交通驱动下湘西地区旅游城镇集聚场强时空演化及影响机制

doi:10.12342/zgstly.20230042

摘要/Abstract

摘要：

交通基础设施在区域旅游一体化建设中发挥着重要作用，对旅游城镇集聚格局的形成和优化具有显著影响。本研究运用复合区位熵和加权平均旅行时间，测度湘西地区2000—2019年旅游城镇集聚水平和交通可达性，借助改进的场强模型和空间计量模型，探讨旅游业发展典型区域的旅游城镇集聚场强时空演化特征及影响机制。研究表明：（1）研究期内湘西地区旅游城镇集聚场强值和交通可达性呈不断提升的态势，交通最优辐射范围在1.6h以内。（2）旅游城镇集聚场强空间格局经历了由“单向主导型”到“双向互动型”的动态演变过程，具体表现为以凤凰为主的高场强区转向以武陵源、永定和凤凰为龙头形成的三大高场强区；交通可达性表现为以中部地区为中心沿南北轴线扩散的不规则圈层结构。（3）在资源禀赋、产业结构和人居环境状况等因素的综合影响下，通过改善交通条件而带来的时空压缩效应，促进旅游城镇人口、土地和产业等要素聚集，提升地方旅游经济效率，并增加资源利用的机会成本，进而推动旅游城镇集聚空间结构实现了由“单中心”向“多中心”的转变。

关键词: 旅游城镇化, 旅游交通, 交通可达性, 交通辐射, 集聚场强

Abstract:

Transportation infrastructure plays a crucial role in the development of integrated regional tourism, significantly influencing the formation and optimization of the agglomeration pattern of tourism towns. By employing composite location entropy and weighted average travel time, the study measures the agglomeration level and transport accessibility of tourist towns in the Xiangxi region from 2000 to 2019. Utilizing an improved gravity model and spatial econometric model, the research explores the spatiotemporal evolution characteristics and influencing mechanisms of agglomeration intensity in typical regions of tourism development. The results show that: (1) During the study period, the agglomeration intensity and transport accessibility in the Xiangxi region exhibited a continuous upward trend, with the optimal transport radiation range within 1.6 hours. (2) The spatial pattern of agglomeration intensity in tourism towns underwent a dynamic transformation from a “unidirectional dominance” to a “bidirectional interactive” pattern, manifested by the emergence of three major high-intensity areas led by Wulingyuan, Yongding, and Fenghuang, shifting away from the dominance of Fenghuang. Meanwhile, transport accessibility demonstrated an irregular concentric structure spreading along the north-south axis, with the central region as the center. (3) Under the comprehensive influence of resource endowment, industrial structure, and living environment conditions, the improvement of transportation conditions brings about the compression effect of space and time, facilitating the agglomeration of population, land, and industries in tourist towns. This, in turn, enhances local tourism economic efficiency and increases the opportunity cost of resource utilization, leading to a spatial structural transformation from “monocentric” to “polycentric” pattern.

Key words: tourism urbanization, tourism traffic, traffic accessibility, traffic radiation, agglomeration field strength

谭佳欣, 麻学锋, 王凯. 交通驱动下湘西地区旅游城镇集聚场强时空演化及影响机制[J]. 中国生态旅游, 2023, 13(3): 380-393.

Tan Jiaxin, Ma Xuefeng, Wang Kai. Spatiotemporal evolution and influencing mechanisms of the agglomeration field in tourist towns in Xiangxi region driven by transportation[J]. ECOTOURISM, 2023, 13(3): 380-393.

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区县	2000年	2010年	2019年	区县	2000年	2010年	2019年
永顺	68.933	89.954	94.326	麻阳	7.904	8.105	9.895
龙山	53.872	50.731	77.035	会同	68.613	171.469	181.208
凤凰	201.110	224.990	225.234	芷江	94.609	124.423	100.154
保靖	57.000	65.471	127.343	中方	30.735	44.151	46.412
泸溪	109.118	125.896	130.540	通道	16.000	120.408	181.590
花垣	18.090	21.168	116.896	靖州	127.631	141.220	183.681
吉首	18.350	38.972	153.301	新晃	174.110	174.434	125.266
古丈	120.000	159.734	170.163	鹤城	6.740	13.053	89.882
溆浦	17.667	23.139	79.739	桑植	33.857	85.690	137.234
洪江	101.652	145.738	153.436	慈利	114.269	88.453	190.547
辰溪	30.413	36.323	46.820	永定	170.841	211.461	383.380
沅陵	8.016	13.810	88.237	武陵源	430.760	224.990	204.870

变量	普通回归模型（OLS）	空间滞后模型（SLM）	空间误差模型（SEM）
_cons	-0.098	—	—
X₁	0.082	0.114^**	0.111^**
X₂	0.142	0.081	0.082
X₃	0.680^***	0.780^***	0.786^***
X₄	-0.212^**	-0.023	-0.024
X₅	0.123	-0.134	-0.133
X₆	0.013	0.073	0.078
X₇	0.315^***	0.348^***	0.341^***
ρ	—	-0.019	—
λ	—	—	0.025
R²	0.664	0.684	0.679
Log-Likelihood	—	66.225	66.219
AIC	-102.126	-114.451	-114.438