基于位置大数据的青藏高原人类活动时空模式

doi:10.11821/dlxb202007006

Abstract

Abstract:

The activities of local people and tourists have great effects on the ecological environment on the Qinghai-Tibet Plateau. Different kinds of activities may cause different impacts on ecology and environment. To effectively protect the ecological environment, it is necessary to study the spatiotemporal patterns of different kinds of human activities. In this paper, two Tencent positioning datasets which record one-week location requests in January and July of 2018, respectively, are used to explore the human activities in off-season and peak season of tourism on the plateau. A Tucker tensor decomposition method is employed to reduce the dimension of massive data and obtain the principle modes of human activities. The data in off-season are decomposed into 3 daily patterns, 3 hourly patterns and 8 spatial patterns, and the data in peak season are decomposed into 2 daily patterns, 4 hourly patterns and 8 spatial patterns. By analyzing the core tensor, different kinds of activities are inferred through the relations among different dimensions of data, and the human activities in off-season and peak season of tourism are analyzed. The human activities on the Qinghai-Tibet Plateau are found to be different from those in other places. Different from ordinary weekday and weekend patterns, there is a mid-week pattern (Tuesday through Friday) and an inter-week pattern (Saturday, Sunday and Monday) on the Qinghai-Tibet Plateau, and there is a special holiday pattern in off-season of tourism. It is also found that the human activities in off-season and peak season of tourism are different, which indicates different activities of the local residents and the tourists. In off-season of tourism, the positioning activities are very active in the morning, however, the activities are less active during the daytime of mid-week days than during the daytime of inter-week days, and the activities are mostly found in the cities in the mid-week days but mostly in the outskirts of the cities or on the way to scenic spots in the inter-week days. In off-season, there exist the activities of local residents. In peak season, there are less activities in the morning, but the activities during the day are more broadly distributed both in the mid-week days and in the inter-week days. It is indicated that the activities of tourists are significant in the peak season. After clustering spatial grids with similar patterns, we find that there are mixed spatial patterns in most parts of the study area, which discloses that there are usually multiple kinds of human activities in a region.

Key words: Qinghai-Tibet Plateau, human activity, off-season and peak season, big data, tensor decomposition

XU Jun, XU Yang, HU Lei, WANG Zhenbo. 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.

