Existing regionalization methods have largely overlooked the temporal dimension, leading to outcomes that predominantly reflect spatial differentiation of regional variables only at a singular instance, rather than across their entire evolutionary process. In response, this research proposes novel model for geographic process based on time-series (STS) data. This is designed amalgamate adjacent entities exhibiting analogous trends in variable fluctuations, while segregating those with...

Extracting spatial network structure (SNS) from large-scale origin-destination flow data is an important approach for understanding interregional association patterns and interaction laws. Currently, the extraction of SNS primarily relies on complex clustering or aggregated statistics with predefined regional constraints. However, these methods often overlook one more fundamental principles essential ensuring correctness accuracy: 1) Aggregation spatially proximate nodes necessary when...

Close-range interpersonal interactions serve as a major channel for virus transmission, with higher infection risks indoors than outdoors. Thus, evaluating indoor infectious disease transmission is vital effective epidemic prevention and control. However, collecting complete individual-level behavioral data faces challenges due to privacy concerns acquisition costs, impeding accurate risk mapping. To address this, we propose an individual-centered, scenario-based simulation framework in this...

City influence is a critical topic in regional studies, reflecting how cities draw attention and exert impact various domains. Understanding city essential for fostering sustainable urban growth. However, existing studies have failed to fully explore the characteristics of reflected by collective behaviors from bottom-up perspective. This study investigates individual search mirror attract, providing insights into their perceived influence. An “attention flow” model developed differentiate...

Both acute myocarditis patients and normal cohort usually present with coronary computed tomography angiography (CCTA) performance, the performance of CCTA radiomics on prediction for is still unclear. This study aims to build a clinical model using CCTA-based radiomics. A total 215 consecutive from Affiliated Jinhua Hospital, Zhejiang University School Medicine (Center 1) Sir Run Shaw 2) who underwent were diagnosed as or enrolled. All images myocardium automatically segmented extract...

In GIScience, the regionalization method is widely used for geographical data mining, spatiotemporal pattern discovery, and regional studies. An ideal should consider spatial contiguity, temporal attribute similarity. Existing approaches mostly focus on contiguity similarity while ignoring characteristics of geographic phenomena. We propose a multivariate (STR) that considers design bottom – up unsupervised hierarchical clustering algorithm with constraints using proximity rules, enabling...

Abstract Traditional studies on food culture mainly focus types of foods. However, these have been limited by the low availability data. With rapid advancement web maps, efficiently obtaining restaurant data is possible. This study examines mid‐eastern China using millions items point interest and explores different spatial patterns between local non‐local restaurants. The are first analyzed hotspot detector G ‐statistics models in global perspectives, correlations then examined preferences,...

The detection of colocation pattern is an important and widely used method to analyze the spatial associations geographical objects events. Existing studies primarily focus on discovering patterns association rules based point data. A broad range flow data types, such as population flow, logistics, information have emerged in recent years. However, are difficult detect because their complex structure. This work proposes a rule discovery approach that treats origin‐destination (OD) Boolean...

As a result of the influence geographical environment and historical heritage, food preference has significant regional differentiation characteristics. However, spatial structure culture represented by cuisine at level not yet been explored from perspective geography. Cultural regionalization is an important way to analyze understand culture. It great significance deeply mine intra-regional homogeneity scientifically cognize inter-regional cultural This study aims explore such patterns...

One of the most crucial topics in spatial interaction studies is mining patterns from extensive origin-destination (OD) flow data to capture interregional associations. However, prevailing methodologies tend disregard importance using relative closeness connections as weights, treat and temporal dimensions independently, or overlook dimension completely. Consequently, identified are susceptible inaccuracies, precise identification pattern occurrence time duration, despite their fundamental...

Spatial autocorrelation analysis is essential for understanding the distribution patterns of spatial flow data. Existing methods focus mainly on origins and destinations units relationships between them. These measure gravity or positional directional autocorrelations that are treated as objects. However, intrinsic complexity actual data necessitates consideration not only gravity, positional, but also variables interest. This study proposes a global method to interest consists three steps....

Large-scale epidemics, such as COVID-19, pose significant threats to human health and social stability due their rapid covert transmission, emerging one of the main challenges maintaining a well-functioning urban system. During pandemic, spatial transmission mechanisms influencing factors infectious diseases have received central topic. Scholars concentrated on researching phase extensive dissemination mandatory intervention, utilizing multiple datasets. However, there remains notable gap in...