GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION UNTUK MENANGANI OVERDISPERSI PADA JUMLAH PENDUDUK MISKIN

Poverty is a condition that is often associated with needs, difficulties an deficiencies in various life circumstances. The number of poor people in Indonesia increase in 2020. This research focus on modelling the number of poor people in Indonesia using Geographically Weighted Negative Binomial Regression (GWNBR) method. The number of poor people is count data, so analysis used to model the count data is poisson regression. If there is overdispersion, it can be overcome using negative binomial regression. Meanwhile to see the spatial effect, we can use the Geographically Weighted Negative Binomial Regression method. GWNBR uses a adaptive bisquare kernel for weighting function. GWNBR is better at modelling the number of poor people because it has the smallest AIC value than poisson regression and negative binomial regression. While the GWNBR method obtained 13 groups of province based on significant variables.

Identification of Cell-Type-Specific Spatially Variable Genes Accounting for Excess Zeros

Spatial transcriptomic techniques can profile gene expressions while retaining the spatial information, thus offering unprecedented opportunities to explore the relationship between gene expression and spatial locations. The spatial relationship may vary across cell types, but there is a lack of statistical methods to identify cell-type-specific spatially variable (SV) genes by simultaneously modeling excess zeros and cell-type proportions. We develop a statistical approach CTSV to detect cell-type-specific SV genes. CTSV directly models spatial raw count data and considers zero-inflation as well as overdispersion using a zero-inflated negative binomial distribution. It then incorporates cell-type proportions and spatial effect functions in the zero-inflated negative binomial regression framework. The R package pscl (Zeileis et al., 2008) is employed to fit the model. For robustness, a Cauchy combination rule is applied to integrate p-values from mutliple choices of spatial effect functions. Simulation studies show that CTSV not only outperforms the competing methods at the aggregated level but also achieves more power at the cell-type level. By analyzing pancreatic ductal adenocarcinoma spatial transcriptomic data, SV genes identified by CTSV reveal meaningful biological insights at the cell-type level. The R package to implement CTSV is available on GitHub https://github.com/jingeyu/CTSV.

Analytical and Modelling Study on the Spatial Stress Distribution and Failure Process of Disc Specimen in the Brazilian Splitting Test

To correctly obtain the spatial stress distribution and failure process of disc specimen in the Brazilian splitting test, an analytical solution of three-dimensional stress is deduced. Then, the effects of height-diameter ratio and clamp radian on the spatial stress distribution and failure process are analyzed and studied combined with numerical modelling. At last, the influence of spatial effect on the tensile strength of disc specimen is discussed. The results show that the cracks firstly generate at the two ends of the specimen in the axial direction and then extend due to the nonuniform distribution of tensile stress. The macrocracks coalescence does not mean the capacity loss of radial bearing. The maximum radial bearing capacity of the disc specimen decreases with the increase of height-diameter ratio due to the spatial effect. The tensile strength obtained by the two-dimensional calculation formula is significantly smaller. Therefore, when the commonly-used height-diameter ratio of 0.5 is used in the Brazilian splitting test, a correction factor k = 1.15 − 1.25 is suggested.

“Childhood Anemia in India: an application of a Bayesian geo-additive model”

