Multifractal Modeling of Worldwide and Canadian Metal Size-Frequency Distributions

The Pareto-lognormal frequency distribution, which can result from multifractal cascade modeling, previously was shown to be useful to describe the worldwide size-frequency distributions of metals including copper, zinc, gold and silver in ore deposits. In this paper, it is shown that the model also can be used for the size-frequency distributions of these metals in Canada which covers 6.6% of the continental crust. Like their worldwide equivalents, these Canadian deposits show two significant departures from the Pareto-lognormal model: (1) there are too many small deposits, and (2) there are too few deposits in the transition zone between the central lognormal and the upper tail Pareto describing the size-frequency distribution of the largest deposits. Probable causes of these departures are: (1) historically, relatively many small ore deposits were mined before bulk mining methods became available in the twentieth century, and (2) economically, giant and supergiant deposits are preferred for mining and these have strongest geophysical and geochemical anomalies. It is shown that there probably exist many large deposits that have not been discovered or mined. Although overall the samples of the size-frequency distributions are very large, frequencies uncertainties associated with the largest deposits are relatively small and it remains difficult to estimate more precisely how many undiscovered mineral deposits there are in the upper tails of the size-frequency distributions of the metals considered.

     

Bayesian Analysis of the Discovery Process Model Using Markov Chain Monte Carlo

Based upon the Bayesian framework for analyzing the discovery sequence in a play, a Markov chain Monte Carlo sampler—the Metropolis–Hastings algorithm, is employed to sample model parameters and pool sizes from their joint posterior distribution. The proposed sampling scheme ensures that the parameter space of changing dimension can be traversed in spite of the unknown number of pools. The equal sample weights make it easy to obtain the confidence intervals and assess the statistical error in the estimates, so that the statistical behaviors of the discovery process modeling can be well understood. Two application examples of the Halten play in Norwegian Sea and the Bashaw reef play in the Western Canada Basin show that, the computational advantage of this method to the simple Monte Carlo integration is considerable. In order to increase the convergence speed of the sample chains to the posterior distributions, several parallel simulations with different starting values are recommended.     

基于蒙特卡罗方法的扎龙湿地水环境质量评价

为了简单有效地评价水质状况,建立了蒙特卡罗水质评价模型,对扎龙湿地北部(进水区)、中部(过水区)和南部(出水区)的污染状况进行评价,并与模糊综合评价结果、灰色关联评价结果和内梅罗评价结果进行比较.基于蒙特卡罗(Monte Carlo,MC)法的评价结果表明,扎龙湿地北部、中部和南部总体水质类别都为Ⅱ类,与模糊综合评价法和灰色关联评价法的评价结果一致,也与湿地水质的实际状况相吻合,从而证明了模型的准确性;而其与内梅罗指数法的评价结果差别较大,究其原因,内梅罗指数法评价结果表现为过保护,较严格,从而使得水质评价结果有些脱离实际,内梅罗指数法比较适于评价参评因子值波动比较小、各参评因子都污染较轻的研究区域.从单指标评价类别分析,溶解氧(DO)、Zn、Cd和Cu含量的评价类别一致;扎龙湿地北部和南部总磷(TP)含量的评价类别一致,中部和南部总氮(TN)含量评价类别一致;而Cr含量在3个区中的评价类别都不同,而且从北部、中部到南部逐渐增大,水质变差;另外由于南部地势较低,Cr在南部长期沉积和聚集也是导致其含量过高的重要原因之一.从各类别概率值之和分析,扎龙湿地内部各区隶属于Ⅱ类的概率值之和都是最大的,但隶属于劣Ⅴ类的概率值之和居第二,表明大部分区域水质较好,局部区域水质较差,考虑到水是流动的,水质有由好转差的风险;从北部到南部,隶属于Ⅰ类、Ⅲ类和劣Ⅴ类的概率值之和趋于减小,隶属于Ⅳ类的概率值之和基本未变,隶属于Ⅴ类的概率值之和稍有增加,水质趋于向Ⅱ类转化;从中部到南部,隶属于Ⅰ类、Ⅱ类和劣Ⅴ类的概率值之和减小,隶属于Ⅳ类的概率值之和基本未变,水质趋于向Ⅲ类和劣Ⅴ类水质转化.从平均值看,TP含量先减少后增大.扎龙湿地水质的评价结果表明,总体符合Ⅱ类水质及以上的概率约为54.5％,但个别指标评价结果不容乐观,尤其是Cd含量、DO含量和南部Cr含量的超标,应引起相关部门的注意.     

