Geo-scape,a granularity depended spatialization tool for visualizing multidimensional data sets

Recently, the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data, on the basis of methods called spatialization methods. Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques. Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of granularity. The present paper makes an investigation on how the issue of granularity is handled in the context of representative examples of spatialization methods. Furthermore, this paper introduces the prototyping tool Geo-Scape, which provides an interactive spatialization environment for representing and exploring multidimensional data at different levels of granularity, by making use of a kernel density estimation technique and on the landscape “smoothness” metaphor. A demonstration scenario is presented next to show how Geo-Scape helps to discover knowledge into a large set of data, by grouping them into meaningful clusters on the basis of a similarity measure and organizing them at different levels of granularity.     

多粒度的GIS数据不确定性粗集表达

粗集是一种处理地理信息不确定性和不精确性的新型数学工具,在揭示和表达多层次(或粒度)的空间知识方面具有较大优势。为此,从空间信息观测或表达的粒度角度来探讨空间数据所蕴含的不确定性。在此基础上,运用粗集理论中的一些基本概念和方法来系统地描述和表达空间目标位置数据、属性数据以及空间关系数据的不确定性,旨在建立一种多粒度的不确定性分析和表达方法。     

Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data

High spatial resolution mapping of natural resources is much needed for monitoring and management of species, habitats and landscapes. Generally, detailed surveillance has been conducted as fieldwork, numerical analysis of satellite images or manual interpretation of aerial images, but methods of object-based image analysis (OBIA) and machine learning have recently produced promising examples of automated classifications of aerial imagery. The spatial application potential of such models is however still questionable since the transferability has rarely been evaluated.We investigated the potential of mosaic aerial orthophoto red, green and blue (RGB)/near infrared (NIR) imagery and digital elevation model (DEM) data for mapping very fine-scale vegetation structure in semi-natural terrestrial coastal areas in Denmark. The Random Forest (RF) algorithm, with a wide range of object-derived image and DEM variables, was applied for classification of vegetation structure types using two hierarchical levels of complexity. Models were constructed and validated by cross-validation using three scenarios: (1) training and validation data without spatial separation, (2) training and validation data spatially separated within sites, and (3) training and validation data spatially separated between different sites.Without spatial separation of training and validation data, high classification accuracies of coastal structures of 92.1% and 91.8% were achieved on coarse and fine thematic levels, respectively. When models were applied to spatially separated observations within sites classification accuracies dropped to 85.8% accuracy at the coarse thematic level, and 81.9% at the fine thematic level. When the models were applied to observations from other sites than those trained upon the ability to discriminate vegetation structures was low, with 69.0% and 54.2% accuracy at the coarse and fine thematic levels, respectively.Evaluating classification models with different degrees of spatial correlation between training and validation data was shown to give highly different prediction accuracies, thereby highlighting model transferability and application potential. Aerial image and DEM-based RF models had low transferability to new areas due to lack of representation of aerial image, landscape and vegetation variation in training data. They do, however, show promise at local scale for supporting conservation and management with vegetation mappings of high spatial and thematic detail based on low-cost image data.     

An assessment of coefficient accuracy in linear regression models with spatially varying coefficients

The realization in the statistical and geographical sciences that a relationship between an explanatory variable and a response variable in a linear regression model is not always constant across a study area has led to the development of regression models that allow for spatially varying coefficients. Two competing models of this type are geographically weighted regression (GWR) and Bayesian regression models with spatially varying coefficient processes (SVCP). In the application of these spatially varying coefficient models, marginal inference on the regression coefficient spatial processes is typically of primary interest. In light of this fact, there is a need to assess the validity of such marginal inferences, since these inferences may be misleading in the presence of explanatory variable collinearity. In this paper, we present the results of a simulation study designed to evaluate the sensitivity of the spatially varying coefficients in the competing models to various levels of collinearity. The simulation study results show that the Bayesian regression model produces more accurate inferences on the regression coefficients than does GWR. In addition, the Bayesian regression model is overall fairly robust in terms of marginal coefficient inference to moderate levels of collinearity, and degrades less substantially than GWR with strong collinearity.     

