Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

IMPORTANCE

The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection.

METHODS

A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling.

RESULTS

Of the 6146 patients that met the inclusion criteria, 3024 (49%) of patients were male and 3122 (51%) of patients were female. There were 2252 (37%) White patients, 1257 (20%) Hispanic patients, 1636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2411 (39%) patients lost weight, and 3735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2058 (33%) were normal weight (18.5 ≤ BMI < 25), 1376 (22%) were overweight (25 ≤ BMI < 30) and 2625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight.

CONCLUSIONS

United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak

We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order r for m chains consisting of s possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, r m 2 s 2 + 2 , remarkably lower than m s r m + 1 required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial-temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control.

What drives the change in China's provincial industrial carbon unlocking efficiency? Evidence from a geographically and temporally weighted regression model

In this paper, we propose the concept of carbon unlocking efficiency based on carbon lock-in. Supported by the "Techno-Institutional Complex" theory, we measure the industrial carbon unlocking efficiency (ICUE) of 30 Chinese provinces and analyze its spatial and temporal jump probabilities through spatial Markov chains, and finally identify and discuss the influencing factors through the GTWR model. We found that the ICUE of each province in China follows a decreasing distribution from east to central to west, with Shanghai, Beijing, and Guangdong having the highest ICUEs among all provinces and cities; although the overall ICUE converges to a higher level in the long run, there is still a certain predatory effect of developed regions on less developed regions in the short term, and the intensification of market competition may adversely affect the growth of ICUE in the lagging regions. The results of GTWR show that factors such as energy use efficiency, FDI, and industrial enterprise size mainly promote ICUE growth, and energy structure mainly shows negative effects on ICUE of each province, while factors such as economic efficiency, R&D intensity, ownership structure, marketization level, share of high-tech industries, and industrial upgrading show obvious spatial heterogeneity, and different regions need to adopt different policy instruments for their strengths and weaknesses. These research results have important policy guidance implications for accelerating the process of industrial carbon unlocking in each region.

Research Orientation and Development of Social Psychology's Concept of Justice in the Era of Cloud Computing

With the maturity and rapid expansion of social psychology, great progress has been made in the integration of social psychology with other disciplines. From the very beginning, social psychology is destined to have a diversified and multidisciplinary research orientation and disciplinary nature, which also makes it difficult for social psychology to be defined in a single disciplinary field and a single research method. With the rapid development of the Internet, the emergence of cloud computing technology not only facilitates the orientation of psychological research, but also promotes the emergence and development of some new psychological disciplines. Therefore, the purpose of this paper is to study the orientation of social psychology and its current development in the context of cloud computing era. This paper collects, organizes, and integrates the research data of college students’ view of justice from the perspective of social psychology through cloud computing technology, and uses empirical research methods to conduct in-depth research on people’s view of justice in social psychology. This paper collects the data reports of college students on social justice issues through cloud computing technology to make the results more accurate. The experimental results show that nearly 70% of college students pay more attention to social justice issues. This data clearly reflects the optimistic trend of people’s attention to justice issues in social psychology.

Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China

The efficient utilization and optimal allocation of natural capital play an important role in economic development and human well-being. The production process of natural capital is the input and output processing of its ecological resources and the environment. Improving the rural natural capital utilization efficiency (RNCUE) is an important goal of natural capital investment, and the efficient utilization of natural capital is an important factor for the efficient operation of the regional economy and society. This study uses the super slack-based measure (SBM) model based on undesirable outputs to measure the RNCUE, combines the exploratory data analysis method (ESDA) and spatial Markov transfer matrix to analyse the spatiotemporal evolution characteristics of efficiency, and analyses the influencing factors of the change of the RNCUE in Chongqing through a spatial econometric model. The results show that: (1) The RNCUE in Chongqing is still at a low level as a whole and there is a large space for efficiency improvement and efficient operation. There is a certain spatial dependence on the interaction of efficiency between adjacent districts and counties. (2) High-high agglomeration is concentrated in the western area of Chongqing One-hour Economic Circle, and low-low agglomeration is concentrated in Southeast and Northeast Chongqing. The probability of a large change in the RNCUE in consecutive years is small, and it is easy to form the phenomenon of “club convergence” in space. (3) The RNCUE in Chongqing has been affected by rainfall, temperature, NDVI, the per capita GDP, proportion of fixed asset investment, expenditure for agriculture, and proportion of primary industry and rural population. The influencing factors show that the spatial heterogeneity is significant. The RNCUE has a negative correlation with forest coverage and the expenditure for agriculture, is not significantly positive or negative with the proportion of the primary industry and is positively correlated with the rural population density. This study points out that we can improve the RNCUE in Chongqing by optimizing the spatial differentiation control mechanism, clarifying property rights, enhancing liquidity, and strengthening scientific and technological innovation.

Robust empirical Bayes approach for Markov chain modeling of air pollution index

Air pollution is a matter of concern among the public, especially for those living in urban and industrial areas. Markov chain modeling is often used to model the underlying dynamics of air pollution, which involves describing the transition probability of going from one air pollution state to another. Thus, estimating the transition probability matrix for the data of the air pollution index (API) is an essential process in the modeling. However, one may observe many zero probabilities in the transition probability matrix, especially when faced with a small sample, interpreting the results with respect to the climate condition less realistic. This study proposes a robust empirical Bayes method, which incorporates a method of smoothing the zero frequencies in the count matrix, contributing to an improved estimation of the transition probability matrix. The robustness of the empirical Bayesian estimation is investigated based on Bayes risk. The transition probability matrices estimated based on the robust empirical Bayes method for the hourly API data collected from seven monitoring stations in Malaysia for the period 2012 to 2014 are used for determining the air pollution characteristics such as the mean residence time, the steady-state probability and the mean recurrence time. Furthermore, the proposed method has been evaluated by Monte Carlo simulations. Results suggest that it is quite effective in producing non-zero transition probability estimates, and superior to the maximum likelihood method in terms of minimizing the mean squared error for individual and entire transition probabilities. Therefore, the robust empirical Bayes method proves to be an improved approach to the estimation of the Markov chain. When applied to API data, it could provide important information on air pollution dynamics that may help guiding the development of proper strategies for managing the impact of air quality.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-020-00607-4.