When compared to classical sciences such as math, with roots in prehistory, and physics, with roots in antiquity, geographical information science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling for spatial data and spatial analysis is an important branch of t

Developments in Geographic Information Technology have raised the expectations of users. A static map is no longer enough; there is now demand for a dynamic representation. Time is of great importance when operating on real world geographical phenomena, especially when these are dynamic. Researchers in the field of Temporal Geographical Infor

Providing an authoritative assessment of the current landscape of spatial analysis in the social sciences, this cutting-edge Handbook covers the full range of standard and emerging methods across the social science domain areas in which these methods are typically applied. Accessible and comprehensive, it expertly answers the key questions regarding the dynamic intersection of spatial analysis and the social sciences.

Offers New Insight on Uncertainty ModellingFocused on major research relative to spatial information, Uncertainty Modelling and Quality Control for Spatial Data introduces methods for managing uncertainties-such as data of questionable quality-in geographic information science (GIS) applications. By using original research, current advancement, and

This Advanced Introduction provides a critical review and discussion of research concerning spatial statistics, differentiating between it and spatial econometrics, to answer a set of core questions covering the geographic-tagging-of-data origins of the concept and its theoretical underpinnings, conceptual advances, and challenges for future scholarly work. It offers a vital tool for understanding spatial statistics and surveys how concerns about violating the independent observations assumption of statistical analysis developed into this discipline.

Geomatics is a field of science that has been intimately intertwined with our daily lives for almost 30 years, to the point where we often forget all the challenges it entails. Who does not have a navigation application on their phone or regularly engage with geolocated data? What is more, in the coming decades, the accumulation of geo-referenced data is expected to increase significantly. This book focuses on the notion of the imperfection of geographic data, an important topic in geomatics. It is essential to be able to define and represent the imperfections that are encountered in geographical data. Ignoring these imperfections can lead to many risks, for example in the use of maps which may be rendered inaccurate. It is, therefore, essential to know how to model and treat the different categories of imperfection. A better awareness of these imperfections will improve the analysis and the use of this type of data.

Uncertainty can take many forms, can be represented in many ways, and can have important implications in decision-making and policy development. This book provides a rigorous scientific framework for dealing with uncertainty in real-world situations, and provides a comprehensive study of concepts, measurements, and applications of uncertainty in ecological modeling and natural resource management. The focus of this book is on the kinds and implications of uncertainty in environmental modeling and management, with practical guidelines and examples for successful modeling and risk analysis in the face of uncertain conditions and incomplete information. Provided is a clear classification of uncertainty; methods for measuring, modeling, and communicating uncertainty; practical guidelines for capturing and representing expert knowledge and judgment; explanations of the role of uncertainty in decision-making; a guideline to avoiding logical fallacies when dealing with uncertainty; and several example cases of real-world ecological modeling and risk analysis to illustrate the concepts and approaches. Case topics provide examples of structured decision-making, statistical modeling, and related topics. A summary provides practical next steps that the reader can take in analyzing and interpreting uncertainty in real-world situations. Also provided is a glossary and a suite of references.