Volume 47 Issue 9
Sep.  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
ZHANG Jiaqi, DU Kaihu, REN Shuliang, WANG Ruifan, GUAN Qingfeng, CHEN Wenhui, YAO Yao. Site Selection of Outdoor Advertisement for Home Decoration Brands Based on Multi-source Spatial Big Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1406-1415. DOI: 10.13203/j.whugis20190468
Citation: ZHANG Jiaqi, DU Kaihu, REN Shuliang, WANG Ruifan, GUAN Qingfeng, CHEN Wenhui, YAO Yao. Site Selection of Outdoor Advertisement for Home Decoration Brands Based on Multi-source Spatial Big Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1406-1415. DOI: 10.13203/j.whugis20190468
shu

Site Selection of Outdoor Advertisement for Home Decoration Brands Based on Multi-source Spatial Big Data

  • 1.

    School of Geography and Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China

  • 2.

    School of Information Engineering, Zhejiang A & F University, Hangzhou 311300, China

  • 3.

    Alibaba Group, Hangzhou 311121, China

Funds: 

The National Key Research and Development Program of China 2019YFB2102903

the National Natural Science Foundation of China 41801306

the National Natural Science Foundation of China 41671408

the National Natural Science Foundation of China 41901332

the Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University 18S01

the Natural Science Fundation of Hubei Province 2017CFA041

the Natural Science Fundation of Zhejiang Province LY18D010001

More Information
  • Author Bio:

    ZHANG Jiaqi, postgraduate, specializes in urban computing. E‐mail: ningzetaolover@cug.edu.cn

  • Corresponding author:

    YAO Yao, PhD, associate professor. E‐mail: yaoy@cug.edu.cn

  • Received Date: June 03, 2020
  • Available Online: September 19, 2022
  • Published Date: September 04, 2022
    Graphical Abstract
    • Abstract
  •   Objectives  Outdoor advertisement can attract the attention of target user groups and increase brand influence, and site selection is the most important influencing factor for its effectiveness. Reasonable site selection plays a positive role in improving brand awareness and expanding the market. However, due to the difficulty in obtaining commercial data, previous studies on a macro and rough scales failed to conduct detailed analysis on the actual effects of site selection.
      Methods  We propose a framework to solve the above problems. Firstly, we extract feature sets from geographical attributes and commercial economic attributes and preprocess them using mathematical statistics and the geographical processing basic method. Then, by coupling of the in-store conversion rate with road network characteristics, point of interest (POI), and other multi-source spatial features, a random forest model is constructed to mine the correlation between them. Finally, the importance of each feature is quantified.
      Results  We choose Beijing as the study area, and build a prediction model for the in-store conversion rate of a home decoration brand based on this framework. The model shows a good performance (Standard R2=0.758). Then we obtain the spatial distribution and factors influencing the suitability of outdoor advertising for this home decoration brand in Beijing. The in-store conversion rate of the brand is high in the center and is low in the periphery of Beijing, which features the phenomena of strong spatial autocorrelation and high-value aggregation. Meanwhile, the in-store conversion rate has a significant correlation with social economy, commercial politics, and crowd activities. The location of continuous advertising exposure to the same group has a great influ‍ence on the in-store conversion rate.
      Conclusions  The fine mapping results of the model constructed in this study can quantitatively evaluate the advertising effect of each location and maximize the efficiency, which can provide a refer‍ence and theoretical basis for relevant studies on the outdoor advertisement or commercial site selection.
    • FullText(HTML)
    • References (42)
  • [1]
    Lichtenthal J D, Yadav V, Donthu N. Outdoor Advertising for Business Markets[J]. Industrial Marketing Management, 2006, 35(2): 236-247 doi: 10.1016/j.indmarman.2005.02.006
    [2]
    Akören A N. Interaction of Outdoor Advertising Im proved by Innovative Methods with Digital Art[J]. Procedia-Social and Behavioral Sciences, 2015, 195: 799-805 doi: 10.1016/j.sbspro.2015.06.178
    [3]
    朱军, 陈敬良, 宗利永. 线下广告信息属性对消费者购买意愿的影响研究[J]. 预测, 2019, 38(4): 32-38 https://www.cnki.com.cn/Article/CJFDTOTAL-YUCE201904005.htm

