Time has become an increasingly important topic in urban studies and urban planning. The spatial-temporal interplay is not only of relevance for the theory of urban development and urban politics, but also for urban planning and governance. The space-time approach focuses on the human being with its various habits and routines in the city. Understanding and taking those habits into account in urban planning and public policies offers a new way to improve the quality of life in our cities. Adapting the supply and accessibility of public spaces and services to the inhabitants’ space-time needs calls for an integrated approach to the physical design of urban space and to the organization of cities. In the last two decades the body of practical and theoretical work on urban space-time topics has grown substantially. The book offers a state of the art overview of the theoretical reasoning, the development of new analytical tools, and practical experience of the space-time design of public cities in major European countries. The contributions were written by academics and practitioners from various fields exploring space-time research and planning.

A state-of-the-art presentation of optimum spatio-temporal sampling design - bridging classic ideas with modern statistical modeling concepts and the latest computational methods. Spatio-temporal Design presents a comprehensive state-of-the-art presentation combining both classical and modern treatments of network design and planning for spatial and spatio-temporal data acquisition. A common problem set is interwoven throughout the chapters, providing various perspectives to illustrate a complete insight to the problem at hand. Motivated by the high demand for statistical analysis of data that takes spatial and spatio-temporal information into account, this book incorporates ideas from the areas of time series, spatial statistics and stochastic processes, and combines them to discuss optimum spatio-temporal sampling design. Spatio-temporal Design: Advances in Efficient Data Acquisition: Provides an up-to-date account of how to collect space-time data for monitoring, with a focus on statistical aspects and the latest computational methods Discusses basic methods and distinguishes between design and model-based approaches to collecting space-time data. Features model-based frequentist design for univariate and multivariate geostatistics, and second-phase spatial sampling. Integrates common data examples and case studies throughout the book in order to demonstrate the different approaches and their integration. Includes real data sets, data generating mechanisms and simulation scenarios. Accompanied by a supporting website featuring R code. Spatio-temporal Design presents an excellent book for graduate level students as well as a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.

From environmental management to land planning and geo-marketing, the number of application domains that may greatly benefit from using data enriched with spatio-temporal features is expanding very rapidly. This book shows that a conceptual design approach for spatio-temporal databases is both feasible and easy to apprehend. While providing a firm basis through extensive discussion of traditional data modeling concepts, the major focus of the book is on modeling spatial and temporal information.

Spatio-temporal networks (STN)are spatial networks whose topology and/or attributes change with time. These are encountered in many critical areas of everyday life such as transportation networks, electric power distribution grids, and social networks of mobile users. STN modeling and computations raise significant challenges. The model must meet the conflicting requirements of simplicity and adequate support for efficient algorithms. Another challenge is to address the change in the semantics of common graph operations, such as, shortest path computation assuming different semantics, or when temporal dimension is added. Also paradigms (e.g. dynamic programming) used in algorithm design may be ineffective since their assumptions (e.g. stationary ranking of candidates) may be violated by the dynamic nature of STNs. In recent years, STNs have attracted attention in research. New representations have been proposed along with algorithms to perform key STN operations, while accounting for their time dependence. Designing a STN database would require the development of data models, query languages, and indexing methods to efficiently represent, query, store, and manage time-variant properties of the network. The purpose of Spatio-temporal Networks: Modeling and Algorithms is to explore this design at the conceptual, logical, and physical level. Models used to represent STNs are explored and analyzed. STN operations, with an emphasis on their altered semantics with the addition of temporal dimension, are also addressed.

Searching for Semantics: Data Mining, Reverse Engineering Stefano Spaccapietra Fred M aryanski Swiss Federal Institute of Technology University of Connecticut Lausanne, Switzerland Storrs, CT, USA REVIEW AND FUTURE DIRECTIONS In the last few years, database semantics research has turned sharply from a highly theoretical domain to one with more focus on practical aspects. The DS- 7 Working Conference held in October 1997 in Leysin, Switzerland, demon strated the more pragmatic orientation of the current generation of leading researchers. The papers presented at the meeting emphasized the two major areas: the discovery of semantics and semantic data modeling. The work in the latter category indicates that although object-oriented database management systems have emerged as commercially viable prod ucts, many fundamental modeling issues require further investigation. Today's object-oriented systems provide the capability to describe complex objects and include techniques for mapping from a relational database to objects. However, we must further explore the expression of information regarding the dimensions of time and space. Semantic models possess the richness to describe systems containing spatial and temporal data. The challenge of in corporating these features in a manner that promotes efficient manipulation by the subject specialist still requires extensive development.

Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological StudiesSpatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and

Winner of the 2013 DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winners of the 2011 PROSE Award in the Mathematics category, for the book “Statistics for Spatio-Temporal Data” (2011), published by John Wiley and Sons. (The PROSE awards, for Professional and Scholarly Excellence, are given by the Association of American Publishers, the national trade association of the US book publishing industry.) Statistics for Spatio-Temporal Data has now been reprinted with small corrections to the text and the bibliography. The overall content and pagination of the new printing remains the same; the difference comes in the form of corrections to typographical errors, editing of incomplete and missing references, and some updated spatio-temporal interpretations. From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive-species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information into account. Statistics for Spatio-Temporal Data presents a systematic approach to key quantitative techniques that incorporate the latest advances in statistical computing as well as hierarchical, particularly Bayesian, statistical modeling, with an emphasis on dynamical spatio-temporal models. Cressie and Wikle supply a unique presentation that incorporates ideas from the areas of time series and spatial statistics as well as stochastic processes. Beginning with separate treatments of temporal data and spatial data, the book combines these concepts to discuss spatio-temporal statistical methods for understanding complex processes. Topics of coverage include: Exploratory methods for spatio-temporal data, including visualization, spectral analysis, empirical orthogonal function analysis, and LISAs Spatio-temporal covariance functions, spatio-temporal kriging, and time series of spatial processes Development of hierarchical dynamical spatio-temporal models (DSTMs), with discussion of linear and nonlinear DSTMs and computational algorithms for their implementation Quantifying and exploring spatio-temporal variability in scientific applications, including case studies based on real-world environmental data Throughout the book, interesting applications demonstrate the relevance of the presented concepts. Vivid, full-color graphics emphasize the visual nature of the topic, and a related FTP site contains supplementary material. Statistics for Spatio-Temporal Data is an excellent book for a graduate-level course on spatio-temporal statistics. It is also a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.