# Exploring Urban Factors with Autoencoders: Relationship Between Static and Dynamic Features

arXiv: https://arxiv.org/abs/2509.06167 · HTML: https://mlsanity.com/paper/2509.06167/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 7 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation](https://mlsanity.com/paper/2106.15814/) (2021)
- [Topology Density Map for Urban Data Visualization and Analysis](https://mlsanity.com/paper/2007.15828/) (2020)
- [Towards Better Bus Networks: A Visual Analytics Approach](https://mlsanity.com/paper/2008.10915/) (2020)
- [IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality](https://mlsanity.com/paper/2202.07349/) (2022)
- [Urban Anomaly Analytics: Description, Detection, and Prediction](https://mlsanity.com/paper/2004.12094/) (2020)
- [Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics](https://mlsanity.com/paper/2408.06139/) (2024)
- [Visualizing and Interacting with Geospatial Networks: A Survey and Design Space](https://mlsanity.com/paper/2101.06322/) (2021)
- [NeighViz: Towards Better Understanding of Neighborhood Effects on Social Groups with Spatial Data](https://mlsanity.com/paper/2309.11454/) (2023)

## Abstract (arXiv)

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.