留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China

XUE Shuyan LI Gang YANG Lan LIU Ling NIE Qifan Muhammad Sajid MEHMOOD

XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD. Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China[J]. 中国地理科学, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3
引用本文: XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD. Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China[J]. 中国地理科学, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3
XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD. Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China[J]. Chinese Geographical Science, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3
Citation: XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD. Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China[J]. Chinese Geographical Science, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3

Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China

doi: 10.1007/s11769-019-1086-3
基金项目: 

Under the auspices of the Tang Scholar Program of Northwest University (No. 2016)

Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China

Funds: 

Under the auspices of the Tang Scholar Program of Northwest University (No. 2016)

  • 摘要: Attended collection and delivery points are vital components of ‘last-mile logistics’. Based on point of interest (POI) data for Cainiao Stations and China Post stations in Changsha City, China, this paper provides a detailed exploration of the basic features, spatial distribution, and location influencing factors of attended collection and delivery points. Specifically, analyses of the types, service objects and location distributions of the attended collection and delivery points alongside a discussion of their spatial pattern and influencing factors provides a reference for their general geographic layout and characteristics. The findings of this study indicate that:1) The main mode of operation of attended collection and delivery points is franchises, with other modes of operation rely on supermarkets and other individual shop types. 2) The main service targets of attended collection and delivery points are communities, schools, and businesses, followed by townships, enterprises, scenic spots, and administrative units. 3) Approximately 77.44% of the attended collection and delivery points are located near the exits of service areas; others are situated in the centre of the service areas. For the Cainiao Stations, 80% are located within 125 m of the exit; for the China Post stations, 80% are located within 175 m of the exit. 4) The spatial distribution of the attended collection and delivery points in Changsha is unbalanced, with ‘more centre and fewer surrounding’. The centre is an ‘inverted triangle’, and the edge is an ‘orphan’, showing a northwest-southeast orientation and symmetrical along the axis. The layout of the attended collection and delivery points forms three core areas, and the number of sites decreases with the distance from the core. 5) The number and distribution of the attended collection and delivery points are strongly consistent with the regional economic development level, population, and roadway system traffic convenience. Most attended collection and delivery points are on residential, scientific and educational, and commercial and financial land.
  • [1] Baldi M M, Manerba D, Perboli G et al., 2019. A generalized bin packing problem for parcel delivery in last-mile logistics. Eu-ropean Journal of Operational Research, 274(3):990-999. doi: 10.1016/j.ejor.2018.10.056
    [2] Ban Ziqi, 2016. Analysis of problems and countermeasures in Alibaba ‘Rookie Post Station’. Labour Security World, (30):53-54. (in Chinese)
    [3] Boysen N, Schwerdfeger S, Weidinger F, 2018. Scheduling last-mile deliveries with truck-based autonomous robots. Journal Citation Reports, 271(3):1085-1099. doi: 10.1016/j.ejor.2018.05.058
    [4] Browne M, Allen J, Anderson S et al., 2001. Overview of Home Delivery in the UK. Westminster:University of Westminster.
    [5] Changsha Municipal Bureau of Statistics, National Bureau of Statistics Changsha Investigation Team, 2018. Changsha Sta-tistical Yearbook 2018, Beijing:China statistics press
    [6] Changsha municipal government, 2014. Changsha urban centre land use planing map (2003-2020)
    [7] Maere B D, 2017. Ecological and Economic Impact of Automated Parcel Lockers Vs Home Delivery. Brussels:University of Brussels.
    [8] Deng Sumei, 2018. Discussion on the ‘last mile’ distribution method of e-commerce logistics under the background of ‘In-ternet +’. Modern Economic Information, (22):324. (in Chi-nese)
    [9] Ehmke J F, Mattfeld D C, 2012. Vehicle routing for attended home delivery in city logistics. Procedia-Social and Behavioral Sciences, 39:622-632. doi: 10.1016/j.sbspro.2012.03.135
    [10] Esper T L, Jensen T D, Turnipseed F L et al., 2003. The last mile:an examination of effects of online retail delivery strategies on consumers. Journal of Business Logistics, 24(2):177-203. doi: 10.1002/j.2158-1592.2003.tb00051.x
    [11] Esser K, Kurte J, 2006. B2C E-commerce:impact on transport in urban areas. In:Proceedings of the 4th International Confer-ence on City Logistics. Amsterdam:Elsevier Science.
