Description
Urban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management, analysis, and manipulation using R statistical software. Designed for undergraduate and above level courses, this book is an ideal onramp for the study of urban informatics and how to translate novel data sets into new insights and practical tools.
The book follows a unique pedagogical approach developed by the author to enable students to build skills by pursuing projects that inspire and motivate them. Each chapter has an Exploratory Data Assignment that prompts readers to practice their new skills on a data set of their choice. These assignments guide readers through the process of becoming familiar with the contents of a novel data set and communicating meaningful insights from the data to others.
Key Features:
- The technical curriculum consists of both data management and analytics, including both as needed to become acquainted with and reveal the content of a new data set.
- Content that is contextualized in real-world applications relevant to community concerns.
- Unit-level assignments that educators might use as midterms or otherwise. These include Community Experience assignments that prompt students to evaluate the assumptions they have made about their data against real world information.
- All data sets are publicly available through the Boston Data Portal.
Table of Contents
Chapter 1 Introduction
Chapter 2 Welcome to R
Chapter 3 Telling a Data Story: Examining Individual Records
Chapter 4 The Pulse of the City: Observing Variable Patterns
Chapter 5 Uncovering Information: Making and Creating Variables
Chapter 6 Measuring with Big Data
Chapter 7 Making Measures from Records: Aggregating and Merging Data
Chapter 8 Mapping Communities
Chapter 9 Advanced Visual Techniques
Chapter 10 Beyond Measurement: Inferential Statistics (and Correlations)
Chapter 11 Identifying Inequities across Groups: ANOVA and t-Test
Chapter 12 Unpacking Mechanisms Driving Inequities: Multivariate Regression
Chapter 13 Advanced Analytic Techniques
Chapter 14 Emergent Technologies
Bibliography
Index