Figures/Tables 14

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Tab. 1

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Tab. 2

Tab.3

References 21

[1]	Li S, Zhang Y, Wang Z, et al. Mapping human influence intensity in the Tibetan Plateau for conservation of ecological service functions. Ecosystem Services, 2018,30:276-286.
[2]	Fang Chuanglin, Li Guangdong. Particularities, gradual patterns and countermeasures of new-type urbanization in Tibet, China. Bulletin of Chinese Academy of Sciences, 2015,30(3):294-305.
	[ 方创琳, 李广东. 西藏新型城镇化发展的特殊性与渐进模式及对策建议. 中国科学院院刊, 2015,30(3):294-305.]
[3]	Li Guangdong, Wang Zhenbo, Liu Shenghe. Strategies and countermeasures for science and technology supporting Tibet and regional scientific and technological cooperation in Tibet. Bulletin of Chinese Academy of Sciences, 2015,30(3):333-341.
	[ 李广东, 王振波, 刘盛和. 西藏区域科技合作与科技援藏的战略思路及对策. 中国科学院院刊, 2015,30(3):333-341.]
[4]	Zhang Huiyuan. Problems and progresses in protecting ecological environment on the Qinghai-Tibetan Plateau. Environmental Protection, 2011(17):20-22.
	[ 张惠远. 青藏高原区域生态环境面临的问题与保护进展. 环境保护, 2011(17):20-22.]
[5]	Degrossi L C, de Albuquerque J P, Rocha R S, et al. A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information. Transactions in GIS, 2018,22(2):542-560. pmid: 29937686
[6]	Pei Tao, Liu Yaxi, Guo Sihui, et al. Principle of big geodata mining. Acta Geographic Sinica, 2019,74(3):586-598.
	[ 裴韬, 刘亚溪, 郭思慧, 等. 地理大数据挖掘的本质. 地理学报, 2019,74(3):586-598.]
[7]	Sorokin P A, Merton R K. Social time: A methodological and functional analysis. American Journal of Sociology, 1937,42(5):615-629.
[8]	Demissie M G, Correia G A, Bento C. Analysis of the pattern and intensity of urban activities through aggregate cellphone usage. Transportmetrica, 2015,11(6):502-524.
[9]	Sagl G, Loidl M, Beinat E. A visual analytics approach for extracting spatio-temporal urban mobility information from mobile network traffic. ISPRS International Journal of Geo-Information, 2012,1(3):256-271.
[10]	Xu Y, Shaw S L, Zhao Z, et al. Understanding aggregate human mobility patterns using passive mobile phone location data: A home-based approach. Transportation, 2015,42(4):625-646.
[11]	Pei T, Sobolevsky S, Ratti C, et al. A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 2014,28(9):1988-2007.
[12]	Tu W, Cao J, Yue Y, et al. Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns. International Journal of Geographical Information Science, 2017,31(12):2331-2358
[13]	Sun L, Axhausen K W. Understanding urban mobility patterns with a probabilistic tensor factorization framework. Transportation Research Part B: Methodological, 2016,91:511-524.
[14]	Wang J, Gao F, Cui P, et al. Discovering urban spatio-temporal structure from time-evolving traffic networks//Chen L, Jia Y, Sellis T, et al. Web Technologies and Applications: 16th Asia-Pacific Web Conference. Switzerland: Springer International Publishing, 2014: 93-104.
[15]	Zhi Y, Li H, Wang D, et al. Latent spatio-temporal activity structures: A new approach to inferring intra-urban functional regions via social media check-in data. Geo-spatial Information Science, 2016,19(2):94-105.
[16]	Ma T, Lu R, Zhao N, et al. An estimate of rural exodus in China using location-aware data. PLoS ONE, 2018,13(7):e0201458. Doi: 10.1371/journal.pone.0201458. pmid: 30063720
[17]	Kolda T G, Bader B W. Tensor decompositions and applications. SIAM Review, 2009,51(3):455-500.
[18]	Zhang Yili, Li Bingyuan, Zheng Du. A discussion on the boundary and area of the Tibetan Plateau in China. Geographical Research, 2002,21(1):1-8.
	[ 张镱锂, 李炳元, 郑度. 论青藏高原范围与面积. 地理研究, 2002,21(1):1-8.]
[19]	Mørup M, Hansen L K, Arnfred S M. Algorithms for sparse non-negative Tucker decompositions. Neural Computation, 2008,28(8):2112-2131.
[20]	Ma T, Pei T, Song C, et al. Understanding geographical patterns of a city's diurnal rhythm from aggregate data of location-aware services. Transactions in GIS, 2019,23(1):104-117. doi: 10.1111/tgis.v23.1
[21]	Cai L, Xu J, Liu J, et al. Sensing multiple semantics of urban space from crowdsourcing positioning data. Cities, 2019,93:31-42.

参数	参数含义	参数范围
S	区域模式特征模数	[3, 15]
T	时段模式特征模数	[3, 6]
D	日模式特征模数	[2, 3]

	T1			T2			T3
	D1	D2	D3	D1	D2	D3	D1	D2	D3
S1	0.1231	0.0000	0.0000	0.2751	0.0000	0.0000	0.0000	0.0000	0.0000
S2	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.3983	0.0000	0.0000
S3	0.2224	0.2617	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
S4	0.0000	0.0000	0.0000	0.0000	0.0000	0.3775	0.0000	0.0000	0.0000
S5	0.0000	0.0021	0.0000	0.0000	0.0000	0.0000	0.0000	0.4192	0.0000
S6	0.0005	0.0000	0.2564	0.0000	0.0000	0.0000	0.0003	0.0012	0.0000
S7	0.0000	0.0958	0.0000	0.0000	0.2938	0.0429	0.0000	0.0000	0.0000
S8	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.3913

	T1		T2		T3		T4
	D1	D2	D1	D2	D1	D2	D1	D2
S1	0.4005	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
S2	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.3904
S3	0.0000	0.0000	0.3422	0.0000	0.0000	0.0000	0.0000	0.0000
S4	0.0000	0.0000	0.0000	0.0000	0.0000	0.3618	0.0000	0.0000
S5	0.0000	0.0000	0.0000	0.0000	0.2748	0.0000	0.0000	0.0000
S6	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.3493	0.0000
S7	0.0000	0.3427	0.0000	0.0000	0.0030	0.0001	0.0062	0.0000
S8	0.0000	0.0000	0.0000	0.3524	0.0000	0.0000	0.0000	0.0000

