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Temporal Data Mining

Temporal Regimes Materiality Politics Technology

The Routledge Handbook of Philosophy of Temporal Experience

The Routledge Handbook of Philosophy of Temporal Experience

Experience is inescapably temporal. But how do we experience time? Temporal experience is a fundamental subject in philosophy – according to Husserl the most important and difficult of all. Its puzzles and paradoxes were of critical interest from the Early Moderns through to the Post-Kantians. After a period of relative neglect temporal experience is again at the forefront of debates across a wealth of areas from philosophy of mind and psychology to metaphysics and aesthetics. The Routledge Handbook of Philosophy of Temporal Experience is an outstanding reference source to the key debates in this exciting subject area and represents the first collection of its kind. Comprising nearly 30 chapters by a team of international contributors the Handbook is organized into seven clear parts: Ancient and early modern perspectives Nineteenth and early twentieth-century perspectives The structure of temporal experience Temporal experience and the philosophy of mind Temporal experience and metaphysics Empirical perspectives Aesthetics Within each part key topics concerning temporal experience are examined including canonical figures such as Locke Kant and Husserl; extensionalism retentionalism and the specious present; interrelations between temporal experience and time agency dreaming and the self; empirical theories of perceiving and attending to time; and temporal awareness in the arts including dance music and film. The Routledge Handbook of Philosophy of Temporal Experience is essential reading for students and researchers of philosophy of mind and psychology. It is also extremely useful for those in related fields such as metaphysics phenomenology and aesthetics as well as for psychologists and cognitive neuroscientists.

GBP 44.99
1

Spatio-Temporal Methods in Environmental Epidemiology

Spatio-Temporal Methods in Environmental Epidemiology

Spatio-Temporal Methods in Environmental Epidemiology with R like its first Edition explores the interface between environmental epidemiology and spatio-temporal modelling. It links recent developments in spatio-temporal theory with epidemiological applications. Drawing on real-life problems it shows how recent advances in methodology can assess the health risks associated with environmental hazards. The book's clear guidelines enable the implementation of the methodology and estimation of risks in practice. New additions to the second edition include : a thorough exploration of the underlying concepts behind knowledge discovery through data; a new chapter on extracting information from data using R and the Tidyverse; additional material on methods for Bayesian computation including the use of NIMBLE and Stan; new methods for performing spatio-temporal analysis and an updated chapter containing further topics. Throughout the book there are new examples and the presentation of R code for examples has been extended. Along with these additions the book now has a GitHub site (https://spacetime-environ. github. io/stepi2) that contains data code and further worked examples. Features • Explores the interface between environmental epidemiology and spatio­ temporal modelling; • Incorporates examples that show how spatio-temporal methodology can inform societal concerns about the effects of environmental hazards on health; • Uses a Bayesian foundation on which to build an integrated approach to spatio-temporal modelling and environmental epidemiology; • Discusses data analysis and topics such as data visualization mapping wrangling and analysis • Shows how to design networks for monitoring hazardous environmental processes networks and the ill-effects of preferential sampling; • Through the listing and application of code shows the power of R tidyverse NIMBLE and Stan and other modern tools in performing complex data analysis and modelling. Representing a continuing important direction in environmental epidemiology this book - in full color throughout - underscores the increasing need to consider dependencies in both space and time when modelling epidemiological data. Readers will learn how to identify and model patterns in spatio-temporal data and how to exploit dependencies over space and time to reduce bias and inefficiency when estimating risks to health.