    Zhu Jun, Chen Jingliang, Zong Liyong. The Impact of Outdoor Advertising Information Attributes on Consumers'Purchase Intention[J]. Forecasting, 2019, 38(4): 32-38 https://www.cnki.com.cn/Article/CJFDTOTAL-YUCE201904005.htm
    [4]
    Tam H. Understanding the Impact of Web Personalization on User Information Processing and Decision Outcomes[J]. MIS Quarterly, 2006, 30(4): 865-890 doi: 10.2307/25148757
    [5]
    Liu D Y, Weng D, Li Y H, et al. SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations[J]. IEEE Transactions on Visualization and Computer Graphics, 2017, 23 (1): 1-10
    [6]
    Wilson R T, Till B D. Effects of Outdoor Advertising: Does Location Matter?[J]. Psychology & Marketing, 2011, 28(9): 909-933
    [7]
    Patel M R, Bhatt M K. A Study to Know Impact of Outdoor Advertising on Customer Purchase Decision [J]. SEMCOM Management & Technology Review, 2018, 6(1): 113-117
    [8]
    Cantrell J, Kreslake J M, Ganz O, et al. Marketing Little Cigars and Cigarillos: Advertising, Price, and Associations with Neighborhood Demographics[J]. American Journal of Public Health, 2013, 103 (10): 1902-1909 doi: 10.2105/AJPH.2013.301362
    [9]
    Rosewarne L. The Men 's Gallery: Outdoor Advertising and Public Space: Gender, Fear, and Feminism[J]. Women's Studies International Forum, 2005, 28(1): 67-78 doi: 10.1016/j.wsif.2005.02.005
    [10]
    Huang M, Fang Z X, Xiong S L, et al. Interest Driven Outdoor Advertising Display Location Selection Using Mobile Phone Data[J]. IEEE Access, 2019, 7: 30878-30889 doi: 10.1109/ACCESS.2019.2903277
    [11]
    Dilchert S, Ones D, Viswesvaran C, et al. Response Distortion in Personality Measurement: Born to Deceive, Yet Capable of Providing Valid Self-Assessments?[J]. Psychology Science, 2006, 48(3): 209-225
    [12]
    Anagnostopoulos A, Petroni F, Sorella M. Targeted Interest-Driven Advertising in Cities Using Twitter [J]. Data Mining and Knowledge Discovery, 2018, 32(3): 737-763 doi: 10.1007/s10618-017-0529-7
    [13]
    北京市统计局, 国家统计局北京调查总队. 北京统计年鉴: 2018[M]. 北京: 中国统计出版社, 2018

    Beijing Municipal Bureau Statistics, Survey Office of the National Bureau of Statistics in Beijing. Beijing Statistical Yearbook: 2018[M]. Beijing: China Statistics Press, 2018
    [14]
    北京市统计局, 国家统计局北京调查总队. 北京区域统计年鉴: 2018[M]. 北京: 中国统计出版社, 2018

    Beijing Municipal Bureau Statistics, Survey Office of the National Bureau of Statistics in Beijing. Beijing Regional Statistical Yearbook: 2018[M]. Beijing: China Statistics Press, 2018
    [15]
    Chen Y M, Liu X P, Li X, et al. Delineating Urban Functional Areas with Building-Level Social Media Data: A Dynamic Time Warping(DTW)Distance Based K-Medoids Method[J]. Landscape and Urban Planning, 2017, 160: 48-60 doi: 10.1016/j.landurbplan.2016.12.001
    [16]
    Jean N, Burke M, Xie M, et al. Combining Satellite Imagery and Machine Learning to Predict Poverty[J]. Science, 2016, 353(6301): 790-794 doi: 10.1126/science.aaf7894
    [17]
    Yao Y, Zhang J B, Hong Y, et al. Mapping FineScale Urban Housing Prices by Fusing Remotely Sensed Imagery and Social Media Data[J]. Transactions in GIS, 2018, 22(2): 561-581 doi: 10.1111/tgis.12330
    [18]
    Wang Z Y, Niu L. A Data Model for Using OpenStreetMap to Integrate Indoor and Outdoor Route Planning[J]. Sensors, 2018, 18(7), DOI: org/ 10.3390/s18072100
    [19]
    许泽宁, 高晓路. 基于电子地图兴趣点的城市建成区边界识别方法[J]. 地理学报, 2016, 71(6): 928-939

