Digital Elevation Model Construction Using Geostatistics and Geological Expert Knowledge -- A Case Study in Oitti Area in Southern Finland

Digital Elevation Models (DEMs) are an important topic in geographical information science as DEMs are commonly used in GIS analysis applications related to physical environment. There are various techniques used for modelling the elevation surface and in this study the focus is on interpolation methods based on geostatistical techniques. This study is further research extended from previous work in which the kriging method was applied in elevation modelling. In the previous research, it was shown that kriging is a suitable tool for constructing an elevation model in a study area which presented glacial and postglacial clays. Therefore, it was rather simple to build a DEM in that area by using the ordinary kriging method for interpolation. However, in many locations in Finland, it is not simple to build a DEM. The complex structure of the land formation may result in a complicated structure. Quaternary deposits consist of elongated moraine ridges that affect the geomorphology and, so, the elevation model. In Finland, the elevation model of moraine ridge areas is important because sources of fresh water are situated in these kinds of land formations. Mapping of the groundwater areas is necessary because of the EU directives. The aim of this research is to present a comparison of two kriging approaches in building elevation models. In the first one, elevation model is built by using the same variogram model in the whole study area. The second kriging approach uses geological expert knowledge in order to divide the study area into three sub-areas, a clay-dominated area in the west and east and a moraine ridge in the centre. It was shown that expert knowledge of Quaternary deposits can be applied in digital elevation modelling in order to produce a higher-quality result

Use of a Fuzzy Decision-making Approach in an Analysis of the Vulnerability of Street Networks for Disaster Management

Disaster management with respect to urban structures has received more attention in recent years. In disaster management, the most vulnerable structures in a modern society are the critical networks, such as transportation networks. The vulnerability analysis of spatial networks should not depend only on the topological structure; some non-topological attributes, such as population information, should also be considered. In a rescue operation, decision-making problems are very often uncertain or vague because of the lack of information. Therefore, the classi cation of a high or low-risk area on the basis of spatial information should not have crisp boundaries and it would be more reasonable to use a fuzzy approach. In this paper, population information and a betweenness centrality measure of the road network were used as the evaluation criteria, and a fuzzy multiple-attribute decision-making (MADM) approach was used to support a vulnerability analysis of the road network of Finland for disaster management. In order to validate the model, results were compared with original population information and a betweenness attribute map. The validation results showed the hotspots in a fuzzy MADM vulnerability map have a similar pattern to an original input attributes map and the number of hotspots were reduced to a reasonable scale in order to improve rescue e ciency

Exploratory vs. Model-based Mobility Analysis

In this paper we describe and analyze a visual analytic process based on interactive visualization methods, clustering, and various forms of user knowledge. We compare this analysis approach to an existing map overlay type model, which has been developed through a traditional modeling approach. In the traditional model the layers represent input data sets and each layer is weighted according to their importance for the result. The aim in map overlay is to identify the best fit areas for the purpose in question. The more generic view is that map overlay reveals the similarity of the areas. Thus an interactive process, which uses clustering, seems to be an alternative method that could be used when the analysis needs to be made rapidly and utilizing whatever data is available. Our method uses visual analytic approach and data mining, and utilizes the user knowledge whenever a decision must be made. The tests carried out show that our method gives acceptable results for the cross-country mobility problem, and fulfills the given requirements about the computational efficiency. The method fits especially to the situations in which available data is incomplete and of low quality and must be completed by the user knowledge. The transparency of the process makes the method suitable also in situations when results based on various user opinions and values must be made. The case in our research is from the crisis management application area in which the above mentioned conditions often take place

Knowledge and Reasoning in Spatial Analysis

Reasoning is an essential part of any analysis process. Especially in visual analytics, the quality of the results depends heavily on the knowledge and reasoning skills of the analyst. In this study, we consider how to make the results transparent by visualizing the reasoning and the knowledge, so that persons from outside can trace and verify them. The focus of this study is in spatial analysis and a case study was carried out on a process of off-road mobility analysis. In the case study, linked views of a map and a PCP were identified as reasoning artifacts. The knowledge used by the analyst was formed by these artifacts and the tangible pieces of information identified in them, along with the mental models of the analyst′s mind. To make the results transparent, the tangible pieces of information were marked with sketches and the mental models were presented in causal graphs because it was found that causality was central to the reasoning process in the case study. The causal graph allows the reasoning of the analyst to be studied, as well as traced back to its origin.Peer reviewe

Insight provenance for spatiotemporal visual analytics: Theory, review, and guidelines

Research on provenance, which focuses on different ways to describe and record the history of changes and advances made throughout an analysis process, is an integral part of visual analytics. This paper focuses on providing the provenance of insight and rationale through visualizations while emphasizing, first, that this entails a profound understanding of human cognition and reasoning and that, second, the special nature of spatiotemporal data needs to be acknowledged in this process. A recently proposed human reasoning framework for spatiotemporal analysis, and four guidelines for the creation of visualizations that provide the provenance of insight and rationale published in relation to that framework, work as a starting point for this paper. While these guidelines are quite abstract, this paper set out to create a set of more concrete guidelines. On the basis of a review of available provenance solutions, this paper identifies a set of key features that are of relevance when providing the provenance of insight and rationale and, on the basis of these features, produces a new set of complementary guidelines that are more practically oriented than the original ones. Together, these two sets of guidelines provide both a theoretical and practical approach to the problem of providing the provenance of insight and rationale. Providing these kinds of guidelines represents a new approach in provenance research

Visualization of Spatial Data Structures on Different Levels of Abstraction

AbstractSpatial data structures are used to manipulate location data. The visualization of such structures faces many challenges that are not relevant in the visualization of one-dimensional data. The visualized data can be represented using several different types of visual metaphors. These metaphors can be divided into several different levels of abstraction depending on the purpose of the visualization. This paper proposes a division of data structure visualization into four levels of abstraction, and shows how these abstractions can be taken into account in the visualization of spatial data structures

A spatial fuzzy influence diagram for modelling spatial objects dependencies : a case study on tree-related electricity outages

We would like to acknowledge the National Emergency Supply Agency of Finland for funding this research. This material is also based in part upon work supported by the U.S. National Science Foundation under grant numbers [1047916 and 1443080]. The National Emergency Supply Agency of Finland funded the Alvar project. This article is the research outcome of the Alvar project.Spatial objects can be interconnected and mutually dependent in complex ways. In Geographical Information Science, spatial objects’ topological relationships are not discussed together with their attributes’ dependencies, and the vagueness of spatial objects is often ignored during the spatial modelling process. To address this, a spatial fuzzy influence diagram (SFID) is introduced. Compared to the traditional statistical or fuzzy modelling approach, the influence diagram brings advantages in helping decision-makers structure complex interdependency problems. A questionnaire was developed to evaluate the applicability of using an influence diagram in modelling spatial objects’ dependencies. As a case study, an SFID is applied to tree-related electric outages. The result of the case study is represented as a vulnerability map of electrical networks. The map shows areas at risk due to tree-related electric outages. The results were first validated by using a visual comparison of the vulnerability map and electricity fault data. In the second validation step, the percentage of fault data, which has received values in different vulnerability categories, was calculated. The results of the case study can be used to support the decision-making process of electrical network maintenance and planning.PostprintPeer reviewe

Geoinformatiikkaa kaikille : kirja-arvostelu

Geographic information systems and science, Paul A. Longley, Michael F. Goodchild, David J. Maguire & David W. Rhind, Chichester (2001