Abstract：With the rapid development of China's economy and society, air travel has become an important part of residents' travel mode, and affects the urban pattern. Based on the Tencent positioning request data of 215 domestic airports from April 28 to May 10, 2019, this paper analyzes the spatio-temporal characteristics of Chinese urban residents' air travel under the two scenarios of May Day holiday and working day, and further explores the relationship between them and urban pattern. The results show that:1) The spatial characteristics of air travel of urban residents in China are generally higher in the eastern coastal area than in the inland area, and lower in the central area; 2) The air travel choice of urban residents in China is mostly concentrated in working day, and the central cities in Beijing-Tianjin-Hebei region, Yangtze River Delta, Guangdong-Hong Kong-Macao Greater Bay Area, Chengdu-Chongqing area and other economically developed regions are more inclined to "working-day advantage type", while the non regional central cities in central and southwest regions are more inclined to "holiday advantage type", and residents' air travel shows a 24-hour cycle of decline, rise and fluctuation. Compared with working days, May Day holiday entered the fluctuation period 2 hours ahead of schedule, and residents preferred to choose the flight time around 3pm and 8pm. 3) In the two scenarios of working day and holiday, there is a strong correlation between the hierarchical differentiation of city cluster and the city scale. To some extent, the cluster distribution of cities is close to the pyramid of city scale.
刘永乐, 张景秋. 基于腾讯位置数据的中国城市居民航空出行时空特征[J]. 人文地理, 2021, 36(5): 131-137,147.
LIU Yong-le, ZHANG Jing-qiu. ANALYSIS OF SPATIAL AND TEMPORAL CHARACTERISTICS OF RESIDENTS' AIR TRAVEL BASED ON TENCENT LOCATION BIG DATA IN CHINESE CITIES. HUMAN GEOGRAPHY, 2021, 36(5): 131-137,147.
郭文炯,白明英.中国城市航空运输职能等级及航空联系特征的实证研究[J].人文地理,1999,14(1):27-31.[Guo Wenjiong, Bai Mingying. A positive research on the functional hierarchy of urban air transportation and the features of air transportation relationship in China[J]. Human Geography, 1999,14(1):27-31.]
[2]
周一星,胡智勇.从航空运输看中国城市体系的空间网络结构[J].地理研究,2002,21(3):276-286.[Zhou Yixing, Hu Zhiyong. Looking into the network structure of Chinese urban system from the perspective of air transportation[J]. Geographical Research, 2002,21(3):276-286.]
[3]
王法辉,金凤君,曾光.中国航空客运网络的空间演化模式研究[J]. 地理科学,2003,23(5):519-525.[Wang Fahui, Jin Fengjun, Zeng Guang. Geographic patterns of air passenger transport in China[J]. Scientia Geographica Sinica, 2003,23(5):519-525.]
[4]
王姣娥,金凤君,孙炜,等.中国机场体系的空间格局及其服务水平[J].地理学报,2006,61(8):829-838.[Wang Jiao'e, Jin Fengjun, Sun Wei, et al. Research on spatial distribution and service level of Chinese airport system[J]. Acta Geographica Sinica, 2006,61(8):829-838.]
[5]
宋伟,李秀伟,修春亮.基于航空客流的中国城市层级结构分析[J]. 地理研究,2008,27(4):917-926.[Song Wei, Li Xiuwei, Xiu Chunliang. Patterns of spatial interaction and hierarchical structure of Chinese cities based on intercity air passenger flows[J]. Geographical Research, 2008,27(4):917-926.]
[6]
陈维忠,黄金川,闫梅,等.北京航空旅客出行特征及新机场旅客分担[J]. 地理科学进展,2012,31(10):1360-1368.[Chen Weizhong, Huang Jinchuan, Yan Mei, et al. Research on the characteristics of Beijing passengers and the distribution rate of new airport[J]. Progress in Geography, 2012,31(10):1360-1368.]
[7]
王绍博,郭建科,罗小龙,等.高速铁路对中心城市航空客运市场的空间影响——基于人均时间价值视角[J].地理科学进展,2019, 38(11):1665-1674.[Wang Shaobo, Guo Jianke, Luo Xiaolong, et al. Spatial differentiation of the impact of high-speed rail on aviation passenger market in central cities of China[J]. Progress in Geography, 2019,38(11):1665-1674.]
