Comparison of multinomial logistic regression and logistic regression： which is more efficient in allocating land use？

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/ allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Investigating the factors affecting traffic violations based on electronic enforcement data:A case study in Shangyu,China

To study the influencing factors of traffic violations,this study investigated the effects of vehicle attribution,day of week,time of day,location of traffic violations,and weather on traffic violations based on the electronic enforcement data and historical weather data obtained in Shangyu,China.Ten categories of traffic violations were determined from the raw data.Then,chi-square tests were used to analyze the relationship between traffic violations and the potential risk factors.Multinomial logistic regression analyses were conducted to further estimate the effects of different risk factors on the likelihood of the occurrence of traffic violations.By analyzing the results of chi-square tests via SPSS,the five factors above were all determined as significant factors associated with traffic violations.The results of the multinomial logistic regression revealed the significant effects of the five factors on the likelihood of the occurrence of corresponding traffic violations.The conclusions are of great significance for the development of effective traffic intervention measures to reduce traffic violations and the improvement of road traffic safety.

Optimal Poisson Subsampling for Softmax Regression

Softmax regression,which is also called multinomial logistic regression,is widely used in various fields for modeling the relationship between covariates and categorical responses with multiple levels.The increasing volumes of data bring new challenges for parameter estimation in softmax regression,and the optimal subsampling method is an effective way to solve them.However,optimal subsampling with replacement requires to access all the sampling probabilities simultaneously to draw a subsample,and the resultant subsample could contain duplicate observations.In this paper,the authors consider Poisson subsampling for its higher estimation accuracy and applicability in the scenario that the data exceed the memory limit.The authors derive the asymptotic properties of the general Poisson subsampling estimator and obtain optimal subsampling probabilities by minimizing the asymptotic variance-covariance matrix under both A-and L-optimality criteria.The optimal subsampling probabilities contain unknown quantities from the full dataset,so the authors suggest an approximately optimal Poisson subsampling algorithm which contains two sampling steps,with the first step as a pilot phase.The authors demonstrate the performance of our optimal Poisson subsampling algorithm through numerical simulations and real data examples.

Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment

A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.

Contraceptive Method Choice Among Newly Married Couples and Influential Factors in Shanghai Municipality

A follow-up study with 7,826 representative newly married couples for fifteen months after their weddings in Shanghai Municipality showed that among the 3, 412 couples who actually adopted contraceptive method, rhythm was the main choice; the proportion for couples taking the contraceptive pill was much higher among sexually active couples before their weddings. The proportions of adopting rhythm or condom or the both, however, increased afterwards.About 86% of couples who had ever planned adopting the rhythm at registration actually used it. In fact, 16% of those who had ever planned to take pills eventually made this choice, because of their worry about any adverse side effects on mother's and fetus' health. Their knowledge about contraception,especially the pills, was incomprehensiue. APProximately 62% of condom users had not been given any instruction regarding its use when they got this contracoptive device one year later. Half of the pill and spermicide users learnt these respective methods from their friends or relatives. The proportion of delivering contraceptiues alter marriage by;F.P.P. was rather low. By fitting the multinomial logistic regression model, it is indicated that couple's evaluation on contraceptiue methods and contraceptiue goal were the main factors determining newlyweds' method of choice. Wife's knowledge on contraception and the accessibility of contraceptives and devices also influenced the method choice to some extent.

Farmer's Perception on Supply-Demand Matching of New Variety and Its Influence Factors

Using disordered multinomial logistic regression and multiple linear regression method,385 copies of questionnaires on farmer are analyzed to explore the relationship between peasant's psychological traits,peasant's cognition on seed technology and perception on supplydemand matching of new variety.Research results show that the vast majority of farmers think that current new variety is at high-level supplydemand balance and the oversupply status,and updating speed of new variety on the market is faster;the farmers preferring risk,seeking innovation and having strong learning and cognition ability may select high-level supply-demand matching state,and the farmers understanding the importance and difference of seed technology tend to choose high-level supply-demand matching situation;the farmers with strong learning and cognition ability can acknowledge the importance and difference of seed technology,while the farmers preferring risk can perceive the difference of seed technology;psychology seeking the innovation and learning and cognition ability affect the farmer's perception on supplydemand matching status of new variety via affecting the farmer's cognition on technical difference.

Characterizing diseases using genetic and clinical variables:A data analytics approach

Predictive analytics is crucial in precision medicine for personalized patient care.To aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease types.To achieve this,experiments were performed on diseased tissues obtained from the L1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical variables.In this study,the k-means technique was used for clustering the diseased tissue types,and the multinomial logistic regression(MLR)technique was applied for classifying the diseased tissue types.Dimensionality reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical variables.The results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 non-landmark genes,and the difference is statistically significant.Furthermore,it was evident that both clinical and genetic variables were important in predicting the diseased tissue types.The top three clinical predictors for predicting diseased tissue types were identified as morphology,gender,and age of diagnosis.Additionally,this study explored the possibility of using the latent representations of the clusters of landmark and non-landmark genes as predictors for an MLR classifier.The classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical variables.This study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine,enhancing diagnostic accuracy.

The Perception of Flood Risks: A Case Study of Babessi in Rural Cameroon

Although risk perception of natural hazards has been identified as an important determinant for sound policy design,there is limited empirical research on it in developing countries.This article narrows the empirical literature gap.It draws from Babessi,a rural town in the Northwest Region of Cameroon.Babessi was hit by a severe flash flood in 2012.The cross-disciplinary lens applied here deciphers the complexity arising from flood hazards,often embedded in contexts characterized by poverty,a state that is constrained in disaster relief,and market-based solutions being absent.Primary data were collected via snowball sampling.Multinomial logistic regression analysis suggests that individuals with leadership functions,for example,heads of households,perceive flood risk higher,probably due to their role as household providers.We found that risk perception is linked to location,which in turn is associated with religious affiliation.Christians perceive floods riskier than Muslims because the former traditionally reside at the foot of hills and the latter uphill;rendering Muslims less exposed and eventually less affected by floods.Finally,public disaster relief appears to have built up trust and subsequently reduced risk perception,even if some victims remained skeptical of state disaster relief.This indicates strong potential benefits of public transfers for flood risk management in developing countries.