居住空间对家庭购物出行决策的影响

doi:10.11820/dlkxjz.2009.03.006

Abstract

Abstract:

The relationship between urban form and non-work activity-travel has received considerable attention in the recent debates over the area of transportation, urban studies and geography. In this paper, we formulate and estimate a nested model structure that includes the choice of shopping frequency of each household in the upper level, the allocation of household member to shopping task in the middle nest, and the choice of shopping tour pattern of residents in the lower nest. Data for the analysis are drawn from a 2007 Beijing activity dairy survey, including 516 households and 1110 residents in 10 communities distributed from urban core to suburb. The estimated results, with the methods of full information maximum likelihood (FIML), indicate that the difference of residential location does not significantly influent the household shopping travel demand such as tour frequency, however, obviously lead to diverse individual's everyday lifestyle such as shopping tour pattern to offset the impact of location. And the variable of residential duration, supposed as measurement of the cognition about residential space, merely significantly affects all levels of the shopping decision-making behaviors. Moreover, from the result of the simulation with changing the residential shopping density from 0.1M to 2.5M (M is current shopping density), we can draw a conclusion that the elasticity of shopping tour demand is not invariable for the reason that in some range (0.7M to 1.4M) the demand is elastic and in other case (<0.7M) is inelastic when the density is changing. And the elasticity varies between the location of intra-four-circle and outer-four-circle. It means that different land-use policies should be carried out in different region inside city, according to residential elasticity of shopping tour demand and the current situation of residential land-use, to optimize the shopping tour frequency. Meanwhile, the result tells us that the probability of choosing only-shop-to-home (OSH) pattern of suburb residents is the most largest, not only for the reason that the lower commercial density in suburb, but also the worse accessibility between suburb residential districts and CBD in urban core, such as lacking of transfer convenience and unpredicted traffic jam in Beijing city, all of which prevent the individual taking risks to carry out other activity in the long-distance activity pattern.

Key words: Beijing city, nested logit model (NL), shopping decision-making behavior；activity-based approach；residential space, tour pattern

ZHANG Wenjia, CHAI Yanwei. The Influence of Residential Space on Household Shopping Tour Decision-Making Behaviors[J].PROGRESS IN GEOGRAPHY, 2009, 28(3): 362-369.

References

[1] Kelly B, Damian E. The transportation and land use link. Journal of Planning Literature, 1994, 9(2):28–32.

[2] 阎小培, 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. 北京:科学出版社,2006.

[3] 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.

[4] Shiftan Y. The use of activity-based modeling to analyze the effect of land-use policies on travel behavior. Annals of Regional Science, 2008, 42:79-97.

[5] Handy S. Regional versus local accessibility: Implications for nonwork travel. Transportation Research Record, 1993, 1400: 58-66.

[6] Boarnet M G, Sarmiento S. Can land-use policy really affect travel behaviour? A study of the link between non-work travel and land-use characteristics. Urban Studies, 1998, 35(7): 1155-1169.

[7] Agyemang-Duah K, Anderson W P, Hall F L. Trip generation for shopping travel. Transportation Research Record, 1996, 1493: 12–20.

[8] Zhang M. Exploring the relationship between urban form and nonwork travel through time use analysis. Landscape and Urban Planning, 2005, 73: 244-261.

[9] Horner M W, O’Kelly M E. Is non-work travel excessive? Journal of Transport Geography, 2007, 15: 411-416.

[10] Ewing R, DeAnna M, Li S C. Land use impacts on trip generation rates. Transportation Research Record, 1996,1518: 1–6.

[11] Handy S. Urban form and pedestrian choices: studies of Austin neighborhoods. Transportation Research Record, 1996, 1552: 135-144.

[12] Handy S, Clifton K J. Local shopping as a strategy for reducing automobile travel. Transportation, 2001, 28: 317–346.

[13] Yun D S, O'Kelly M E. Modeling the day-of-the-week shopping activity and travel patterns. Socio-Economic Planning Science, 1997, 31(4): 307–319.

[14] 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.

[15] 仵宗卿, 柴彦威, 张志斌. 天津市民购物行为特征研究. 地理科学, 2000，20(06): 534-539.

[16] 陈零极, 柴彦威. 上海市民大型超市购物行为特征研究. 人文地理, 2006(5): 124-129.

[17] 王德, 朱玮, 农耘之, 等. 王府井商业街消费者行为特征分析. 商业经济, 2007(9): 16-19.

[18] 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, 200l ，20(4)：479-488.

[19] 王德.上海市消费者出行特征与商业空间结构分析. 城市规划, 2001, 25(10): 6-14.

[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] 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): 898-907.

[22] 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.

[23] 朱玮, 王德, 齐藤参郎. 南京东路消费者的回游消费行为研. 城市规划, 2006, 50 (2): 9-17.

[24] 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.

[25] Ben-Akiva M and Lerman S R. Discrete Choice Analysis: Theory and Application to Travel Demand. US: Cambridge: MIT Press, 1985.

[26] Train K E. Discrete Choice Methods with Simulation. UK: Cambridge University Press, 2003.

The Influence of Residential Space on Household Shopping Tour Decision-Making Behaviors

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 6

Metrics

Comments

Recommended 0

[1]	ZHANG Xinyu, CHEN Peng. The impact of COVID-19 prevention and control measures on residential burglary hotspots: A case study of the core urban areas of Beijing [J]. PROGRESS IN GEOGRAPHY, 2023, 42(2): 328-340.
[2]	ZHAO Ziyu, ZHAO Shiyao, HAN Zhonghui, XU Yunxiao, JIN Jie, WANG Shijun. Impact of the COVID-19 pandemic on population heat map in leisure areas in Beijing on holidays [J]. PROGRESS IN GEOGRAPHY, 2021, 40(7): 1073-1085.
[3]	Zhenshan YANG, Di WU, Ding YANG. Willingness to move, place dependence and community identity: An investigation of residential choice in the Zhongguancun area in Beijing [J]. PROGRESS IN GEOGRAPHY, 2019, 38(3): 417-427.
[4]	Yajuan SHI, Yongbin ZHU, Jinchuan HUANG. Relationship between infrastructure and socioeconomic development in Beijing [J]. PROGRESS IN GEOGRAPHY, 2016, 35(4): 450-461.
[5]	Yiqing XU, Fenghua PAN, Xiaoyu JIANG, Yalin QU, Jinshe LIANG. The geography and syndication investment networks of venture capital in Beijing [J]. PROGRESS IN GEOGRAPHY, 2016, 35(3): 358-367.
[6]	Teqi DAI, Liang WANG, Yuchao ZHANG, Cong LIAO. Optimizing school distribution with constraints of school size after school consolidation in rural China: A case study of Yanqing District, Beijing City [J]. PROGRESS IN GEOGRAPHY, 2016, 35(11): 1352-1359.