中国中心城市旅游出行的空间分布规律与结构图谱研究

doi:10.13249/j.cnki.sgs.2021.11.004

摘要/Abstract

摘要：

通过全国338个地级及以上城市旅游出行大数据，应用基于O-D联系的ArcGIS空间分析和Gephi网络分析，深入挖掘旅游数字足迹所蕴涵的“位空间”与“流空间”信息，全景式解析中国旅游出行的空间分布规律与结构特征。研究显示，全国旅游出行的总体空间分布呈现出“菱形”空间架构。全国旅游出行的“核心-边缘”结构特征显著，国家主要中心城市及城市群在旅游出行方面拥有强大的辐射带动作用。依据旅游出行强度指标，并综合考虑地理区位、空间联系、职能定位等因素，将中心城市按旅游出行能力划分为国家综合中心、国家门户中心、区域中心、省域中心、地方中心5个等级体系，旅游出行的城市等级规模递增特征极其明显。大数据显示，全国旅游出行的适宜距离为600~2 000 km，最佳出行距离为1 200 km，人均旅游出行距离为1 060 km，不同等级中心城市旅游出行随着空间距离增加总体上呈现波动衰减特征。旅游联系网络分析显示，在区域尺度上，全国形成了以北京-天津、上海-杭州、广州-深圳、成都-重庆为核心的4个旅游社团联系网络，区域旅游出行“各自为政”，而大空间尺度上旅游出行是“全国一盘棋”。研究通过划分不同空间距离区段，对中心城市旅游出行分布规律及其社团结构进行多尺度、分层次的扫描式空间解构，从而实现更加精准和细致的空间刻画与结构图谱展现。

关键词: 中心城市, 旅游流, 流空间, 结构图谱, 菱形结构

Abstract:

In this article, “location space” and “flow space” information were mined from travel big data of 338 central cities in China by using OD connection model and Gephi network analysis method. The law of travel space and travel structure of China’s central cities was revealed. The results showed that the form of tourists’ travel space in China’s central cities was diamond-shaped, which was response of new pattern of national strategy to tourist travel. Chinese tourists travelled with a “core-edge” network structure, and central cities and urban agglomerations had a powerful radiation-driven role. According to the index of tourism travel intensity, combined with factors of geographical location, spatial connection and functional orientation, the central cities were classified into 5 kinds of national comprehensive center, national portal center, regional center, provincial center and local center. Big data showed that the reasonable travel distance is 600-2 000 km, the optimal travel distance is 1 200 km and the average travel distance is 1 060 km. Travel of tourists in different levels of central cities was characterized by distance decay. At a regional scale of 500 km, 4 contact networks of tourism associations with Beijing-Tianjin, Shanghai-Hangzhou, Guangzhou-Shenzhen and Chengdu-Chongqing as the core have been formed nationwide, regional tourism travel is characterized by “fragmented”. In the space of 500-1 000 km, the community structure of tourism contact network is not obvious, it’s difficult to distinguish the regional communities. In national scale of more than 1 000 km, the tourist travel network has gradually changed into a multi-core network with the whole country.

Key words: central city, tourism flows, flow space, structure graph spectra, rhombus structure

中图分类号:

K591.99

王海江, 苏景轩, 苗长虹, 袁占良. 中国中心城市旅游出行的空间分布规律与结构图谱研究[J]. 地理科学, 2021, 41(11): 1907-1916.

Wang Haijiang, Su Jingxuan, Miao Changhong, Yuan Zhanliang. Spatial Distribution Law and Its Structure Graph Spectra of China’s Central City Tourism Travel[J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(11): 1907-1916.

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对比指标	全国	国家中心城市与门户城市（双核）	区域中心城市	省域中心城市	地方中心城市
注：未包含港澳台数据。
出行线路占比/%	100	7.77	4.27	18.19	69.77
出行人数占比/%	100	49.98	9.86	21.42	18.74
平均出行强度	1	27.40	8.65	3.73	0.28
适宜出行距离/km （累积30%~90%）	600~2 000	600~2 100	600~2 000	600~2 100	450~2 000
最佳出行距离/km （累积达60%）	1 200	1 200	1 100	1 200	1 000
人均出行距离/km	1 060	1 100	1 010	1 070	970
距离衰减规律	较明显	不明显	较明显	较明显	明显

城市	连入度	连出度	度	加权入度	加权出度	加权度	出行强度	城市连接率
注：1. 某一城市与区域内其他城市具有旅游联系的城市数量占区域全部城市数量的比值，反映城市对外旅游联系的区域覆盖度。如值为1，代表该城市与区域内其他全部城市都有旅游联系；2.未包含港澳台数据。
北京	298	317	615	52 865	167 968	220 833	73.28	0.94
上海	292	315	607	37 596	102 761	140 357	44.83	0.94
广州	244	283	527	15 332	59 229	74 561	25.84	0.84
成都	294	290	584	34 622	47 534	82 156	20.74	0.86
深圳	210	292	502	6 682	36 860	43 542	16.08	0.87
天津	228	278	506	13 650	34 900	48 550	15.23	0.83
杭州	288	259	547	36 001	28 064	64 065	12.24	0.77
重庆	274	266	540	21 722	25 151	46 873	10.97	0.79
南京	268	258	526	27 999	23 996	51 995	10.47	0.77
西安	299	261	560	36 025	22 336	58 361	9.75	0.78
沈阳	167	245	412	4 655	20 480	25 135	8.94	0.73
武汉	255	259	514	14 615	20 368	34 983	8.89	0.77
苏州	269	246	515	24 593	17 683	42 276	7.72	0.73
大连	210	239	449	8 800	15 816	24 616	6.90	0.71
长沙	221	250	471	8 093	14 202	22 295	6.20	0.74
福州	143	233	376	2 788	13 078	15 866	5.71	0.69
青岛	259	241	500	24 172	12 031	36 203	5.25	0.72
厦门	280	234	514	45 919	11 948	57 867	5.21	0.70
郑州	202	242	444	4 342	11 945	16 287	5.21	0.72
济南	204	234	438	6 298	11 043	17 341	4.82	0.70