Discovering spatio-temporal patterns of human activity on the Qinghai-Tibet Plateau based on crowdsourcing positioning data

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 21

Related Articles 15

Metrics

Comments

[1]	WEI Shimei, PAN Jinghu. Network structure resilience of cities at the prefecture level and above in China [J]. Acta Geographica Sinica, 2021, 76(6): 1394-1407.
[2]	LI Meng, YUAN Wen, YUAN Wu, NIU Fangqu, LI Hanqin, HU Duanmu. Big data analysis on geographical relationship of the Arctic based on news reports [J]. Acta Geographica Sinica, 2021, 76(5): 1090-1104.
[3]	HOU Guangliang, , ZHU Yan, PANG Longhui. Communication route and its evolution on the Qinghai-Tibet Plateau during the prehistoric time [J]. Acta Geographica Sinica, 2021, 76(5): 1294-1313.
[4]	WANG Lucang, LIU Haiyang, LIU Qing. China's city network based on Tencent's migration big data [J]. Acta Geographica Sinica, 2021, 76(4): 853-869.
[5]	HUANG Hai, TIAN You, LIU Jiankang, ZHANG Jiajia, YANG Dongxu, YANG Shun. The mechanism and sensitivity analysis of soil freeze-thaw erosion on slope in eastern Tibet [J]. Acta Geographica Sinica, 2021, 76(1): 87-100.
[6]	DONG Guanghui, QIU Menghan, LI Ruo, CHEN Fahu. Using the Fulcrum Cognitive Model to explore the mechanismof past human-land co-evolution [J]. Acta Geographica Sinica, 2021, 76(1): 15-29.
[7]	SUN Siao, WANG Jing, QI Wei. Urban-and-rural virtual water trade of Qinghai-Tibet Plateau: Patterns and influencing factors [J]. Acta Geographica Sinica, 2020, 75(7): 1346-1358.
[8]	FENG Zhiming, LI Wenjun, LI Peng, XIAO Chiwei. Relief degree of land surface and its geographical meanings in the Qinghai-Tibet Plateau, China [J]. Acta Geographica Sinica, 2020, 75(7): 1359-1372.
[9]	LIANG Xinyue, XU Mengzhen, LYU Liqun, CUI Yifei, ZHANG Fengbao. Geomorphological characteristics of debris flow gullies on the edge of the Qinghai-Tibet Plateau [J]. Acta Geographica Sinica, 2020, 75(7): 1373-1385.
[10]	WANG Nan, WANG Huimeng, DU Yunyan, YI Jiawei, LIU Zhang, TU Wenna. Spatiotemporal patterns of in- and out-bound population flows of the Qinghai-Tibet Plateau [J]. Acta Geographica Sinica, 2020, 75(7): 1418-1431.
[11]	LIU Yu, YAO Xin, GONG Yongxi, KANG Chaogui, SHI Xun, WANG Fahui, WANG Jiao'e, ZHANG Yi, ZHAO Pengfei, ZHU Di, ZHU Xinyan. Analytical methods and applications of spatial interactions in the era of big data [J]. Acta Geographica Sinica, 2020, 75(7): 1523-1538.
[12]	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.
[13]	YI Jiawei, WANG Nan, QIAN Jiale, MA Ting, DU Yunyan, PEI Tao, ZHOU Chenghu, TU Wenna, LIU Zhang, WANG Huimeng. Spatio-temporal responses of urban road traffic and human activities in an extreme rainfall event using big data [J]. Acta Geographica Sinica, 2020, 75(3): 497-508.
[14]	TONG Yun, MA Yong, LIU Haimeng. The short-term impact of COVID-19 epidemic on the migration of Chinese urban population and the evaluation of Chinese urban resilience [J]. Acta Geographica Sinica, 2020, 75(11): 2505-2520.
[15]	CHEN Xiaoqiang, YUAN Lihua, SHEN Shi, LIANG Xiaoyao, WANG Yuanhui, WANG Xiangyu, YE Sijing, CHENG Changxiu, SONG Changqing. Analysis of the geo-relationships between China and its neighboring countries [J]. Acta Geographica Sinica, 2019, 74(8): 1534-1547.