Quantitative measurement and development evaluation of logistics clusters in China

The logistics clusters are the result of concentration, scale and specialization of logistics activities, and their quantitative measurement and development evaluation provide an important foundation for improving the land use efficiency and achieving economies of scale. Taking 289 cities at prefecture-level and above as research objects, this paper collected macro-statistical data of transport, postal and warehousing industry during 2000–2014, business registration data of more than 290 thousand logistics enterprises, and 170 thousand logistics points of interest（POI）. With the integration of multi-index and multi-source data, the evolution process and spatial pattern of logistics clusters in China were explored with the methods of Location Quotient（LQ）, Horizontal Cluster Location Quotient（HCLQ）, Logistics Employment Density（LED） and modified Logistics Establishments＇ Participation（LEP）. The development levels, types and modes of different logistics clusters were quantified. Several important findings are derived from the study.（1） The logistics clusters are mainly located on the east side of the Hu Huanyong Line, and the accumulative pattern evolves from group to block structure, featuring wide coverage and high concentration. The evolution of logistics clusters has two stages of rapid convergence and stable change, resulting in gradual increase in the development level and efficiency of logistics clusters and in emergence of spillover effect.（2） 21 mature logistics clusters are distributed in the core and sub-cities of the main metropolitan areas of 16 provincial-level administrative divisions, conforming to the government logistics and transport planning. 43 emerging logistics clusters are distributed in 21 provincial administrative divisions, and different types of cities have huge disparities which highlight the differentiation of the market behaviors and government planning among them.（3） The logistics clusters present differentiated development modes with the change of scales. In urban agglomerations scale, the nested ＂center-periphery＂ structures with ＂main nucleus-secondary cores-general nodes＂ are clarified. The polar nuclear development, networked and balanced development, single core and multipoint, multi-core multipoint hub-spoke development patterns are formed in different provincial administrative divisions.

A Study of Mathematic Model to the Development of Regional Industry Cluster

This paper analyzes the factors that influence the development of regional industry cluster, which are location factors, accumulatable factors, and external factors. Then regarding the similarity between the development of industry cluster and biology community, a modified logistic model is built, and a field study is made between the real instances and the model.

PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.