基于家庭的购物行为时、空间 决策模型及其应用
收稿日期: 2009-04-20
修回日期: 2009-08-17
网络出版日期: 2010-02-20
基金资助
国家自然科学基金项目(40671058)
A household-based model of shopping decision-making behavior in timing and destination choice: Formulation and application to spatial policy evaluation
Received date: 2009-04-20
Revised date: 2009-08-17
Online published: 2010-02-20
基于家庭的购物行为决策的概念模型,利用2007年北京市活动日志调查的第一手数据,建立购物出发时间决策和购物目的地决策的条件Logit模型,并探讨其在相关政策评估和预测上的应用。结果发现:家庭内部相互作用和决策层面要素对购物时、空间决策的影响比较显著;早晚高峰出行的人群具有不同的社会经济属性和购物行为属性;居民休息日的购物活动集中在离家6km以内或者高密度的商业中心;影响购物目的地选择的因素更多是购物地距离,而非商业密度。通过模拟发现,土地混合利用的政策可能会增加高峰期的交通拥挤程度,而商业设施分散布局可能更有利于城市交通状况的改善和居民日常生活质量的提升。
张文佳,柴彦威 . 基于家庭的购物行为时、空间 决策模型及其应用[J]. 地理研究, 2010 , 29(2) : 338 -350 . DOI: 10.11821/yj2010020016
The researches of shopping behavior in urban geography and planning mainly focus on how to manage urban residential non-work travel, how to regulate and optimize commercial spatial structure, and how to improve residential everyday life quality. In this paper, we put forward the conceptual model of the shopping decision-making behavior on the perspective of intra-household interaction. Hereby, using the first-hand data from a activity-travel daily survey of Beijing in 2007, we derive and formulate a multinomial logit model (MNL) for shopping timing decision-making, and a conditional logit model (CLM) for shopping destination choice, to interpret the behavioral diversity among different groups and evaluate the spatial policies. The results indicate that: First, the influences of the factors about intra-household interaction and different decision-making levels on the shopping space-time decision-making process are more significant than that of traditional socio-demographic characters. Therefore, the characters of household-based decision should be paid more attention by the studies of activity-travel behavior. Then, the groups travelling in the morning peak period seem to be low-income or exurb-living, with non-motor mode or longer shopping duration. Also, the dwellers travelling in the evening peak period seem to be with family companions, suburb-living or choosing the tour mode of HWSH. Moreover, it does mean to attract more shopping activities with higher commercial density. At the weekend, residential shopping activities are mostly concentrated within 6 km far from the home, or appear at the high-density CBD. Furthermore, the impacts of personal characters on the destination choice give more attention to the home-to-shop distance, rather than the density of the shopping district. Finally, the results from estimation and simulation evaluation show that the policy of mixed land-use might indeed reduce the travel time, but increase the probability of multi-purpose shopping tour, which will more probably choose the peak-time travelling, then aggravate the traffic jam. Besides, suppose that the commercial establishment would be uniformly distributed, the more shopping activities will be concentrated surrounding the home. Although the cities have commercial hierarchical structure, it would be better to use the policy of decentralization, rather than aggregation, to improve the urban transportation and advance the residential everyday life quality.
[1] 孙立平. 断裂——20世纪90年代以来的中国社会.北京: 社会科学文献出版社, 2004,1.
[2] 袁志刚, 宋铮.城镇居民消费行为变异与我国经济增长. 经济研究, 1999,(11):20~28.
[3] Jones P M, Dix M C, Clarke M I, Heggie I G. Understanding Travel Behavior. Aldershot, UK:Gower Pulishing Co., Ltd. 1983.
[4] Horner Mark W, Morton E. O'Kelly. Is non-work travel excessive? Journal of Transport Geography, 2007, 15( 6): 411~416.
[5] 宁越敏.上海市区商业中心区位的探讨. 地理学报, 1984, 39(2): 163~172.
[6] 杨吾扬.北京市零售商业与服务业中心和网点的过去、现在和未来.地理学报, 1994, 49(1): 9~15.
[7] 张文忠,李业锦.北京城市居民消费区位偏好与决策行为分析——以西城区和海淀中心地区为例.地理学报, 2006, 61(10): 1037~1045.
