An integrated framework of mobile crowd estimation for the 2019, July 1st rally in Hong Kong.

Traditional approach of mobile crowd estimation involves counting a group of individuals at a specific place, manually, in real-time. It is a laborious exercise that can be physically and mentally demanding. In Hong Kong, a large rally can last more than six hours, making the manual count method susceptible to human errors. While crowd counting using object detection and tracking has been well-established in computer vision, such application has remained relatively small scale within a controlled indoor setting (e.g. counting people at fixed gateways in a mall). No attempt to date has applied the automatic crowd counting method to count hundreds of thousands of people along an open stretch of rally route within the complex urban outdoor landscape. This research proposed an integrated approach that combines the capture-recapture method in statistics and a Convolutional Neural Network (CNN) method in computer vision to count the mobile crowd. The research teams implemented the integrative approach and counted 276,970 people with a 95% confidence interval of 263,663 to 290,276 in the 2019, July 1st Rally in Hong Kong. This work counted the attendance of a large-scale rally as a proof of concept to fill in a gap in the empirical studies. The intellectual merits and research findings shed useful insights to improve mobile population estimation and leverage alternative data sources to support related scientific applications.

Space-Time Cluster's Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties.

As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020 and the first quarter in 2021. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental impact's indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, adult population, natural supply, economic condition, air quality or medical care. We established the GWR model to seek the sensitive factors. The result shows that adult population, economic condition, air quality, and medical care are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).

Identifying risk factors for disparities in breast cancer mortality among African-American and Hispanic women.

BACKGROUND: This study evaluated the risk factors associated with racial disparities in female breast cancer mortality for African-American and Hispanic women at the census tract level in Texas from 1995 to 2005. METHODS: Data on female breast cancer cases were obtained from the Texas Cancer Registry. Socioeconomic and demographic data were collected from Census 2000. Network distance and driving times to mammography facilities were estimated using Geographic Information System techniques. Demographic, poverty and spatial accessibility factors were constructed using principal component analysis. Logistic regression models were developed to predict the census tracts with significant racial disparities in breast cancer mortality based on racial disparities in late-stage diagnosis and structured factors from the principal component analysis. RESULTS: Late-stage diagnosis, poverty factors, and demographic factors were found to be significant predictors of a census tract showing significant racial disparities in breast cancer mortality. Census tracts with higher poverty status were more likely to display significant racial disparities in breast cancer mortality for both African Americans (odds ratio [OR], 2.43; 95% confidence interval [CI], 1.95-3.04) and Hispanics (OR, 5.30; 95% CI, 4.26-6.59). Spatial accessibility was not a consistent predictor of racial disparities in breast cancer mortality for African-American and Hispanic women. CONCLUSION: Physical access to mammography facilities does not necessarily reflect a greater utilization of mammogram screening, possibly owing to financial constraints. Therefore, a metric measuring access to health care facilities is needed to capture all aspects of access to preventive care. Despite easier physical access to mammography facilities in metropolitan areas, great resources and efforts should also be devoted to these areas where racial disparities in breast cancer mortality are often found.