Seamless integration of spatial statistics and GIS: The S-PLUS for ArcView and the S+Grassland Links

The extension of the functional capacity of geographic information systems (GIS) with tools for statistical analysis in general and exploratory spatial data analysis (ESDA) in particular has been an increasingly active area of research in recent years. In this paper, two operational implementations that combine the functionality of spatial data analysis software with a GIS are considered more closely. They consist of linkages between the S-PLUS software for data analysis and two different GIS implementations, the ArcView desktop system, which is mostly vector-oriented, and the primarily raster-based Grassland open GIS environment. We emphasize conceptual and technical issues related to the software implementation of these approaches and suggest future directions for linking spatial statistics and GIS. Received: 14 January 1999 / Accepted: 11 May 1999     

Spatial analysis using clustering methods: Evaluating central point and median approaches

Spatial analysis is an important area of research which continues to make major contributions to the exploratory capabilities of geographical information systems. The use and application of classic clustering methods is being pursued as an exploratory approach for the analysis of spatially referenced data. Numerous potential clustering approaches exist, so research assessing the relative differences of these approaches is important. This paper evaluates the median and central points optimization based clustering approaches for use in the context of exploratory spatial data analysis. Functional and visual comparisons using three spatial applications across a range of cluster values are carried out. The empirical results suggest that these two clustering approaches identify very similar groupings. The significance of this finding is that the development of clustering tools for exploratory analysis may be limited to the median based approach given relative computational and solvability considerations. Received: 28 September 1998/Accepted: 9 August 1999     

Interface Design for Geographic Visualization: Tools for Representing Reliability

Digital "softcopy" maps are becoming the norm—replacing static paper maps in applications from wayfinding to scientific research. As a result, the design of interface tools that allow users to manipulate map parameters effectively and efficiently is likely to become as fundamental to cartography as the design of maps themselves. This article presents some principles for the design of interfaces to geo-referenced data. These principles are summarized in a hierarchical approach to interface design with conceptual, operational, and implementational levels. This hierarchical approach leads designers from questions about the goals of the system and the users of that system to the creation of tools to accomplish those goals and interface controls that allow effective interaction with the tools. The article goes on to describe the application of these principles to a prototype geographic visualization system designed for exploration of spatial data sets and visualization of reliability of both data and data abstractions associated with environmental change. The prototype involves a synthesis of concepts and methods derived from cartography, scientific visualization, and exploratory data analysis into a system for exploratory spatial data analysis and spatial decision support.     

Developing a Geographic Visualization Tool to Support Earth Science Learning

This paper reports on the development and assessment of the EarthSystemsVisualizer (ESV), a geovisualization tool designed to facilitate learning about global weather. Our goals in designing ESV were to evaluate two exploratory spatial data analysis (ESDA) techniques, temporal brushing and temporal focusing,and to determine whether interactive geovisualization tools influence problem solving strategies, approaches to learning, and students' ability to generate hypotheses about earth-science processes. Focus group sessions were conducted with both expert and novice users to assess an initial design for the ESV interface prior to conducting a task-based assessment of ESV use. Changes were implemented in response to the focus group results, including the redesign of a temporal legend and improved speed and direction controls. Our task-based assessment considered student reactions to components of ESV, especially whether they could use it to answer questions about global-scale weather processes, and whether the system (particularly its focusing and brushing tools) had an impact on the hypotheses generated about relationships among weather variables. The assessment revealed that focusing and brushing had little impact on students' ability to answer questions about weather processes, and that performance suffered for students who were confused by the focusing and brushing tools. In fact, students who understood the tools performed the best, but students without the tools performed better than those who had the tools but were unsure how to use them. We also concluded that the level of the visualization system must be well matched to the knowledge users have about the application domain: students who already possessed an advanced understanding of meteorology or climatology benefited less and were more critical of the system than students with an intermediate or a novice level of understanding.     

