Understanding an Urban Park through Big Data

To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.

Understanding the Impact of Clinical Training on EHR Use Optimization

Electronic health records usability creates challenges to the delivery of care. This paper presents a novel approach to user analysis. Fixation counts have been analyzed to identify differences among physicians of 3 experience levels - residents, fellows and attending physicians. The findings indicate that users with different training levels had varied experiences while interacting with the same interface. EHRs will always be used by a variety of user groups, each with their own unique characteristics and therefore user analysis must be an important component of EHR usability testing. Eye tracking technology could serve as a valuable tool in this context.