深圳轨道交通对城市空间结构的影响——基于地价梯度和开发强度梯度的分析

doi:10.11821/dlyj201611007

摘要/Abstract

摘要：

大运量的城市轨道交通系统在一定程度上决定了城市的布局结构和土地利用格局。从时空两个维度出发,基于地价和开发强度两个要素,探讨轨道交通影响下的深圳市空间结构动态演变过程。通过描述性分析和空间计量模型实证发现,轨道交通站点周边地价和开发强度在受到距市中心或组团中心距离影响而空间衰减的同时,呈现出明显的站点导向性。轨道交通对城市空间结构的影响在时间上是超前的,规划方案的公示和轨道系统的建成运营均塑造了土地价格梯度和容积率梯度。并且,由于城市建设和历史原因,深圳原特区内外在轨道交通的影响下,地价和开发强度呈现了差异化的增长趋势。研究结果对理解轨道交通对城市空间结构影响及政府财政效应评估等具有积极意义。

关键词: 轨道交通, 空间梯度, 城市空间结构, 地块交易, 深圳市

Abstract:

High capacity transit system offers a solution to traffic problems and shapes the city structure at the same time. A great number of articles have examined the close relationship between rail transit and urban structure. Our study attempts to enrich the existing literature by providing empirical evidences of how the metro system in Shenzhen has helped to change urban structure. We use data of land transaction from 2000 to 2014, which includes information on price, parcel location, land use type and transaction mode. We calculate distances from each parcel to its nearest metro station, nearest hub station, and the city center. We also divide all land parcels into three groups according to its temporal relationship with the planning and operation of its closest metro station. When analyzing the data, we firstly plot land price and FAR in relation to the three distance variables to see how they vary spatially. We then use spatial regression models to test whether land price and FAR are significantly affected by the distance to stations. We find that price and FAR of parcels transacted before metro plan announcement shows weak or even no station-orientation. However, price and FAR gradients appear after metro plan announcement and metro operation, which result from relatively fast price increase at station proximity. It is shown that land price and FAR's rising within 1 km are greater than the ones within 2 km. In addition, we find that metro investment has a greater impact on land price in relatively underdeveloped outer part of Shenzhen than that in the inner part by dividing all land parcels into two groups. Metro stations in the outer part then become a series of new economic growth points. Shenzhen is a city of multiple centers. The station-oriented development could further move the city into a fine tuned multi-center structure.

Key words: rail transit, gradient, spatial structure, land transaction, Shenzhen

乐晓辉, 陈君娴, 杨家文. 深圳轨道交通对城市空间结构的影响——基于地价梯度和开发强度梯度的分析[J]. 地理研究, 2016, 35(11): 2091-2104.

Xiaohui LE, Junxian CHEN, Jiawen YANG. Impact of rail transit on urban spatial structure in Shenzhen: Analysis based on land parcel price and FAR gradients[J]. GEOGRAPHICAL RESEARCH, 2016, 35(11): 2091-2104.

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变量类型	变量	变量描述	变量特性	最大值	最小值	平均数
因变量	lnP	地块交易价格	对数值	12.73	3.17	8.63
因变量	FAR	容积率	连续	20	0.05	4.75
距离变量(km)	D1	地块距最近地铁站的直线距离	连续	1.99	0	0.73
	D2	地块就近地铁站沿地铁线到最近枢纽站的最近距离	连续	24.78	0	4.01
	D3	地块就近地铁站沿地铁线到市民中心站的最近距离	连续	37.08	0	16.54
时间变量(以Group A 为参考基准)	Group B	成交时间位于最近地铁站公示后,开通运营前为1,否为0	虚拟	1	0	0.35
时间变量(以Group A 为参考基准)	Group C	成交时间位于最近地铁站开通运营后为1,否为0	虚拟	1	0	0.32
用地类型（以其他用地类型为参考基准）	商业用地(COM_LD)	商业用地为1,非商为0	虚拟	1	0	0.30
	居住用地(RES_LD)	居住用地为1,非居为0	虚拟	1	0	0.28
	工业用地(IND_LD)	工业用地为1,非工为0	虚拟	1	0	0.25
土地交易类型（以“挂牌”为参考基准）	拍卖(AUC)	拍卖为1,非拍卖为0	虚拟	1	0	0.07
土地交易类型（以“挂牌”为参考基准）	招标(TEN)	招标为1,非招标为0	虚拟	1	0	0.03
POI可达性	医院(HOSP)	地块1 km内有医院为1,没有为0	虚拟	1	0	0.35
	公园(PARK)	地块1 km内有公园为1,没有为0	虚拟	1	0	0.20
	小学(PRIM)	地块1 km内有小学为1,没有为0	虚拟	1	0	0.66
	中学(MIDD)	地块1 km内有中学为1,没有为0	虚拟	1	0	0.61
	高校(COLL)	地块2 km内有高校为1,没有为0	虚拟	1	0	0.25

