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Spatial analysis along networks : statistical and computational methods
In the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. Spatial Analysis Along Networks provides a practical guide to the necessary statistical techniques and their computational implementation.
Each chapter illustrates a specific technique, from Stochastic Point Processes on a Network and Network Voronoi Diagrams, to Network K-function and Point Density Estimation Methods, and the Network Huff Model. The authors also discuss and illustrate the undertaking of the statistical tests described in a Geographical Information System (GIS) environment as well as demonstrating the user-friendly free software package SANET.
Spatial Analysis Along Networks:
* Presents a much-needed practical guide to statistical spatial analysis of events on and alongside a network, in a logical, user-friendly order.
* Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics.
* Dedicates a separate chapter to each of the major techniques involved.
* Demonstrates the practicalities of undertaking the tests described in the book, using a GIS.
* Is supported by a supplementary website, providing readers with a link to the free software package SANET, so they can execute the statistical methods described in the book.
Students and researchers studying spatial statistics, spatial analysis, geography, GIS, OR, traffic accident analysis, criminology, retail marketing, facility management and ecology will benefit from this book.
eBook
Handbook of Spatial Statistics
Based on the work of prominent researchers, this handbook provides broad, thorough coverage of this vibrant area, from historical to contemporary topics. It explores the modeling advances, computational approaches, and methodology that have emerged in recent years. The book focuses on continuous and discrete spatial variation, spatial point patterns, and spatio-temporal processes. It also covers multivariate spatial process models, spatial aggregation, spatial misalignment, and spatial gradients in depth. The theory and applications are illustrated with many real-world data examples.
eBook
Spatial and spatio-temporal bayesian models with R-INLA
Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations
eBook
Spatio-temporal approaches
Spatio-temporal Approaches presents a well-built set of concepts, methods and approaches, in order to represent and understand the evolution of social and environmental phenomena within the space. It is basedon examples in human geography and archeology (which will enable us to explore questions regarding various temporalities) and tackles social and environmental phenomena. Chapter 1 discusses how to apprehend change: objects, attributes, relations, processes. Chapter 2 introduces multiple points of view about modeling and the authors try to shed a new light on the different, but complementary approaches of geomaticians and thematicians. Chapter 3 is devoted to the construction of spatio-temporal indicators, to various measurements of the change, while highlighting the advantage of an approach crossing several points of view, in order to understand the phenomenon at hand. Chapter 4 presents different categories of simulation model in line with complexity sciences. These models rely notably on the concepts of emergence and self-organization and allow us to highlight the roles of interaction within change. Chapter 5 provides ideas on research concerning the various construction approaches of hybrid objects and model couplings.
eBook
Applied spatial statistics for public health data
While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially correlated data.
This introductory text is designed to serve as both an introduction for the novice and a reference for practitioners in the field
Requires only minimal background in public health and only some knowledge of statistics through multiple regression
Touches upon some advanced topics, such as random effects, hierarchical models and spatial point processes, but does not require prior exposure
Includes lavish use of figures/illustrations throughout the volume as well as analyses of several data sets (in the form of \"data breaks\")
Exercises based on data analyses reinforce concepts
eBook