A Unit Study of Externality of Shrimp Farming on Provisioning Services (Paddy Farming)

Externality;the term can define as a positive or negative impact from either production or consumption of goods or services.Services provided by particular location have very specific dependency on spatial characteristics of that region.A region’s distinct characteristics make it ecologically unique from other such regions.Ecosystem services are offered by these regions thus differ according to these unique ecological features.In this particular study,artificially imposed expansion of coastal shrimp farming towards the inland and its impact over paddy cultivation have been addressed.Optimization of the extent of this manipulative coastal expansion has been supported by little modification of a previously described model.Here the investment prediction for both shrimp and paddy farming has been investigated by calculating net present value(NPV).Shrimp farming has very specific externality on local ecosystem services.In this particular case,some contradictory results are presented and with respect to positive or negative externality;but the externalities are strong.NPV results indicate that there is no long-term profitability in case of shrimp farming.Hence,an overall externality of shrimp farming has been described in context of this study.

An evaluation and query algorithm for the influence of spatial location based on RkNN

This paper is devoted to the investigation of the evaluation and query algorithm problem for the influence of spatial location based on RkNN(reverse k nearest neighbor).On the one hand,an object can make contribution to multiple locations.However,for the existing measures for evaluating the influence of spatial location,an object only makes contribution to one location,and its influence is usually measured by the number of spatial objects in the region.In this case,a new measure for evaluating the influence of spatial location based on the RkNN is proposed.Since the weight of the contribution is determined by the distance between the object and the location,the influence weight definition is given,which meets the actual applications.On the other hand,a query algorithm for the influence of spatial location is introduced based on the proposed measure.Firstly,an algorithm named INCH(INtersection’s Convex Hull)is applied to get candidate regions,where all objects are candidates.Then,kNN and Range-k are used to refine results.Then,according to the proposed measure,the weights of objects in RkNN results are computed,and the influence of the location is accumulated.The experimental results on the real data show that the optimized algorithms outperform the basic algorithm on efficiency.In addition,in order to provide the best customer service in the location problem and make the best use of all infrastructures,a location algorithm with the query is presented based on RkNN.The influence of each facility is calculated in the location program and the equilibrium coefficient is used to evaluate the reasonability of the location in the paper.The smaller the equilibrium coefficient is,the more reasonability the program is.The actual application shows that the location based on influence makes the location algorithm more reasonable and available.

The spatial pattern of farming and factors influencing it during the Peiligang culture period in the middle Yellow River valley, China

The Peiligang culture(ca.7000–5000 cal BC)is the earliest Neolithic culture in the middle Yellow River valley and represents the emergence of cereal farming in the region[1].It has long been considered representative of millet farming[2],which requires a dry field,a certain amount of rainfall,and minimal crop management,but new records reveal that mixed farming of millet and rice(Oryza sp.)took place during this period[3].Therefore,it is possible