模型		模型 1	模型 2	模型 3	模型 4	模型 5
数据范围		2 km以内	2 km以内Group A	2 km以内	1 km以内	2 km以内
距离变量	D1	-0.39^*	-0.21	-0.16	0.32	0.07
	D2	0.012	0.03	0.02	0.02	0.01
	D3	-0.04^**	-0.05^***	-0.04^**	-0.04^**	-0.02
时间变量	Group B	-	-	0.97^***	1.12^***	0.70^***
时间变量	Group C	-	-	1.60^***	1.65^***	1.08^***
开发强度	FAR	-	-	-	-	0.16^***
用地类型	COM_LD	2.19^***	1.59^***	2.33^***	2.52^***	1.41^***
	RES_LD	0.56^**	1.02^**	1.07^***	1.11^***	0.46^*
	IND_LD	0.23	0.55	0.26	0.26	-0.27
交易类型	AUC	-0.05	-0.26	0.12	-0.01	0.43
交易类型	TEN	1.07^**	0.45	0.87^*	0.84	1.08^**
POI可达性	HOSP	-0.32^*	-0.24	-0.11	-0.03	-0.03
	PARK	0.10	0.22	0.28	0.25	0.26
	PRIM	0.44^**	0.71^***	0.36^*	0.52^**	0.26
	MIDD	-0.33^*	-0.37	-0.37^**	-0.35	-0.27^*
	COLL	0.28	0.29	0.25	0.25	-0.10
常数项	CONS	6.79^***	5.40^***	5.56^***	6.16^***	5.78^***
空间自相关	W_lnP	0.20^***	0.24^*	0.19^***	0.07	0.14^**
统计结果	R2	0.42	0.47	0.50	0.46	0.54
	AIC	1177.94	286.90	1134.82	893.147	1081.12
	Log Likelihood	-573.97	-128.45	-550.41	-429.57	-522.56
	样本量	314	103	314	236	307

模型		模型 6	模型 7	模型 8	模型 9
数据范围		2 km以内	2 km以内Group A	2 km以内	1 km以内
距离变量	D1	-1.25^***	-0.37	-0.80^**	-0.49
	D2	0.03	-0.01	0.06	0.07
	D3	-0.07^**	-0.04	-0.07^***	-0.09^***
时间变量	Group B	-	-	1.95^***	2.67^***
时间变量	Group C	-	-	3.08^***	3.62^***
价格变量	P	-	-	-	-
用地类型	COM_LD	5.10^***	3.42^***	5.50^***	6.45^***
	RES_LD	2.19^***	1.92^**	3.25^***	4.06^***
	IND_LD	2.93^***	1.16	2.97^***	3.68^***
交易类型	AUC	-2.02^***	-1.28^**	-1.72^***	-1.88^**
交易类型	TEN	-0.16	0.61	-0.66	-0.16
POI可达性	HOSP	-0.26	0.09	0.07	0.21
	PARK	-0.41	-0.34	-0.10	-0.32
	PRIM	0.61^*	-0.03	0.53	0.76^*
	MIDD	-0.13	0.45	-0.22	-0.17
	COLL	1.49^***	-0.64	1.64^***	1.89^***
常数项	CONS	2.01^**	0.83	-0.30	-1.41
空间自相关	W_FAR	0.30^***	0.33^***	0.24^***	0.19^***
统计结果	R2	0.53	0.33	0.60	0.60
	AIC	1530.71	443.48	1485.34	1182.4
	Log Likelihood	-750.36	-206.74	-725.67	-574.20
	样本量	307	103	307	236

模型		模型10（以ln(P)为因变量）		模型11（以FAR为因变量）
数据范围		2 km以内原关内	2 km以内原关外	2 km以内原关内	2 km以内原关外
距离变量	D1	0.21	0.23	-0.78	-0.21
	D2	-0.09	-0.05	-0.09	-0.08^***
	D3	-0.04^*	0.05	-0.13^**	0.08^***
时间变量	Group B	0.47	1.08^***	3.18^***	0.69^***
时间变量	Group C	1.26^***	2.30^***	3.94^***	2.06^***
用地类型	COM_LD	2.52^***	1.30^***	7.58^***	1.80^***
	RES_LD	0.41	0.86^***	4.36^***	1.15^***
	IND_LD	0.70^*	-0.47	5.34^***	0.34
交易类型	AUC	0.72	0.36	-1.67	-0.86^**
交易类型	TEN	1.62^**	0.76	1.12	-0.60
POI可达性	HOSP	-0.11	0.02	0.75	0.17
	PARK	0.20	0.78^***	-0.22	0.36
	PRIM	0.73^**	-0.27	0.12	0.06
	MIDD	-0.69^***	-0.29	0.14	-0.20
	COLL	-0.06	-1.89^*	1.33^**	-0.05
常数项	CONS	8.31^***	5.51^***	-0.81	-0.37
空间自相关	W_lnP	-0.07	0.64^***	0.12	0.17^*
统计结果	R2	0.50	0.62	0.57	0.46
	AIC	633.46	464.87	855.66	467.50
	Log Likelihood	-299.73	-216.43	-410.83	-216.75
	样本量	168	146	164	143