This paper fills the gap in rich housing literature by testing the spatio-temporal stability of
real estate submarkets. We start with standard Geographically Weighted Regression (GWR) …

近年来, 疾病的时空聚集分析受到越来越多的关注, 其在了解疾病在时间和空间上的分布特征
方面发挥着重要的作用, 可为探索病因和疾病防控提供参考依据. 为了更好地理解疾病时空聚集 …

Identifying clusters from individual origin–destination (OD) flows is vital for investigating
spatial interactions and flow mapping. However, detecting arbitrarily-shaped and non …

Urban hotspots are regions with intensive passenger flow, sound infrastructure, and thriving
business during a certain period of time, which mirror the travel behavior of residents. Taxi …

空间聚类是探索性空间数据分析的有力手段, 不仅可以直接用于发现地理现象的分布格局与分布
特征, 亦可以为其他空间数据分析任务提供重要的预处理步骤. 空间聚类有望成为大数据认知的 …

Forecasting in geophysical time series is a challenging problem with numerous applications.
The presence of correlation (ie spatial correlation across several sites and time correlation …

Clustering of multi-source geospatial big data provides opportunities to comprehensively
describe urban structures. Most existing studies focus only on the clustering of a single type …

Spatiotemporal raster (STR) data employ an array of grids to represent temporally varying
and spatially distributed information, commonly utilized for recording environmental …

This paper presents a parallel varied density-based clustering algorithm with optimized data
partition (PVDB). First, we improve the partition with reduced boundary points algorithm …

Spatiotemporal clustering patterns of marine anomaly variations are the focus of much
current global climate change research. Marine anomaly variations have multidimensional …