Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach (Paperback)

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.

《空間和空間-時間數據建模：貝葉斯方法》針對統計學家、量化社會、經濟和公共衛生學生和研究人員，他們處理小區域空間和空間-時間數據。本書假設讀者對統計理論有基礎了解，包括標準線性回歸模型。本書比較了階層和空間計量建模，提供了參考和教學文本，每章都有練習題。本書提供了完全貝葉斯的、自包含的統計理論，並有專門應用的章節。本書包含WinBUGS代碼和R代碼，所有數據集都可以在線上獲取。

第一部分涵蓋了建模空間和空間-時間數據時出現的基本問題。第二部分專注於建模橫斷面空間數據，首先描述了幫助指導建模過程的探索性方法。然後有兩個關於貝葉斯模型的理論章節和一個應用章節。接下來是兩個關於空間計量建模的章節，一個描述不同模型，另一個是實際應用。第三部分討論了建模空間-時間數據，首先介紹了時間序列數據的模型。然後介紹了檢測不同類型的時空交互作用的探索性方法，接著是兩個關於時空可分離（無時空交互作用）和不可分離（有時空交互作用）模型的理論章節。應用章節包括：政策干預評估；分析犯罪熱點的時間動態；慢性病監測；以及檢測傳染病傳播中的空間溢出證據。最後一章提出了一些未來的方向和挑戰。

羅伯特·海寧（Robert Haining）是英國劍橋大學人文地理學名譽教授。他是《社會和環境科學中的空間數據分析》（1990年）和《空間數據分析：理論與實踐》（2003年）的作者。他是皇家地理學會和社會科學院的院士。

李廣權（Guangquan Li）是英國紐卡斯爾北安布里亞大學數學、物理和電子工程系的高級講師。他的研究包括在社會和健康科學中發展和應用貝葉斯方法。他是皇家統計學會的院士。

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.

Robert Haining是英國劍橋大學人文地理學的名譽教授。他是《社會與環境科學中的空間數據分析》（1990年）和《空間數據分析：理論與實踐》（2003年）的作者。他是英國地理學會和社會科學院的院士。

Guangquan Li是英國紐卡斯爾北安布里亞大學數學、物理和電子工程系的高級講師。他的研究包括在社會和健康科學中開發和應用貝葉斯方法。他是英國皇家統計學會的院士。