时空间行为调查的回顾与未来展望

柴彦威; 陈梓烽

doi:10.13959/j.issn.1003-2398.2021.02.002

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人文地理 ›› 2021, Vol. 36 ›› Issue (2) : 3-10. DOI: 10.13959/j.issn.1003-2398.2021.02.002

中国时空间行为研究前沿

时空间行为调查的回顾与未来展望

柴彦威¹, 陈梓烽²

作者信息 +

SPACE-TIME BEHAVIOR SURVEYS: STATE-OF-THE-ART AND PROSPECTS

CHAI Yan-wei¹, CHEN Zi-feng²

Author information +

文章历史 +

摘要

随着理论与方法体系不断完善，时空间行为研究近年来越来越重视实践应用导向，同时也面临构建解释时空间行为的一般理论的使命，对研究数据提出了新的要求。本文回顾时空间行为调查的进展，认为时空间行为调查在方法上与移动定位技术的结合日益密切，数据质量得到较大提升；在内容上越来越重视个体行为的能动性以及行为的社会、环境意义。在此基础上，本文提出未来时空间行为调查的两个发展趋势：面向实践应用，融合时空间行为调查与大数据采集；面向理论构建，加强质性调查、开展追踪调查。同时，本文试图审视我国时空间行为调查的进展、优势及不足，为我国时空间行为调查的未来发展指出方向。

Abstract

Space-time behavior as a well-established construct in urban studies and planning has received increasing scholarly attention since being proposed in early 1970s. The studies of space-time behavior, facilitated by individual activity-travel data, have constituted an effective theoretical lens to decipher the associations between human behavior and geographical spaces. More recently, researchers have endeavored to inform policies and planning through analyses of space-time behavior data, as well as to generalize empirical findings into new discourses on how human behavior interacts with geographical spaces. These attempts necessitate an effort to collect large-sized and real-time data for realistic planning programs, as well as longitudinal data to validate causality between geographical spaces and human behavior. This paper presents a review of state-of-the-art of space-time behavior surveys. It articulates that beyond paper-based questionnaires as the most commonly used data collection method, the fast development of GPS tracking technologies in the recent decades has enabled collection of high-resolution data and realistic travelling trajectories. Also, space-time behavior surveys have increasingly expanded their focuses to subjective and socio-cultural aspects of human behavior, as well as social and environmental implications of space-time behavior. Through identifying the challenges and prospects confronting space-time behavior surveys, this paper highlights the needs to integrate space-time behavior surveys with big data and to conduct qualitative and longitudinal surveys in the future studies in order to facilitate planning applications and theory development. The review and prospects presented in this paper can also inform future studies of space-time behavior in Chinese cities.

导出引用

柴彦威, 陈梓烽. 时空间行为调查的回顾与未来展望[J]. 人文地理. 2021, 36(2): 3-10 https://doi.org/10.13959/j.issn.1003-2398.2021.02.002

CHAI Yan-wei, CHEN Zi-feng. SPACE-TIME BEHAVIOR SURVEYS: STATE-OF-THE-ART AND PROSPECTS[J]. HUMAN GEOGRAPHY. 2021, 36(2): 3-10 https://doi.org/10.13959/j.issn.1003-2398.2021.02.002

