Vector representation and analysis of areally distributed variables

When areally distributed variables are represented by vectors relative to a single reference point location, resultant vectors (distribution vectors) can be used to measure the geographic distribution of the variables simultaneously taking into account (1) thevalues of the variables, (2) thedirection, relative to the reference location, of the points at which the values are located, and (3) thedistances between the reference point and the data points. The distribution vector method provides measures of weighted mean location (distribution mean location) and dispersion (standard distance) that reflect the value direction, and distance of variables relative to the reference location. The distribution vector method has versatile applicability to problems involving areally distributed variables and can be used with other quantitative methods. An illustrative application of the distribution vector method with a statistical spatial interaction model of airline passenger traffic from St. Louis to 27 selected SMSAs for each year from 1960 through 1965 is included.I wish to thank my colleagues Prem S. Laumas and Eldon Reiling as well as Gerhard Tintner, Laurence Jay Mauer and Stanley Johnson, for their comments and suggestions on an earlier version of this paper.     

基于P中值模型的村镇文化体育设施配置研究

村镇公共服务设施布局的均等化对我国新农村建设水平有着重要的影响。作为公共服务设施重要组成的文化体育设施的配置直接影响到我国村镇居民的生活质量。分析村镇文体设施配置研究的必要性,结合公共文体设施的特殊属性,提出了村镇配置文体设施中应遵循的原则,并采用区位配置模型中的P中值模型研究村镇文化体育设施配置中的选址问题,同时针对每一个具体的文体设施点建立了村镇文体设施配置矩阵,为村镇在实际配置文体设施时提供参考,以实现资源的最大化利用。     

Energy efficiency benchmarks and the performance of LEED rated buildings for Information Technology facilities in Bangalore, India

This paper provides a summary of an energy benchmarking study that uses performance data of a sample of Information Technology facilities in Bangalore. Information provided by the sample of occupiers was used to develop an Energy Performance Index (EPI) and an Annual Average hourly Energy Performance Index (AAhEPI), which takes into account the variations in operation hours and days for these facilities. The EPI and AAhEPI were modelled to identify the factors that influence energy efficiency. Employment density, size of facility, operating hours per week, type of chiller and age of facility were found to be significant factors in regression models with EPI and AAhEPI as dependent variables. Employment density, size of facility and operating hours per week were standardised and used in a separate regression analysis. Parameter estimates from this regression were used to normalize the EPI and AAhEPI for variance in the independent variables. Three benchmark ranges - the bottom third, middle third and top third - were developed for the two normalised indices. The normalised EPI and AAhEPI of LEED rated building, which were also part of the sample, indicate that, on average, LEED rated buildings outperform the other buildings.     

Presentation of regression analysis,GP and GMDH models to predict the pedestrian density in various urban facilities

In this study, the relationship between space mean speed (SMS), flow rate and density of pedestrians was investigated in different pedestrian facilities, including 1 walkway, 2 sidewalks, 2 signalized crosswalks and 2 mid-block crosswalks. First, statistical analysis was performed to investigate the normality of data and correlation of variables. Regression analysis was then applied to determine the relationship between SMS, flow rate, and density of pedestrians. Finally, two prediction models of density were obtained using genetic programming (GP) and group method of data handling (GMDH) models, and k-fold and holdout cross-validation methods were used to evaluate the models. By the use of regression analysis, the mathematical relationships between variables in all facilities were calculated and plotted, and the best relationships were observed in flow rate-density diagrams. Results also indicated that GP had a higher R² than GMDH in the prediction of pedestrian density in terms of flow rate and SMS, suggesting that GP was better able to model SMS and pedestrian density. Moreover, the application of k-fold cross-validation method in the models led to better performances compared to the holdout cross-validation method, which shows that the prediction models using k-fold were more reliable. Finally, density relationships in all facilities were obtained in terms of SMS and flow rate.     

