基于Shapley值的特色小镇PPP项目收益分配研究——以吉安市遂川县红圩特色小镇项目为例

特色小镇PPP项目因参与方众多、合作形式复杂造成各方利益分配纠葛,直接阻碍项目开发.因此结合特色小镇的特点,依据现有研究成果以投资比例、风险分担及满意度等作为收益分配影响因素,运用AHP-GEM法分别计算了影响因素及风险分担指标权重,结合TOPSIS法对计算出的收益分配影响系数进行修正,构建了基于改进的区间Shaple...     

基于交通网络的特色小镇建设路径研究

特色小镇发展模式研究多定位于特色产业支撑下产城融合、因地制宜的发展,其中区位优势与完善的基础设施是产业及资源优势得以发展的前提条件.交通基础设施建设作为带动区域发展及周边辐射的关键节点,是特色小镇发展和运营的先决条件之一.综合利用交通可达性、道路网络密度、和交通设施水平等级三个方面构建区域交通优势度的评价指标体系,以陕西省14个特色小镇为研究对象应用Arcgis工具分析,对陕西省特色小镇现有交通体系做出定量评价,并分析对城市空间格局的影响,为特色小镇交通道路规划、加强低值区的交通基础设施建设、改善各镇与县市区域的经济联系以及提升城镇化空间格局等提供决策评估.     

基于多方博弈的村落遗产地旅游开发模式形成机理研究——以开平碉楼与村落为例

借助博弈论的思想和方法,以开平碉楼与村落为例,对村落遗产地旅游开发中的核心利益相关者为实现各自利益诉求所实施的博弈行为进行分析,建立两阶段博弈模型,试图找出博弈均衡,通过典型的博弈关系来剖析他们之间的相互关系和作用机制,从而揭示村落遗产地旅游开发模式的形成机理.     

城市旅游开发潜力综合评价——以山东省为例

城市旅游的评价体现着系统性,往往很难以单一指标反映城市旅游发展潜力.本文针对我国旅游业的发展支持力、保障力和市场潜力等关键因素,建立山东省旅游开发潜力综合评价指标,以2017—2019年数据为基础,山东省16市为实证分析对象,首先,使用随机森林与熵值法确定指标重要性和权重;其次,利用主成分分析、层次分析法及支持向量机分...     

结构视域下的高中数学课堂教学实践——以“简单复合函数的导数”教学设计为例

在新课程实施与新教材使用的背景下,笔者结合数学学科特点,关注数学知识、方法内在的逻辑体系,以“简单复合函数的导数”一课为例,尝试进行结构视域下的教学设计与实践,以期促进学生数学抽象、数学运算、直观想象和逻辑推理等数学学科核心素养的形成和发展.     

基于系统聚类方法的养老区域分析——以上海市为例

随着上海迈入人口深度老龄化阶段,上海市各区的养老问题呈现不同的特征,而民政数据海中蕴含着老年人口的大量信息.如何利用民政业务数据海挖掘价值信息是当前养老服务的重点研究内容.本文采用系统聚类方法对养老服务数据进行分析,根据确立的聚类指标体系将上海市各区分为三类,聚类结果很好地反应了不同区域特征.同时,针对不同的养老服务特征提出了基于问题导向的养老服务资源配置建议,为有针对性地提供相应的服务指明方向.此外,聚类结果可以为政府合理构建养老保障体系提供一定的参考.     

基于网络文本挖掘的冰雪旅游形象感知研究——以哈尔滨市为例

旅游形象塑造是制约旅游市场竞争的关键因素,形象感知研究对旅游形象塑造具有重要意义.随着在线旅行社(OTA)的快速发展,产生了大量旅游城市和景点的在线评论数据,为形象感知研究提供了新的视角.以Baidu旅游、携程网、马蜂窝等多家OTA网络点评数据为例,采用词频-逆文档率(TF-IDF)和LDA文档主题生成模型两种典型的文本分析方法,分析游客对冰雪大世界、太阳岛、圣索菲亚大教堂、中央大街、雪乡、亚布力滑雪场等哈尔滨6个冰雪旅游景点的认知形象、情感形象和总体形象感知.研究表明:1)冰雪旅游认知形象要素可归纳为相关地标、旅游活动、建筑、餐饮住宿、游玩价格等5个群组,相关地标是第一要素,其余认知形象要素在各景点权重有所差异;2)冰雪旅游情感形象以积极正面为主,除雪乡外,其余5个景点均具有强烈的推荐意愿;3)冰雪旅游的整体形象感知主题涵盖冰雪娱乐、冰雪观光、冰雪运动、特色美食和文化.研究丰富了旅游大数据研究方法,为哈尔滨市冰雪旅游形象塑造提供建议.     

城市二手住房特征价格研究——以石家庄市为例

将特征价格理论应用于具体的二手住房市场,通过搜集、整理得到2075个石家庄市二手住房价格和特征样本数据,选取了住房的建筑自身、区位和邻里等三类特征作为解释变量,采用最小二乘法（OLS）进行回归,分析了城市二手住房特征价格,构建了适用于二手住房市场的特征价格模型并解释了住房特征与住房价格之间的定量关系．     

旅游环境承载力子系统耦合协调关系研究——以兰州市为例

耦合指两个及以上系统的相互作用关系,协调强调旅游环境系统之间或系统组成要素之间的相互关系,旅游环境系统要素之间的协调是系统内外耦合运行的基础与发展方向,通过分析旅游环境承载力要素之间的协调性,探讨其整体效应,以保持旅游环境系统的整体输出功能和整体正效应发挥.通过旅游环境承载力子系统耦合协调的内涵分析,从自然、经济、社会三个角度构建旅游环境承载力子系统耦合的指标体系,通过熵值法确定权重,构建目的地旅游环境承载力的自然、经济、社会子系统耦合协调度模型,并以兰州市为例进行实证研究.通过研究发现,伴随着2004-2016年兰州市旅游环境承载力指数稳步提升,兰州市旅游环境承载力子系统的耦合协调度从0.1517提升到0.5535,根据兰州市旅游环境承载力子系统的耦合协调状况的分析,提出提升旅游环境承载力子系统的相关对策,以促进旅游目的地的可持续发展.     

