Spatial Data Analysis

～專業推薦～Emeritus Editor of Information and Management (I&M) Prof. Patrick Y.K. ChauSingapore Institute of Management Prof. Kwok Kee WeiHamburg University of Technology Prof. Christian M. Ringle (SmartPLS開發者之一)Ludwig-Maximilians University, Germany Prof. Marko Sarstedt蕭教授在量化研究方法領域深耕多年，著作等身，對於結構方程模型（SEM）的研究與應用更是享譽學界。在本書中，他不僅系統性地介紹了 SPSS 的分析方法，亦詳盡闡述了最新的結構方程模型工具 SmartPLS 4，提供讀者深入理解並掌握現代統計分析技術的重要指南。對於有志於應用 SPSS 及 SEM 進行教學和學術研究的讀者而言，是一本不可多得的寶貴書籍。　------長庚大學 湯明哲校長------■ 國內第一本提供正確的多變量分析(含SPSS實務操作)的研究論文參考範例。■ 國內第一本全面介紹SmartPLS 4操作、CB-SEM & PLS-SEM結構方程模式的實用書。■ 以統計分析(多變量分析)為主軸，整合了理論的介紹、量化的研究、量表的發展、卡方檢定、因素分析、迴歸分析、區別分析和邏輯迴歸、單因子變異數分析、多變量變異數分析、典型相關分析、信度和效度分析、聯合分析多、元尺度和集群分析，第二代統計技術–結構方程模式(SEM) ，正確的多變量分析研究論文參考範例。■ 內容涵蓋SmartPLS 4基本操作、CB-SEM & PLS-SEM結構方程模式的學習範例、反映性和形成性指標與模式的指定、二階和高階因果關係、SEM結構方程模式實例、中介分析、調節分析、測量恆等性(Measurement Invariance)、MGA呈現的範例、內生性 (Endogeneity) ，混合方法Mixed method和論文結構與研究範例。■ 本書可作為統計分析和多變量分析的教科書，也是Hair, Black, Babin, and Anderson 所撰寫的 Multivariate data analysis Multivariate

[學會關鍵 SQL 技能，開啟你的資料科學職業生涯]現今，許多企業與組織都要求資料科學家、資料分析師，必須具備從資料庫提取與結合原始資料、設計並生成所需資料集的能力，不需要靠資料工程師或資料庫管理者處理。[SQL 是從事資料科學必學的 3 大技能之一]本書是由與資料為伍 18 年經驗的資料科學家、資料庫開發者親自撰寫，教導有志於從事資料科學者一定要學會的 SQL 查詢技能，以及發現可能問題的解決方法。你將學到如何設計查詢程式，建構用於探索、分析的資料集，並於過程中培養資料分析思維。此書會引導你建立用於商業智慧軟體，以及機器學習演算法等應用的資料集。教導做為資料科學家最需要的 SQL 查詢技能，並透過專家經驗學習如何從資料中獲得有價值的訊息或知識，並避免處理資料時會遇到的陷阱，幫助企業主管快速掌握情況做出正確決策。[搭配 ChatGPT 輔助學習 SQL] 本書以業界主流也最普及的 MySQL 8.0 資料庫系統與 MySQL Workbench 工具做示範。在各章進行過程中依情境穿插 “ChatGPT 來幫忙” 說明框 (總共有 40 多個) 在你可能需要的地方補充相關背景知識或額外的學習技巧，例如請 ChatGPT 依指示產生 SQL 程式、挑出程式錯誤並修改、調整程式寫法增加執行效率、用 Show Me Diagram plugin 為 SQL 查詢繪製執行流程圖、用 Code Interpreter 執行 SQL 程式查詢 Excel 檔內容等等，讓 ChatGPT 與學習 SQL 融為一體。[SQL 查詢技巧重點]●瞭解基本 SQL 語法並設計有效的 SQL 查詢●使用 SQL 進行探索性資料分析●從資料庫中建構、篩選和排序需要的資料集●使用 SQL JOIN 技巧，連結多個表格的資料●為分析報表和機器學習應用設計資料集●應用更進階的 SQL 技術，如窗口函數和 CTE●建立資料庫表格和視圖，儲存並引用查詢結果本書特色: ● 資深資料科學家寫給有志從事資料科學者的貼心指導● 學習作者觀察資料的視角，培養資料分析思維● 提供書中 SQL 程式碼下載，節省讀者輸入時間● 依學習情境所需穿插 40 多個 "ChatGPT 來幫忙" 說明框● 各章練習題皆附參考答案，非常適合自主學習