[8]
丁金学,金凤君,王姣娥,等.高铁与民航的竞争博弈及其空间效应——以京沪高铁为例[J].经济地理,2013,33(5):104-110.[Ding Jinxue, Jin Fengjun, Wang Jiao'e, et al. Competition game of high-speed rail and civil aviation and its spatial effect:A case study of BeijingShanghai high-speed rail[J]. Economic Geography, 2013,33(5):104-110.]
[9]
李涛,王姣娥,高兴川.中国居民工作日与节假日的城际出行网络异同性研究[J].地理学报,2020,75(4):833-848.[Li Tao, Wang Jiao'e, Gao Xingchuan. Comparison of inter-city travel network during weekdays and holiday in China[J]. Acta Geographica Sinica, 2020, 75(4):833-848.]
[10]
李涛,王姣娥,黄洁.基于腾讯迁徙数据的中国城市群国庆长假城际出行模式与网络特征[J].地球信息科学学报,2020,22(6):1240-1253.[Li Tao, Wang Jiao'e, Huang Jie. Research on travel pattern and network characteristics of inter-city travel in China's urban agglomeration during National Day week based on Tencent migration data[J]. Journal of Geo-Information Science, 2020,22(6):1240-1253.]
[11]
唐锦玥,张维阳,王逸飞.长三角城际日常人口移动网络的格局与影响机制[J]. 地理研究,2020,39(5):1166-1181.[Tang Jinyue, Zhang Weiyang, Wang Yifei. The pattern and influence mechanism of the daily population movement network between cities in the Yangtze River Delta[J]. Geographical Research, 2020,39(5):1166-1181.]
[12]
张蓉,潘竟虎,赖建波.不同交通方式下居民城际出行网络结构特征——以"春运" 为例[J].地理科学进展,2021,40(5):759-773.[Zhang Rong, Pan Jinghu, Lai Jianbo. Characteristics of intercity trip network structure of residents under different traffic modes:A case study of Spring Festival travel rush[J]. Progress in Geography, 2021,40(5):759-773.]
[13]
杨超,朱荣荣,涂然.基于智能手机调查数据的居民出行活动特征分析[J]. 交通信息与安全,2015,33(6):25-32.[Yang Chao, Zhu Rongrong, Tu Ran. Analysis of the travel characteristics of residents in Shanghai using the itinerary data collected from smartphones[J]. Journal of Transport Information and Safety, 2015,33(6):25-32.]
[14]
丁鹏程,杨明,郑长江,等.基于手机信令数据的城市通勤出行特征研究[J]. 交通科技与经济,2020,22(3):29-34.[Ding Pengcheng, Yang Ming, Zheng Changjiang, et al. Research on urban commuting travel characteristics based on mobile signaling data[J]. Technology & Economy in Areas of Communications, 2020,22(3):29-34.]
[15]
陈旭,郑浩毅.基于手机定位数据的个体出行行为特征分析综述[J].综合运输,2019,41(9):38-44.[Chen Xu, Zheng Haoyi. A review of individual travel behavior characteristics based on mobile phone location data[J]. China Transportation Review, 2019,41(9):38-44.]
[16]
罗勇,王晏民,张健钦.基于手机位置数据的居民出行信息挖掘和分析方法研究[J].北京建筑工程学院学报,2012,28(1):40-44,72.[Luo Yong, Wang Yanmin, Zhang Jiangqin. Research of residents' travel information mining and analysis methods based on mobile phone location data[J]. Journal of Beijing University of Civil Engineering and Architecture, 2012,28(1):40-44,72.]
[17]
倪玲霖,张帅超,陈喜群.基于手机信令数据的居民出行空间效应[J]. 浙江大学学报(工学版),2017,51(5):887-895.[Ni Linglin, Zhang Shuaichao, Chen Xiqun. Spatial effects of urban using cellular signaling data[J]. Journal of Zhejiang University (Engineering Science), 2017,51(5):887-895.]
[18]
罗名海,秦思娴,谭波,等.大数据视角下的武汉市综合交通特征分析[J].地理空间信息,2020,18(5):1-7,149.[Luo Minghai, Qing Sixian, Tan bo, et al. Characteristic analysis of the urban integrated transportation in Wuhan city from the perspective of big data[J]. Geospatial Information, 2020,18(5):1-7,149.]
