Data Science

by Ruiji Sun. We introduce and apply regression discontinuity to thermal comfort field studies, which are typically observational. The method utilizes policy thresholds in China, where the winter district heating policy is based on cities' geographical locations relative to the Huai River. Using the regression discontinuity method, we quantify the causal effects of the experiment treatment (district heating) on the physical indoor environments and subjective responses of building occupants. In contrast, using conventional correlational analysis, we demonstrate that the correlation between indoor operative temperature and thermal sensation votes does not accurately reflect the causal relationship between the two. This highlights the importance of causal inference methods in thermal comfort field studies and other observational studies in building science where the regression discontinuity method might apply.

by Nanqin Ying. As social media platforms enforce stricter policies against unauthorized data collection, businesses and researchers must adapt to new API-based access models. This shift limits large-scale web scraping, impacting industries reliant on social media insights. The transition to paid API access and stringent compliance measures raises concerns about accessibility, cost, and ethical data collection. This article explores the evolving regulatory landscape, the enforcement of API restrictions, and how organizations can legally and ethically navigate data access in a world where scraping is becoming increasingly difficult. Understanding these changes is crucial for staying compliant while maintaining valuable insights from social media data.