基于弹性规划理念的城乡建设用地需求预测

文章在回归预测方法的基础上,通过对影响土地需求量的一些不确定性因素的讨论并运用计算机软件Crystal ball进行Monte Carlo模拟,对蚌埠市新一轮土地利用规划目标期2020年的城乡建设用地需求量进行了预测;并最终确定了合理的城乡建设用地需求量区间,为土地利用弹性规划提供了一条重要的用地需求预测途径,有助于土地利用规划在定量的基础上更加符合现实和未来的发展趋势。     

基于灰色理论的西安土地利用预测研究

研究土地利用的变化并做好预测,有利于了解土地变化的原因及发展趋势,有利于调整土地利用结构,使土地利用更趋于合理。依据1998—2006年西安土地利用的变化数据,基于灰色关联度模型分析西安土地利用变化的主要影响因素,计算得到人口因素、固定资产投资是影响土地利用结构的主要因素,并在此基础上运用灰色系统预测理论对2009—2011年土地利用进行了预测,得到耕地林地将不断减少、建设用地将迅速增加的结论。     

提高土地节约集约利用水平的思考

正一、概述按照国务院、国土资源部的安排和部署,包头市近年来在节约集约用地方面做了大量工作,在保护资源和保障发展的双重压力之下,较好的解决了城市发展用地需求和土地供应的矛盾。按照"控制总量、限制增量、盘活存量"的总体目标,主要从土地审批、供应、利用、监管和存量土地内涵挖潜等方面上实现土地的节约集约利用。一是对不符合土地利用总体规划和城市规划的项目用地,不予批准;二是投资规模不足、容积率等指标不符合要求的项目用地,不予批准;三是工业、     

信息科学中软计算法在城市建设用地需求量预测中的应用

0引言建设用地量预测是实现土地可持续发展的核心内容之一,它是根据规划区建设用地的历史情况,以及未来社会对建设用地的客观需求,对某区域的各项建设的用地总规模在规划期内的变化进行测算,目的是弄清楚用地的变化机制,切实为保护土地提供科学依据。上世纪80年代以来,随着信息     

基于能值的土地利用结构优化方案的评价

土地利用结构调整方案的评价及决策是土地利用总体规划编制的核心任务.文章把能值理论与土地利用生态经济效益评价二者紧密结合,并对现阶段农用地转为建设用地时土壤能值的损失未计入能值分析的缺陷进行了改进.在此基础上,以杭州市为例对土地利用结构调整的四个方案进行了能值产出/投入、能值密度、土地环境负载指数、可持续发展指数等指标评价.并建立综合评价模型对土地利用结构调整的四个方案进行了综合评价,结果表明2010年和2020年土地利用生态经济综合效益最优均为BP网络模型方案.文章为土地利用规划方案评价与决策提供了新的思路,也为杭州市土地利用规划编制提供了理论支持.     

开发区土地集约利用潜力的评价

运用协同论,探讨开发区土地系统集约利用的协同机制,测算开发区土地集约利用的潜力,并以合肥高新技术产业开发区为例进行实证检验。研究结果表明:合肥高新技术产业开发区土地集约利用水平较高,建设用地供需基本平衡,但是可供开发的潜力较低。在今后开发区扩展过程中,通过构建集约用地制度体系、研究合理评价方法、提高用地企业准入门槛、加强土地供后监管等是确保土地集约利用的关键。     

推进城乡一体化 建设和谐新农村——成都城乡一体化的探索实践

成都城乡一体化的探索实践以县城和有条件的区域中心镇为重点,提高以城带乡的能力;以科学规划为龙头和基础,加大调控力度。成都的各级领导在工作实践中,坚持用科学的思想来制订科学的规划,并且严格执行规划。他们抓住成都作为全国土地利用总体规划和城市总体规划修编试点城市的契机,坚持把统筹推进工业向集中发展区集中、土地向规模经营集中、农民向城镇集中这“三个集中”原则,在规划思路中加以贯穿;坚持把新一轮城市总体规划、土地利用总体规划、工业布局规划、区(市)县和重点镇规划等各项规划的衔接协调,在规划编制中加以体现;坚持把提高…     