中图分类号： K901

参考文献

[1] Hägerstrand T. What about people in regional science[J]. Papers and Proceedings of the Regional Science Association, 1970,24(1):7-21.
[2] Chai Y. Space-time behavior research in China:Recent development and future prospect[J]. Annals of the Association of American Geographers, 2013,103(5):1093-1099.
[3] 柴彦威,申悦,肖作鹏,等.时空间行为研究动态及其实践应用前景[J].地理科学进展,2012,31(6):667-675.[Chai Yanwei, Shen Yue, Xiao Zuopeng, et al. Review for space-time behavior research:Theory frontiers and application in the future[J]. Progress in Geography, 2012,31(6):667-675.]
[4] 柴彦威,申悦, 陈梓烽.基于时空间行为的人本导向的智慧城市规划与管理[J].国际城市规划,2014(6):31-37.[Chai Yanwei, Shen Yue, Chen Zifeng. Towards smarter cities:Human-oriented urban planning and management based on space-time behavior research[J]. Urban Planning International, 2014(6):31-37.]
[5] 柴彦威,谭一洺,申悦,等.空间-行为互动理论构建的基本思路[J]. 地理研究,2017,36(10):145-156.[Chai Yanwei, Tan Yiming, Sh-en Yue, et al. Space-behavior interaction theory:Basic thinking of general construction[J]. Geographical Research, 2017,36(10):145-156.]
[6] 柴彦威,塔娜.中国时空间行为研究进展[J].地理科学进展,2013,32(9):1362-1373.[Chai Yanwei, Ta Na. Progress in space-time behavior research in China[J]. Progress in Geography, 2013,32(9):1362-1373.]
[7] Kwan M P. Beyond space (as we knew it):Toward temporally integrated geographies of segregation, health, and accessibility:Spacetime integration in geography and GIScience[J]. Annals of the Association of American Geographers, 2013,103(5):1078-1086.
[8] Timmermans H, Arentze T, Joh C H. Analysing space-time behaviour:New approaches to old problems[J]. Progress in Human Geography, 2002,26(2):175-190.
[9] 柴彦威,张文佳,张艳,等.微观个体行为时空数据的生产过程与质量管理[J].人文地理,2009,24(6):1-9.[Chai Yanwei, Zhang Wenjia, Zhang Yan, et al. The production and quality management of disaggregated space-time data of individual's behaviors:A case study of activity-diary survey in Beijing[J]. Human Geography, 2009,24(6):1-9.]
[10] 柴彦威,申悦,马修军,等.北京居民活动与出行行为时空数据采集与管理[J].地理研究,2013,32(1):441-451.[Chai Yanwei, Shen Yue, Ma Xiujun, et al. The collection and management of space-time data of individual behavior based on location-based technologies:A case study of activity-travel survey in Beijing[J]. Geographical Research, 2013,32(1):441-451.]
[11] Cottrill C D, Pereira F C, Zhao F, et al. Future mobility survey:Experience in developing a smartphone-based travel survey in Singapore[J]. Transportation Research Record, 2013,2354(1):59-67.
[12] Lundberg G A, Komarovsky M, McInerny M A. Leisure:A Suburban Study[M]. New York:Columbia University Press, 1934:87-125.
[13] Szalai A. The Use of Time:Daily Activities of Urban and Suburban Populations in Twelve Countries[M]. The Hague:Mouton, 1972:43-68.
[14] Jones P M, Dix M C, Clarke M I, et al. Understanding Travel Behaviour[M]. Aldershot:Gower Publishing Company Limited, 1983:26.
[15] 柴彦威,刘志林,李峥嵘,等.中国城市的时空间结构[M].北京:北京大学出版社,2002:31-59.[Chai Yanwei, Liu Zhilin, Li Zhengrong, et al. The Time-space Structure of Chinese Cities[M]. Beijing:Peking University Press, 2002:31-59.]
[16] Shen Y, Ta N, Chai Y. The Internet and the space-time flexibilityof daily activities:A case study of Beijing, China[J]. Cities, 2020.https://www.sciencedirect.com/science/article/abs/pii/S026427511835993. DOI:10.1016/j.cities.2019.102493.
[17] Krivo L J, Washington H M, Peterson R D, et al. Social isolation of disadvantage and advantage:The reproduction of inequality in urban space[J]. Social Forces, 2013,92(1):141-164.
[18] Schwanen T, Wang D. Well-being, context, and everyday activities in space and time[J]. Annals of the Association of American Geographers, 2014,104(4):833-851.
[19] Lee J, Miller H J. Measuring the impacts of new public transit services on space-time accessibility:An analysis of transit system redesign and new bus rapid transit in Columbus, Ohio, USA[J]. Applied Geography, 2018,93:47-63.
[20] Kwan M P. GIS methods in time-geographic research:Geocomputation and geovisualization of human activity patterns[J]. Geografiska Annaler:Series B, Human Geography, 2004,86(4):267-280.