An analysis of the lessons learned from tuberculosis control in Shanghai. Tuberculosis Control and Institutional Change in Shanghai, 1911-2011 is the first book on the most widespread and deadly infectious disease in China, both historically and today. Weaving together interviews with data from periodicals and local archives in Shanghai, Rachel Core examines the rise and fall of tuberculosis control in China from the 1950s to the 1990s. Under the socialist work unit system, the vast majority of people had guaranteed employment, a host of benefits tied to their workplace, and there was little mobility--factors that made the delivery of medical and public health services possible in both urban and rural areas. The dismantling of work units amid wider market reforms in the 1980s and 1990s led to the rise of temporary and casual employment and a huge migrant worker population, with little access to health care, creating new challenges in TB control. This study of Shanghai will provide valu

本書第二版除了介紹個人理財的相關財務決策外，更加入逐漸受到重視的風險性投資主題。為了讓讀者能深入瞭解投資活動與個人理財的互動運作原理，特別融入相關實作的操作教程內容，讓讀者能親自動手練習「如何正確編製家庭財務報表？」（Personal / Household Financial Statements）。本書的內容架構融入目前世界各國一流商管教育重視「大數據量化分析」(Quantitative Analysis with Big Data) 的最新趨勢。讀者透過本書的精進研讀，不僅能徹底瞭解個人理財的運作邏輯，更能學習透過應用軟體來獨力完成家庭財務規劃。這就是本書提供給社會大眾自我提升「理財素養」，以增進家庭經營效率與效用的方便法門。

Designed for advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II as well as courses on Generalized Linear Models, Longitudinal Data Analysis, Correl

Social media has put mass communication in the hands of normal people on an unprecedented scale, and has also given social scientists the tools necessary to listen to the voices of everyday people around the world. This book gives social scientists the skills necessary to leverage that opportunity, and transform social media's vast stream of information into social science data. The book combines the big data techniques of computer science with social science methodology. Intended as a text for advanced undergraduates, graduate students, and researchers in the social sciences, this book provides a methodological pathway for scholars who want to make use of this new and evolving source of data. It provides a framework for building one's own data collection and analysis infrastructure, a toolkit of content analysis, geographic analysis, and network analysis, and meditations on the ethical implications of social media data.

Social media has put mass communication in the hands of normal people on an unprecedented scale, and has also given social scientists the tools necessary to listen to the voices of everyday people around the world. This book gives social scientists the skills necessary to leverage that opportunity, and transform social media's vast stream of information into social science data. The book combines the big data techniques of computer science with social science methodology. Intended as a text for advanced undergraduates, graduate students, and researchers in the social sciences, this book provides a methodological pathway for scholars who want to make use of this new and evolving source of data. It provides a framework for building one's own data collection and analysis infrastructure, a toolkit of content analysis, geographic analysis, and network analysis, and meditations on the ethical implications of social media data.

This book assesses data protection rules that are applicable to the processing of personal data in a law enforcement context. It offers the first extensive analysis of the LED and Regulation (EU) 2018/1725. It illustrates the challenges arising from the unclear delineation between the different data protection instruments at both national and EU level. Taking a practical approach, it exemplifies situations where the application of data protection instruments could give rise to a lowering of data protection standards where the data protection rules applicable in the law enforcement context are interpreted broadly. The scope of data protection instruments applied by law enforcement authorities impacts processing for purposes of border control, migration management and asylum because there is an unclear delineation between the different data protection instruments.

1 Introduction2 Representatives2.1 Representative of data sets with one feature2.1.1 Best LS-representativ2.1.2 Best 1-representative2.1.3 Best representative of weighted data2.1.4 Bregman divergences2.2 Representative of data sets with two features2.2.1 Fermat-Torricelli-Weber problem2.2.2 Centroid of a set in the plane2.2.3 Median of a set in the plane 2.2.4 Geometric median of a set in the plane2.3 Representative of data sets with several features2.3.1 Representative of weighted data2.4 Representative of periodic data2.4.1 Representative of data on the unit circle2.4.2 Burn diagram3 Data clustering3.1 Optimal k-partition3.1.1 Minimal distance principle and Voronoi diagram3.1.2 k-means algorithm3.2 Clustering data with one feature3.2.1 Application of the LS-distance-like function3.2.2 The dual problem3.2.3 Least absolute deviation principle3.2.4 Clustering weighted data3.3 Clustering data with two or several features3.3.1 Least squares principle3.3.2 The dual problem3.3.3 Least absol