Geospatial analytics increasingly rely on data fusion methods to extract patterns from data; however robust results are difficult to achieve because of the need for spatial and temporal regularization and latent structures within data. Tensor decomposition is a promising approach because it can accommodate multidimensional structure of data (e.g., trajectory information about users, locations, and time periods). To address these challenges, we introduce Multi-Type Clustering using Regularized tensor Decomposition (MCRD), an innovative method for data analysis that provides insight not just about groupings within data types (e.g., clusters of users), but also about the interactions between data types (e.g., clusters of users and locations) in the latent features of complex multi-type datasets. This is done by combining two innovations. First, a tensor representing spatiotemporal data is decomposed using a novel regularization method to account for structure within the data. Next, within- and cross-type groups are found through the application of novel hypergraph community detection methods to the decomposed results. Experimentation on both synthetic and real trajectory data demonstrates MCRD’s capacity to reveal the within- and cross-type grouping in data, and MCRD outperforms related methods including tensor decomposition without regularization, unfolding of tensors, Laplacian regularization, and tensor block models. The robust and versatile analysis provided by combining new regularization and clustering techniques outlined in this paper likely have utility in geospatial analytics beyond the movement applications explicitly studied.

地理空间分析越来越依赖数据融合方法从数据中提取模式；然而，由于需要空间和时间正则化以及数据中的潜在结构，因此很难获得稳健的结果。张量分解是一种很有前途的方法，因为它可以适应数据的多维结构（例如，关于用户、位置和时间段的轨迹信息）。为了应对这些挑战，我们引入了使用正则化张量分解 (MCRD) 的多类型聚类，这是一种创新的数据分析方法，不仅可以提供关于数据类型内分组（例如，用户集群）的见解，还可以提供关于数据之间的交互的见解复杂多类型数据集的潜在特征中的类型（例如，用户和位置的集群）。这是通过结合两项创新来实现的。第一的，使用一种新颖的正则化方法分解表示时空数据的张量，以解释数据中的结构。接下来，通过将新的超图社区检测方法应用于分解结果，发现了内部和交叉类型的组。对合成和真实轨迹数据的实验证明了 MCRD 能够揭示数据中的内部和交叉类型分组，并且 MCRD 优于相关方法，包括无正则化的张量分解、张量展开、拉普拉斯正则化和张量块模型。通过结合本文中概述的新的正则化和聚类技术所提供的强大而通用的分析可能在地理空间分析中具有实用性，超出了明确研究的运动应用程序。