村落遗产地政府主导开发模式的多层次模糊综合评价——以开平碉楼与村落为例

科学评价村落遗产地旅游开发模式,监测其实施效果,对村落遗产地旅游开发的状况和水平作出合理评定,既能全面深入地挖掘村落遗产地旅游开发模式蕴涵的内涵,又能诊断出旅游开发中存在的优势与不足,对促进村落遗产地旅游业可持续发展具有重要的实践意义.在文献研究和专家调查的基础上,构建村落遗产地政府主导开发模式评价指标体系,以开平碉楼与村落为例,采用多层次模糊综合评判法对村落遗产地政府主导开发模式进行定量评价,以期为当地旅游行政管理部门科学制定决策提供理论依据.     

A Com binatorial Approach for Topological Classification of Graphlike M anifolds

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A Simple Analytical and Numerical Approach for Pricing Compound Options

A compound option is simply an option on an option. In this short paper, by using a martingale technique, we obtain an analytical formula for pricing compound European call options. Numerical results are given to explain some economic phenomenon.     

A Multiobjective Hybrid Metaheuristic Approach for GIS-based Spatial Zoning Model

This paper presents a multiobjective hybrid metaheuristic approach for an intelligent spatial zoning model in order to draw territory line for geographical or spatial zone for the purpose of space control. The model employs a Geographic Information System (GIS) and uses multiobjective combinatorial optimization techniques as its components. The proposed hybrid metaheuristic consists of the symbiosis between tabu search and scatter search method and it is used heuristically to generate non-dominated alternatives. The approach works with a set of current solution, which through manipulation of weights are optimized towards the non-dominated frontier while at the same time, seek to disperse over the frontier by a strategic oscillation concept. The general procedure and its algorithms are given as well as its implementation in the GIS environment. The computation has resulted in tremendous improvements in spatial zoning.     

A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models

Inference for spatial generalized linear mixed models (SGLMMs) for high-dimensional non-Gaussian spatial data is computationally intensive. The computational challenge is due to the high-dimensional random effects and because Markov chain Monte Carlo (MCMC) algorithms for these models tend to be slow mixing. Moreover, spatial confounding inflates the variance of fixed effect (regression coefficient) estimates. Our approach addresses both the computational and confounding issues by replacing the high-dimensional spatial random effects with a reduced-dimensional representation based on random projections. Standard MCMC algorithms mix well and the reduced-dimensional setting speeds up computations per iteration. We show, via simulated examples, that Bayesian inference for this reduced-dimensional approach works well both in terms of inference as well as prediction; our methods also compare favorably to existing “reduced-rank” approaches. We also apply our methods to two real world data examples, one on bird count data and the other classifying rock types. Supplementary material for this article is available online.     

A Variational Approach for the Mixed Problem in the Elastostatics of Bodies with Dipolar Structure

In this study, we address the mixed initial boundary value problem in the elastostatics of dipolar bodies. Using the equilibrium equations, we build the operator of dipolar elasticity and prove that this operator is positively defined even in the general case of an elastic inhomogeneous and anisotropic dipolar solid. This helps us to prove the existence of a generalized solution for first boundary value problem and also the uniqueness of the solution. Moreover, relying on this property of the operator of dipolar elasticity to be positively defined, we can apply the known variational method proposed by Mikhlin.     

A Monte Carlo Filtering Approach for Estimating the Term Structure of Interest Rates

We develop new methodology for estimation of general class of term structure models based on a Monte Carlo filtering approach. We utilize the generalized state space model which can be naturally applied to the estimation of the term structure models based on the Markov state processes. It is also possible to introduce measurement errors in the general way without any bias. Moreover, the Monte Carlo filter can be applied even to the models in which the zero-coupon bonds' prices can not be analytically obtained. As an example, we apply the method to LIBORs (London Inter Bank Offered Rates) and interest rates swaps in the Japanese market and show the usefulness of our approach.     

A Complete Classification of the Simultaneously Bireflective and Bicoreflective Subconstructs of Approach Spaces

In this paper, using a technique developed by Herrlich and Lowen [3], we classify and describe all subconstructs of A P, the construct of approach spaces, which are at the same time bireflective and bicoreflective in A P.     

The Latent Scale Covariogram: A Tool for Exploring the Spatial Dependence Structure of Nonnormal Responses

When modeling spatially distributed normal responses Y_i in terms of vectors x_i of explanatory variables, one may fit a linear model assuming independence, and then use the empirical variogram of the residuals to determine an appropriate parametric form for the autocorrelation function. Suppose, however, that the responses are not normally distributed—for example, Poisson or Bernoulli. One may model spatial dependence using a hierarchical generalized linear model in which, conditional on a latent Gaussian field Z = {Z_i}, the Y_i have independent distributions from the exponential family, with an appropriate link function connecting their conditional means with the linear predictors x^t_iβ + Z_i. The question then is how to determine an appropriate model for the autocorrelation function of Z. The empirical variogram of the Y_i is no longer appropriate, since (unless the link function is the identity) it is on the wrong scale. We propose here an alternative, the latent scale covariogram, whose graph reflects the autocorrelation structure of the underlying normal field. We illustrate its use on several real datasets, together with a simulated dataset, and obtain results quite different from those obtained using the variogram. Supplementary materials for this article are available online.