Long-term behaviour and cross-correlation water quality analysis of the River Elbe, Germany

This study analyses weekly data samples from the river Elbe at Magdeburg between 1984 and 1996 to investigate the changes in metabolism and water quality in the river Elbe since the German reunification in 1990. Modelling water quality variables by autoregressive component models and ARIMA models reveals the improvement of water quality due to the reduction of waste water emissions since 1990. The models are used to determine the long-term and seasonal behaviour of important water quality variables. Organic and heavy metal pollution parameters showed a significant decrease since 1990, however, no significant change of chlorophyll-a as a measure for primary production could be found. A new procedure for testing the significance of a sample correlation coefficient is discussed, which is able to detect spurious sample correlation coefficients without making use of time-consuming prewhitening. The cross-correlation analysis is applied to hydrophysical, biological, and chemical water quality variables of the river Elbe since 1984. Special emphasis is laid on the detection of spurious sample correlation coefficients.     

Spatial-temporal prediction of secondary compression using random field theory

A methodology is presented for observation-based settlement predictions by considering the spatial correlation structure of soil. The spatial correlation is introduced among the settlement model parameters, and the settlements at various points are spatially correlated through these geotechnical parameters, which naturally describe the phenomenon. The method is based on Bayesian estimations, considering both prior information, including spatial correlation, and observed settlements, to search for the best estimates of the parameters. Within the Bayesian framework, the optimized selection of the auto-correlation distance, by Akaike’s Bayesian Information Criterion (ABIC), and the spatial interpolation of the model parameters, by the kriging method, are also proposed. The application of the proposed approach in secondary compression settlement predictions, based on the linear relationship between settlement and the logarithm of time, is presented in this paper. Several case studies are carried out using both simulated settlement data and actual field observation data. It is concluded that the accuracy of settlement predictions can be improved by taking into account the spatial correlation structure, especially when the spacing of the observation points is shorter than half of the auto-correlation distance, and that the proposed approach produces rational predictions of settlements at any location and at any time with quantified errors.     

偏最小二乘回归神经网络模型在大坝观测资料分析中的应用

偏最小二乘回归方法能较好地解决自变量之间的严重相关性问题，神经网络模型可以克服模型必须是基本观测数据的线性和非线性组合的局限。研究将两者有机地联系起来，建立偏最小二乘回归神经网络模型，以克服上述不足。实例分析表明，本模型预报精度较高。     

The fixed charge maximal covering location problem

The maximal covering location problem is based on locatingp facilities in such a manner that coverage is maximized within set distance or time standards. This problem has been extended in a number of different ways where the main theme still involves locating a fixed number of facilities. In many applications site costs are not equal; this should cast doubt on the use of a constraint that fixes the number of facilities at a given number as compared to an approach that minimizes site costs and maximizes coverage. This paper addresses the use of site costs in a maximal covering location problem and presents several approaches to solutions, along with computational results. Theoretical linkages to other location models are also presented.     

Applying Directed Acyclic Graphs to Assist Specification of a Hedonic Model

This research empirically tests the hypothesis that utilizing directed acyclic graphs (DAGs) as an ex-ante process to select variables for a hedonic model improves the model's performance. The results for both new and existing house submarkets indicated that DAG analysis mitigated the multicollinearity issue commonly observed in hedonic models. Using DAG analysis also improved the goodness-of-fit of the hedonic model for the new submarket. However, model specification through DAG analysis does not offer clear implications for improving forecasting accuracy, efficiency, and spatial error autocorrelation. The findings imply that DAG analysis for model specification can be a complementary step in the process of estimating hedonic models, especially when reducing standard error bias by alleviating potential multicollinearity is important in determining the attributes that affect housing prices.     

The application of the ant colony optimization algorithm to the construction site layout planning problem

A good site layout is vital to ensure the safety of the working environment, and for effective and efficient operations. Moreover, it minimizes travel distance, decreases materials handling, and avoids the obstruction of materials and plant movement. Based on studies in the manufacturing industry, the cost of materials handling could be reduced by 20–60% if an appropriate facility layout is adopted. In designing a site layout, a planner will first position the key facilities that influence the method and sequence of construction, and then assign the remaining facilities in the available space that is left over. This process is similar to the positioning of facilities in the ant colony optimization (ACO) algorithm. The general principle of the ACO algorithm is to assign facilities to a location one by one, and the occupied locations are deleted from the location scope in the next assignment. In the study, ACO algorithm is employed to resolve the construction site layout planning problem in a hypothetical medium‐sized construction project. By applying fuzzy reasoning and the entropy technique, the study calculates the closeness relationship between facilities, in which the optimal site layout is affected by the mutual interaction of facilities.     

