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. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 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, age structure, natural supply, economic condition, air quality, and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition 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).

Medicaid is a unique approach in ensuring the below poverty population obtains free insurance coverage under federal and state provisions in the United States. Twelve states without expanded Medicaid caused two million people who were under the poverty line into health insecurity. Principal Component-based logistical regression (PCA-LA) is used to consider health status (HS) as a dependent variable and fourteen social-economic indexes as independent variables. Four composite components incorporated health conditions (i.e., “no regular source of care” (NRC), “last check-up more than a year ago” (LCT)), demographic impacts (i.e., four categorized adults (AS)), education (ED), and marital status (MS). Compared to the unadjusted LA, direct adjusted LA, and PCA-unadjusted LA three methods, the PCA-LA approach exhibited objective and reasonable outcomes in presenting an odd ratio (OR). They included that health condition is positively significant to HS due to beyond one OR, and negatively significant to ED, AS, and MS. This paper provided quantitative evidence for the Medicaid gap in Texas to extend Medicaid, exposed healthcare geographical inequity, offered a sight for the Centers for Disease Control and Prevention (CDC) to improve the Medicaid program and make political justice for the Medicaid gap.

Lung cancer remains the leading cause for cancer mortality worldwide. While it is well-known that smoking is an avoidable high-risk factor for lung cancer, it is necessary to identify the extent to which other modified risk factors might further affect the cell’s genetic predisposition for lung cancer susceptibility, and the spreading of carcinogens in various geographical zones. This study aims to examine the association between lung cancer mortality (LCM) and major risk factors. We used Fuzzy Inference Modeling (FIM) and Random Forest Modeling (RFM) approaches to analyze LCM and its possible links to 30 risk factors in 100 countries over the period from 2006 to 2016. Analysis results suggest that in addition to smoking, low physical activity, child wasting, low birth weight due to short gestation, iron deficiency, diet low in nuts and seeds, vitamin A deficiency, low bone mineral density, air pollution, and a diet high in sodium are potential risk factors associated with LCM. This study demonstrates the usefulness of two approaches for multi-factor analysis of determining risk factors associated with cancer mortality.

Due to the teacher shortage in the U.S., an automatic toolbox with secondary development based on the ArcPy package was created to explore the spatial imbalance of the pupil–teacher ratio. It consists of four tools (or toolsets) for multi-scale spatial visualization, a sensitivity analysis with a heat map, the ordinary least squares regression with spatial autocorrelation, and the random forest tree regression. This study demonstrated the application of the toolset in the evaluation of educational resource spatial misallocation. Firstly, multi-scale analysis results showed that, the loss of teachers was mainly distributed in Oregon, Nevada, Arizona, and California from the state level, while it focused on such counties as Terrebonne Parish, Concordia Parish, and Bienville Parish in Louisiana in the county level. Secondly, it was found through heatmap sensitivity analysis that pupil–teacher ratios were highly related to low levels of student support services staff, free lunch programs, and low levels of local education agency (LEA) administrators. Then, the OLS tool was used to automatically calculate the spatial weighted matrix, the Moran I, R2, and AICC indices, AdjR2, F-Stat, F-Prob, and the Wald statistic, which showed whether the model was significant or not. This was followed by random forest tree regression modeling, which found that the LEA administrative support staff and the totally free lunch number highly impacted pupil–teacher ratios. Besides, the designed tool provided ribbons for the Common Core of Data (CCD) to link to other data sources.