    Xu Zening, Gao Xiaolu. A Novel Method for Identifying the Boundary of Urban Built-Up Areas with POI Data[J]. Acta Geographica Sinica, 2016, 71 (6): 928-939
    [20]
    张珣, 钟耳顺, 张小虎, 等. 2004—2008年北京城区商业网点空间分布与集聚特征[J]. 地理科学进展, 2013, 32(8): 1207-1215 https://www.cnki.com.cn/Article/CJFDTOTAL-DLKJ201308005.htm

    Zhang Xun, Zhong Ershun, Zhang Xiaohu, et al. Spatial Distribution and Clustering of Commercial Network in Beijing During 2004—2008[J]. Progress in Geography, 2013, 32(8): 1207-1215 https://www.cnki.com.cn/Article/CJFDTOTAL-DLKJ201308005.htm
    [21]
    韩会然, 杨成凤, 宋金平. 北京批发企业空间格局演化与区位选择因素[J]. 地理学报, 2018, 73(2): 219-231 https://www.cnki.com.cn/Article/CJFDTOTAL-DLXB201802003.htm

    Han Huiran, Yang Chengfeng, Song Jinping. Impact Factors of Location Choice and Spatial Pattern Evolution of Wholesale Enterprises in Beijing[J]. Acta Geographica Sinica, 2018, 73(2): 219 - 231 https://www.cnki.com.cn/Article/CJFDTOTAL-DLXB201802003.htm
    [22]
    谷岩岩, 焦利民, 董婷, 等. 基于多源数据的城市功能区识别及相互作用分析[J]. 武汉大学学报·信息科学版, 2018, 43 (7): 1113-1121 doi: 10.13203/j.whugis20160192

    Gu Yanyan, Jiao Limin, Dong Ting, et al. Spatial Distribution and Interaction Analysis of Urban Functional Areas Based on Multi-source Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1113-1121 doi: 10.13203/j.whugis20160192
    [23]
    姚尧, 任书良, 王君毅, 等. 卷积神经网络和随机森林的城市房价微观尺度制图方法[J]. 地球信息科学学报, 2019, 21(2): 168-177

    Yao Yao, Ren Shuliang, Wang Junyi, et al. Mapping the Fine-Scale Housing Price Distribution by Integrating a Convolutional Neural Network and Random Forest[J]. Journal of Geo-Information Science, 2019, 21(2): 168-177
    [24]
    Gallin J. The Long-Run Relationship Between House Prices and Income: Evidence from Local Housing Markets[J]. Real Estate Economics, 2006, 34(3): 417-438 doi: 10.1111/j.1540-6229.2006.00172.x
    [25]
    于瀚祥, 王利, 裴倩. 大连市商品住宅价格空间分异及其影响因素分析[J]. 国土与自然资源研究, 2018(6): 17-22 doi: 10.3969/j.issn.1003-7853.2018.06.004