GBP 39.99
1

The Passing of Temporal Well-Being

Spatio–Temporal Methods in Environmental Epidemiology with R

Spatio–Temporal Methods in Environmental Epidemiology with R

Spatio-Temporal Methods in Environmental Epidemiology with R like its First Edition explores the interface between environmental epidemiology and spatio-temporal modeling. It links recent developments in spatio-temporal theory with epidemiological applications. Drawing on real-life problems it shows how recent advances in methodology can assess the health risks associated with environmental hazards. The book's clear guidelines enable the implementation of the methodology and estimation of risks in practice. New additions to the Second Edition include: a thorough exploration of the underlying concepts behind knowledge discovery through data; a new chapter on extracting information from data using R and the tidyverse; additional material on methods for Bayesian computation including the use of NIMBLE and Stan; new methods for performing spatio-temporal analysis and an updated chapter containing further topics. Throughout the book there are new examples and the presentation of R code for examples has been extended. Along with these additions the book now has a GitHub site (https://spacetime-environ. github. io/stepi2) that contains data code and further worked examples. Features:• Explores the interface between environmental epidemiology and spatio­-temporal modeling• Incorporates examples that show how spatio-temporal methodology can inform societal concerns about the effects of environmental hazards on health• Uses a Bayesian foundation on which to build an integrated approach to spatio-temporal modeling and environmental epidemiology• Discusses data analysis and topics such as data visualization mapping wrangling and analysis• Shows how to design networks for monitoring hazardous environmental processes and the ill effects of preferential sampling• Through the listing and application of code shows the power of R tidyverse NIMBLE and Stan and other modern tools in performing complex data analysis and modelingRepresenting a continuing important direction in environmental epidemiology this book – in full color throughout – underscores the increasing need to consider dependencies in both space and time when modeling epidemiological data. Readers will learn how to identify and model patterns in spatio-temporal data and how to exploit dependencies over space and time to reduce bias and inefficiency when estimating risks to health. | Spatio–Temporal Methods in Environmental Epidemiology with R

GBP 89.99
1

Excavating Modernity Physical Temporal and Psychological Strata in Literature 1900-1930

Temporal Urban Design Temporality Rhythm and Place

Temporal Urban Design Temporality Rhythm and Place

Temporal Urban Design: Temporality Rhythm and Place examines an alternative design approach focusing on the temporal aesthetics of urban places and the importance of the sense of time and rhythm in the urban environment. The book departs from concerns on the acceleration of cities its impact on the urban quality of life and the liveability of urban spaces and questions on what influences the sense of time and how it expresses itself in the urban environment. From here it poses the questions: what time is this place and how do we design for it? It offers a new aesthetic perspective akin to music brings forward the methodological framework of urban place-rhythmanalysis and explores principles and modes of practice towards better temporal design quality in our cities. The book demonstrates that notions of time have long been intrinsic to planning and urban design research agendas and whilst learning from philosophy urban critical theory and both the natural and social sciences debate on time it argues for a shift in perspective towards the design of everyday urban time and place timescapes. Overall the book explores the value of the everyday sense of time and rhythmicity in the urban environment and discusses how urban designers can understand analyse and ultimately play a role in the creation of temporally unique both sensorial and affective places in the city. The book will be of interest to urban planners designers landscape architects and architects as well as urban geographers and all those researching within these disciplines. It will also interest students of planning urban design architecture urban studies and of urban planning and design theory. | Temporal Urban Design Temporality Rhythm and Place

GBP 130.00
1

Modelling Spatial and Spatial-Temporal Data A Bayesian Approach

Modelling Spatial and Spatial-Temporal Data A Bayesian Approach

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social economic and public health students and researchers who work with small-area spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian self-contained treatment of the underlying statistical theory with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling one describing different models the other substantive applications. Part III discusses modelling spatial-temporal data first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges. Robert Haining is Emeritus Professor in Human Geography University of Cambridge England. He is the author of Spatial Data Analysis in the Social and Environmental Sciences (1990) and Spatial Data Analysis: Theory and Practice (2003). He is a Fellow of the RGS-IBG and of the Academy of Social Sciences. Guangquan Li is Senior Lecturer in Statistics in the Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle England. His research includes the development and application of Bayesian methods in the social and health sciences. He is a Fellow of the Royal Statistical Society. | Modelling Spatial and Spatial-Temporal Data A Bayesian Approach

GBP 44.99
1

Statistical Analysis of Spatial and Spatio-Temporal Point Patterns

Statistical Methods for Spatio-Temporal Systems

Statistical Methods for Spatio-Temporal Systems

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities. Contributed by leading researchers in the field each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis gastroenteric disease and the U. K. foot-and-mouth outbreak the first chapter uses stochastic models such as point process models to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems such as bacteria colonies tumors and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data followed by a chapter on space-time covariance functions. The contributors then describe stochastic and statistical models that are used to generate simulated rainfall sequences for hydrological use such as flood risk assessment. The final chapter explores Gaussian Markov random field specifications and Bayesian computational inference via Gibbs sampling and Markov chain Monte Carlo illustrating the methods with a variety of data examples such as temperature surfaces dioxin concentrations ozone concentrations and a well-established deterministic dynamical weather model.

GBP 59.99
1

Temporal Identities and Security Policy in Postwar Japan

Temporal Boundaries of Law and Politics Time Out of Joint