寄递大数据城市画像

doi:10.3785/j.issn.1008-973X.2019.09.017

浙江大学学报(工学版)

2019, Vol. 53

Issue (9): 1779-1787 DOI: 10.3785/j.issn.1008-973X.2019.09.017

计算机科学与人工智能

寄递大数据城市画像

任思源(

),郭斌*(

),张曼,岳超刚,李青洋,於志文

西北工业大学计算机学院, 陕西西安 710072

Urban profiling using express big data

Si-yuan REN(

),Bin GUO*(

),Man ZHANG,Chao-gang YUE,Qing-yang LI,Zhi-wen YU

School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China

全文: PDF(1159 KB) HTML

摘要：

为了充分利用快递面单中所包含的时间、地址、物品等信息对城市进行数据分析，基于大量城市历史快递数据，提出一种城市画像系统框架. 通过数据补全、地址转换、物品类型提取以及数据格式转换等方法，对多家快递公司的数据进行汇聚和预处理. 提出寄递频次、寄递时间、寄递地址、寄递物品4个分析指标，基于西安市真实历史数据集，分别对城市中不同社会群体与城市区域的快递数据进行分析，并基于数据分析结果进行城市画像；结合社会实际情况对分析结果中存在的规律与异常情况作出合理解释，通过可视化平台对城市画像内容进行集成与演示. 结果表明，采用提出的城市画像系统能够发现不同社会群体和区域之间存在的寄递行为规律与异常.

关键词： 城市画像; 可视化; 寄递大数据; 社会群体; 城市区域; 数据分析

Abstract:

A framework of urban profiling system was proposed based on a large number of urban history express data for urban data analysis through making full use of the express sheet information, including time, address and item. The data of several express companies were gathered and pre-processed by means of data completion, address conversion, item type extraction and data format conversion method. And four indicators were proposed: delivering frequency, time, address and item. Based on Xi’an real historical data set, express data of different social groups and urban areas in the city was analyzed, respectively, and an urban profiling was made through data analysis results. In combination with the actual situation of the society, a reasonable explanation of the laws and anomalies was made in the analysis results. Finally, urban profiling results were integrated and demonstrated through a visualization platform. Results show that the proposed urban profiling system can detect the law and anomaly of the delivery behavior between different social groups and regions.

Key words: urban profiling visualization express big data social groups urban areas data analysis

收稿日期: 2018-12-12 出版日期: 2019-09-12

CLC:

TP 399

通讯作者: 郭斌 E-mail: rensiyuan@mail.nwpu.edu.cn;guob@nwpu.edu.cn

作者简介: 任思源（1993—），男，博士生，从事城市计算研究. orcid.org/0000-0001-9265-3161. E-mail： rensiyuan@mail.nwpu.edu.cn

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	作者相关文章
	任思源
	郭斌
	张曼
	岳超刚
	李青洋
	於志文

引用本文:

任思源,郭斌,张曼,岳超刚,李青洋,於志文. 寄递大数据城市画像[J]. 浙江大学学报(工学版), 2019, 53(9): 1779-1787.

Si-yuan REN,Bin GUO,Man ZHANG,Chao-gang YUE,Qing-yang LI,Zhi-wen YU. Urban profiling using express big data. Journal of ZheJiang University (Engineering Science), 2019, 53(9): 1779-1787.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.09.017 或 http://www.zjujournals.com/eng/CN/Y2019/V53/I9/1779