Markov链在预测助学贷款违约率中的作用

贷后风险难以监控是国家助学贷款发放难的重要原因之一。为此,在发放助学贷款的同时,预测其违约率成为了关键。根据我国商业银行的规定,助学贷款的类别变化符合Markov链的状态转移规律。在搜集了足够的数据之后,可以利用Markov链进行助学贷款违约率的预测,以期帮助商业银行加强贷后风险管理。     

Markov链在预测助学贷款违约率中的运用

贷后风险难以监控是国家助学贷款发放难的重要原因之一。为此,在发放助学贷款的同时,预测其违约率成为了关键。根据我国商业银行的规定,助学贷款的类别变化符合Markov链的状态转移规律。在搜集了足够的数据之后,可以利用Markov链进行助学贷款违约率的预测,以期帮助商业银行加强贷后风险管理。     

区域经济文摘

新型城市化要走土地集约利用的道路 研究表明，1980～2005年中国城市化水平每增加一个百分点，所占用的建设用地为1004平方公里，2006～2030年，中国城市化进程每增加一个百分点，所占用的建设用地将达3460平方公里，未来中国推进城镇化面临着日益严峻的土地供应保障瓶颈。为此，今后应着力：优化新区建设用地，提高新区土地集约利用效率。应充分利用城市存量空间，加强对现有建成区的再开发，以减少基础设施和公共服务设施建设的成本，提高新区土地集约利用效率。     

Bayesian inference and prediction of order statistics for a Type-II censored Weibull distribution

This paper describes the Bayesian inference and prediction of the two-parameter Weibull distribution when the data are Type-II censored data. The aim of this paper is twofold. First we consider the Bayesian inference of the unknown parameters under different loss functions. The Bayes estimates cannot be obtained in closed form. We use Gibbs sampling procedure to draw Markov Chain Monte Carlo (MCMC) samples and it has been used to compute the Bayes estimates and also to construct symmetric credible intervals. Further we consider the Bayes prediction of the future order statistics based on the observed sample. We consider the posterior predictive density of the future observations and also construct a predictive interval with a given coverage probability. Monte Carlo simulations are performed to compare different methods and one data analysis is performed for illustration purposes.     

基于马尔可夫模型的绿洲土地利用变化预测研究

文章阐述了马尔可夫模型在绿洲土地利用变化预测研究中的应用,并选取位于柴达木盆地东端都兰县境内的香日德绿洲为研究区,分别利用1987年和1999年覆盖该研究区的TM和ETM遥感影像,在RS和GIS技术的支持下,运用马尔可夫预测模型预测了2011年和2023年该区土地利用的变化情况。研究发现:在1987~1999年间,研究区面积变化最大的是土地利用类型为荒漠草场和高寒干旱草场,未来24年间,面积变化幅度将有所减小,各个土地利用类型的面积变化将达到一个较为平稳的状态。     

贝叶斯隐马尔科夫异质面板模型及应用研究

针对不可观测异质性非时变假设导致的删失变量偏差及推断无效问题,构建贝叶斯隐马尔科夫异质面板模型,刻画截面个体间的动态时变不可观测异质性,诊断经济系统环境中可能存在的隐性变点,设计相应的马尔科夫链蒙特卡洛抽样算法估计模型参数,并对中国各地区的金融发展与城乡收入差距关系进行实证分析,捕捉到金融发展与城乡收入差距间长期稳定关系的隐性变化,发现了区域个体不可观测异质性存在的动态时变特征。研究结果表明各参数的迭代轨迹收敛且估计误差非常小,验证了贝叶斯隐马尔科夫异质面板模型的有效性。     

Markov Chain Monte Carlo methods for estimating surgery duration

Developing prediction bounds for surgery duration is difficult due to the large number of distinct procedures. The variety of procedures at a multi-speciality surgery suite means that even with several years of historical data a large fraction of surgical cases will have little or no historical data for use in predicting case duration. Bayesian methods can be used to combine historical data with expert judgement to provide estimates to overcome this, but eliciting expert opinion for a probability distribution can be difficult. We combine expert judgement, expert classification of procedures by complexity category and historical data in a Markov Chain Monte Carlo model and test it against one year of actual surgery cases at a multi-speciality surgical suite.     

