Multicollinearity and correlation among local regression coefficients in geographically weighted regression

Present methodological research on geographically weighted regression (GWR) focuses primarily on extensions of the basic GWR model, while ignoring well-established diagnostics tests commonly used in standard global regression analysis. This paper investigates multicollinearity issues surrounding the local GWR coefficients at a single location and the overall correlation between GWR coefficients associated with two different exogenous variables. Results indicate that the local regression coefficients are potentially collinear even if the underlying exogenous variables in the data generating process are uncorrelated. Based on these findings, applied GWR research should practice caution in substantively interpreting the spatial patterns of local GWR coefficients. An empirical disease-mapping example is used to motivate the GWR multicollinearity problem. Controlled experiments are performed to systematically explore coefficient dependency issues in GWR. These experiments specify global models that use eigenvectors from a spatial link matrix as exogenous variables.This study was supported by grant number 1 R1 CA95982-01, Geographic-Based Research in Cancer Control and Epidermiology, from the National Cancer Institute. The author thank the anonymous reviewers and the editor for their helpful comments.     

Space-time visualization and analysis in the Cancer Atlas Viewer

This article describes the Cancer Atlas Viewer: free, downloadable software for the exploration of United States cancer mortality data. We demonstrate the software by exploring spatio-temporal patterns in colon cancer mortality rates for African-American and white females and males in the southeastern United States over the period 1970–1995. We compare the results of two cluster statistics: the local Moran and the local G*, through time. Overall, the two statistics reach similar conclusions for most locations. Where they disagree reveals functional differences in the kinds of local spatial variation to which the statistics are sensitive and identifies some interesting patterns in the data. There are only two persistent clusters of colon cancer mortality through time, and these are clusters of low values.This project was funded by grant CA92669 from the National Cancer Institute to BioMedware, Inc. The perspectives stated in this article are those of the authors and do not necessarily represent the official views of the National Cancer Institute. Constructive criticism from Heidi Durbeck of BioMedware, Peter Rogerson of SUNY-Buffalo, and three anonymous reviewers helped us improve the interpretation and presentation of these results.     

Expert Classification Method Based on Patch-Based Neighborhood Searching Algorithm

The expert knowledge has been widely used to improve the remotely sensed classification accuracy. Generally, the ex-pert classification system mainly depends on DEM and some thematic maps. The spatial relationship information in pixel level was commonly introduced into the expert classification. Because the geographic objects were found spatially dependent relationship to a certain degree, the commonly used basic unit of spatial relationship information in pixel greatly limited the efficiency of spatial in-formation. A patch-based neighborhood searching algorithm was proposed to implement the expert classification. The homogene-ous spectral unit, patch, was used as the basic unit in the spatial object granularity, and different types of patches' relationship in-formation were obtained through a spatial neighborhood searching algorithm. And then the neighborhood information and DEM data were added into the expert classification system and used to modify the primitive classification errors. In this case, the classi-fication accuracies of wetland, grassland and cropland were obviously improved. In this work, water was used as base object, and different types of water extraction methods were tested to get a result in a high accuracy.     

Improving exposure assessment in environmental epidemiology: Application of spatio-temporal visualization tools