Combining Geo‐SOM and Hierarchical Clustering to Explore Geospatial Data

Geo‐SOM is a useful geovisualization technique for revealing patterns in spatial data, but is ineffective in supporting interactive exploration of patterns hidden in different Geo‐SOM sizes. Based on the divide and group principle in geovisualization, the article proposes a new methodology that combines Geo‐SOM and hierarchical clustering to tackle this problem. Geo‐SOM was used to “divide” the dataset into several homogeneous subsets; hierarchical clustering was then used to “group” neighboring homogeneous subsets for pattern exploration in different levels of granularity, thus permitting exploration of patterns at multiple scales. An artificial dataset was used for validating the method's effectiveness. As a case study, the rush hour motorcycle flow data in Taipei City, Taiwan were analyzed. Compared with the best result generated solely by Geo‐SOM, the proposed method performed better in capturing the homogeneous zones in the artificial dataset. For the case study, the proposed method discovered six clusters with unique data and spatial patterns at different levels of granularity, while the original Geo‐SOM only identified two. Among the four hierarchical clustering methods, Ward's clustering performed the best in pattern discovery. The results demonstrated the effectiveness of the approach in visually and interactively exploring data and spatial patterns in geospatial data.     

基于空间粒度的矢量空间数据比例尺估算方法

志愿者地理信息近年已得到广泛应用,鉴于有些来源数据可能缺少比例尺说明,因此对矢量空间数据比例尺的估算十分必要。本文提出了一种通过计算和统计矢量空间数据的空间粒度,估算矢量空间数据比例尺的方法。首先,以道路要素为例选取多种空间粒度,包括单类别道路空间数据的最短直线段长度和弯曲的最小面积等,并通过线性插值方法拟合空间数据比例尺估算函数;其次,以北京市1∶25万、1∶100万、1∶400万比例尺道路矢量空间数据作为试验样本数据,详细阐述了基于空间粒度估算比例尺的过程;然后,统计了不同类型空间粒度的数量分布,基于开方根规律量化了比例尺与空间粒度单元数量之间的对应关系;最后,试验验证了该方法的可行性和有效性,有利于多尺度多源数据的融合和应用。     

Study on gis data directly integrated into image processing

The basic properties to be dealt with ,when considering initially the research needs related to the integration of remote sensing (RS) information into a geographic information system (GIS),are many-sided.The primary combination of remote sensing and GIS is mainly realized by the transforms of data structure .Because of its own limitations,there is an urgent need to investigate the integration of RS and GIS in higher levels.In this paper,we discuss the different types of combinations of RS with GIS,and propose that GIS data should be directly brought into image processing from the beginning.A tentative idea of how to use the method of granularity to study the common processing unit of RS and GIS is described.The example for the determination of granularity of spatial data processing related to run-length-code line is also given.     

Study on gis data directly integrated into image processing

The basic properties to be dealt with, when considering initially the research needs related to the integration of remote sensing (RS) information into a geographic information system (GIS), are many-sided. The primary combination of remote sensing and GIS is mainly realized by the transforms of data structure. Because of its own limitations, there is an urgent need to investigate the integration of RS and GIS in higher levels. In this paper, we discuss the different types of combinations of RS with GIS, and propose that GIS data should be directly brought into image processing from the beginning. A tentative idea of how to use the method of granularity to study the common processing unit of RS and GIS is described. The example for the determination of granularity of spatial data processing related to run-length-code line is also given.     

Spatially constrained regionalization with multilayer perceptron

In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non‐stationarity and autocorrelation in spatial data. Supervised MLP machine learning algorithms with spatial constraints have been implemented and tested on a point dataset. MLP spatially weighted classification models and an MLP contiguity‐constrained classification model are developed to conduct spatially constrained regionalization. The proposed methods have been tested with an attribute‐rich point dataset of geological surveys in Ukraine. The experiments show that consideration of the spatial effects, such as the use of spatial attributes and their respective whitening, improve the output of regionalization. It is also shown that spatial sorting used to preserve spatial contiguity leads to improved regionalization performance.     

