用自编码器融合城市动静态数据，发现联合表征更易发现规律。
Exploring Urban Factors with Autoencoders: Relationship Between Static and Dynamic Features
- 用自编码器融合街级粒度的动静态城市数据
- 联合表征比独立分析产生更清晰的模式结构
- 适合城市规划与多源数据融合研究者
城市分析利用包含丰富城市信息的海量数据，用于模拟、预测趋势并揭示复杂模式。尽管这些数据支持高级分析，但其细粒度、异构性和多模态特性也带来了挑战。为此，已开发出可视化分析工具，以支持对融合异构与多模态数据的潜在表示进行探索，数据在街级粒度上离散化。然而，现有可视化辅助工具很少评估融合数据是否在集成可视化框架中提供比单独分析各数据源更深入的洞见。本文构建了一个可视化辅助框架，分析融合后的潜在表示相较于独立表示，在揭示动态与静态城市数据模式方面的有效性。分析表明，联合潜在表示能生成更结构化的模式，而独立表示在特定情况下仍具价值。
原文摘要 · Abstract (English)
Urban analytics utilizes extensive datasets with diverse urban information to simulate, predict trends, and uncover complex patterns within cities. While these data enables advanced analysis, it also presents challenges due to its granularity, heterogeneity, and multimodality. To address these challenges, visual analytics tools have been developed to support the exploration of latent representations of fused heterogeneous and multimodal data, discretized at a street-level of detail. However, visualization-assisted tools seldom explore the extent to which fused data can offer deeper insights than examining each data source independently within an integrated visualization framework. In this work, we developed a visualization-assisted framework to analyze whether fused latent data representations are more effective than separate representations in uncovering patterns from dynamic and static urban data. The analysis reveals that combined latent representations produce more structured patterns, while separate ones are useful in particular cases.
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