Titlebook: Spatial Data and Intelligence; Third International Huayi Wu,Yu Liu,Guoliang Li Conference proceedings 2022 The Editor(s) (if applicable) a

insomnia

Heterogeneous Network Representation Learning Based on Adaptive Multi-channel Graph Convolution of nodes and edges, which describe the personalized information of nodes and complex relationships between nodes. In this paper, we propose a Heterogeneous Adaptive Multi-Channel Graph Convolutional Networks (HAM-GCN) for HIN representation learning. In HAM-GCN, we design three channels to extract

RLS898

Albinism

清澈

OWL

GUISE

Hot Public Appeal Extraction and Visual Analysis Combined BERT and Spatio-Temporal Locationn and valuable for early warning information and intelligence clues in large-scale text data remains a challenging task, especially with the explosion of public appeals. This paper proposes an intelligent extraction model of hot public appeal based on BERT and Spatio-Temporal location. For the unstr

黄瓜

Study on the Differences of Urban Thermal Environment Under the Framework of Local Climate Zonesscribe the urban thermal environment, but how to objectively and scientifically obtain the urban heat island intensity is a research difficulty. This paper uses remote sensing data, OpenStreetMap data, climate data and other data, based on the local climate zones framework, combined with surface tem

持久

Research on the Spatial Characteristics of High-Density Urban Road Network and Functional Agglomerat is evaluated, and suggestions for optimizing the urban road network and functional layout are proposed, aiming to provide methodological reference and guidance for the urban planning, functional layout, and development and construction of high-density urban areas.

信任

Conference proceedings 2022 Papers. .The 19 full papers and 1 short paper included in this book were carefully reviewed and selected from 77 submissions. They were organized in topical sections as follows: Traffic Management, Data science, City Analysis..

梯田

Diversified Top-, Spatial Pattern Matchingbjective function . to select diversified top-. matches. Then, we present an approximate algorithm to efficiently find . matches which maximize the objective function based on level-by-level searching strategy. Experiment results on four real datasets show that DivMatch is more effective and efficient than the algorithm IncMatch.