A thorough assessment of human exposure to environmental agents should incorporate mobility patterns and temporal changes in human behaviors and concentrations of contaminants; yet the temporal dimension is often under-emphasized in exposure assessment endeavors, due in part to insufficient tools for visualizing and examining temporal datasets. Spatio-temporal visualization tools are valuable for integrating a temporal component, thus allowing for examination of continuous exposure histories in environmental epidemiologic investigations. An application of these tools to a bladder cancer case-control study in Michigan illustrates continuous exposure life-lines and maps that display smooth, continuous changes over time. Preliminary results suggest increased risk of bladder cancer from combined exposure to arsenic in drinking water (>25 g/day) and heavy smoking (>30 cigarettes/day) in the 1970s and 1980s, and a possible cancer cluster around automotive, paint, and organic chemical industries in the early 1970s. These tools have broad application for examining spatially- and temporally-specific relationships between exposures to environmental risk factors and disease.This study was supported by grant R01 CA96002-10, Geographic-Based Research in Cancer Control and Epidemiology, from the National Cancer Institute. Development of the STIS^TM software was funded by grants R43 ES10220 from the National Institutes of Environmental Health Sciences and R01 CA92669 from the National Cancer Institute. Access to cancer case records was provided by Michigan Cancer Surveillance Program within the Division for Vital Records and Health Statistics, Michigan Department of Community Health. The authors thank Michigan Public Health Institute for conducting the telephone interview and Stacey Fedewa and Lisa Bailey for entering written surveys into a database. The authors thank 3 anonymous reviewers for their helpful comments.     

我国省级空间数据库的一种构建方案(英文)

In order to provide a provincial spatial database, this paper presents a scheme for spatial database construction to meet the needs of China. The objective and overall technical route of spatial database construction are described. The logical and physical database models are designed. Key issues are addressed, such as integration of multi-scale heterogeneous spatial databases, spatial data version management based on metadata and integrative management of map cartography and spatial database. Supported by the 863 High Technology Program of China (No. 2007AA12Z214), the National Natural Science Foundation of China (No. 40601083) and the National Key Basic Research and Development Program of China ( No. 2004CB318206).     

Design and implementation of a Space-Time Intelligence System for disease surveillance

Modeling chronic and infectious diseases entails tracking and describing individuals and their attributes (such as disease status, date of diagnosis, risk factors and so on) as they move and change through space and time. Using Geographic Information Systems, researchers can model, visualize and query spatial data, but their ability to address time has been limited by the lack of temporal referencing in the underlying data structures. In this paper, we discuss issues in designing data structures, indexing, and queries for spatio-temporal data within the context of health surveillance. We describe a space-time object model that treats modeled individuals as a chain of linked observations comprised of an ID, space-time coordinate, and time-referenced attributes. Movement models for these modeled individuals are functions that may be simple (e.g. linear, using vector representation) or more complex. We present several spatial, temporal, spatio-temporal and epidemiological queries emergent from the data model. We demonstrate this approach in a representative application, a simulation of the spread of influenza in a hospital ward.This research was supported by grant R44ES010220 from the National Institute of Environmental Health Sciences (NIEHS) and by grants R01CA092669 and R01CA96002 from the National Cancer Institute (NCI). The content of this paper does not necessarily represent the official views of the NIEHS or the NCI.     

基于簇模型的矢量地图数据的高效压缩方法(英文)

This paper proposes a new method for the compression of vector data map. Three key steps are encompassed in the proposed method, namely, the simplification of vector data map via the elimination of vertices, the compression of removed vertices based on a clustering model, and the decoding of the compressed vector data map. The proposed compression method was implemented and applied to compress vector data map to investigate its performance in terms of the compression ratio and distortions of geometric shapes. The results show that the proposed method provides a feasible and efficient solution for the compression of vector data map and is able to achieve a promising ratio of compression and maintain the main shape characteristics of the spatial objects within the compressed vector data map. Supported by the National 863 Program of China (No. 2007AAl2Z241), the Program for New Century Excellent Talents in University (No. NCET-07-0643), the National Natural Science Foundation of China (No. 40571134, No. 40871185), the National 973 Program of China (No. 108085).     

