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
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摘要
人类活动是引起青藏高原生态环境发生改变的重要因素。很多学者对青藏高原史前人类活动和近几十年的人口分布格局与人口流动开展了大量研究,但有关人群时空分布的精细尺度研究相对缺乏。海量的位置大数据为认识高原人群短期的动态变化提供了新途径。本文利用手机定位数据、人口迁徙数据等高时空分辨率的位置大数据,通过时间序列分解方法和基于统计检验的异常判别方法,分析了2017年国庆期间青海与西藏的人群分布时空变化特征,并探讨了假期旅游行为及人口迁徙与变化特征之间的关系。研究结果显示:①在省级和城市整体尺度上,定位请求量的假期变化在时间上呈现先降后升的"潮汐"变化模式;②在精细网格尺度上,西宁和拉萨城市及周边地区的人群分布变化在空间上呈现中心跌、周边涨的"离心化"变化模式。国庆假期人们向城市周边热门景点移动聚集的旅游行为和城市之间的人口迁徙都是导致西宁和拉萨周边地区定位请求量上涨的重要潜在原因,而两座高原城市中心定位请求的下跌不仅与人口迁徙有关,还与假期人类日常行为及定位请求频次的变化等因素有关。通过位置大数据挖掘节庆假期人群分布的时空变化,不仅加深了对高原人口分布格局与流动变化的认识,也为高原城镇化与生态保护的精细化管理与决策提供支撑。
Human activities play an important role in transforming the eco-environment of the Qinghai-Tibet Plateau(QTP). Extensive studies have been conducted on the human activities in the prehistoric QTP and on population distribution and migration in the recent decades, yet, most of them rely on limited demographic materials of coarse spatial resolutions. It remains understudied regarding the fine-scale spatiotemporal pattern of human distribution in the QTP. In this context, geospatial big data generated from ubiquitous mobile communication technology and internet provide a great opportunity to investigate the dynamic human distribution at very fine scales. This study took the advantage of the geospatial big data, including the mobile phone location requests(LR) and population migration, and employed time series decomposition and anomaly detection approaches to explore the population distribution changes in the QTP during the 2017 National Day holidays. Results show that, at the provincial and prefectural scales, Qinghai, Tibet, and their provincial cities exhibit a featured "tidal change" pattern that the LR first decreased then increased. Such fluctuation in Qinghai were stronger than that in Tibet, and cities in the same province demonstrated significant differences. At the grid scale, the LR in the surrounding areas of Xining and Lhasa displayed a spatially"decentralized pattern"that the LP dropped in the central areas yet increased in the peripheral. Based on the anomaly detection approach, we found the number of anomaly grids and deviation magnitude increased in Xining, Haidong, Haibei, Hainan, and Huangnan of Qinghai since the holiday. More positive anomalies were observed than the negative ones, and the negative anomalies were concentrated in cities of large population densities such as Xining and Lhasa. Further analysis combining the population migration data reveals that the travel behaviors potentially drove people swarming to the nearby scenic spots and that the massive migration between cities was an important reason for the increase of LR in areas surrounding Xining and Lhasa. The decrease of LR in the central areas of cities could be partly attributed to significant population migration, but the different daily routines and location request frequencies during holidays may also be important reasons. Our findings demonstrate the potential of using geospatial big data to improve our understanding of human distribution and migration, which could further support fine management and decision-making for plateau urbanization and ecological protection.