Web-enabled spatial decision analysis using Ordered Weighted Averaging (OWA)

 This paper presents a spatial decision support tool that implements the Ordered Weighted Averaging (OWA) method. OWA is a family of multicriteria evaluation operators characterised by two sets of weights: criterion importance weights and order weights. We propose a highly interactive way of choosing, modifying, and fine-tuning the decision strategy defined by the order weights. This exploratory approach to OWA is supported by a graphical representation of the operator's behaviour in terms of decision risk and tradeoff/dispersion between criteria. Our prototype implementation is based on the CommonGIS software, and thus, Web-enabled and working with vector data. We successfully demonstrate online, exploratory support of spatial decision strategies using a data set of skiing resorts in Wallis, Switzerland. Received: 24 September 2002 / Accepted: 10 January 2003     

用ESDA技术从GIS数据库中发现知识

从GIS数据库中可发现许多知识，这在GIS界已引起了相当的重视。ESDSA技术注重研究数据的空间依赖与空间异质性，在知识发现中用于选取感兴趣的数据子集，并可初步发现隐含在数据中的某些特征和规律。一些标准全程和局部空间统计包括MoranI,GearyG,G统计以及LISA等是ESDA技术的基本核心内容之一。在目前专家系统尚不成熟的条件下，充分利用GIS的可视化和空间分析技术是实现ESDA技术与GIS紧密完全结合的关系。实例说明在ESDA技术结合其它相关领域的知识从GIS数据库中发现知识的方法是可行的。     

Exploratory Analysis of Spatial Data Using Interactive Maps and Data Mining

We present new methods for analyzing geo-referenced statistical data. These methods combine visualization and direct manipulation techniques of exploratory data analysis and algorithms for data mining. The methods have been implemented by integrating two hitherto separate software tools: Descartes for interactive thematic mapping, and the data mining toolbox Kepler. In using these tools, data analysis may proceed as a steady interaction between visual inspiration and insights gained from mathematical–statistical calculations. After introducing the various components of the methods and tools, the paper guides the reader through in-depth examples of using the tools in the context of analysis of urban demographic data. In particular, it is shown how geography-based classifications of urban districts can be related to available thematic characteristics by applying the data mining algorithms classification tree derivation, attribute weighting, and subgroup discovery.     

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     

A comprehensive framework for exploratory spatial data analysis: Moran location and variance scatterplots

Abstract

A significant Geographic Information Science (GIS) issue is closely related to spatial autocorrelation, a burning question in the phase of information extraction from the statistical analysis of georeferenced data. At present, spatial autocorrelation presents two types of measures: continuous and discrete. Is it possible to use Moran's I and the Moran scatterplot with continuous data? Is it possible to use the same methodology with discrete data? A particular and cumbersome problem is the choice of the spatial-neighborhood matrix (W) for points data. This paper addresses these issues by introducing the concept of covariogram contiguity, where each weight is based on the variogram model for that particular dataset: (1) the variogram, whose range equals the distance with the highest Moran I value, defines the weights for points separated by less than the estimated range and (2) weights equal zero for points widely separated from the variogram range considered. After the W matrix is computed, the Moran location scatterplot is created in an iterative process. In accordance with various lag distances, Moran's I is presented as a good search factor for the optimal neighborhood area. Uncertainty/transition regions are also emphasized. At the same time, a new Exploratory Spatial Data Analysis (ESDA) tool is developed, the Moran variance scatterplot, since the conventional Moran scatterplot is not sensitive to neighbor variance. This computer-mapping framework allows the study of spatial patterns, outliers, changeover areas, and trends in an ESDA process. All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb© (or, in the near future, myGeooffice.org).     