图 1 寄递大数据城市画像的可视化分析框架

表 1 快递数据中物品类型提取算法实验结果

表 2 数据预处理后的快递数据样例

表 3 高校寄递信息统计

图 2 中国某高校寄递关系网

图 3 高校寄递量时间分布图

图 4 高校与公司寄递行为的时间分布对比图

图 5 高校与公司寄递物品类型对比

图 6 高校寄递物品分布

图 7 本科与专科院校3月、11月购买物品类型Top5

图 8 西安城市区域划分

图 9 高档小区寄递量随时间分布图

图 10 高档小区寄递物品分布图

图 11 不同类型区域间收件量随时间分布图

图 12 不同类型区域用户购买物品类型Top5

图 13 递大数据城市画像系统界面可视化系统界面

1	谭旭. 基于物流数据的快递网络分析与建模[D]. 杭州: 浙江大学, 2015. TAN Xu. Analysis and modeling of express delivery network based on logistics data [D]. Hangzhou: Zhejiang University, 2015.
2	郝晟. 面向侦查的快递数据分析挖掘系统[D]. 天津: 天津大学,2014. HAO Miao. Express data analysis and mining system for investigation [D]. Tianjin: Tianjin University, 2014.
3	文杰锋. 快递物流配送异常检测方法研究[D]. 重庆: 重庆邮电大学, 2016. WEN Jie-feng. Research on abnormal detection methods for express delivery logistics [D]. Chongqing: Chongqing University of Posts and Telecommunications, 2016.
4	GAO F, ZHAO Q L. Evidential reasoning-based airline network design for long-haul transportation in express delivery[J]. Tehnicki Vjesnik-Technical Gazette, 2017, 24 (5): 1551- 1559
5	刘二超. 快递服务便利店选址问题研究[D]. 北京: 清华大学, 2014. LIU Er-chao. Study on the location of express service convenience stores [D]. Beijing: Tsinghua University, 2014.
6	TANG S Y, DENG G M. Based on the theory of grey system to forecast China's business volume of express services[J]. Modern Economy, 2015, 6 (2): 283 doi: 10.4236/me.2015.62025
7	YIN S, JIANG Y C, TIAN Y, et al. A data-driven fuzzy information granulation approach for freight volume forecasting[J]. IEEE Transactions on Industrial Electronics, 2017, 64 (2): 1447- 1456 doi: 10.1109/TIE.2016.2613974
8	李万彪, 余志, 龚峻峰, 等基于关系数据模型的犯罪网络挖掘研究[J]. 中山大学学报: 自然科学版, 2014, 53 (5): 1- 7 LI Wan-xi, YU Zhi, GONG Jun-feng, et al Criminal network mining based on relational data model[J]. Journal of Sun Yatsen University: Natural Science, 2014, 53 (5): 1- 7
9	范文兵, 吴宇昊. 基于SaaS模式的快递投递业务系统设计[J]. 计算机应用, 2017, 37(增 1): 312-316. FAN Wen-bing, WU Yu-hao. Design of integrated express delivery system based on SaaS[J]. Journal of Computer Applications, 2017, 37(Suppl. 1): 312-316.
10	GUO B, ZHANG D Q, YU Z W, et al. Extracting social and community intelligence from digital footprints[J]. Journal of Ambient Intelligence and Humanized Computing, 2014, 5 (1): 1- 2
11	GUO B, LI J, YU Z, et al CityTransfer: transferring inter-and Intra-City knowledge for chain store site recommendation based on multi-Source urban data[J]. Wearable and Ubiquitous Technologies, 2018, 1 (4): 135
12	KOOTI F , GRBOVIC M , AIELLO L, et al. Analyzing Uber's Ride- sharing economy [C]// International Conference. International World Wide Web Conferences Steering Committee, 2017: 574-582.
13	YUAN J, ZHENG Y, XIE X. Discovering regions of different functions in a city using human mobility and POIs [C] // ACM 18th SIGKDD International Conference on Knowledge Discovery and Data mining. Beijing: ACM, 2012: 186-194.
14	DENG C, ZHAO H, ZHANG Z S, et al. Fast and accurate neural word segmentation for chinese[J]. Association for Computational Linguistics, 2017, 608- 615
15	CHEN X , SHI Z , QIU X , et al Adversarial multi-criteria learning for Chinese word segmentation[J]. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017, 1193- 1203
16	JENSEN P Network-based predictions of retail store commercial categories and optimal locations[J]. Physical Review E, 2006, 74 (3): 035101
17	ZHANG J, HUANG D G, HUANG K Y, et al. λ-active learning based microblog-oriented Chinese word segmentation [J]. Journal of Tsinghua University: Science and Technology, 2018, 58 (3): 260- 265
18	JENSEN P. Analyzing the localization of retail stores with complex systems tools [C]// International Symposium on Intelligent Data Analysis. 2009: 10-20.
19	LATHIA N, QUERCIA D, CROWCROFT J. The hidden image of the city: sensing community well-being from urban mobility [C]// International Conference on Pervasive Computing. 2012: 91-98.
20	MACKINLAY A Event studies in economics and finance[J]. Journal of Economic Literature, 1997, 35 (1): 13- 39

[1]	白大鹏,张斌,洪昊岑,李洋,季清华,杨华勇. 生物3D打印装置及打印模型形貌检测[J]. 浙江大学学报(工学版), 2021, 55(2): 289-298.
[2]	黄钰期,陈卓烈,胡军强,李梅,牛昊一. 活塞内冷油腔两相流振荡可视化模拟[J]. 浙江大学学报(工学版), 2020, 54(3): 435-441.
[3]	扶建辉,王进,陆国栋,JUNGYoong-ho. 基于体素的汽车装配体漏水缝隙识别与可视化[J]. 浙江大学学报(工学版), 2020, 54(2): 357-364.
[4]	赵洁,瞿海斌,田埂,魏燕定. 基于粉体学性质的流化床制粒质量一致性评价方法[J]. 浙江大学学报(工学版), 2020, 54(2): 374-380.
[5]	沙东辉,骆仲泱,鲁梦诗,江建平,方梦祥,周栋,陈浩. 带正电颗粒电凝并的显微可视化研究[J]. 浙江大学学报(工学版), 2016, 50(1): 93-101.
[6]	刘智慧, 张泉灵. 大数据技术研究综述[J]. 浙江大学学报(工学版), 2014, 48(6): 957-972.
[7]	方锡武,刘振宇,谭建荣,程丰备. 异构有限元网格多场信息的等效集成方法[J]. 浙江大学学报(工学版), 2014, 48(6): 973-879.
[8]	刘智慧, 张泉灵. 大数据技术研究综述[J]. J4, 2014, 48(1): 0-00.
[9]	刘芳, 孙芸, 杨庚, 林海. 基于粒子群优化算法的社交网络可视化[J]. J4, 2013, 47(1): 37-43.
[10]	欧海英, 李晓宇, 付战平. 设计优化中的非线性主轴降维映射法[J]. J4, 2010, 44(1): 87-93.
[11]	吴培宁谭建荣刘振宇冯义雄. 基于Voronoi图的环评等值线快速拓扑填充[J]. J4, 2009, 43(2): 321-327.
[12]	田景红, 潘晓弘, 王正肖. 基于频繁模式挖掘的实时供应链数据分析[J]. J4, 2009, 43(12): 2259-2263.
[13]	沈国江. 城市区域交通智能分散控制研究[J]. J4, 2006, 40(4): 585-589.
[14]	刁现芬陈思平梁长虹汪元美. 基于阈值区间的水平集算法在耳蜗分割中的应[J]. J4, 2006, 40(2): 262-266.
[15]	沈国江孙优贤. 面向控制的城市交通网络宏观动态模型[J]. J4, 2005, 39(10): 1485-1489.

Viewed

Full text

Abstract

Cited

Shared

Discussed