[21] Shoval N. Tracking technologies and urban analysis[J]. Cities, 2008, 25(1):21-28.
[22] Neves A, Brand C. Assessing the potential for carbon emissions savings from replacing short car trips with walking and cycling using a mixed GPS-travel diary approach[J]. Transportation Research Part A:Policy and Practice, 2019,123:130-146.
[23] 柴彦威.中日城市结构比较硏究[M].北京:北京大学出版社,1999:44-51.[Chai Yanwei. A Comparative Study of Urban Structure of Chinese and Japanese Cities[M]. Beijing:Peking University Press, 1999:44-51.]
[24] 闫小培,周素红,毛蒋兴,等.高密度开发城市的交通系统与土地利用——以广州为例[M].北京:科学出版社,2006:213-234.[Yan Xiaopei, Zhou Suhong, Mao Jiangxing, et al. Urban Transport System and Land Use in Highly-densely Developed Cities:A Case Study of Guangzhou[M]. Beijing:Science Press, 2006:213-234.]
[25] 周素红,邓丽芳.基于T-GIS的广州市居民日常活动时空关系[J]. 地理学报,2011,65(12):1454-1463.[Zhou Suhong, Deng Lifang. Spatio-temporal pattern of residents' daily activities based on TGIS:A case study in Guangzhou, China[J]. Acta Geographica Sini-ca, 2011,65(12):1454-1463.]
[26] 申悦,柴彦威.转型期深圳居民日常活动的时空特征及其变化[J]. 地域研究与开发,2010,29(4):69-73.[Shen Yue, Chai Yanwei. A study on the temporal-spatial characteristics of daily activities of Shenzhen residents during the transition[J]. Areal Research and Development, 2010,29(4):69-73.]
[27] 刘玉亭,何深静,李志刚.南京城市贫困群体的日常活动时空间结构分析[J].中国人口科学,2005(S1):87-95.[Liu Yuting, He Shenjing, Li Zhigang. Analysis of the space-time structure of daily activities of urban poverty group in Nanjing[J]. China Population Science, 2005(S1):87-95.]
[28] 郑凯,金海龙,贾丽娟,等.城市中少数民族购物活动时空特征:以乌鲁木齐市维吾尔族为例[J].云南地理环境研究,2009,21(3):16-21.[Zheng Kai, Jin Hailong, Jia Lijuan, et al. A study on temporal and spatial characteristics of the shopping activities of minorities in city:A case study of the Uygur residents of Urumqi[J]. Yunnan Geographic Environment Research, 2009,21(3):16-21.]
[29] 石天戈,张小雷,杜宏茹,等.乌鲁木齐市居民出行行为的空间特征和碳排放分析[J].地理科学进展,2013,32(6):897-905.[Shi Tiange, Zhang Xiaolei, Du Hongru, et al. Spatial characteristics of residents' outings and carbon emissions in Urumqi city[J]. Progress in Geography, 2013,32(6):897-905.]
[30] 柴彦威,谭一洺.中国西部城市居民时空间行为特征研究——以西宁市为例[J].人文地理,2017,32(4):37-44.[Chai Yanwei, Tan Yiming. Examining the space-time behavior patterns of residents in cities of the Western China:Evidence from Xining[J]. Human Geography, 2017,32(4):37-44.]
[31] 唐雪琼.县级城镇居民休闲行为研究——以云南蒙自县为例[J]. 人文地理,2004,19(4):41-44.[Tang Xueqiong. Recreational behavior research of county residents:A case study of Mengzi county[J]. Human Geography, 2004,19(4):41-44.]
[32] 李昌霞,柴彦威,刘璇.北京城市老年人购物决策过程中的评价性认知特征[J].人文地理,2004,19(6):89-92.[Li Changxia, Chai Yanwei, Liu Xuan. The characteristics of appraisive cognition of the elderly in Beijing during shopping decision-making process[J]. Human Geography, 2004,19(6):89-92.]
[33] Ma J, Tao Y, Kwan M P, et al. Assessing mobility-based real-time air pollution exposure in space and time using smart sensors and GPS trajectories in Beijing[J]. Annals of the American Association of Geographers, 2020,110(2):434-448.
[34] 黄潇婷,马修军.基于GPS数据的旅游者活动节奏研究[J].旅游学刊,2011,26(12):26-29.[Huang Xiaoting, Ma Xiujun. Study on tourists' rhythm of activities based on GPS data[J]. Tourism Tribune, 2011,26(12):26-29.]
[35] 王德,朱玮,黄万枢,等.基于人流分析的上海世博会规划方案评价与调整[J]. 城市规划,2009,33(8):26-32.[Wang De, Zhu Wei, Huang Wanshu, et al. In-site visitor flow analysis and planning adjustment of the Shanghai expo[J]. City Planning Review, 2009,33(8):26-32.]
[36] Chai Y W, Chen Z, Liu Y, et al. Space-time behavior survey for smart travel planning in Beijing, China[M]//Rasouli S, Timmermans H. Mobile Technologies for Activity-Travel Data Collection and Analysis. Hershey, PA:IGI-Global, 2014:79-90.
[37] Wang J, Kwan M P, Chai Y W. An innovative context-based crystalgrowth activity space method for environmental exposure assessment:A study using GIS and GPS trajectory data collected in Chicago[J]. International Journal of Environmental Research and Public Health, 2018,15(4):703. DOI:10.3390/ijerph15040703.
