analysis, it provides a new perspective of urban transportation research and its spatial effects.

Key words: transportation network, housing rent, spatial pattern, influence mode, Beijing

DU Chao, WANG Jiaoe, LIU Binquan, HUANG Dingxi. Impacts of street and public transport network centralities on housing rent:A case study of Beijing[J].PROGRESS IN GEOGRAPHY, 2019, 38(12): 1831-1842.

Figures/Tables 12

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References 30

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变量	回归估计结果
变量	模型1	模型2	模型3	模型4	模型5	模型6	模型7	模型8	模型9	模型10	模型11
房屋面积	0.029	-0.078	0.022	0.021	0.021	-0.079	-0.072	-0.076	-0.067	-0.063	-0.066
与最近企业距离	-0.248	-8.813	0.837	1.474	1.668	-10.386	-8.222	-8.343	-10.893	-9.337	-9.496
与最近医院距离	1.075	0.664	1.411	1.191	1.357	1.042	0.754	1.007	0.854	0.673	0.808
与最近公园绿地距离	-2.037	-3.597	-1.588	-1.642	-1.632	-3.938	-3.950	-3.952	-4.576	-4.621	-4.591
与最近商场距离	-2.668	-3.332	-2.692	-2.649	-2.860	-3.410	-3.327	-3.507	-3.226	-3.114	-3.237
与最近餐饮服务距离	11.419	12.669	12.834	11.627	12.094	13.650	11.693	11.624	12.008	9.978	10.242
道路网络邻近中心性	12.840	—	13.972	13.908	14.197	—	—	—	—	—	—
道路网络介中心性	-0.480	—	—	—	—	-0.095	0.004	-0.004	—	—	—
道路网络直达中心性	-5.326	—	—	—	—	—	—	—	-7.400	-7.349	-7.440
公交网络邻近中心性	—	1.676	0.828	—	—	1.608	—	—	1.469	—	—
公交网络介中心性	—	-0.039	—	0.692	—	—	0.074	—	—	-0.036	—
公交网络度中心性	—	-0.363	—	—	0.480	—	—	0.238	—	—	0.344
R²	0.555	0.661	0.558	0.558	0.559	0.655	0.649	0.652	0.638	0.634	0.635

变量	邻近中心性		介中心性		公交网络度中心性	道路网络直达中心性
变量	道路网络	公交网络	道路网络	公交网络	公交网络度中心性	道路网络直达中心性
房屋面积	0.021	-7.892	-0.077	-0.084	-0.079	-0.065
与最近企业距离	1.020	-7.892	-9.267	-9.829	-7.834	-9.868
与最近医院距离	1.257	0.639	1.005	0.863	0.768	0.759
与最近公园绿地距离	-1.726	-3.743	-3.943	-3.606	-3.733	-4.667
与最近商场距离	-2.812	-3.309	-3.454	-3.490	-3.583	-3.251
与最近餐饮服务距离	12.252	12.537	12.452	14.597	12.404	10.733
交通单一变量	14.002	0.027	0.048	1.689	0.136	-7.159
R²	0.555	0.655	0.646	0.648	0.647	0.629

	道路网络			公共交通网络
	邻近中心性	介中心性	直达中心性	度中心性	邻近中心性	介中心性
高度正影响(>1.00)	2258	760	594	725	917	789
中度正影响(0.50~1.00)	0	147	34	208	171	201
低度正影响(0~0.50)	0	125	58	219	220	286
低度负影响(-0.50~0)	0	166	118	159	253	202
中度负影响(-1.00~-0.50)	0	150	54	190	208	167
高度负影响(<-1.00)	0	910	1400	757	489	613
合计	2258	2258	2258	2258	2258	2258

区位	环路	邻近中心性			介中心性			直达中心性	度中心性
区位	环路	道路	公交		道路	公交		道路	公交
城市中心	二环内	23.207	7.280		-2.500	1.391		-27.193	0.307
城市中心	二环与三环之间	14.552	3.446		-0.420	1.352		-11.814	0.367
城市中部	三环与四环之间	13.689	1.810		0.123	0.239		-6.437	0.809
城市中部	四环与五环之间	11.717	1.558		0.992			-1.385	-6.921	0.217
城市外围	五环与六环之间	13.557	-0.444	0.145	-0.194	-1.108	-0.570

Impacts of street and public transport network centralities on housing rent:A case study of Beijing

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