Annals of GIS最新文献

The COVID-19 pandemic has resulted in more than 600 million confirmed cases worldwide since December 2021. Cardiovascular disease (CVD) is both a risk factor for COVID-19 mortality and a complication that many COVID-19 patients develop. This study uses Twitter data to identify the spatiotemporal patterns and correlation of related tweets with daily COVID-19 cases and deaths at the national, regional, and state levels. We collected tweets mentioning both COVID-19 and CVD-related words from February to July 2020 (Eastern Time) and geocoded the tweets to the state level using GIScience techniques. We further proposed and validated that the Twitter user registration state can be a feasible proxy of geotags. We applied geographical and temporal analysis to investigate where and when people talked about COVID-19 and CVD. Our results indicated that the trend of COVID-19 and CVD-related tweets is correlated to the trend of COVID-19, especially the daily deaths. These social media messages revealed widespread recognition of CVD's important role in the COVID-19 pandemic, even before the medical community started to develop consensus and theory supports about CVD aspects of COVID-19. The second wave of the pandemic caused another rise in the related tweets but not as much as the first one, as tweet frequency increased from February to April, decreased till June, and bounced back in July. At the regional level, four regions (Northeast, Midwest, North, and West) had the same trend of related tweets compared to the country as a whole. However, only the Northeast region had a high correlation (0.8-0.9) between the tweet count, new cases, and new deaths. For the second wave of confirmed new cases, the major contributing regions, South and West, did not ripple as many related tweets as the first wave. Our understanding is that the early news attracted more attention and discussion all over the U.S. in the first wave, even though some regions were not impacted as much as the Northeast at that time. The study can be expanded to more geographic and temporal scales, and with more physical and socioeconomic variables, with better data acquisition in the future.

Since the Dartmouth hospital service areas (HSAs) were proposed three decades ago, there has been a large body of work using the unit in examining the geographic variation in health care in the U.S. for evaluating health care system performance and informing health policy. However, many studies question the replicability and reliability of the Dartmouth HSAs in meeting the challenges of ever-changing and a diverse set of health care services. This research develops a reproducible, automated, and efficient GIS tool to implement Dartmouth method for defining HSAs. Moreover, the research adapts two popular network community detection methods to account for spatial constraints for defining HSAs that are scale flexible and optimize an important property such as maximum service flows within HSAs. A case study based on the state inpatient database in Florida from the Healthcare Cost and Utilization Project is used to evaluate the efficiency and effectiveness of the methods. The study represents a major step toward developing HSA delineation methods that are computationally efficient, adaptable for various scales (from a local region to as large as a national market), and automated without a steep learning curve for public health professionals.