    Yu Hanxiang, Wang Li, Pei Qian. A Study on Spatial Differentiation of Commercial Housing Price and Its Influencing Factors in Dalian[J]. Territory & Natural Resources Study, 2018(6): 17-22 doi: 10.3969/j.issn.1003-7853.2018.06.004
    [26]
    Yao Y, Liu P H, Hong Y, et al. Fine-Scale Intraand Inter-City Commercial Store Site Recommendations Using Knowledge Transfer[J]. Transactions in GIS, 2019, 23(5): 1029-1047 doi: 10.1111/tgis.12553
    [27]
    Guo B, Li J, Zheng V W, et al. CityTransfer: Transferring Inter-and Intra-City Knowledge for Chain Store Site Recommendation Based on Multi Source Urban Data[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2017, 1(4), DOI: org/ 10.1145/3161411
    [28]
    Gong Z Y, Ma Q W, Kan C C, et al. Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions[J]. Sustainability, 2019, 11(22), DOI: org/ 10.3390/su11226424
    [29]
    Guan C H, Rowe P G. The Concept of Urban Intensity and China's Townization Policy: Cases from Zhejiang Province[J]. Cities, 2016, 55: 22-41 doi: 10.1016/j.cities.2016.03.012
    [30]
    Zhou X G, Chen Z F, Yeh A G, et al. Workplace Segregation of Rural Migrants in Urban China: A Case Study of Shenzhen Using Cellphone Big Data [J]. Environment and Planning B: Urban Analytics and City Science, 2021, 48(1): 25-42 doi: 10.1177/2399808319846903
    [31]
    Zheng Y, Liu F R, Hsieh H P. U-Air: When Urban Air Quality Inference Meets Big Data[C]//The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, USA, 2013
    [32]
    Ließ M, Glaser B, Huwe B. Uncertainty in the Spatial Prediction of Soil Texture: Comparison of Regression Tree and Random Forest Models[J]. Geoderma, 2012, 170: 70-79 doi: 10.1016/j.geoderma.2011.10.010
    [33]
    Carranza E J M, Laborte A G. Data -Driven Predictive Mapping of Gold Prospectivity, Baguio District, Philippines: Application of Random Forests Algorithm[J]. Ore Geology Reviews, 2015, 71: 777-787 doi: 10.1016/j.oregeorev.2014.08.010
    [34]
    Chen W, Xie X S, Wang J L, et al. A Comparative Study of Logistic Model Tree, Random Forest, and Classification and Regression Tree Models for Spatial Prediction of Landslide Susceptibility[J]. Catena, 2017, 151: 147-160 doi: 10.1016/j.catena.2016.11.032
    [35]
    Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1): 5-32 doi: 10.1023/A:1010933404324
    [36]
    吴辰文, 梁靖涵, 王伟, 等. 基于递归特征消除方法的随机森林算法[J]. 统计与决策, 2017(21): 60-63 https://www.cnki.com.cn/Article/CJFDTOTAL-DDKX202003009.htm

    Wu Chenwen, Liang Jinghan, Wang Wei, et al. Random Forest Algorithm Based on Recursive Feature Elimination[J]. Statistics & Decision, 2017(21): 60-63 https://www.cnki.com.cn/Article/CJFDTOTAL-DDKX202003009.htm
    [37]
    Mao Y, Xia Z, Yin Z, et al. Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection[J]. Chinese Journal of Chemical Engineering, 2007, 15(2): 233-239 doi: 10.1016/S1004-9541(07)60064-0
    [38]
    Grömping U. Variable Importance Assessment in Regression: Linear Regression Versus Random Forest[J]. The American Statistician, 2009, 63(4): 308-319 doi: 10.1198/tast.2009.08199
    [39]
    李秋萍, 刘逸诗, 巩诗瑶, 等. 基于居民出行活动特征的个体经济水平推断方法[J]. 武汉大学学报·信息科学版, 2019, 44(10): 1575-1580 doi: 10.13203/j.whugis20170426

    Li Qiuping, Liu Yishi, Gong Shiyao, et al. Individual Income Level Inference Method Based on Travel Behavior of Urban Residents[J]. Geomatics and Information Science of Wuhan University, 2019, 44 (10): 1575-1580 doi: 10.13203/j.whugis20170426
    [40]
    Tobler W R. A Computer Movie Simulating Urban Growth in the Detroit Region[J]. Economic Geography, 1970, 46(sup1): 234-240
    [41]
    Friedmann J. Regional Development Policy: A Case Study of Venezuela[J]. Urban Studies, 1966, 4(3): 309-311
    [42]
    胡望舒, 孙威. 基于泰尔指数的北京市区域经济差异[J]. 中国科学院研究生院学报, 2013, 30(3): 353-360 https://www.cnki.com.cn/Article/CJFDTOTAL-ZKYB201303013.htm