地学可视化探讨

提出了关于地学可视化的研究框架。地学可视化包括地图可视化、地理信息系统（ Ｇ Ｉ Ｓ） 可视化及其在专业应用领域的可视化。地图可视化与 Ｇ Ｉ Ｓ可视化是地学可视化理论和技术的２ 个基础部分。地学多维图解是从非可视化角度提出的,关于图的地学知识表达、获取及其图的地学问题解的求证原理和方法,是地学可视化的高一级发展阶段。地图可视化中的信息表达交流模型和地理视觉认知决策模型的研究,将从理论上促进地学多维图解模型的进一步发展。基于虚拟现实技术和计算机网络技术的虚拟地理环境是地学可视化、地学多维图解理论和技术发展的最后集成系统。     

ESDA支持下的城市地价分布信息提取

在城市地价分布信息提取中，因地价信息空间位置的相关性和样本数量的有限性，传统统计学的两个基本假设（样本独立与大样本）不易实现。为此，采用探索性空间数据分析（ESDA）技术，对地价数据作空间统计分析。在ESDA分析中，数据结构必须满足正态分布，同时，要对全局趋势分析和方向分析等予以考虑，才能得到误差较小的分析结果。镇江市实例研究表明，该地区地价样点的空间数据结构呈对数分布，存在二阶趋势和北北东方向，合理步长为375 m。     

From fields to objects: A review of geographic boundary analysis

Geographic boundary analysis is a relatively new approach unfamiliar to many spatial analysts. It is best viewed as a technique for defining objects – geographic boundaries – on spatial fields, and for evaluating the statistical significance of characteristics of those boundary objects. This is accomplished using null spatial models representative of the spatial processes expected in the absence of boundary-generating phenomena. Close ties to the object-field dialectic eminently suit boundary analysis to GIS data. The majority of existing spatial methods are field-based in that they describe, estimate, or predict how attributes (variables defining the field) vary through geographic space. Such methods are appropriate for field representations but not object representations. As the object-field paradigm gains currency in geographic information science, appropriate techniques for the statistical analysis of objects are required. The methods reviewed in this paper are a promising foundation. Geographic boundary analysis is clearly a valuable addition to the spatial statistical toolbox.? This paper presents the philosophy of, and motivations for geographic boundary analysis. It defines commonly used statistics for quantifying boundaries and their characteristics, as well as simulation procedures for evaluating their significance. We review applications of these techniques, with the objective of making this promising approach accessible to the GIS-spatial analysis community. We also describe the implementation of these methods within geographic boundary analysis software: GEM. Received: 22 March 1999 / Accepted: 7 September 1999     

经济空间场与ESDA结合的公共卫生事件应急策略制定

新型冠状病毒肺炎疫情引起了人们对国家公共卫生事件应对能力的广泛关注。基于波及效应梯度场提出了生产诱发梯度,进一步完善了经济空间场理论,并将其与探索性空间数据分析(exploratory spatial data analysis,ESDA)相结合,对医药制造业波及效应及空间布局进行了研究,制定了有效的公共卫生事件应急策略。经济空间场理论分析结果揭示了产业经济空间中医药制造业与各产业部门之间的波及效应,并由此得到了医药密切相关产业。同时,经济空间场理论与ESDA方法的结合能在区分中国大陆各省空间聚集类型的基础上,评估各省公共卫生事件的应对能力,从而为各省提出相应的应急策略和医药制造业发展建议。该方法综合考虑了产业波及效应和产业空间布局现状,不仅能够促进各省医药制造业的健康合理发展,也有助于增强各省突发公共卫生事件的应对能力。     

Interactive visual cluster detection in large geospatial datasets based on dynamic density volume visualization

The emerging spatial big data (e.g. detailed spatial trajectories, geo-referenced social media data) provide tremendous opportunities for GIScientists and geographers. However, their large volume also poses challenges to existing spatial data analytical techniques (including visual analytical techniques). This article presents an interactive visual approach to detect clusters from those emerging data sets based on dynamic density volume visualization in a three-dimensional space (two spatial dimensions plus a third temporal or thematic dimension of interest). Cluster can be visually discovered through dynamic adjustment of density to colour/opacity mapping and extracted through flexible selection tools. The approach was tested on a large simulated data-set and a spatial trajectory data-set. The results show that the approach can overcome the visual clotting problem in traditional visualization tools caused by large data volume and facilitate the involvement of domain knowledge in analysis. It can effectively support visual cluster detection in the emerging large geospatial data sets.     