GIS-based stochastic modeling of physical accessibility using GPS-based floating car data and Monte Carlo simulation

The term physical accessibility has long been used by geographers, economists, and urban planners and reflects the relative ease of access to/from several urban/rural services by considering the traveling costs. Numerous accessibility measures, ranging from simple to sophisticated, can be observed in the geographical information systems (GIS)-based accessibility modeling literature. However, these measures are generally calculated from a constant catchment boundary (a most likely or average catchment boundary) based on constant deterministic transportation costs. This is one of the fundamental shortcomings of the current GIS-based accessibility modeling and creates uncertainty about the accuracy and reliability of the accessibility measures, especially when highly variable speeds in road segments are considered. The development of a new stochastic approach by using global positioning system (GPS)-based floating car data and Monte Carlo simulation (MCS) technique could enable handling the variations in transportation costs in a probabilistic manner and help to consider all possible catchment boundaries, instead of one average catchment boundary, in accessibility modeling process. Therefore, this article proposes a stochastic methodology for GIS-based accessibility modeling by using GPS-based floating car data and MCS technique. The proposed methodology is illustrated with a case study on medical emergency service accessibility in Eskisehir, Turkey. Moreover, deterministic and stochastic accessibility models are compared to demonstrate the differences between the models. The proposed model could provide better decision support for the decision-makers who are supposed to deal with accessibility, location/allocation, and service/catchment area related issues.     

基于蒙特卡洛生存分析探究东北森林物候的影响因素

植被是生态环境变化的指示器,分析植被物候的影响因素不仅有助于气候变化分析,提高区域气候模式的模拟精度,而且对于准确评估植被生长趋势、生产力以及全球碳收支均具有重要意义。基于遥感的植物物候监测已取得了长足的发展和进步,但当前利用大范围、长时间序列的遥感数据分析植被物候影响因素的研究尚不多,采用线性回归模型对非线性的植被物候影响因素进行分析可能存在偏误。因此,本文提出一种基于蒙特卡洛模拟的生存分析方法,对东北森林物候的影响因素进行量化分析。首先利用东北森林地区1982-2009年间AVHRR GIMMS NDVI数据,应用双Logistic曲线拟合方法对植被春季返青期（SOS）、秋季落叶期（EOS）及植被生长期（GSL）进行提取;然后基于蒙特卡洛模拟和生存分析构建植被物候影响因素分析模型;最后运用所构建模型探讨了东北森林区春季返青期、秋季落叶期的可能影响因素。结果发现：温度、降水和风力对中国东北森林关键物候期有一定影响,其中温度是春季返青期和秋季落叶期的最主要驱动因素,长期平均温度比短期内的温度突变对物候影响更显著,落叶期前的风速增加有可能使落叶时间提前;除了环境因素,春季返青早的年间秋季落叶倾向于更晚。研究表明,结合蒙特卡洛方法的生存分析可以较好地对物候期的影响因素进行定量分析,可为物候现象的归因分析提供一种新的方法。     

Modelling uncertainty in natural resource analysis using fuzzy sets and Monte Carlo simulation: slope stability prediction

The techniques of fuzzy logic and Monte Carlo simulation are combined to address two incompatible types of uncertainty present in most natural resource data: thematic classification uncertainty and variance in unclassified continuously distributed data. The resultant model of uncertainty is applied to an infinite slope stability model using data from Louise Island, British Columbia. Results are summarized so as to answer forestry decision support queries. The proposed model of uncertainty in resource data analysis is found to have utility in combining different types of uncertainty, and efficiently utilizing available metadata. Integration of uncertainty data models with visualization tools is considered a necessary prerequisite to effective implementation in decision support systems.     

Inference of past climate from borehole temperature data using Bayesian Reversible Jump Markov chain Monte Carlo

Estimates of past climate derived from borehole temperatures are assuming a greater importance in context of the millennial temperature variation debate. However, recovery of these signals is usually performed with regularization which can potentially lead to underestimation of past variation when noise is present. In this work Bayesian inference is applied to this problem with no explicit regularization. To achieve this Reversible Jump Markov chain Monte Carlo is employed, and this allows models of varying complexity (i.e. variable dimensions) to be sampled so that it is possible to infer the level of ground surface temperature (GST) history resolution appropriate to the data. Using synthetic examples, we show that the inference of the GST signal back to more than 500 yr is robust given boreholes of 500 m depth and moderate noise levels and discuss the associated uncertainties. We compare the prior information we have used with the inferred posterior distribution to show which parts of the GST reconstructions are independent of this prior information. We demonstrate the application of the method to real data using five boreholes from southern England. These are modelled both individually and jointly, and appear to indicate a spatial trend of warming over 500 yr across the south of the country.     

Spatial conjunctive analysis of (crime) case configurations: Using Monte Carlo methods for significance testing

While research has repeatedly demonstrated how spatial distributions of crime can be shaped by the presence of facilities such as bars and public transport hubs, the joint influence of different facility types has rarely been explored. Spatial conjunctive analysis of case configurations (also known as qualitative comparative analysis) offers a means to identify the combinations of facility types that are most commonly found around crime events, and has been used in a small number of studies focusing on street robbery. This study extends this limited evidence base by implementing a significance test based on the Monte Carlo method using street robbery data for Austin, Texas. The results show that some of the top-ranking facility type combinations had observed frequencies that were not significantly greater than chance expectations. The accurate identification of the highest-risk environments has important implications for crime prevention.     

Sensitivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model

We analysed the sensitivity of a decision tree derived forest type mapping to simulated data errors in input digital elevation model (DEM), geology and remotely sensed (Landsat Thematic Mapper) variables. We used a stochastic Monte Carlo simulation model coupled with a one‐at‐a‐time approach. The DEM error was assumed to be spatially autocorrelated with its magnitude being a percentage of the elevation value. The error of categorical geology data was assumed to be positional and limited to boundary areas. The Landsat data error was assumed to be spatially random following a Gaussian distribution. Each layer was perturbed using its error model with increasing levels of error, and the effect on the forest type mapping was assessed. The results of the three sensitivity analyses were markedly different, with the classification being most sensitive to the DEM error, than to the Landsat data errors, but with only a limited sensitivity to the geology data error used. A linear increase in error resulted in non‐linear increases in effect for the DEM and Landsat errors, while it was linear for geology. As an example, a DEM error of as small as ±2% reduced the overall test accuracy by more than 2%. More importantly, the same uncertainty level has caused nearly 10% of the study area to change its initial class assignment at each perturbation, on average. A spatial assessment of the sensitivities indicates that most of the pixel changes occurred within those forest classes expected to be more sensitive to data error. In addition to characterising the effect of errors on forest type mapping using decision trees, this study has demonstrated the generality of employing Monte Carlo analysis for the sensitivity and uncertainty analysis of categorical outputs that have distinctive characteristics from that of numerical outputs.     

Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering,Bayesian probability and Monte Carlo simulation

ABSTRACT

Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips.     

书评——《城市对称分布与中国城市化趋势》(英文)

YE Danian, HE Wei, LI Zhe, XU Wendong Anhui Education Press,Hefei,2011,x+260 pp,ISBN 978 7 5336 5737 6(hardback),CHY 58.00 As products of urbanization, cities are focal points for social and economic interaction. Though China is an ancient civilization, only recently has the urban population surpassed that in rural areas. During the last three decades, unprecedented urbanization has reshaped China’s geographical, social, economic and cultural landscapes. Among the social-economic transformations having occurred in China in recent history, urbanization is probably the most impressive and dynamic process; it has changed daily life of Chinese population and profoundly impacted the regional and global economy.     

Probability Estimates of Field Areas and Trapped Oil Volumes

A portion of a sedimentary basin is subdivided conceptually into hexagons of equal area. The area of each hexagon is equal to the minimum area an oil field should have to be commercial. Hexagons can be full of oil or empty. A field size 1 consists of a cell with oil surrounded by six empty cells; a field size 2 consists of two adjacent cells with oil surrounded by eight empty cells, etc. Principles of Percolation Theory are used to determine the probability distribution of the areas of the oil fields existing in this portion of the basin. The only piece of information necessary to determine this probability distribution is the Success Ratio (number of successful exploration wells/total number of exploration wells drilled in this portion of the basin). This approach has several practical applications.A probabilistic model is introduced to predict to which extent potential oil traps are filled with oil. The model assumes that the probability that an oil unit will end up in a particular trap, is proportional to the surface area of the trap. The model predicts that independently of the distribution of the trap volumes, there will be a critical trap volume. All the traps having a volume less than this critical volume, will be filled to spill point. An equation is deduced to predict, for all traps having a volume greater than the critical, the volume of oil that can be encountered in the trap, provided the volume of the trap is known.     

湛江市土壤有机氯农药残留状况及空间分布特征

选取位于中国大陆最南端的湛江市为研究区域,对该市土壤进行了系统采样分析,测定了61个表层土壤样品中的17种有机氯农药的含量。利用多元地统计和GIS相结合的方法,研究了17种有机氯农药的残留状况及空间分布情况。结果表明,17种有机氯农药在42个土壤样品中均有不同程度的检出,其中p,p'-DDE检出率为100%,3种有机氯农药检出率在90%以上。其中六六六和滴滴涕残留较普遍,六六六未达到污染水平,有一个土壤样品滴滴涕超过国家土壤环境质量一级标准。分析显示,近期可能仍有新的六六六输入土壤环境,而滴滴涕则主要来自过去施用农药的残留。主成分分析显示17种有机氯农药可以由6个主成分反映,分别对应于工业HCH和林丹的使用、农药使用习惯以及当地病虫害特点等。采用克里格插值法对有机氯农药在研究区的空间分布进行了研究,结果表明不同有机氯农药在研究区分布存在很大差异。     

中国外资星级宾馆区位研究

本文研究了外资星级宾馆的地理分布与区位。整体而言,外资星级饭店集聚在少数省区和城市,尤其是珠三角、长三角以及京津冀地区。基于服务业外资区位理论,本文采用负二项回归模型检验了追随国外客户、市场规模、集聚经济、对外经济联系及基础设施等对外资星级宾馆区位选择的影响。统计分析表明外资星级宾馆在区位选择上,倾向于追随国外客户在中国布局,同时通过与国内宾馆的共聚而获得成本节约与竞争力提升。城市的对外联系强化涉外市场潜力,进一步吸引外资星级宾馆;而交通通讯基础设施强化宾馆业集聚经济,引导外资星级宾馆的区位选择。