Background The geographical differences that cause anaemia can be partially explained by the variability in environmental factors, particularly nutrition and infections. The studies failed to explain the non-linear effect of the continuous covariates on childhood anaemia. The present paper aims to investigate the risk factors of childhood anaemia in India with focus on geographical spatial effect. Methods Geo-additive logistic regression models were fitted to the data to understand fixed as well as spatial effects of childhood anaemia. Logistic regression was fitted for the categorical variable with outcomes (anaemia (Hb < 11) and no anaemia (Hb ≥ 11)). Continuous covariates were modelled by the penalized spline and spatial effects were smoothed by the two-dimensional spline. Results At 95% posterior credible interval, the influence of unobserved factors on childhood anaemia is very strong in the Northern and Central part of India. However, most of the states in North Eastern part of India showed negative spatial effects. A U-shape non-linear relationship was observed between childhood anaemia and mother’s age. This indicates that mothers of young and old ages are more likely to have anaemic children; in particular mothers aged 15 years to about 25 years. Then the risk of childhood anaemia starts declining after the age of 25 years and it continues till the age of around 37 years, thereafter again starts increasing. Further, the non-linear effects of duration of breastfeeding on childhood anaemia show that the risk of childhood anaemia decreases till 29 months thereafter increases. Conclusion Strong evidence of residual spatial effect to childhood anaemia in India is observed. Government child health programme should gear up in treating childhood anaemia by focusing on known measurable factors such as mother’s education, mother’s anaemia status, family wealth status, child health (fever), stunting, underweight, and wasting which have been found to be significant in this study. Attention should also be given to effects of unknown or unmeasured factors to childhood anaemia at the community level. Special attention to unmeasurable factors should be focused in the states of central and northern India which have shown significant positive spatial effects.

The Spatial Effect of Aging Population on Innovation in Entrepreneurship--Based on Empirical Analysis of Western China

This study researches the impact of an aging population on Innovation in Entrepreneurship (IE), and applying fixed effects models (FE), mediated effects models and spatial lag regression model (SAR) to panel data of Western China (excluding Tibet) from 2004 to 2019.The results showed that an aging population and IE inverted significantly U-curve, and human capital plays a significant partial mediation between the two. A theoretical perspective based on the First Law of Geography, in the western China, aging population and IE are both positive spatial correlation, and both show the characteristics of "High-High" spatial agglomeration. Under the spatial model, aging population and IE also inverted U-curve.

Contribution and Mechanism of Different Levels of Educational Human Capital to the Identification of Regional Green Economic Growth

With the rapid development of economy and society and the continuous optimization of industrial structure, the demand for high skilled talents is increasing. Education plays an irreplaceable role in China’s economic growth. Therefore, it is of great significance to study the impact of talent supply of vocational education on economic growth from the perspective of human capital and comprehensively consider the spatial interaction of economic growth. Taking intelligent image recognition technology as the main research technology, this paper discusses the contribution of educational human capital to regional green economic growth. This paper expounds the content of intelligent image recognition technology, constructs an image recognition system based on neural network, and studies the relationship between human resource utilization efficiency and regional economy under intelligent image recognition technology based on the empirical analysis of intelligent image recognition technology. Finally, it makes an empirical study on the spatial effect in the image recognition system and expounds the relationship between feature space and economic growth. It verifies the relationship between educational human capital and green economic growth. The results show that the intelligent image recognition technology has a good effect in the research of spatial effect.

An Analysis of the Impact of the Emissions Trading System on the Green Total Factor Productivity Based on the Spatial Difference-in-Differences Approach: The Case of China

How to effectively identify the spatial effect of the emissions trading system(ETS) on urban green total factor productivity(GTFP) generated through the linkage of economic factors between cities is a necessary part of scientifically evaluating the effect of ETS policy in emerging- market countries. This study aims to examine the spatial effect, mechanism, and heterogeneity of the ETS on urban GTFP based on the panel data of 281 cities from 2004 to 2017 in China, applying spatial difference-in-differences(DID) Durbin model (SDID-SDM) with multidimensional fixed effect (FE). The results show that ETS significantly improves the GTFP of the pilot cities, produces a spatial spillover effect and the results are robust to the placebo test, propensity score matching SDID (PSM-SDID) test, and Carbon-ETS interference test. Further analysis shows that the policy effect is mainly driven by improving energy efficiency, promoting green innovation, and optimizing the industrial structure. In addition, we found that ETS performs better in regions with a high degree of marketization, strong environmental law enforcement, and a low proportion of coal consumption. In general, the identification method of this study can be used as a scientific reference for conducting similar research in other emerging countries.