面向“15分钟生活圈”社区结构的表示学习

利用城市大数据发现社区结构是城市计算中重要的研究方向。有效表示面向“15分钟生活圈”社区的结构特征可以细粒度地评价生活圈社区周围的设施情况，有利于城市规划建设，创造宜居的生活环境。首先，定义了面向“15分钟生活圈”的城市社区结构，并采用表示学习方法获取生活圈社区的结构特征；然后，提出了生活圈社区结构的嵌入表示框架，框架中利用居民的出行轨迹数据确定兴趣点（POI）与居民区的关系，构建反映不同时段居民出行规律的动态活动图；最后，对构建的动态活动图采用自编码器进行表示学习得到生活圈社区潜在特征的向量表示，从而有效概括居民日常活动所形成的社区结构。针对生活圈社区便利性评价、相似性度量等应用，利用真实数据集进行了实验评估，结果表明，分POI类别的日周期的潜在表示方法优于星期周期的潜在表示方法，且前者的归一化折损累计增益（NDCG）比后者最少提升了24.28%，最多提升了60.71%，验证了所提方法的有效性。

Community structure representation learning for "15-minute living circle"

The discovery of community structures using urban big data is an important research direction in urban computing. Effective representation of the structural characteristics of the communities in the "15-minute living circle" can be used to evaluate the facilities around the living circle communities in a fine-grained manner， which is conducive to urban planning as well as the construction and creation of a livable living environment. Firstly， the urban community structure oriented to "15-minute living circle" was defined， and the structural characteristics of the living circle communities were obtained by representation learning method. Then， the embedding representation framework of the living circle community structure was proposed， in which the relationship between the Points Of Interest （POI） and the residential area was determined by using the travel trajectory data of the residents， and a dynamic activity map reflecting the travel rules of the residents at different times was constructed. Finally， the representation learning to the constructed dynamic activity map was performed by an auto-encoder to obtain the vector representations of the potential characteristics of the communities in the living circle， thus effectively summarizing the community structure formed by the residents’ daily activities. Experimental evaluations were conducted using real datasets for applications such as community convenience evaluation and similarity metrics in living circles. The results show that the daily latent feature expression method based on POI categories is better than the weekly latent feature expression method. Compared to the latter， the minimum increase of Normalized Discounted Cumulative Gain （NDCG） of the former is 24.28% and the maximum increase of NDCG is 60.71%， which verifies the effectiveness of the proposed method.