Human activities play an important role in transforming the eco-environment of the Qinghai-Tibet Plateau(QTP). Extensive studies have been conducted on the human activities in the prehistoric QTP and on population distribution and migration in the recent decades, yet, most of them rely on limited demographic materials of coarse spatial resolutions. It remains understudied regarding the fine-scale spatiotemporal pattern of human distribution in the QTP. In this context, geospatial big data generated from ubiquitous mobile communication technology and internet provide a great opportunity to investigate the dynamic human distribution at very fine scales. This study took the advantage of the geospatial big data, including the mobile phone location requests(LR) and population migration, and employed time series decomposition and anomaly detection approaches to explore the population distribution changes in the QTP during the 2017 National Day holidays. Results show that, at the provincial and prefectural scales, Qinghai, Tibet, and their provincial cities exhibit a featured "tidal change" pattern that the LR first decreased then increased. Such fluctuation in Qinghai were stronger than that in Tibet, and cities in the same province demonstrated significant differences. At the grid scale, the LR in the surrounding areas of Xining and Lhasa displayed a spatially"decentralized pattern"that the LP dropped in the central areas yet increased in the peripheral. Based on the anomaly detection approach, we found the number of anomaly grids and deviation magnitude increased in Xining, Haidong, Haibei, Hainan, and Huangnan of Qinghai since the holiday. More positive anomalies were observed than the negative ones, and the negative anomalies were concentrated in cities of large population densities such as Xining and Lhasa. Further analysis combining the population migration data reveals that the travel behaviors potentially drove people swarming to the nearby scenic spots and that the massive migration between cities was an important reason for the increase of LR in areas surrounding Xining and Lhasa. The decrease of LR in the central areas of cities could be partly attributed to significant population migration, but the different daily routines and location request frequencies during holidays may also be important reasons. Our findings demonstrate the potential of using geospatial big data to improve our understanding of human distribution and migration, which could further support fine management and decision-making for plateau urbanization and ecological protection.
引文
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[2]刘荣高,刘洋,徐新良,等.近30年青藏高原南缘地理环境状况及变迁研究[J].中国科学院院刊,2017,32(9):1003-1013.[Liu R G, Liu Y, Xu X L, et al. Change of geographical environment in southern margin of Tibetan Plateau since 1980s[J]. Bulletin of Chinese Academy of Sciences, 2017,32(9):1003-1013.]
[3]姚檀栋,陈发虎,崔鹏,等.从青藏高原到第三极和泛第三极[J].中国科学院院刊,2017,32(9):924-931.[Yao T D,Chen F H, Cui P, et al. From Tibetan Plateau to third pole and pan-third pole[J]. Bulletin of Chinese Academy of Sciences, 2017,32(9):924-931.]
[4]张宪洲,杨永平,朴世龙,等.青藏高原生态变化[J].科学通报,2015,60(32):3048-3056.[Zhang X Z, Yang Y P, Piao S L, et al. Ecological change on the Tibetan Plateau[J].Chinese Science Bulletin, 2015,60(32):3048-3056.]
[5]樊杰,徐勇,王传胜,等.西藏近半个世纪以来人类活动的生态环境效应[J].科学通报,2015,60(32):3057-3066.[Fan J, Xu Y, Wang C S, et al. The effects of human activities on the ecological environment of Tibet over the past half century[J]. Chinese Science Bulletin, 2015,60(32):3057-3066.]
[6]陈发虎,刘峰文,张东菊,等.史前时代人类向青藏高原扩散的过程与动力[J].自然杂志,2016,38(4):235-240.[Chen F H, Liu F W, Zhang D J, et al. The process and driving force for peopling the Tibetan Plateau during prehistoric periods[J]. Chinese Journal of Nature, 2016,38(4):235-240.]
[7]侯光良.青藏高原的史前人类活动[J].盐湖研究,2016,24(2):68-74.[Hou G L. The prehistoric human activities on the Qinghai-Tibet Plateau[J]. Journal of Salt Lake Research, 2016,24(2):68-74.]
[8]郭兵,孔维华,姜琳,等.青藏高原高寒生态区生态系统脆弱性时空变化及驱动机制分析[J].生态科学,2018,37(3):96-106.[Guo B, Kong W H, Jiang L, et al. Analysis of spatial and temporal changes and its driving mechanism of ecological vulnerability of alpine ecosystem in Qinghai Tibet Plateau[J]. Ecological Science, 2018,37(3):96-106.]
[9] Li S C, Zhang Y L, Wang Z F, et al. Mapping human influence intensity in the Tibetan Plateau for conservation of ecological service functions[J]. Ecosystem Services, 2018,30:276-286.
[10] Sanderson E W, Jaiteh M, Levy M A, et al. The human footprint and the last of the wild[J]. BioScience, 2002,52(10):891-904.
[11]王超阚瑷珂,曾业隆,等.基于随机森林模型的西藏人口分布格局及影响因素[J].地理学报,2019,74(4):664-680.[Wang C, Kan Y K, Zeng Y L, et al. Population distribution pattern and influencing factors in Tibet based on random forest model[J]. Acta Geographica Sinica, 2019,74(4):664-680.]