Monte Carlo Methods for Bayesian Inference on the Linear Hazard Rate Distribution

The Bayesian estimation and prediction problems for the linear hazard rate distribution under general progressively Type-II censored samples are considered in this article. The conventional Bayesian framework as well as the Markov Chain Monte Carlo (MCMC) method to generate the Bayesian conditional probabilities of interest are discussed. Sensitivity of the prior for the model is also examined. The flood data on Fox River, Wisconsin, from 1918 to 1950, are used to illustrate all the methods of inference discussed in this article.     

The double chain markov model

In the class of discrete time Markovian processes, two models are widely used, the Markov chain and the hidden Markov model. A major difference between these two models lies in the relation between successive outputs of the observed variable. In a visible Markov chain, these are directly correlated while in hidden models they are not. However, in some situations it is possible to observe both a hidden Markov chain and a direct relation between successive observed outputs. Unfortunately, the use of either a visible or a hidden model implies the suppression of one of these hypothesis. This paper prsents a Markovian model under random environment called the Double Chain Markov Model which takes into account the maijn features of both visible and hidden models. Its main purpose is the modeling of non-homogeneous time-series. It is very flexible and can be estimated with traditional methods. The model is applied on a sequence of wind speeds and it appears to model data more successfully than both the usual Markov chains and hidden Markov models.     

Markov Chain Monte Carlo model selection for DAG models

We present a methodology for Bayesian model choice and averaging in Gaussian directed acyclic graphs (dags). The dimension-changing move involves adding or dropping a (directed) edge from the graph. The methodology employs the results in Geiger and Heckerman and searches directly in the space of all dags. Model determination is carried out by implementing a reversible jump Markov Chain Monte Carlo sampler. To achieve this aim we rely on the concept of adjacency matrices, which provides a relatively inexpensive check for acyclicity. The performance of our procedure is illustrated by means of two simulated datasets, as well as one real dataset.     

Modeling attainment of steady state of drug concentration in plasma by means of a Bayesian approach using MCMC methods

The approach of Bayesian mixed effects modeling is an appropriate method for estimating both population-specific as well as subject-specific times to steady state. In addition to pure estimation, the approach allows to determine the time until a certain fraction of individuals of a population has reached steady state with a pre-specified certainty. In this paper a mixed effects model for the parameters of a nonlinear pharmacokinetic model is used within a Bayesian framework. Model fitting by means of Markov Chain Monte Carlo methods as implemented in the Gibbs sampler as well as the extraction of estimates and probability statements of interest are described. Finally, the proposed approach is illustrated by application to trough data from a multiple dose clinical trial.     

High-order extensions of the Double Chain Markov Model

The Double Chain Markov Model is a fully Markovian model for the representation of time-series in random environments. In this article, we show that it can handle transitions of high-order between both a set of observations and a set of hidden states. In order to reduce the number of parameters, each transition matrix can be replaced by a Mixture Transition Distribution model. We provide a complete derivation of the algorithms needed to compute the model. Three applications, the analysis of a sequence of DNA, the song of the wood pewee, and the behavior of young monkeys show that this model is of great interest for the representation of data that can be decomposed into a finite set of patterns.     

Joint modeling of multivariate longitudinal mixed measurements and time to event data using a Bayesian approach

In many longitudinal studies multiple characteristics of each individual, along with time to occurrence of an event of interest, are often collected. In such data set, some of the correlated characteristics may be discrete and some of them may be continuous. In this paper, a joint model for analysing multivariate longitudinal data comprising mixed continuous and ordinal responses and a time to event variable is proposed. We model the association structure between longitudinal mixed data and time to event data using a multivariate zero-mean Gaussian process. For modeling discrete ordinal data we assume a continuous latent variable follows the logistic distribution and for continuous data a Gaussian mixed effects model is used. For the event time variable, an accelerated failure time model is considered under different distributional assumptions. For parameter estimation, a Bayesian approach using Markov Chain Monte Carlo is adopted. The performance of the proposed methods is illustrated using some simulation studies. A real data set is also analyzed, where different model structures are used. Model comparison is performed using a variety of statistical criteria.