[19]
李浩,王旭智,万旺根.基于位置数据的居民出行时空特征研究——以上海市为例[J].电子测量技术,2019,42(19):25-30.[Li Hao, Wang Xuzhi, Wan Wanggen. Research on temporal and spatial characteristics of residents' travel based on location data:A case of Shanghai[J]. Electronic Measurement Technology, 2019,42(19):25-30.]
[20]
Li Z F, Yu L, Gao Y, et al. Identifying temporal and spatial characteristics of residents' trips from cellular signaling data:Case study of Beijing[J]. Transportation Research Record, 2018,2672(42):81-90.
[21]
花磊,彭宏杰,杨秀锋,等.基于腾讯位置大数据的长江经济带人口流动空间分析[J].华中师范大学学报(自然科学版),2019,53(5):815-820.[Hua Lei, Peng Hongjie, Yang Xiufeng, et al. Analysis of population flow space in the Yangtze River Economic Belt based on Tencent location big data[J]. Journal of Central China Normal University (Natural Sciences), 2019,53(5):815-820.]
[22]
张伟丽,叶信岳,李栋,等.网络关联、空间溢出效应与中国区域经济增长——基于腾讯位置大数据的研究[J].地理科学,2019,39(9):1371-1377.[Zhang Weili, Ye Xinyue, Li Dong, et al. Network association, spillover effect and China's regional economic growth based on Tencent's location big data[J]. Scientia Geographica Sinica, 2019,39(9):1371-1377.]
[23]
许珺,徐阳,胡蕾,等.基于位置大数据的青藏高原人类活动时空模式[J].地理学报,2020,75(7):1406-1417.[Xu Jun, Xu Yang, Hu Lei, et al. Discovering spatio-temporal patterns of human activity on the Qinghai-Tibet Plateau based on crowdsourcing positioning data[J]. Acta Geographica Sinica, 2020,75(7):1406-1417.]
[24]
易嘉伟,杜云艳,涂文娜.基于位置大数据的国庆假期青藏高原人群分布时空变化模式挖掘[J].地球信息科学学报,2019,21(9):1367-1381.[Yi Jiawei, Du Yunyan, Tu Wenna. Spatiotemporal pattern of population distribution in the Qinghai-Tibet Plateau during the National Day holidays:Based on geospatial big data mining[J]. Journal of Geo-information Science, 2019,21(9):1367-1381.]
[25]
吴中元,许捍卫,胡钟敏.基于腾讯位置大数据的精细尺度人口空间化——以南京市江宁区秣陵街道为例[J].地理与地理信息科学,2019,35(6):61-65.[Wu Zhongyuan, Xu Hanwei, Hu Zhongmin. Fine-scale population spatialization based on Tencent location big data:A case study of Moling Subdistrict, Jiangning District, Nanjing[J]. Geography and Geo-Information Science, 2019,35(6):61-65.]
[26]
Ma T, Lu R, Zhao N, et al. An estimate of rural exodus in China using location-aware data[J]. PloS ONE, 2018,13(7):1-14.
[27]
戚伟,李颖,刘盛和,等.城市昼夜人口空间分布的估算及其特征——以北京市海淀区为例[J].地理学报,2013,68(10):1344-1356.[Qi Wei, Li Ying, Liu Shenghe, et al. Estimation of urban population at daytime and nighttime and analyses of their spatial pattern:A case study of Haidian District, Beijing[J]. Acta Geographica Sinica, 2013,68(10):1344-1356.]
[28]
符海月,李满春,赵军,等.人口数据格网化模型研究进展综述[J].人文地理,2006,21(3):115-119,114.[Fu Haiyue, Li Manchun, Zhao Jun, et al. Summary of grid transformation models of population data[J]. Human Geography, 2006,21(3):115-119,114.]
[29]
董南,杨小唤,蔡红艳.人口数据空间化研究进展[J].地球信息科学学报,2016,18(10):1295-1304.[Dong Nan, Yang Xiaohuan, Cai Hongyan. Research progress and perspective on the spatialization of population data[J]. Journal of Geo-information Science, 2016, 18(10):1295-1304.]
[30]
孟斌,王劲峰.地理数据尺度转换方法研究进展[J].地理学报,2005, 60(2):277-288.[Meng Bin, Wang Jinfeng. A review on the methodology of scaling with Geo-Data[J]. Acta Geographica Sinica, 2005, 60(2):277-288.]
