| 多源空间大数据场景下的家装品牌线下广告选址 |
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| 引用本文： | 张嘉琪,杜开虎,任书良,王瑞凡,关庆锋,陈文辉,姚尧.多源空间大数据场景下的家装品牌线下广告选址[J].武汉大学学报(信息科学版),2022,47(9):1406-1415. |
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| 作者姓名： | 张嘉琪 杜开虎 任书良 王瑞凡 关庆锋 陈文辉 姚尧 |
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| 作者单位： | 1.中国地质大学（武汉）地理与信息工程学院，湖北 武汉，430074 |
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| 基金项目： | 国家重点研发计划2019YFB2102903国家自然科学基金41801306国家自然科学基金41671408国家自然科学基金41901332武汉大学测绘遥感信息工程国家重点实验室开放基金18S01湖北省自然科学基金2017CFA041浙江省自然科学基金LY18D010001 |
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| 摘 要： | 合理进行线下广告牌投放位置的选择对商家宣传品牌以及扩大营销市场具有十分积极的作用。由于商业数据较难获取，以往研究多停留在宏观理论层面，未能对线下广告选址的实际布局进行细尺度分析。以北京为研究区，通过耦合某大型家装品牌线下广告到店转化率和路网、感兴趣点数据等表征地理特征和商业经济特征的多源空间数据，构建了基于随机森林的广告到店转化率预测模型（=0.758），得到该品牌连锁家装商店在北京市广告选址适宜性空间分布结果，并对各影响特征进行分析。研究结果表明:该家装品牌线下广告到店转化率在北京整体呈现“中心高、外围低”的格局，且存在较强的空间自相关和高值聚集现象；同时，线下广告到店转化率与社会经济、商业政治和人群活动等具有较强相关性，且对同一群体持续进行广告曝光的位置对到店转化率的影响较大。该结果可为线下广告牌布局和商业选址等相关研究提供参考依据和理论基础。
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| 关 键 词： | 多源数据 线下广告 随机森林 地块特征集 到店转化率 |
| 收稿时间： | 2020-06-04 |
Site Selection of Outdoor Advertisement for Home Decoration Brands Based on Multi-source Spatial Big Data |
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| Institution: | 1.School of Geography and Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China2.School of Information Engineering, Zhejiang A & F University, Hangzhou 311300, China3.Alibaba Group, Hangzhou 311121, China |
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| 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 influ?ence 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 refer?ence and theoretical basis for relevant studies on the outdoor advertisement or commercial site selection. |
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