A study of geographic information has become a significant field of concentrate in software engineering because of the expansion of much information created by electronic gadgets fit together geographic data from people, like advanced mobile phones and GPS gadgets. Area facts allow a deeper understanding of users’ options and actions by bridging the gap between the physical and digital worlds. This expansion of wide geo-spatial datasets has inspired studies into novel recommender systems that aim to make users’ travels and social interactions easier. This huge amount of data generated by these devices has prompted an increase in the number of research activities and procedures aimed at breaking down and recovering useful data based on these large datasets. The aim of this challenge is to study GPS directions from a variety of people, as well as examine and apply computational strategies to recover useful data from those directions, useful data from GPS directions, including areas of interest and people’s proximity, and then create an instrument for information representation. This paper demonstrates how data mining techniques is used to recover valuable information from spatial data. As well as how such data can be useful in understanding people and areas within a district. Depending on the outcomes, we recommend a HITS (Hypertext Induced Topic Search) based POI recommendation calculation that can take into account the effect of social connections when recommending POIs to individual users.

地理信息研究已成为软件工程的一个重要研究领域，因为电子设备将人类的地理数据（如先进的移动电话和 GPS 设备）结合在一起所产生的大量信息不断增加。区域事实通过弥合物理世界和数字世界之间的差距，可以更深入地了解用户的选择和行动。广泛的地理空间数据集的这种扩展激发了对旨在使用户的旅行和社交互动更容易的新型推荐系统的研究。这些设备生成的大量数据促使旨在分解和恢复基于这些大型数据集的有用数据的研究活动和程序的数量增加。这项挑战的目的是研究来自不同人群的 GPS 方向，以及检查和应用计算策略以从这些方向恢复有用数据，从 GPS 方向恢复有用数据，包括感兴趣的区域和人们的接近度，然后创建用于信息表示的工具。本文演示了如何使用数据挖掘技术从空间数据中恢复有价值的信息。以及此类数据如何有助于了解区域内的人员和区域。根据结果​​，我们推荐基于 HITS（超文本诱导主题搜索）的 POI 推荐计算，在向个人用户推荐 POI 时可以考虑社交关系的影响。然后创建用于信息表示的工具。本文演示了如何使用数据挖掘技术从空间数据中恢复有价值的信息。以及此类数据如何有助于了解区域内的人员和区域。根据结果​​，我们推荐基于 HITS（超文本诱导主题搜索）的 POI 推荐计算，在向个人用户推荐 POI 时可以考虑社交关系的影响。然后创建用于信息表示的工具。本文演示了如何使用数据挖掘技术从空间数据中恢复有价值的信息。以及此类数据如何有助于了解区域内的人员和区域。根据结果​​，我们推荐基于 HITS（超文本诱导主题搜索）的 POI 推荐计算，在向个人用户推荐 POI 时可以考虑社交关系的影响。