Fields as a framework for integrating GIS and environmental process models. Part 1: Representing spatial continuity

Linking a GIS to a spatially distributed, physically-based environmental model offers many advantages. However, the implementation of such linkages is generally problematic. Many problems arise because the relationship between the reality being represented by the mathematical model and the data model used to organize the spatial data in the GIS has not been rigorously defined. In particular, while many environmental models are based on theories that assume continuity and incorporate physical fields as independent variables, current GISs can only represent continuous phenomena in a variety of discrete data models. This paper outlines a strategy in which field variables are used to enable modellers to work directly with the spatial data as spatially continuous phenomena. This allows the manner in which the spatial data has been discretized and the ways in which it can be manipulated to be treated independently from the conceptual modelling of physical processes. Modellers can express their spatial data needs as representations of reality, rather than as elements of a GIS database, and a GIS-independent language for model development may result. By providing a formal linkage between the various models of spatial phenomena, a mechanism is created for the explicit expression of transformation rules between the different spatial data models stored and manipulated by a GIS.     

Using GIS in Areawide Pest Management: A Case Study in South Dakota

Infestations of corn rootworms (Coleoptera: Chrysomelidae) create economic and environmental concerns in the Corn Belt region of the United States. To supplement the population control tactics of areawide pest management programs, we believe that a better understanding of the spatial relationships between biotic and abiotic or physical factors at the landscape scale is needed. Our research used several geographical information systems (GIS) and spatial analytical techniques to examine relationships between corn rootworm metapopulation dynamics, soil texture, and elevation. Within GIS, several spatially explicit procedures were used that include an interpolation technique, spatial autocorrelation analysis, and contingency analysis. Corn rootworm metapopulation distributions were found to be aggregated and related to soil texture and elevation. We review techniques and discuss our preferences for using particular spatially explicit procedures. The information derived from the spatial analyses demonstrates how GIS can be used in areawide pest management to provide inputs for spatially explicit models to predict future pest populations and formulate more well‐informed pest management decisions. The techniques described in this paper could easily be extended to study the spatial dynamics between other pest populations in agricultural landscapes.     

Spatial Approaches to Modeling Dispersion of Communicable Diseases – A Review

The dispersion of communicable diseases in a population is intrinsically spatial. In the last several decades, a range of spatial approaches has been devised to model epidemiological processes; and they differ significantly from each other. A review of spatially oriented epidemiological models is necessary to assess advances in spatial approaches to modeling disease dispersion and to help identify those most appropriate for specific research goals. The most notable difference in the design of these spatially oriented models is the scale and mobility of the modeling unit. Using two criteria, this review identifies six types of spatially oriented models. These include: (1) population‐based wave models, (2) sub‐population models, (3) individual‐based cellular automata models, (4) mobile sub‐population models, (5) individual‐based spatially implicit models, and (6) individual‐based mobile models. Each model type is evaluated in terms of its design principles, assumptions, and intended applications. For the evaluation of design, four aspects of design principles are discussed: the modeling unit, the interaction between the modeling units, the spatial process, and the temporal process utilized in a design. Insights gained from this review can be useful for devising much‐needed spatially and temporally oriented strategies to forecast, prevent, and control communicable diseases.     

将GIS数据直接纳入图像处理

探讨了RS与GIS在空间数据处理过程中各种可能的结合方式,明确了目前RS与GIS集成中的一个重要任务就是将GIS数据直接纳入图像处理;同时提出,解决这个问题应先从空间数据处理的粒度入手,即寻找RS与GIS共同的处理单元。给出了将GIS数据直接纳入图像处理的实例,说明在行程线这个粒度上已能将RS与GIS很好地结合在一起了。     

Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.      