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[13]吴江,张秀香,叶玲翠,等.不同时间尺度周期的旅游客流量波动特征研究——以西藏林芝市为例[J].地理研究,2016,35(12):2347-2362.[Wu J, Zhang X X, Ye L C, et al. A time scale study on the tourist flow in Nyingchi, Tibet[J]. Geographical Research, 2016,35(12):2347-2362.]
[14]陆锋,刘康,陈洁.大数据时代的人类移动性研究[J].地球信息科学学报,2014,16(5):665-672.[Lu F, Liu K, Chen J. Research on human mobility in big data era[J]. Journal of Geo-Information Science, 2014,16(5):665-672.]
[15]牛方曲,刘卫东.基于互联网大数据的区域多层次空间结构分析研究[J].地球信息科学学报,2016,18(6):719-726.[Niu F Q, Liu W D. Identifying the hierarchical regional spatial structure using internet big data[J]. Journal of Geoinformation Science, 2016,18(6):719-726.]
[16]赵梓渝,魏冶,庞瑞秋,等.中国春运人口省际流动的时空与结构特征[J].地理科学进展,2017,36(8):952-964.[Zhao Z Y, Wei Y, Pang R Q, et al. Spatiotemporal and structural characteristics of interprovincial population flow during the 2015 Spring Festival travel rush[J]. Progress in Geography, 2017,36(8):952-964.]
[17]吴吉东,王旭,王菜林,等.社会经济数据空间化现状与发展趋势[J].地球信息科学学报,2018,20(9):1252-1262.[Wu D J, Wang X, Wang C L, et al. The status and development trend of disaggregation of socio-economic data[J]. Journal of Geo-information Science, 2018,20(9):1252-1262.]
[18] Ma T. Quantitative responses of satellite-derived nighttime lighting signals to anthropogenic land-use and landcover changes across china[J]. Remote Sensing, 2018,10(9):1447.
[19]潘碧麟,王江浩,葛咏,等.基于微博签到数据的成渝城市群空间结构及其城际人口流动研究[J].地球信息科学学报,2019,21(1):68-76.[Pan B L, Wang J H, Ge Y, et al.Spatial structure and population flow analysis in Chengdu-Chongqing urban agglomeration based on Weibo check-in big data[J]. Journal of Geo-information Science,2019,21(1):68-76.]
[20]张晓瑞,华茜,程志刚.基于空间句法和LBS大数据的合肥市人口分布空间格局研究[J].地理科学,2018,38(11):1809-1816.[Zhang X R, Hua Q, Cheng Z G. Space pattern of urban population distribution based on space syntax and LBS big data[J]. Scientia Geographica Sinica,2018,38(11):1809-1816.]
[21] Boris T, Derek B, Meentemeyer R K, et al. Continentalscale quantification of landscape values using social media data[J]. Proceedings of the National Academy of Sciences, 2016,113(46):12974-12979.
[22] Walden-Schreiner C, Leung Y F, Tateosian L. Digital footprints:Incorporating crowdsourced geographic information for protected area management[J]. Applied Geography, 2018,90:44-54.
[23]段玉珊,王娜.西藏人口城镇化进程及发展趋势预测[J].西藏研究,2015(4):67-74.[Duan Y S, Wang N. Analysis on the process and trends of urbanization in Tibet[J]. Tibetan Studies, 2015(4):67-74.]
[24]张宇,曹卫东,梁双波,等.西部欠发达区人口城镇化与产业城镇化演化进程对比研究——以青海省为例[J].经济地理,2017,37(2):61-67.[Zhang Y, Cao W D, Liang S B,et al. A comparative study on the evolution process of urbanization and industrialization in the underdeveloped areas of western China:A case study of Qinghai province[J]. Economic Geography, 2017,37(2):61-67.]
[25]国家统计局.中国统计年鉴2018[M].北京:中国统计出版社,2018.[National bureau of statistics. China statistical yearbook 2018[M]. Beijing:China Statistics Press, 2018.]
[26]丁生喜,王晓鹏,秦真凤,等.基于人口-经济-生态协调发展的青海省新型城镇化研究[J].生态经济,2015,31(3):74-77.[Ding S X, Wang X P, Qin Z F, et al. Research on the new type urbanization of Qinghai based on the coordinated development of population, ecology and economy[J]. Ecological Economy, 2015,31(3):74-77.]
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