    [12] Huang Qian, Liu Zhe, 2016. The case study on urban logistics joint distribution based on two-markets——In the case of rookie station. Logistics Sci-Tech, 39(6):43-45. (in Chinese)
    [13] Huang Tao, 2017. Study on the Layout of Express Self-pickup Network Based on GIS. Xi'an:Chang'an University. (in Chi-nese)
    [14] Kämäräinen V, Punakivi M, 2002. Developing cost-effective op-erations for the e-grocery supply chain. International Journal of Logistics Research and Applications, 5(3):285-298. doi: 10.1080/1367556021000026727
    [15] Kedia A, Kusumastuti D, Nicholson A, 2017. Acceptability of collection and delivery points from consumers' perspective:a qualitative case study of Christchurch city. Case Studies on Transport Policy, 5(4):587-595. doi: 10.1016/j.cstp.2017.10.009
    [16] Li Na, 2013. Research on the Layout of the Self Pick up Points of KB Company. Beijing:Beijing Jiaotong University. (in Chinese)
    [17] Li Guoqi, Jin Fengjun, Chen Yu et al., 2015. Spatial patterns of logistics industry based on a geographic analysis of hotness degree. Progress in Geography, 34(5):629-637. (in Chinese)
    [18] Li Gang, Yang Lan, He Jianxiong et al., 2018. The spatial pattern and organization relation of the pickup points based on POI data in Xi'an:focus on Cainiao stations. Scientia Geographica Sinica, 38(12):2024-2030. (in Chinese)
    [19] Li Gang, Chen Weiyu, Yang Lan et al., 2019. Spatial pattern and agglomeration mode of parcel collection and delivery points in Wuhan City. Progress in Geography, 38(3):407-416. (in Chi-nese)
    [20] Morganti E, Dablanc L, Fortin F, 2014a. Final deliveries for online shopping:the deployment of pickup point networks in urban and suburban areas. Research in Transportation Business & Management, 11:23-31. doi: 10.1016/j.rtbm.2014.03.002
    [21] Morganti E, Seidel S, Blanquart C et al., 2014b. The impact of e-commerce on final deliveries:alternative parcel delivery services in France and Germany. Transportation Research Procedia, 4:178-190. doi: 10.1016/j.trpro.2014.11.014
    [22] Punakivi M, 2003. Comparing Alternative Home Delivery Models for E-grocery Business. Helsinki:Helsinki University of Technology.
    [23] Rowlands P, 2006. Unattended delivery solutions-finally picking up. Fulfillment and E. Logistics, 39:19-20.
    [24] Tan Rushi, Xu Yilun, Chen Dong et al., 2016. Research on the spatial distribution of pickup points from the perspective of residents' behaviour:A case study of Cainiao network pickup points in Nanjing. World Regional Studies, 25(5):111-120. (in Chinese)
    [25] Weltevreden J W J, 2008. B2C E-commerce logistics:the rise of collection-and-delivery points in the Netherlands. International Journal of Retail & Distribution Management, 36(8):638-660. doi: 10.1108/09590550810883487
    [26] Wu Xiaoyan, 2017. Successful delivery mode and experience enlightenment of ‘Lase One-kilometer’ from foreign E-commerce logistics. Prices Monthly, (12):47-50. (in Chi-nese)
    [27] Xia Litao, Gu Fengyun, Qu Chongchong, 2015. Study on network optimization of rookie station. Logistics Technology, 34(10):142-145. (in Chinese)
    [28] Xu Junjie, Jiang Ling, Hong Liang, 2012. Developing schemes of pick-up points for E-commerce parcels. Logistics Engineering and Management, 34(11):142-144. (in Chinese)
    [29] Yu Wenhao, Ai Tinghua, 2015. The visualization and analysis of POI features under network space supported by kernel density estimation. Acta Geodaetica et Cartographica Sinica, 44(1):82-90. (in Chinese)
    [30] Yu Wenhao, Ai Tinghua, Liu Pengcheng et al., 2015. Network kernel density estimation for the analysis of facility POI hotspots. Acta Geodaetica et Cartographica Sinica, 44(12):1378-1383, 1400. (in Chinese)
    [31] Yu Wenhao, Ai Tinghua, Yang Min et al., 2016. Detecting ‘Hot Spots’ of facility pois based on kernel density estimation and spatial autocorrelation technique. Geomatics and Information Science of Wuhan University, 41(2):221-227. (in Chinese)
    [32] Zeng Guojun, Lu Yirui, 2017. The spatial layout and influencing factors of multinational retail diet brands:a case study of star-bucks in Guangzhou. Human Geography, 32(6):47-55. (in Chinese)
    [33] Zhan Bin, Gu Ziqi, Li Yang, 2016. Research on optimization of ‘last mile’ distribution mode of e-commerce logistics under the background of ‘Internet +’. Logistics Technology, 35(01):1-4+11. (in Chinese)
    [34] Zhang Shunli, 2015. Research and Application of Modern Logis-tics and Express Industry of E-commercein China. Chongqing:Chongqing University. (in Chinese)
    [35] Zhang Jin, Chen Yiyou, 2015. The review of research on the ‘Last Mile’ in logistics. China Business and Market, (4):23-32. (in Chinese)
    [36] Zhang Huiyun, Shang Xin, 2015. Analysis of last mile distribution modes for express delivery industry:in the case of rookie station and fengchao delivery cabinet. Logistics Technology, 34(11):48-51. (in Chinese)
    [37] Zhou Chunshan, Jin Wanfu, Zhang Guojun, 2018. Impact of shipping distance on online retailers' sales:a case study of Maiyang on Tmall. Chinese Geographical Science, 28(2):261-273. doi: 10.1007/s11769-018-0945-7