A Multiscale Approach for Spatio-Temporal Outlier Detection

A spatial outlier is a spatially referenced object whose thematic attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents an object that is significantly different from its neighbourhoods even though it may not be significantly different from the entire population. Here we extend this concept to the spatio‐temporal domain and define a spatial‐temporal outlier (ST‐outlier) to be a spatial‐temporal object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial or/and temporal neighbourhoods. Identification of ST‐outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability or deformation. Many methods have been recently proposed to detect spatial outliers, but how to detect the temporal outliers or spatial‐temporal outliers has been seldom discussed. In this paper we propose a multiscale approach to detect ST‐outliers by evaluating the change between consecutive spatial and temporal scales. A four‐step procedure consisting of classification, aggregation, comparison and verification is put forward to address the semantic and dynamic properties of geographic phenomena for ST‐outlier detection. The effectiveness of the approach is illustrated by a practical coastal geomorphic study.     

Exploring nationally and regionally defined models for large area population mapping

Interactions between humans, diseases, and the environment take place across a range of temporal and spatial scales, making accurate, contemporary data on human population distributions critical for a variety of disciplines. Methods for disaggregating census data to finer-scale, gridded population density estimates continue to be refined as computational power increases and more detailed census, input, and validation datasets become available. However, the availability of spatially detailed census data still varies widely by country. In this study, we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models. We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts. Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia. Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country. This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.     

A Spatial Analysis System for Integrating Data,Methods and Models on Environmental Risks and Health Outcomes

Integrating data on health outcomes with methods of disease mapping and spatially explicit models of environmental contaminants are important aspects of environmental health surveillance. In this article, we describe a modular, web‐based spatial analysis system that uses GIS, spatial analysis methods and software services delivered over computer networks to achieve this end. The Environmental Health Surveillance System (EHSS) is a prototype system that is designed to serve three purposes: a secure environment for producing maps of disease outcomes from individual‐level data while preserving privacy; an automated process of linking environmental data, environmental models, and GIS tasks like geocoding for the purposes of estimating individual exposures to environmental contaminants; and mechanisms to visualize the spatial patterns of disease outcomes via Web‐based mapping interfaces and interactive tools like Google Earth.     

An integrated environment for developing knowledge-based spatial decision support systems

Knowledge-based spatial decision support systems (KBSDSS) incorporate heuristic reasoning, and analytical and spatial modelling capabilities to support decision making in resource and environmental management. They are able to provide the assistance for both the structuring and formulation of the spatial problem, and the design and execution of a solution process by automatically integrating different types of models and data. This paper presents a KBSDSS development environment, which connects a GIS tool (ARC/INFO) with an expert system tool (CLIPS) and a hypertext diagramming tool (HARDY). ARC/INFO provides capabilities for spatial data handling. CLIPS is used to develop knowledge bases and heuristic reasoning. HARDY is used to represent spatial problems in terms of diagrams that can then be translated into a CLIPS knowledge base for evaluation. The development environment allows users to structure their problems and develop models for particular requirements and domains.     

A GIS-Based Tool for Spatial and Distributed Hydrological Modelling: CLASS Spatial Analyst

CLASS Spatial Analyst is a GIS tool which can be used to support spatially distributed hydrological modelling. The tool generates a number of spatial layers that can be used for many practical applications. These include climate zoning, multi‐resolution DEMs, Compound Topographic Index (CTI) (also referred to as Topographic Wetness Index, TWI), lateral multiple flow paths, accumulation and dispersion of water and solutes from hazard areas, estimation of soil depth, soil material/horizon distribution and soil moisture storage capacity in different parts of the landscape. Although each of these tasks can be undertaken separately using spatial software packages such as ArcGIS, GRASS, TIME, and MapInfo, this tool puts together all these tasks into a single application which provides the user with an option of undertaking some or all of them within this application. The technology used in this tool is supported by various peer‐reviewed publications (e.g. Tarboton 1997 , Gallant and Dowling 2003 ).