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 …

The detection of colocation pattern is an important and widely used method to analyze the
spatial associations of geographical objects and events. Existing studies primarily focus on …

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

Spatial association rule mining can reveal the inherent laws of spatial object
interdependence and is an important part of spatial data mining. Most of the existing …

The analysis of association rules within ecosystems is crucial for monitoring, managing, and
conserving natural resources. As widely adopted approaches for this task, geospatial …

Groups of spatial features whose instances frequently appear together in nearby areas are
regarded as prevalent co-location patterns (PCPs). Traditional PCP mining ignores the …

The discovery of spatio-temporal co-occurrence patterns (STCPs) among multiple types of
crimes whose events frequently co-occur in neighboring space and time is crucial to the joint …

Machine translation facilitates cross-linguistic communication by converting text between
languages. However, producing contextually accurate translations remains a challenge …

Spatial co-location pattern mining is a process of finding a group of distinct spatial features
whose instances frequently appear in close proximity to each other. The proximity of …

Discovering spatial prevalent co-location patterns (SPCPs) has become an important branch
of spatial data mining because it can effectively reveal the hidden knowledge between …