A factor analysis of socio-economic determinants of property crimes in cities

In the past a number of studies on the economics of crime have emphasized the importance of deterrence in crime prevention while assigning lesser importance to socio-economic determinants. Others have concentrated on the role of the socio-economic variables in crime production and have utilized the multiple regression analysis which has produced ambiguous results due to the presence of strong multicollinearity among independent variables. This paper is concerned with socio-economic determinants of urban property crimes, and utilizes factor analysis to overcome the problems associated with multicollinearity. Three factors are extracted out of twelve variables with data from the 47 of the largest cities in Ohio in 1970. The three factors are used as independent variables in the linear regression analysis for different types of property crimes. The highlights of the findings are that economic forces play an important role in the determination of property crimes but in addition other sociological variables which represent attitudes, tradition, mores and values are also important. Ethnicity or variables associated with community stability seem to discourage deviant behavior and thus crime. Economists may, therefore, need to give greater attention to these variables.The author thanks David Croyle for providing the data, Usha Mohan for computations and Harvey Rosen for helping with the computer program. He is grateful to Belton Fleisher of Ohio State University, and E. Peterson of University of Utah.     

Location and quality selection for new facilities on a network market

In this paper, the problem of determining the location and quality of new facilities in a network market is analyzed. Customers make their choice according to an attraction function, which is directly proportional to the facility quality level and decreasing with respect to the distance between customers and facilities. In order to solve the location problem, both an integer linear program and an exact algorithm are proposed. These algorithms are embedded into a branch and bound-based algorithm for solving the joint location–quality problem. An illustrative example where customers present different distance perception is presented.     

Using the principal component analysis method as a tool in contractor pre‐qualification

Contractor pre‐qualification can be regarded as a complicated, two‐group, non‐linear classification problem. It involves a variety of subjective and uncertain information extracted from various parties such as contractors, pre‐qualifiers and project teams. Non‐linearity, uncertainty and subjectivity are the three predominant characteristics of the contractor pre‐qualification process. This makes the process more of an art than a scientific evaluation. In addition to non‐linearity, uncertainty and subjectivity, contractor pre‐qualification is further complicated by the large number of contractor pre‐qualification criteria (CPC) used in current practice and the multicollinearity existing between contractor attributes. An alternative empirical method using principal component analysis (PCA) is proposed for contractor pre‐qualification in this study. The proposed method may alleviate the existing amount of multicollinearity and largely reduce the dimensionality of the pre‐qualification data set. The applicability and potential of PCA for contractor pre‐qualification has been examined by way of two data sets: (1) 73 pre‐qualification cases (37 qualified and 36 disqualified) collected in England and (2) 85 (45 qualified and 40 disqualified) pre‐qualification cases relating to 10 public sector projects in Hong Kong. The PCA‐based results demonstrated that strong and positive inter‐correlations existed between most of the qualifying variables, with the minimum correlation coefficient being 0.121 and the maximum being 0.899, and that qualified and disqualified contractors could be satisfactorily separated.     

中继泵的选择与计算

针对管路较长、地势复杂的供热管网施工，从管网加设中继泵的目的、中继泵位置的确定、中继泵流量扬程的选择计算等方面进行了介绍，指出中继泵的设置减少了管网整体投资，有效地改善了供热状况。     

城郊结合区域公共服务设施配置的理论思考

城郊结合区域是城市空间扩展的主要方向,城郊结合区域公共服务设施的配置与城市运营密切联系、与居民生活息息相关,其建设应融入更多经济层面、社会层面的思考。遵循“以人为本”的配置理念,统筹的配置原则、动态的配置过程,促使城郊结合区域公共服务设施良好运营,保障服务。     