    [38] Zhu Tao, Ni Weiying, 2017. Research on the status quo and countermeasures of the ‘last mile’ express delivery point in e-commerce logistics. Modern Business, (17):9-12. (in Chi-nese)
  • [1] Dalai MA, Fengtai ZHANG, Lei GAO, Guangming YANG, Qing YANG, Youzhi AN.  Spatiotemporal Dynamics of Green Total-factor Water-use Efficiency and Its Influencing Factors in China . Chinese Geographical Science, 2021, 31(5): 795-814. doi: 10.1007/s11769-021-1227-3
    [2] Xingchuan GAO, Tao LI, Dongqi SUN.  Regional Differentiation Regularity and Influencing Factors of Population Change in the Qinghai-Tibet Plateau, China . Chinese Geographical Science, 2021, 31(5): 888-899. doi: 10.1007/s11769-021-1223-7
    [3] Le CHEN, Meijun XI, Wanfu JIN, Ya HU.  Spatial Pattern of Long-term Residence in the Urban Floating Population of China and its Influencing Factors . Chinese Geographical Science, 2021, 31(2): 342-358. doi: 10.1007/s11769-021-1193-9
    [4] Xuelan TAN, Hangling YU, Yue AN, Zhenkai WANG, Lingxiao JIANG, Hui REN.  Spatial Differentiation and Influencing Factors of Poverty Alleviation Performance Under the Background of Sustainable Development: A Case Study of Contiguous Destitute Areas in Hunan Province, China . Chinese Geographical Science, 2021, 31(6): 1029-1044. doi: 10.1007/s11769-021-1242-4
    [5] LIU Yuanxin, LYU Yihe, BAI Yingfei, ZHANG Buyun, TONG Xiaolin.  Vegetation Mapping for Regional Ecological Research and Management: A Case of the Loess Plateau in China . Chinese Geographical Science, 2020, 30(3): 410-426. doi: 10.1007/s11769-020-1120-5
    [6] NAN Ying, WANG Bingbing, ZHANG Da, LIU Zhifeng, QI Dekang, ZHOU Haohao.  Spatial Patterns of LULC and Driving Forces in the Transnational Area of Tumen River: A Comparative Analysis of the Sub-regions of China, the DPRK, and Russia . Chinese Geographical Science, 2020, 30(4): 588-599. doi: 10.1007/s11769-020-1136-x
    [7] DU Yan, QIN Weishan, SUN Jianfeng, WANG Xiaohui, GU Haoxin.  Spatial Pattern and Influencing Factors of Regional Ecological Civilisa-tion Construction in China . Chinese Geographical Science, 2020, 30(5): 776-790. doi: 10.1007/s11769-020-1145-9
    [8] CHU Nanchen, ZHANG Pingyu, LI He.  Transnational Economic Connection Analysis Based on Railway Class Ac-cessibility Between China and Russia . Chinese Geographical Science, 2019, 20(5): 872-886. doi: 10.1007/s11769-019-1064-9
    [9] SUN Zhe, ZHAN Dongsheng, JIN Fengjun.  Spatio-temporal Characteristics and Geographical Determinants of Air Quality in Cities at the Prefecture Level and Above in China . Chinese Geographical Science, 2019, 20(2): 316-324. doi: 10.1007/s11769-019-1031-5
    [10] DIAO Shuo, YUAN Jiadong, WU Yanyan.  Performance Evaluation of Urban Comprehensive Carrying Capacity of Harbin, Heilongjiang Province in China . Chinese Geographical Science, 2019, 20(4): 579-590. doi: 10.1007/s11769-019-1056-9
    [11] QIAO Xuning, GU Yangyang, ZOU Changxin, WANG Lei, LUO Juhua, HUANG Xianfeng.  Trade-offs and Synergies of Ecosystem Services in the Taihu Lake Basin of China . Chinese Geographical Science, 2018, 28(1): 86-99. doi: 10.1007/s11769-018-0933-y
    [12] JU Hongrun, ZHANG Zengxiang, WEN Qingke, WANG Jiao, ZHONG Lijin, ZUO Lijun.  Spatial Patterns of Irrigation Water Withdrawals in China and Implications for Water Saving . Chinese Geographical Science, 2017, 27(3): 362-373. doi: 10.1007/s11769-017-0871-0
    [13] YU Chao, MA Yanji.  Carbon Emission Trends of Manufacturing and Influencing Factors in Jilin Province, China . Chinese Geographical Science, 2016, 26(5): 656-669. doi: 10.1007/s11769-016-0823-0
    [14] DAI Dandan, ZHOU Chunshan, YE Changdong.  Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China . Chinese Geographical Science, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1
    [15] DI Xianghong, HOU Xiyong, WANG Yuandong, WU Li.  Spatial-temporal Characteristics of Land Use Intensity of Coastal Zone in China During 2000-2010 . Chinese Geographical Science, 2015, 25(1): 51-61. doi: 10.1007/s11769-014-0707-0
    [16] XIE Miaomiao, WANG Yanglin, FU Meichen, ZHANG Dingxuan.  Pattern Dynamics of Thermal-environment Effect During Urbanization: A Case Study in Shenzhen City, China . Chinese Geographical Science, 2013, 23(1): 101-112.
    [17] MA Xiaodong, QIU Fangdao, LI Quanlin, SHAN Yongbin, CAO Yong.  Spatial Pattern and Regional Types of Rural Settlements in Xuzhou City, Jiangsu Province, China . Chinese Geographical Science, 2013, 23(4): 482-491. doi: 10.1007/s11769-013-0615-8
    [18] DAI Junliang, WANG Kaiyong, GAO Xiaolu.  Spatial Structure and Land Use Control in Extended Metropolitan Region of Zhujiang River Delta, China . Chinese Geographical Science, 2010, 20(4): 298-308. doi: 10.1007/s11769-010-0402-8
    [19] KONG Fan-hua, Nobukazu NAKAGOSHI, YIN Hai-wei, Akira KIKUCHI.  SPATIAL GRADIENT ANALYSIS OF URBAN GREEN SPACES COMBINED WITH LANDSCAPE METRICS IN JINAN CITY OF CHINA . Chinese Geographical Science, 2005, 15(3): 254-261.
    [20] LIU Ji-yuan, DENG Xiang-zheng, LIU Ming-liang, ZHANG Shu-wen.  STUDY ON THE SPATIAL PATTERNS OF LAND—USE CHANGE AND ANALYSES OF DRIVING FORCES IN NORTHEASTERN CHINA DURING 1990-2000 . Chinese Geographical Science, 2002, 12(4): 299-308.
  • 加载中
计量
  • 文章访问数:  97
  • HTML全文浏览量:  0
  • PDF下载量:  194
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-03-07

Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China

doi: 10.1007/s11769-019-1086-3
    基金项目:

    Under the auspices of the Tang Scholar Program of Northwest University (No. 2016)

摘要: Attended collection and delivery points are vital components of ‘last-mile logistics’. Based on point of interest (POI) data for Cainiao Stations and China Post stations in Changsha City, China, this paper provides a detailed exploration of the basic features, spatial distribution, and location influencing factors of attended collection and delivery points. Specifically, analyses of the types, service objects and location distributions of the attended collection and delivery points alongside a discussion of their spatial pattern and influencing factors provides a reference for their general geographic layout and characteristics. The findings of this study indicate that:1) The main mode of operation of attended collection and delivery points is franchises, with other modes of operation rely on supermarkets and other individual shop types. 2) The main service targets of attended collection and delivery points are communities, schools, and businesses, followed by townships, enterprises, scenic spots, and administrative units. 3) Approximately 77.44% of the attended collection and delivery points are located near the exits of service areas; others are situated in the centre of the service areas. For the Cainiao Stations, 80% are located within 125 m of the exit; for the China Post stations, 80% are located within 175 m of the exit. 4) The spatial distribution of the attended collection and delivery points in Changsha is unbalanced, with ‘more centre and fewer surrounding’. The centre is an ‘inverted triangle’, and the edge is an ‘orphan’, showing a northwest-southeast orientation and symmetrical along the axis. The layout of the attended collection and delivery points forms three core areas, and the number of sites decreases with the distance from the core. 5) The number and distribution of the attended collection and delivery points are strongly consistent with the regional economic development level, population, and roadway system traffic convenience. Most attended collection and delivery points are on residential, scientific and educational, and commercial and financial land.

English Abstract

XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD. Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China[J]. 中国地理科学, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3
引用本文: XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD. Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China[J]. 中国地理科学, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3
XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD. Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China[J]. Chinese Geographical Science, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3
Citation: XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD. Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China[J]. Chinese Geographical Science, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3
参考文献 (38)

目录

    /

    返回文章
    返回