[8] 柴彦威,翁桂兰,沈洁.基于居民购物消费行为的上海城市商业空间结构研究. 地理研究, 2008, 27(4) : 897~906.
[9] Banister David, Ann Bowling. Quality of life for the elderly: The transport dimension.Transport Policy, 2004, 11(2):105~115.
[10] Van Eck Jan Ritsema, Guillaume Burghouwt, Martin Dijst. Lifestyles, spatial configurations and quality of life in daily travel: An explorative simulation study. Journal of Transport Geography, 2005, 13(2):123~134.
[11] 柴彦威, 沈洁, 翁桂兰.上海居民购物行为的时空间特征及其影响因素.经济地理.2008, 28(2):221~227.
[12] 沃姆斯利 D J, 刘易斯D J著.行为地理学.王兴中,郑国强,李贵才译.西安:陕西人民出版社. 1988.
[13] Golledge R,Stimson R.Spatial Behavior:A Geographic Perspective.London:The Guilford Press,1997.
[14] Timmermans H, Arentze T and Joh C H. Analysing space-time behaviour: New approaches to old problems. Progress in Human Geography, 2002, 26(2): 175~190.
[15] 王德, 张晋庆. 上海市消费者出行特征与商业空间结构分析. 规划研究, 2001, (10):6~14.
[16] 仵宗卿, 柴彦威, 戴学珍, 等. 购物出行空间的等级结构研究——以天津市为例. 地理研究, 2001, (4): 479~488.
[17] 柴彦威, 翁桂兰, 龚华. 深圳居民购物消费行为的时空间特征. 人文地理, 2004, 19(6): 79~84.
[18] 柴彦威, 李昌霞. 中国城市老年人日常购物行为的空间特征:以北京、深圳和上海为例. 地理学报, 2005, 60(3): 401~408.
[19] 沈洁, 柴彦威. 郊区化背景下北京市民城市中心商业区的利用特征.人文地理, 2006 (5),113~118.
[20] 冯健, 陈秀欣, 兰宗敏.北京市居民购物行为空间结构演变.地理学报, 2007, 62(10):1083~1096.
[21] 朱玮, 王德, 齐藤参郎. 南京东路消费者的入口消费行为研究. 城市规划, 2005, 29 (5): 14~21.
[22] 朱玮, 王德, 齐藤参郎. 南京东路消费者的回游消费行为研究. 城市规划, 2006, 50 (2): 9~17.
[23] 柴彦威, 林涛, 龚华. 深圳居民购物行为空间决策因素分析. 人文地理, 2004, 19(6): 85~88.
[24] 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.人文地理,2004,19(6):89~93.
[25] 张文佳, 柴彦威.基于家庭的城市居民出行需求理论与验证模型. 地理学报, 2008, 63(12): 1246~1256.
[26] 薛娟娟, 朱青.北京市零售商业空间分布研究.商业研究, 2006, 14:32~34.
[27] Greene W H. Econometric Analysis.Upper Saddle River, New Jersey: Pearson Education, Inc. 2003:720.
[28] Yun D S, O'Kelly M E.Modeling the day-of-the-week shopping activity and travel patterns. Socio-Econ. Plann. Sci.1997,31(4), 307~319.
[29] Limanond T, Niemeier D.Effect of land use on decisions of shopping tour generation:A case study of three traditional neighborhoods in WA.Transportation,2004,31: 153~181.
[30] 张文佳, 柴彦威.基于家庭的购物行为时空间决策研究.上海,同济大学,第三届空间与行为规划研究会议. 2009,1.
[31] 张文佳,柴彦威.居住空间对家庭购物出行决策的影响.地理科学进展, 2009, 28(3): 362~369.
[32] 周一星.北京的郊区化及引发的思考.地理科学, 1996, 16(3): 198~206.
[33] 楚波,梁进社.基于OPM模型的北京制造业区位因子的影响分析.地理研究, 2007, 26(4): 724~735.
[34] 丁成日,宋彦,等.城市规划与空间结构——城市可持续发展战略.北京:中国建筑工业出版社. 2005,10.
[35] Boarnet M G, Crane R. Travel by Design: The Influence of Urban Form on Travel. New York: Oxford University Press, 2001.
/
| 〈 |
|
〉 |