Residual-based specification of a hidden random field included in a hierarchical spatial model

A method for specifying a hidden random field (HRF) included in a hierarchical spatial model is proposed. In hierarchical models of interest the first stage describes, conditional on a realization of the HRF, a response variable which is observable on a continuous spatial domain; the second stage models the HRF which reflects unobserved spatial heterogeneity. The question which is investigated is how can the HRF be modeled, i.e. specified. The method developed to address this question is based on residuals obtained when the base model, i.e. the hierarchical model in which the HRF is assumed constant, is fitted to data. It is shown that the residuals are linked with the HRF, and the link is used to specify the HRF. The method is applied to simulated data in order to assess its performance, and then to real data on radionuclide concentrations on Rongelap Island.     

A mathematical morphology based method for hierarchical clustering analysis of spatial points on street networks

Spatial clustering analysis is an important issue that has been widely studied to extract the meaningful subgroups of geo-referenced data. Although many approaches have been developed in the literature, efficiently modeling the network constraint that objects (e.g. urban facility) are observed on or alongside a street network remains a challenging task for spatial clustering. Based on the techniques of mathematical morphology, this paper presents a new spatial clustering approach NMMSC designed for mining the grouping patterns of network-constrained point objects. NMMSC is essentially a hierarchical clustering approach, and it generally consists of two main steps: first, the original vector data is converted to raster data by utilizing basic linear unit of network as the pixel in network space; second, based on the specified 1-dimensional raster structure, an extended mathematical morphology operator (i.e. dilation) is iteratively performed to identify spatial point agglomerations with hierarchical structure snapped on a network. Compared to existing methods of network-constrained hierarchical clustering, our method is more efficient for cluster similarity computation with linear time complexity. The effectiveness and efficiency of our approach are verified through the experiments with real and synthetic data sets.     

基于OO4O访问Oracle9iSpatial空间数据

GIS的数据组织在经历了CAD数据模型和Coverage数据模型两个阶段后,现已发展到对象关系数据模型阶段。该文阐述了采用对象关系模型的Oracle9iSpatial技术;分析了用VC++开发基于Oracle9iSpatial的GIS应用系统时,访问Spatial中空间数据的关键技术;并给出了实现代码。     

Hierarchical gradient based iterative parameter estimation algorithm for multivariable output error moving average systems

According to the hierarchical identification principle, a hierarchical gradient based iterative estimation algorithm is derived for multivariable output error moving average systems (i.e., multivariable OEMA-like models) which is different from multivariable CARMA-like models. As there exist unmeasurable noise-free outputs and unknown noise terms in the information vector/matrix of the corresponding identification model, this paper is, by means of the auxiliary model identification idea, to replace the unmeasurable variables in the information vector/matrix with the estimated residuals and the outputs of the auxiliary model. A numerical example is provided.     

Assessing the spatial sensitivity of a random forest model: Application in gridded population modeling

Gridded human population data provide a spatial denominator to identify populations at risk, quantify burdens, and inform our understanding of human-environment systems. When modeling gridded population, the information used for training the model may differ in spatial resolution than what is produced by the model prediction. This case arises when approaching population modeling from a top-down, dasymetric approach in which one redistributes coarse administrative unit level population data (i.e., source unit) to a finer scale (i.e., target unit). However, often overlooked are issues associated with the differing variance across the scale, spatial autocorrelation and bias in sampling techniques. In this study, we examine the effects of intentionally biasing our sampling from the source to target scale within the context of a weighted, dasymetric mapping approach. The weighted component is based on a Random Forest estimator, which is a non-parametric ensemble-based prediction model. We investigate issues of autocorrelation and heterogeneity in the training data using 18 different types of samples to show the variations in training, census-level (i.e., source) and output, grid-level (i.e., target) predictions. We compare results to simple random sampling and geographically stratified random sampling. Results indicate that the Random Forest model is sensitive to the spatial autocorrelation inherent in the training data, which leads to an increase in the variance of the residuals. Sample training datasets that are at a spatial scale representative of the true population produced the best fitting models. However, the true representative dataset varied in autocorrelation for both scales. More attention is needed with ensemble-based learning and spatially-heterogeneous data as underlying issues of spatial autocorrelation influence results for both the census-level and grid-level estimations.     

Leak localization in water distribution networks using a mixed model-based/data-driven approach

This paper proposes a new method for leak localization in water distribution networks (WDNs). In a first stage, residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model. In a second stage, a classifier is applied to the residuals with the aim of determining the leak location. The classifier is trained with data generated by simulation of the WDN under different leak scenarios and uncertainty conditions. The proposed method is tested both by using synthetic and experimental data with real WDNs of different sizes. The comparison with the current existing approaches shows a performance improvement.     

