The presence or absence of some urban facilities can shape the spatial distribution of crime
occurrence. Exploring the joint influence of various types of facilities on crime occurrence …

Spatiotemporal association pattern mining can discover interesting interdependent
relationships among various types of geospatial data. However, existing mining methods for …

Spatial flow co-location patterns (FCLPs) are important for understanding the spatial
dynamics and associations of movements. However, conventional point-based co-location …

多模态地理大数据时空分析旨在融合地理大数据的多模态信息发现有价值的时空分布规律,
异常表现, 关联模式与变化趋势, 是全空间信息系统的核心研究内容, 并有望成为推进地理学人地 …

Urban crime incidents always exhibit a structure of spatio-temporal dependence. Exploration
of the spatio-temporal interactions of crime incidents is critical to understanding the …

Multilevel co-location patterns embedded in spatial datasets are difficult to discern due to the
complexity of neighboring relationships among spatial features. The neighboring …

Given instances (spatial points) of different spatial features (categories), significant spatial co-
distribution pattern discovery aims to find subsets of spatial features whose spatial …

In the era of big data, a significant volume of spatiotemporal data exists in a multiscale
format, describing diverse phenomena in the objective world across different spatial and …

Urban crimes are not homogeneously distributed but exhibit spatial heterogeneity across a
range of spatial scales. Meanwhile, while geographic space shapes human activities, it is …

Spatial flows represent spatial interactions or movements. Mining colocation patterns of
different types of flows may uncover the spatial dependences and associations among flows …