Mapping determinants of rural poverty in Guangxi–a less developed region of China

Rapid urbanization in China has led to an increasing imbalance in regional development.The Guangxi Zhuang Autonomous Region,a less developed border region with unique cultural diversity,has a relatively large population(4.52 million people in 2015)under the poverty line,according to the national standard of poverty.China has launched a national campaign to reduce poverty using a wide range of new development policies and large-scale investment.However,there have been few studies on the determinants of poverty at the county level across a province.This paper aims to explore the spatial and social differences related to poverty among 109 counties by considering the spatial heterogeneity of poverty determinants.Spatial statistical models revealed that slope(Slp),GDP per capita(GDPP),the ethnic minority population ratio(EMPR),medical and technical personnel of healthcare institutions(MTP)and illiteracy rate(IR)significantly affect the patterns of the poverty rate,with a high adjusted R2(0.67),while the poverty rate affects GDPP,IR,MTP and EMPR;i.e.,the effects are interactional.Furthermore,the IR is significantly affected by the provision of schools and transportation conditions.Among these determinants,social factors may be key.The spatial patterns of these relationships demonstrate remarkable variation across the province and between minor and major groups.This quantitative evidence is enhanced by indepth interviews with selected groups.These results are expected to be useful for the anti-poverty project in Guangxi.

Effect of time-of-day and day-of-the-week on congestion duration and breakdown:A case study at a bottleneck in Salem,NH

This work uses regression models to analyze two characteristics of recurrent congestion： breakdown, the transition from freely flowing conditions to a congested state, and duration, the time between the onset and clearance of recurrent congestion. First, we apply a binary logistic regression model where a continuous measurement for traffic flow and a dichoto- mous categorical variable for time-of-day （AM- or PM-rush hours） is used to predict the probability of breakdown. Second, we apply an ordinary least squares regression model where categorical variables for time-of-day （AM- or PM-rush hours） and day-of-the-week （Monday-Thursday or Friday） are used to predict recurrent congestion duration. Models are fitted to data collected from a bottleneck on 1-93 in Salem, NH, over a period of 9 months. Results from the breakdown model, predict probabilities of recurrent congestion, are consistent with observed traffic and illustrate an upshift in breakdown probabilities between the AM- and PM-rush periods. Results from the regression model for congestion duration reveal the presences of significant interaction between time-of-day and day-of-the-week. Thus, the effect of time-of-day on congestion duration depends on the day-of-the-week. This work provides a simplification of recurrent congestion and recovery, very noisy processes. Simplification, conveying complex relationships with simple statistical summaries-facts, is a practical and powerful tool for traffic administrators to use in the decision-making process.