# Smart City Data Analysis via Visualization of Correlated Attribute Patterns

arXiv: https://arxiv.org/abs/2104.06701 · HTML: https://mlsanity.com/paper/2104.06701/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander](https://mlsanity.com/paper/2206.10160/) (2022)

## Abstract (arXiv)

Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute pattern (CAP) mining discovers correlations among multiple attributes from the sets of sensors spatially close to each other and temporally correlated in their measurements. In this paper, we develop a visualization system for CAP mining and demonstrate analysis of smart city data. Our visualization system supports an intuitive understanding of mining results via sensor locations on maps and temporal changes of their measurements. In our demonstration scenarios, we provide four smart city datasets collected from China and Santander, Spain. We demonstrate that our system helps interactive analysis of smart city data.