Monitoring spatial maxima

When assessing maps consisting of comparable regional values, it is of interest to know whether the peak, or maximum value, is higher than it would likely be by chance alone. Peaks on maps of crime or disease might be attributable to random fluctuation, or they might be due to an important deviation from the baseline process that produces the regional values. This paper addresses the situation where a series of such maps are observed over time, and it is of interest to detect statistically significant deviations between the observed and expected peaks as quickly as possible. The Gumbel distribution is used as a model for the statistical distribution of extreme values; this distribution does not require the underlying distributions of regional values to be either normal, known, or identical. Cumulative sum surveillance methods are used to monitor these Gumbel variates, and these methods are also extended for use when monitoring smoothed regional values (where the quantity monitored is a weighted sum of values in the immediate geographical neighborhood). The new methods are illustrated by using data on breast cancer mortality for the 217 counties of the northeastern United States, and prostate cancer mortality for the entire United States, during the period 1968-1998.The research assistance of Ikuho Yamada is gratefully acknowledged. I also am grateful for the support of Grant 1R01 ES09816-01 from the National Institutes of Health, the support of National Cancer Institute Grant R01 CA92693-0, and the helpful comments made by the referees     

Space-Time Intelligence Systems: Technology, applications and methods

Geographic Information System (GIS) software is constrained, to a greater or lesser extent, by a static world view that is not well-suited to the representation of time (Goodchild 2000). Space Time Intelligence System (STIS) software holds the promise of relaxing some of the technological constraints of spatial only GIS, making possible visualization approaches and analysis methods that are appropriate for temporally dynamic geospatial data. This special issue of the Journal of Geographical Systems describes some recent advances in STIS technology and methods, with an emphasis on applications in public health and spatial epidemiology.The STIS expert workshops were funded in part by grants R01CA092669 and R01CA096002 from the National Cancer Institute, and by grants R43-ES010220 and R44-ES010220 from the National Institute of Environmental Health Sciences. Gillian AvRuskin provided cheerful editorial assistance. We thank the participants at the workshops for providing invaluable expertise and critical insights.     

一种面向网络发布的海洋数据库引擎(英文)

To meet the requirements of efficient management and web publishing for marine remote sensing data, a spatial database engine, named MRSSDE, is designed independently. The logical model, physical model, and optimization method of MRSSDE are discussed in detail. Compared to the ArcSDE, which is the leading product of Spatial Database Engine, the MRSSDE proved to be more effective.     

Detection of temporal changes in the spatial distribution of cancer rates using local Moran’s I and geostatistically simulated spatial neutral models

This paper presents the first application of spatially correlated neutral models to the detection of changes in mortality rates across space and time using the local Morans I statistic. Sequential Gaussian simulation is used to generate realizations of the spatial distribution of mortality rates under increasingly stringent conditions: 1) reproduction of the sample histogram, 2) reproduction of the pattern of spatial autocorrelation modeled from the data, 3) incorporation of regional background obtained by geostatistical smoothing of observed mortality rates, and 4) incorporation of smooth regional background observed at a prior time interval. The simulated neutral models are then processed using two new spatio-temporal variants of the Morans I statistic, which allow one to identify significant changes in mortality rates above and beyond past spatial patterns. Last, the results are displayed using an original classification of clusters/outliers tailored to the space-time nature of the data. Using this new methodology the space-time distribution of cervix cancer mortality rates recorded over all US State Economic Areas (SEA) is explored for 9 time periods of 5 years each. Incorporation of spatial autocorrelation leads to fewer significant SEA units than obtained under the traditional assumption of spatial independence, confirming earlier claims that Type I errors may increase when tests using the assumption of independence are applied to spatially correlated data. Integration of regional background into the neutral models yields substantially different spatial clusters and outliers, highlighting local patterns which were blurred when local Morans I was applied under the null hypothesis of constant risk.This research was funded by grants R01 CA92669 and 1R43CA105819-01 from the National Cancer Institute and R43CA92807 under the Innovation in Biomedical Information Science and Technology Initiative at the National Institute of Health. The views stated in this publication are those of the authors and do not necessarily represent the official views of the NCI. The authors also thank three anonymous reviewers for their comments that helped improve the presentation of the methodology.     