偏最小二乘回归在边坡工程安全监测中的应用

偏最小二乘回归法利用了成分提取的工作思路,在变量系统中选取若干对系统具有最佳解释能力的新综合变量,而非直接考虑因变量与自变量进行回归建模,因而能有效地解决自变量之间具有多重相关性的问题。应用该法针对边坡工程安全监测变量及其影响因子间的复杂相关性进行研究。通过对锦屏一级水电站左岸边坡深部变形的实测资料的建模分析表明,偏最小二乘回归法适用于对复杂相关的因子进行建模,通过交叉有效性检验确保模型的精度,同时对引起边坡变形的影响因子的重要程度进行排序,因而在边坡工程安全监测资料的统计分析方向具有广阔的应用前景。     

上海与香港公园体育设施配置标准 比较研究

中国内地与香港公共开放空间游憩规划标准的最主要差异在于游憩设施分类系统及其配置指标,内地《公园设计规范》关于游憩设施分类标准相对较粗,只有人均公园陆地面积、不同陆地面积的公园设施类型用地比例以及停车位3项定量指标,这些指标主要是公园设施大类上的配置要求,缺少对游憩设施类型及人均用地和类型指标等细致、具体的规定,未对不同规模公园的游憩设施类型配置差异进行规定。现有指标主要源自经验的总结,在公园游憩设施类型与居民游憩需求对应关系上缺少具体、科学的配置方法,特别是针对特殊人群的体育运动需求,而香港的公园设计标准在地域性、安全性等方面更加详尽,增加了设计风格和针对特殊人群如残障人士的设计要求。适应需求、分类设置、复合使用、已建公园和新建公园区别对待等是对上海公园体育设施配置的主要启发。     

Multicut‐High Dimensional Model Representation for Structural Reliability Bounds Estimation Under Mixed Uncertainties

Abstract: In reliability analysis of structural systems involving both aleatory and epistemic uncertainties, in conjunction with multiple design points, every configuration of the interval variables is to be explored to determine the bounds on reliability. To reduce the computational cost involved, this article presents a novel uncertain analysis method for estimating the bounds on reliability of structural systems involving multiple design points in the presence of mixed uncertain (both random and fuzzy) variables. The proposed method involves Multicut‐High Dimensional Model Representation (MHDMR) technique for the limit state/performance function approximation, the transformation technique to obtain the contribution of the fuzzy variables to the convolution integral and fast Fourier transform for solving the convolution integral. The limit state function approximation is obtained by linear and quadratic approximations of the first‐order HDMR component functions at the most probable point. In the proposed method, efforts are required in evaluating conditional responses at a selected input determined by the sample points, as compared to full‐scale simulation methods. Therefore, the proposed technique estimates the failure probability accurately with significantly less computational effort compared to the direct Monte Carlo simulation. The methodology developed is applicable for structural reliability estimation involving any number of fuzzy variables and random variables with any kind of distribution. The accuracy and efficiency of the proposed method is demonstrated through four examples involving explicit/implicit performance functions.     

通用航空应急救援点选址布局优化研究

为了使通用航空应急救援与城市综合应急救援体系的快速发展保持一致,根据城市航空应急救援的需要,提出了通用航空应急救援点的选址布局方法。首先,基于层次分析法构建通用航空应急救援点选址布局的影响因素分析模型;其次,使用ArcGIS软件对影响选址因素中可矢量化的指标进行空间分析,得到符合要求的预选址区域;然后,构建通用航空应急救援点选址布局数学模型,即加权距离总和最小化以及优先选取权重较高的救援候选点,并通过Lingo软件求解;最后,通过案例进行可行性分析,验证模型和方法的合理性。结果表明,该模型和方法具有较高的准确性和较好的适用性。该方法的提出为通用航空应急救援点的选址布局提供了理论依据,具有一定的参考意义。     

基于 AHP 与广义最短距离的 GIS 选址方法

GIS的空间分析功能使其应用于选址时有独到的优势,现普遍应用的GIS多准则决策分析选址模型多为层次分析模型,其得结果为非劣解集,且没考虑交通网络。而距离是很多设施空间选址时必须考虑的因素,本文将基于层次分析模型的GIS选址方法和传统选址方法中的步行距离最小模型结合起来,得到考虑多种因素和路网结构下的步行距离最小的选址结果,使选址更符合实际,且通过实例验证该方法的可行性。