基于位置大数据的青藏高原人类活动时空模式

doi:10.11821/dlxb202007006

地理学报 ›› 2020, Vol. 75 ›› Issue (7): 1406-1417.doi: 10.11821/dlxb202007006

基于位置大数据的青藏高原人类活动时空模式

许珺¹(), 徐阳¹^,², 胡蕾¹^,², 王振波³

1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2. 中国科学院大学资源与环境学院,北京 100049
3. 中国科学院地理科学与资源研究所中国科学院区域可持续发展分析与模拟院重点实验室,北京 100101

收稿日期:2019-03-29 修回日期:2020-04-20 出版日期:2020-07-25 发布日期:2020-09-25
作者简介:许珺(1972-), 女, 博士, 副研究员, 中国地理学会会员(S110007304M), 主要从事地理空间认知、知识表达、空间数据挖掘研究。E-mail: xujun@lreis.ac.cn
基金资助:
中国科学院战略性先导科技专项(XDA20040401);国家自然科学基金项目(41771477)

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

XU Jun¹(), XU Yang¹^,², HU Lei¹^,², WANG Zhenbo³

1. State Key laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received:2019-03-29 Revised:2020-04-20 Published:2020-07-25 Online:2020-09-25
Supported by:
Strategic Priority Research Program of Chinese Academy of Sciences(XDA20040401);National Natural Science Foundation of China(41771477)

摘要/Abstract

摘要：

随着青藏高原城镇化和旅游的发展,人类活动强度不断加剧,有必要对青藏高原不同类型的人类活动时空模式进行研究,以便了解不同区域人类活动对自然环境的影响以及更好地采取生态环境保护措施。利用腾讯位置大数据,分析青藏高原在旅游淡季和旅游旺季的人类活动时空模式,研究旅游人口对青藏高原人类活动的影响。本文分别选取2018年1月和7月的腾讯定位请求数据,构建多时空维度的高阶张量,采用张量分解的方法对数据进行降维,获取旅游淡季和旺季不同时空维度的主要特征,分析活动模式,发现青藏高原在旅游淡季和旺季的活动模式在时间上和空间上都有很大不同,体现了旅游人口造成的影响。在旅游淡季,青藏高原居民的活动在凌晨有个高峰,在午间有个低谷,并且表现有特殊的节日模式,工作日的人类活动主要分布在城镇等人口聚集区,休息日向城镇周边发散;旺季凌晨活动分为两个高峰,一个在时间上较淡季提前,与午夜活动相连,另一个较淡季的时间推迟,旺季活动没有午间的低谷和特殊的节日模式,且工作日和休息日的日间活动都分布较广。

关键词: 青藏高原, 人类活动, 淡旺季, 大数据, 张量分解

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

许珺, 徐阳, 胡蕾, 王振波. 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, 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.

图/表 14

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参考文献 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

基于位置大数据的青藏高原人类活动时空模式

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

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