基于关系数据库系统的空间数据处理方法

目前,大量的数据都是通过数据库管理系统(DBMS)进行存储和管理,关系数据库是解决数据处理问题的最成熟和最有效的工具在地理信息系统(GIs)的应用中,是利用所谓的空间数据库来管理、分析和观察空间数据因为空间数据包含许多不同的数据格式和结构,这些复杂数据导致了对空间数据的操作和处理是非常复杂和困难的一项工作提出了一种利用关系数据库的成熟技术来解决空间数据处理的方法,思路是引入RSDD(regularly spatialdiscrete domains)概念,并定义基于RSDD的基本对象RPO(sDD_Based primary object)和对象RO(RSDD-Basedobject)概念,这些概念能够解决空间对象实数表示的无限精确性和计算机处理的有限精度之间的矛盾.     

Combining spatial and sociodemographic regression techniques to predict residential fire counts at the census tract level

This work examines different spatial and sociodemographic models for predicting residential fire counts at the census tract level for 118 U.S. fire departments across 25 states. The models give five-year forecasts of residential fire counts for 3392 census tracts which contain over 13 million residents in total. All models described in this paper train on fire incident data from the National Fire Incident Reporting System (NFIRS) over the interval 2006–2011 (inclusive) and are evaluated based on their ability to predict the fire counts that occurred over the interval 2012–2016. Two strictly spatial models are considered- a simple “count” model that serves as a baseline for all other models described in the paper and a model that utilizes kernel density estimation (KDE) with statistically optimized bandwidths. Using data from the American Community Survey (ACS), an examination of the effects of demographic and housing factors on the fire risk is presented. The data suggest that the fire risk per person is generally higher in census tracts with attributes corresponding to socioeconomic disadvantage such as low median incomes and small fractions of residents with college degrees. These trends inform the design of a Bayesian hierarchical Poisson regression model, which is shown to make predictions with a 9% lower root mean squared error (RMSE) relative to the base model. A spatial kernel regression is then conducted on the residuals of this regression, which results in a 15% RMSE improvement relative to the base model. These results are compared to a conditional autoregressive (CAR) model, which incorporates spatial information directly into the hierarchical Poisson regression. Although the RMSE is higher for the CAR model's point estimate forecasts (7% lower than the base model), it allows for the generation of probabilistic forecasts and gives spatially-informed statistical estimates of the effects of the sociodemographic variables. This work highlights the utility of geocoded fire incident and demographic data as well as machine learning techniques that can utilize these datasets to make improved predictions.     

用云解释空间数据挖掘的有关问题

空间数据挖掘正在兴起,该文分析了空间数据挖掘中从数据到知识、粒度和尺度等有关问题,构筑了空间数据挖掘金字塔,并利用云模型予以解释和表达。     

Mining multiple-level spatial association rules for objects with a broad boundary

Spatial data mining, i.e., mining knowledge from large amounts of spatial data, is a demanding field since huge amounts of spatial data have been collected in various applications, ranging from remote sensing to geographical information systems (GIS), computer cartography, environmental assessment and planning. The collected data far exceeds people's ability to analyze it. Thus, new and efficient methods are needed to discover knowledge from large spatial databases. Most of the spatial data mining methods do not take into account the uncertainty of spatial information. In our work we use objects with broad boundaries, the concept that absorbs all the uncertainty by which spatial data is commonly affected and allows computations in the presence of uncertainty without rough simplifications of the reality. The topological relations between objects with a broad boundary can be organized into a three-level concept hierarchy. We developed and implemented a method for an efficient determination of such topological relations. Based on the hierarchy of topological relations we present a method for mining spatial association rules for objects with uncertainty. The progressive refinement approach is used for the optimization of the mining process.     

基于几何元素的城市三维地理信息系统空间数据模型

空间数据模型是城市三维地理信息系统建立的核心问题。基于不同的城市空间对象划分方法,提出了城市三维地理信息系统的十八个空间数据模型:把空间现象抽象为六类对象:点对象、线对象、规则面对象、规则体对象、不规则对象和DTM,基于六种几何元素(点、直线段、多边形、函数构造面、TIN面片、规则体素)的不同组合,提出了城市三维地理信息系统的九个空间数据模型;把空间现象抽象为五类对象:0维空间对象(点对象)、一维空间对象(线对象)、二维空间对象(面对象)、三维空间对象(体对象)和DTM,基于六种几何元素的不同组合提出了八个空间数据模型;把空间现象抽象为0维对象(点对象)、一维对象(线对象)、二维对象(面对象)、三维对象(体对象)、DTM,基于点、直线段、TIN面片、多边形、规则面、不规则面六类几何元素提出了一个空间数据模型。通过对一个数据模型的实验验证和各模型的理论分析,表明了模型的特点及可行性。     

基于GIS的空间过程模拟建模方法研究

ＧＩＳ空间过程建模分析功能是ＧＩＳ走向广泛应用的基础,为了实现这一目标,不公要求ＧＩＳ的数据模型能够充分表达过程模型所需的基本数据集和模拟模模型的输出数据集,还要求ＧＩＳ提供更加丰富的空间分析功能,尤其是多时相空间分析功能。     