[38] Buliung R N, Roorda M J, Remmel T K. Exploring spatial variety in patterns of activity-travel behaviour:Initial results from the Toronto Travel-Activity Panel Survey (TTAPS)[J]. Transportation, 2008,35(6):697-722.
[39] Battelle. Lexington Area Travel Data Collection Test:Final Report[R]. Columbus, OH:Battelle Memorial Institute, 1997:1.
[40] Stopher P, Fitzgerald C, Xu M. Assessing the accuracy of the Sydney household travel survey with GPS[J]. Transportation, 2007,34(6):723-741.
[41] Prelipcean A C, Yamamoto T. Workshop synthesis:New developments in travel diary collection systems based on smartphones and GPS receivers[J]. Transportation Research Procedia, 2018,32:119-125.
[42] Fan Y, Chen Q, Liao C, Douma F. Smartphone-based Travel Experience Sampling and Behavior Intervention Among Young Adults[R]. Minnesota:Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012:7-20.
[43] Vlassenroot S, Gillis D, Bellens R, et al. The use of smartphone applications in the collection of travel behaviour data[J]. International Journal of Intelligent Transportation Systems Research, 2015,13(1):17-27.
[44] 翟青,甄峰,汪侠,等.城市商务人士的行为活动特征[J].地理研究, 2014,33(8):1477-1488.[Zhai Qing, Zhen Feng, Wang Xia, et al. SEM-based study on the characteristics of business activities[J]. Geographical Research, 2014,3(8):1477-1488.]
[45] 李渊,王秋颖,王德.GPS与回忆日志在旅游者空间行为上的精度比对——鼓浪屿实证研究[J].旅游学刊,2017,32(8):81-92.[Li Yuan, Wang Qiuying, Wang De. Precision and differential analysis by using GPS and recall diaries in the tourist's behavior research:The case of Gulangyu[J]. Tourism Tribune, 2017,32(8):81-92.]
[46] 李清泉,周宝定.基于智能手机的个体室内时空行为分析[J].地理科学进展,2015,34(4):457-465.[Li Qingquan, Zhou Baoding. Smartphone-based individual indoor spatiotemporal behavior analysis[J]. Progress in Geography, 2015,34(4):457-465.]
[47] Abou-Zeid M. Measuring and Modeling Activity and Travel Wellbeing[D]. Boston:Massachusetts Institute of Technology, 2009:80-82.
[48] Ettema D, Gärling T, Olsson L E, et al. Out-of-home activities, daily travel, and subjective well-being[J]. Transportation Research Part A:Policy and Practice, 2010,44(9):723-732.
[49] Bergstad C J, Gamble A, Gärling T, et al. Subjective well-being related to satisfaction with daily travel[J]. Transportation, 2011,38(1):1-15.
[50] Roorda M J, Lee-Gosselin M, Doherty S T, et al. Travel Activity Panel Surveys in the Toronto and Quebec City Regions:Comparison of Methods and Preliminary Results[C]. Toronto:2nd International Colloquium on the Behavioural Foundations of Integrated Land Use and Transportation Models, 2005.
[51] Shiffman S. Ecological momentary assessment (EMA) in studies of substance use[J]. Psychological Assessment, 2009,21(4):486-497.
[52] Kwan M P, Ding G. Geo-narrative:Extending geographic information systems for narrative analysis in qualitative and mixed-method research[J]. The Professional Geographer, 2008,60(4):443-465.
[53] Ellegård K. A time-geographical approach to the study of everyday life of individuals:A challenge of complexity[J]. GeoJournal, 1999, 48(3):167-175.
[54] Ellegård K, Cooper M. Complexity in daily life:A 3D-visualization showing activity patterns in their contexts[J]. Electronic International Journal of Time Use Research, 2004,1(1):37-59.
[55] Hellgren M. Extracting more knowledge from time diaries?[J]. Social Indicators Research, 2014,119(3):1517-1534.
[56] Ellegård K,张艳,蒋晨,等.复杂情境中的日常活动可视化与应用研究[J].人文地理,2016,31(5):39-46.[Ellegård Kajsa, Zhang Yan, Jiang Chen, et al. Visualization and applications of daily activities in the complex context[J]. Human Geography, 2016,31(5):39-46.]
[57] 张艳,柴彦威. "新"时间地理学——瑞典Kajsa团队的创新研究[J]. 人文地理, 2016,31(5):19-24,46.[Zhang Yan, Chai Yanwei. "New" time-geography:A review of recent progresses of time geographical research from Kajsa Ellegård in Sweden[J]. Human Geography, 2016,31(5):19-24,46.]