    Hu Wangshu, Sun Wei. Regional Economic Disparity in Beijing Based on Theil Index[J]. Journal of Graduate University of Chinese Academy of Sciences, 2013, 30(3): 353-360 https://www.cnki.com.cn/Article/CJFDTOTAL-ZKYB201303013.htm
    • Related Articles

  • Related Articles

    [1]YANG Renfei. Research on Multi-level Classification and Change Detection Using Remote Sensing Images for Urban Wetland[J]. Geomatics and Information Science of Wuhan University, 2023, 48(12): 2105-2105. DOI: 10.13203/j.whugis20230130
    [2]WANG Zhipan, SHEN Yan, WANG Liang, ZHANG Qingling, YOU Shucheng. High-Resolution Remote Sensing Image Building Change Detection Based on One-Class Classifier Framework[J]. Geomatics and Information Science of Wuhan University, 2020, 45(10): 1610-1618. DOI: 10.13203/j.whugis20180485
    [3]LUO Ling, MAO Dehua, ZHANG Bai, WANG Zongming, YANG Guang. Remote Sensing Estimation for Light Use Efficiency of Phragmites australis Based on Landsat OLI over Typical Wetlands[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 524-533. DOI: 10.13203/j.whugis20180294
    [4]YANG Lamei, JIA Yonghong. A Method for Determining the Natural Boundary of Seasonal Saltwater Lake Wetland with Multi-source Data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(3): 419-425. DOI: 10.13203/j.whugis20180207
    [5]LI Peng, LI Dahui, LI Zhenhong, WANG Houjie. Wetland Classification Through Integration of GF-3 SAR and Sentinel-2B Multispectral Data over the Yellow River Delta[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1641-1649. DOI: 10.13203/j.whugis20180258
    [6]FENG Wenqing, ZHANG Yongjun. Object-oriented Change Detection for Remote Sensing Images Based on Fuzzy Comprehensive Evaluation[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 875-881. DOI: 10.13203/j.whugis20140291
    [7]CHEN Jianqun, WU Xie, WANG Zhenxing, ZHU Jianjun. Establishment of Fundamental Geographic Information System and Associated Key Technologies for Poyang Lake Wetland[J]. Geomatics and Information Science of Wuhan University, 2012, 37(8): 888-891.
    [8]GONG Hao, ZHANG Jingxiong, SHEN Shaohong. Object-Based Correspondence Analysis for Improved Accuracy in Remote Sensing Change Detection[J]. Geomatics and Information Science of Wuhan University, 2009, 34(5): 544-547.
    [9]ZHANG Jianqing, SHE Qiong, PAN Li. Change Detection of Residential Area by Remote Sensing Image Based on LBP/C Texture[J]. Geomatics and Information Science of Wuhan University, 2008, 33(1): 7-11.
    [10]ZHANG Xiaodong, LI Deren, GONG Jianya, QIN Qianqing. A Change Detection Method of Integrating Remote Sensing and GIS[J]. Geomatics and Information Science of Wuhan University, 2006, 31(3): 266-269.

  • Cited by

    Periodical cited type(3)

    1. 钟振，文麒麟，梁金福. 应用重力场模型二阶位系数及新近岁差率约束火星内核大小及密度组成. 物理学报. 2023(02): 386-392 .
    2. 文麒麟，钟振. 应用模拟退火算法估算月核大小及其密度组成. 物理学报. 2023(08): 347-353 .
    3. 魏二虎，任晓斌，刘经南，李连艳，聂桂根，李岩林. 利用VLBI观测量对月球天平动参数解算及着陆器定位和测速的改进. 武汉大学学报(信息科学版). 2022(04): 483-491 .

    Other cited types(1)

Catalog

    Get Citation
    PDF
    XML
    Article views (848) PDF downloads (102) Cited by(4)
    Related

    Copyright © 2013 《武汉大学学报·信息科学版》编辑部

    Address：武汉大学文理学部本科生院楼北5楼Post Code：430072

    Tel：027-68778465,68788631E-mail:whuxxb@vip.163.com

    Submit Article

    Submission Guidelines

    e

    Contact Us

    Qrcode

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return