Computational Techniques for Spatial Logistic Regression with Large Datasets

In epidemiological research, outcomes are frequently non-normal, sample sizes may be large, and effect sizes are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. I focus on binary outcomes, with the risk surface a smooth function of space, but the development herein is relevant for non-normal data in general. I compare penalized likelihood (PL) models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation.A Bayesian model using a spectral basis (SB) representation of the spatial surface via the Fourier basis provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being reasonably computationally efficient. One of the contributions of this work is further development of this underused representation. The SB model outperforms the PL methods, which are prone to overfitting, but is slower to fit and not as easily implemented. A Bayesian Markov random field model performs less well statistically than the SB model, but is very computationally efficient. We illustrate the methods on a real data set of cancer cases in Taiwan.The success of the SB with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.     

Least squares analysis—propagation of random errors and Newton-Raphson iterative formula

The standard deviation and/or variances of least squares residuals in parameter evaluation in nonlinear models are often calculated approximately using Taylor's series, i.e. using the equation of propagation of random errors. A new iterative method is described to evaluate the standard deviations of least squares residuals without any approximation. It is suitable for nonlinear models with several independent variables, for implicit models and for experimental data with nonzero covariances as well.     

On sampling regional data

The region quadtree is a very popular hierarchical data structure for the representation of binary images (regional data) and it is heavily used at the physical level of many spatial databases. Random sampling algorithms obtain approximate answers of aggregate queries on these databases efficiently. In the present report, we examine how four different sampling methods are applied to specific quadtree implementations (to the most widely used linear implementations). In addition, we examine how two probabilistic models (a parametric model of random images and a model of random trees) can be used for analysing the cost of these methods.     

空间数据质量模型研究

空间数据对于地理信息系统就如同汽油对于汽车一样重要。空间数据的质量更是地理信息系统分析结果不否准确和合理的重要因素。因此根据质量评价方法的不同,提出了相应的空间数据质量模型,并进而提出空间数据质量的度量模型,这对科学、合理地进行数据质量评定及准确、统一的质量表达将起到概念级指导意义。     

Knowledge discovery from spatial transactions

We propose a general mechanism to represent the spatial transactions in a way that allows the use of the existing data mining methods. Our proposal allows the analyst to exploit the layered structure of geographical information systems in order to define the layers of interest and the relevant spatial relations among them. Given a reference object, it is possible to describe its neighborhood by considering the attribute of the object itself and the objects related by the chosen relations. The resulting spatial transactions may be either considered like “traditional” transactions, by considering only the qualitative spatial relations, or their spatial extension can be exploited during the data mining process. We explore both these cases. First we tackle the problem of classifying a spatial dataset, by taking into account the spatial component of the data to compute the statistical measure (i.e., the entropy) necessary to learn the model. Then, we consider the task of extracting spatial association rules, by focusing on the qualitative representation of the spatial relations. The feasibility of the process has been tested by implementing the proposed method on top of a GIS tool and by analyzing real world data.     

Information visualization; assisting low spatial individuals with information access tasks through the use of visual mediators

This study investigated the use of visual mediators to facilitate information access by low spatial individuals. Based on theories of adaptive learning and field-dependence, two human-computer interfaces were developed which were intended to compensate for the inability of low spatial individuals to readily construct visual mental models of a menu system's structure. The two compensatory interfaces included: a 2D visual hierarchy and a linear structure. The information search performance of high and low spatial individuals was compared on the two compensatory interfaces and a third challenge match interface, which challenged individuals to construct a mental model of a hierarchical menu system in order to perform efficiently. The visual mediators were successful in accommodating low spatial individuals, as indicated by the lack of any significant performance differences being detected between the high and low spatial groups on the two compensatory interfaces. High spatial individuals outperformed low spatial individuals only when information search tasks required the use of spatial ability in mentally constructing a model of the organization and structure of embedded task information. The key factor in the accommodation process was the elimination of the need to mentally visualize the structure of embedded task information. These results indicate that visualization techniques can be successfully used to enhance the information search performance of low spatial individuals.     

A new algorithm for recursive estimation of parameters in controlled ARMA processes

A recursive algorithm for estimating the constant but unknown parameters of a controlled ARMA process is presented. The algorithm is a recursive version of an off-line algorithm using three stages of standard least-squares. In the first stage the parameters of a controlled AR model of degree p are estimated. The residuals used in this stage are employed in the second stage to estimate the parameters of a controlled ARMA process. The first two stages constitute a recursive version of Durbin's algorithm. The model obtained in the second stage is used to filter the input, output and residuals and these filtered variables are used in the final stage to obtain improved estimates of the controlled ARMA process. It is shown that the estimate is (globally) p-consistent, i.e. that the estimate converges a.s. as the number of data tends to infinity, to a vector which, in turn, converges to the true parameter vector as the degree p of the AR model tends to infinity.