Visual Analysis Of Heterogeneous Relational Spatial-temporal Data From Diverse Urban Data Sources

With the development of sensing technology, people can collect a variety of urban data, such as meteorology data in the meteorology domain, air quality data in the environment domain, and traffic flow data in the traffic domain. We can understand the problems in cities better by properly processing the urban data, since it implies rich knowledge about cities. Among this, a meaningful idea is extracting correlation patterns from cross domain urban data. Such cross domain correlation patterns can serve many applications, such as air quality diagnosis, selection of business addresses. However, the researchers encountered the following challenges in analyzing, understanding and examining the correlation patterns:1. It contains a large number of correlation patterns. Thousands of correlation patterns.2. The data structure of the correlation patterns is very complex. First, it contains multiple dimensions since it implies some correlation. Second, the value of each dimension is either a range(for numerical observation) or a category(for categorical observation).In addition, it contains three other properties, including spatial property, temporal property and probability property.In this paper, we proposed a visual analysis system to help analysts visually analyze and make sense of the correlation patterns. With an integration of multiple visualization and interaction methods, we design a few linked visualizations to allow users to understand and analyze the data from different views on different levels of detail. Through the linked visualizations, the system provides analysts with a quick and comprehensive overview of the data. In the end, case studies are conducted to demonstrate the effectiveness of our system in understanding and analyzing the complex correlation data.