Interactive maps for visual exploration of grid and vector geodata

This article considers two types of tools used for the analysis of spatial data: computational tools for grid data and visualization-based interactive exploratory techniques. The main purpose is to demonstrate that computational and visual techniques are complementary, and their combined use in data analysis may produce a synergistic effect. To this end, we describe two example scenarios of data analysis in the evaluation of potential damage from earthquakes and in analysis of forest resources. The analyses were performed with the use of CommonGIS, featuring the availability of powerful geocomputational tools and interactive visualization-related facilities that can be efficiently used in combination in a desktop environment or via the World Wide Web.     

Map-Based Exploratory Evaluation of Non-Medical Determinants of Population Health

Multi‐criteria evaluation (MCE) and decision‐making are increasingly combined with interactive tools to assist users with visual thinking and exploring decision strategies. The interactive control of criterion combination rules and the simultaneous observation of geographic space and criterion space provide a means of investigating the sensitivity of the decision outcome to the decision‐maker's preferences. The Analytic Hierarchy Process (AHP) is an MCE method that has been successfully implemented in management processes including those addressed by Geographic Information Systems. In this paper, we present a map‐based, interactive AHP implementation, which provides a link between a well‐understood decision support method and exploratory geographic visualization. Using a case study with public health data for the Province of Ontario, Canada, we demonstrate that exploratory map use increases the effectiveness of the AHP‐based evaluation of population health.     

A Space-Time GIS Approach to Exploring Large Individual-based Spatiotemporal Datasets

The increasing number of large individual-based spatiotemporal datasets in various research fields has challenged the GIS community to develop analysis tools that can efficiently help researchers explore the datasets in order to uncover useful information. Rooted in Hägerstrand's time geography, this study presents a generalized space-time path (GSTP) approach to facilitating visualization and exploration of spatiotemporal changes among individuals in a large dataset. The fundamental idea of this approach is to derive a small number of representative space-time paths (i.e. GSTPs) from the raw dataset by identifying spatial cluster centers of observed individuals at different time periods and connecting them according to their temporal sequence. A space-time GIS environment is developed to implement the GSTP concept. Different methods of handling temporal data aggregation and the creation of GSTPs are discussed in this article. Using a large individual-based migration history dataset, this study successfully develops an operational space-time GIS prototype in ESRI's ArcScene and ArcMap to provide a proof-of-concept study of this approach. This space-time GIS system demonstrates that the proposed GSTP approach can provide a useful exploratory analysis and geovisualization environment to help researchers effectively search for hidden patterns and trends in such datasets.     

The visualization of socio-economic GIS data using virtual reality tools

This paper addresses two issues: the use of 'realistic' environments for the visualization of socio-economic data, and the use of virtual reality tools for the exploration of data from existing GIS databases. 'Realistic' is here used in a narrow sense to imply the reconstruction of real-world scenes in which major components of the physical environment are reconstructed to facilitate geographical visualization. A review of visualization techniques for geographically-referenced 3-D data is presented, and the translation of an existing urban GIS database into virtual reality modelling language (VRML) demonstrated for a study area in Cardiff, UK. This work shows that the export of conventional 2-D GIS data into virtual reality toolkits can be relatively straightforward, and offers exciting new avenues for visualization and exploration. We argue that some of the parameters of the true scene such as colour, building shape, and texture may be directly modified in order to represent otherwise unobservable socio-economic characteristics, in the spirit of exploratory data analysis. These issues are of particular importance in the context of current advances towards the dynamic linkage of GIS and virtual reality modelling.     

扶贫GIS中的分析方法研究

贫困地区的发展问题是全面建设小康社会进程中十分重要和紧迫的问题。地理信息系统由于具有独特的空间分析和信息综合优势,在有关扶贫问题……。本文以黑龙江省为例,演示了空间可视化方法在扶贫定性分析中的应用,介绍了传统统计分析方法在扶贫分析中应用的主要方面,并指出了传统统计分析方法明显存在的空间关联性分析不足的问题,同时针对该问题引入了探索性空间数据分析方法。