[58] Rainham D, McDowell I, Krewski D, et al. Conceptualizing the healthscape:Contributions of time geography, location technologies and spatial ecology to place and health research[J]. Social Science & Medicine, 2010,70(5):668-676.
[59] Manzoni V, Maniloff D, Kloeckl K, et al. Transportation mode identification and real-time CO₂ emission estimation using smartphones[R]. Boston:SENSEable City Lab, Massachusetts Institute of Technology, 2010:4-10.
[60] Schwanen T, Kwan M P. The internet, mobile phone and space-time constraints[J]. Geoforum, 2008,39(3):1362-1377.
[61] Ren F, Kwan M P. The impact of the internet on human activitytravel patterns:Analysis of gender differences using multi-group structural equation models[J]. Journal of Transport Geography, 2009,17(6):440-450.
[62] Lavieri P S, Dai Q, Bhat C R. Using virtual accessibility and physical accessibility as joint predictors of activity-travel behavior[J]. Transportation Research Part A:Policy and Practice, 2018,118:527-544.
[63] Loo B P Y, Wang B. Factors associated with home-based e-working and e-shopping in Nanjing, China[J]. Transportation, 2018,45(2):365-384.
[64] Ratti C, Frenchman D, Pulselli R M, et al. Mobile landscapes:Using location data from cell phones for urban analysis[J]. Environment and Planning B:Planning and Design, 2006,33(5):727-748.
[65] 龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J].地理学报,2012,67(10):1339-1352.[Long Ying, Zhang Yu, Cui Chengyin. Identifying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica, 2012,67(10):1339-1352.]
[66] 甄峰,王波,陈映雪.基于网络社会空间的中国城市网络特征——以新浪微博为例[J].地理学报,2012,67(8):1031-1043.[Zhen Feng, Wang Bo, Chen Yingxue. China's city network characteristics based on social network space:An empirical analysis of Sina Micro-blog[J]. Acta Geographica Sinica, 2012,67(8):1031-1043.]
[67] Liu Y, Kang C, Gao S, et al. Understanding intra-urban trip patterns from taxi trajectory data[J]. Journal of Geographical Systems, 2012, 14(4):463-483.
[68] Zheng Z, Zhou S H. Scaling laws of spatial visitation frequency:Applications for trip frequency prediction[J]. Computers, Environment and Urban Systems, 2017,64:332-343.
[69] Zhou X, Yeh A G O, Yue Y. Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data[J]. Journal of Transport Geography, 2018,68:102-108.
[70] Long Y, Liu X, Zhou J, et al. Early birds, night owls, and tireless/recurring itinerants:An exploratory analysis of extreme transit behaviors in Beijing, China[J]. Habitat International, 2016,57:223-232.
[71] Chen C, Ma J, Susilo Y, et al. The promises of big data and small data for travel behavior (aka human mobility) analysis[J]. Transportation Research Part C:Emerging Technologies, 2016,68:285-299.
[72] 柴彦威,塔娜,张艳.融入生命历程理论、面向长期空间行为的时间地理学再思考[J].人文地理,2013,28(2):1-6.[Chai Yanwei, Ta Na, Zhang Yan. Rethinking time-geography in long-term space-time behavior study:Integrating with life course theory[J]. Human Geography, 2013,28(2):1-6.]
[73] Boarnet M G. A broader context for land use and travel behavior, and a research agenda[J]. Journal of the American Planning Association, 2011,77(3):197-213.
[74] Singleton R, Straits B C. Approaches to Social Research (4th Ed).[M]. New York:Oxford University Press, 2005:96-103.
[75] 曹新宇.居住自选择在建成环境和交通行为关系中的作用[M]//柴彦威.时空间行为研究前沿.南京:东南大学出版社,2015:122-133.[Cao Xinyu. The role of residential self-selection in the associations of built environment and travel behavior[M]//Chai Yanwei, et al. The Frontier of Space-time Behavior Research. Nanjing:Southeast University Press, 2015:122-133.]
[76] Creswell J W. Qualitative Inquiry and Research Design:Choosing among Five Approaches[M]. London:Sage, 2007:39-40.
[77] 柴宏博,冯健.基于家庭生命历程的北京郊区居民行为空间研究[J].地理科学进展,2016,35(12):1506-1516.[Chai Hongbo, Feng Jian. Behavior space of suburban residents in Beijing based on family life course[J]. Progress in Geography, 2016,35(12):1506-1516.]
[78] Fitzgerald J, Gottschalk P. An analysis of sample attrition in panel data[J]. Journal of Human Resources, 1998,33(2):251-299.