Location plays a very important role in geomarketing. Location tells where the customers are, identifies something in the surrounding area or solves problems regarding the location of a new outlet. However, in an urban area, the locations have a vertical component due to high-rise and multilevel buildings. This situation requires a new approach that can handle three-dimensional data for location analysis. In this research, a novel 3D data structure is introduced to manage and constellate locations in three-dimensional space. The data structure is designed based on a group of classifications and clusters, and supplemented with the additional element of nearest-neighbour information. The locations are analysed to determine a geomarketing strategy by using several methods, such as single-nearest-neighbour, k-nearest-neighbour (kNN) and reverse-k-nearest-neighbour (RkNN) analyses. These analyses are performed based on encoded neighbour information of the Voronoi diagram that is extracted from the data structure. From the results, various tasks pertaining to geomarketing strategy can be carried out, such as identifying nearby competitors, locating target customers for marketing purposes and analysing the impact of opening a new outlet on competitors. Additionally, the proposed method is tested for its ability to handle large amounts of geomarketing data in terms of its efficiency in time retrieval and storage. The data structure is compared with 3D R-Tree to analyse its performance and efficiency. 3D R-Tree is chosen because it is the most commonly used structure in spatial databases. The test demonstrates that the proposed method requires the least amount of Input/Output than 3D R-Tree. The performance of the data structure is also evaluated; the results indicate that it is outperforms it competitors by responding 60–80% faster to query operations.

地理位置在地理营销中起着非常重要的作用。位置告诉顾客在哪里，识别周围区域的东西或解决有关新商店位置的问题。但是，在市区，由于高层和多层建筑，这些位置具有垂直分量。这种情况需要一种新的方法，该方法可以处理三维数据以进行位置分析。在这项研究中，引入了一种新颖的3D数据结构来管理和构筑三维空间中的位置。数据结构是基于一组分类和聚类设计的，并补充了最近邻居信息的其他元素。通过使用多种方法（例如，最近邻，k，最近邻（k NN）和反向k最近邻（R kNN）分析。这些分析是基于从数据结构中提取的Voronoi图的已编码邻居信息执行的。根据结果​​，可以执行与地理营销策略有关的各种任务，例如识别附近的竞争对手，出于营销目的定位目标客户并分析开设新商店对竞争对手的影响。此外，就其在时间检索和存储方面的效率而言，所提出的方法已经处理了处理大量地理营销数据的能力。将数据结构与3D R-Tree进行比较，以分析其性能和效率。选择3D R-Tree是因为它是空间数据库中最常用的结构。测试表明，与3D R-Tree相比，该方法所需的输入/输出量最少。还评估了数据结构的性能；结果表明，它对查询操作的响应速度提高了60-80％，胜过竞争对手。