从国家自然科学基金项目分析遥感领域热点

以2000~2013年国家自然科学基金资助的遥感领域1172个项目为样本,系统分析了遥感领域近10年来研究方向：遥感机理研究、遥感数据处理与分析方法、遥感定量反演模型与方法、遥感动态监测与预测以及遥感产品真实性检验等。根据研究方向的具体研究内容细化子研究方向,得到遥感领域的研究热点及其变迁情况,并进一步以关键词统计方式阐述了近年来相对集中的热点问题。     

基于数据场的空间目标行为挖掘(英文)

The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining. Supported by the National 973 Program of China(No.2006CB701305,No.2007CB310804), the National Natural Science Fundation of China (No.60743001), the Best National Thesis Fundation (No.2005047), the National New Century Excellent Talent Fundation (No.NCET-06-0618).     

地图制图学在人文地理学中的应用及不足

参与国家自然科学基金项目和国家社会科学基金项目,在绘制专题图时,得到了些心得体会,结合分析近些年人文地理学的发展,并结合现代制图学的发展,以及现代制图学在人文地理学中的应用,指出地图制图学在人文地理应用中的不足。为地图制图学和人文地理学更好的发展提供理论依据。     

An elliptical basis function network for classification of remote sensing images

An elliptical basis function (EBF) network is employed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and employing the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture-density distributions in the feature space, the network not only possesses the advantage of the RBF mechanism, but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is more effective in training and simpler in structure than an RBF network constructed for the same task.The research was supported by grant 40101021 from the Natural Science Foundation of China, and grant 2002AA135230 from Hi-Tech research and development program of China. The authors would like to thank the reviewers for their valuable comments.     

Transformation of coordinates using piecewise conformal mapping

A multiresolution-like algorithm for piecewise conformal mapping is presented. The algorithm is based on partitioning the complex plane into convex polygons, defining appropriate window functions for these polygons, and finding a conformal mapping for each polygon. The window functions are used to glue the pieces together into a single mapping. An application of this algorithm to determine a transformation from the Estonian coordinate system of L-EST92 into the respective system L-EST97, is presentedAcknowledgments.This work has been supported by Estonian Science Foundation grant nr. 5037. The author also thanks the Estonian Land Board, especially U. Mets and L. Tamme for their help in preparing the data, and the reviewers for valuable suggestions.     

New mathematical model based on affine transformation for remote sensing image with high resolution

This paper calculates the parameters of image position and orientation,proposes a mathematical model and adopts a mew method with three steps of transformations based on parallel ray projection.Every step of the model is strict,and the map function of each transformation is the first order polynomials and other simple function.The final calculation of the parameters is for the linear equation with good status.As a result,the problem of the relativity of image parameter calculation is solved completely.Some experiments are carried out.     

基于地图空间认知的室内消防救援地图设计

室内地图可为消防救援提供有效的空间信息服务。探索面向消防救援的室内地图空间认知规律,使室内地图能够更好地描述特定的室内环境,更切合消防救援人员的认知需求;借鉴传统地图的空间认知过程,探讨了面向消防救援需求的室内地图空间认知过程,提出了3种不同消防救援空间认知视角的室内空间划分方法和基于空间认知的室内消防救援地图设计原则,为面向消防救援室内地图的制作与使用提供了理论支撑。     

Interactive analysis of the spatial distribution of disease using a geographic information systems

Geographic information systems (GIS) provide a variety of tools for the manipulation and display of public health data. Few, however, enable users to interactively evaluate hypotheses on spatial trends in disease risk that may be suggested by maps of measures of disease impact. We addressed this limitation by developing a seamless interface between a commercial GIS and a suite of spatial analysis algorithms. Users of the system can utilize the GIS's capability to interactively select and manipulate geographically referenced data and, through a series of pull-down menus, apply a variety of exploratory analysis methods to this information. In the presented application, we illustrate this capability by including algorithms for the reduction of random noise in observed incidence rates, for the detection of unusual aggregations of disease events, and for the statistical evaluation of inferences drawn from spatial trends. We demonstrate this application by examining lung cancer mortality in the state of Ohio. Received: 22 September 1999 / Accepted: 8 March 2000