Analyzing Us Census Data

Census data are widely used by practitioners to understand demographic change, allocate resources, address inequalities, and make sound business decisions. Until recently, projects using US Census data have required proficiency with multiple web interfaces and software platforms to prepare, map, and present data products. This book introduces readers to tools in the R programming language for accessing and analyzing Census data, helping analysts manage these types of projects in a single computing environment. Chapters in this book cover the following key topics: • Rapidly acquiring data from the decennial US Census and American Community Survey using R, then analyzing these datasets using tidyverse tools; • Visualizing US Census data with a wide range of methods including charts in ggplot2 as well as both static and interactive maps; • Using R as a geographic information system (GIS) to manage, analyze, and model spatial demographic data from the US Census; • Working with and modeling individual-level microdata from the American Community Survey’s PUMS datasets; • Applying these tools and workflows to the analysis of historical Census data, other US government datasets, and international Census data from countries like Canada, Brazil, Kenya, and Mexico. Kyle Walker is an associate professor of geography at Texas Christian University, director of TCU’s Center for Urban Studies, and a spatial data science consultant. His research focuses on demographic trends in the United States, demographic data visualization, and software tools for open spatial data science. He is the lead author of a number of R packages including tigris, tidycensus, and mapboxapi.

Exploring the U.S. Census gives social science students and researchers alike the tools to understand, extract, process, and analyze data from the decennial census, the American Community Survey, and other data collected by the U.S. Census Bureau. Donnelly′s text provides a thorough background on the data collection methods, structures, and potential pitfalls of the census for unfamiliar researchers, collecting information previously available only in widely disparate sources into one handy guide. Hands-on, applied exercises at the end of the chapters help readers dive into the data. Along the way, the author shows how best to analyze census data with open-source software and tools. Readers can freely evaluate the data on their own computers, in keeping with the free and open data provided by the Census Bureau. By placing the census in the context of the open data movement, this text makes the history and practice of the census relevant so readers can understand what a crucial resource the census is for research and knowledge.

How to parse, analyze, and incorporate census data This handy resource offers a reference guide for anyone interested in tailoring specific Census data to their needs. It includes computer coding (SAS v9.x) software for extracting targeted data from thousands of Census files, as well as primers on using online tools and mapping software for analyzing data. The book offers thorough coverage of all aspects of census data including its historical significance, suggestions for parsing housing, occupation, transportation, economic, health, and other data from the census, and much more. Offers an guide to analyzing Census data that can have an impact on financial markets as well as housing and economic data boding ill or well for the future of the economy It includes computer coding (SAS v9.x) scripts for extracting specific data from Census files Offers guidance on using thousands of variables from Census results released every year and American Community Survey data now released every year The only one-stop guide to analyzing and using annual and decennial Census data Bass offers a practical guide for leveraging information compiled by the Census to further research as well as business interests.

In this book you will learn how to use R with the tidycensus and tidyverse packages to explore and visualize US Census data.tidycensus is an R package that allows users to interface with the US Census Bureau's decennial Census and five-year American Community APIs and return tidyverse-ready data frames, optionally with simple feature geometry included. tidycensus is designed to help R users get Census data that is pre-prepared for exploration within the tidyverse, and optionally spatially with the sf package.If your work involves the use of data from the US Census Bureau and would like to use R to explore, manipulate, and visualize these datasets, the tidycensus and tidyverse packages are great tools for accomplishing these tasks. Beyond this, the sf package now allows R users to work with spatial data in an integrated way with tidyverse tools, and updates to the tigris package provide access to Census boundary data as sf objects.This book will also allow the student to learn, in detail, the fundamentals of the R language and additionally master some of the most efficient libraries for data visualization in chart, graph, and map formats. The student will learn the language and applications through examples and practice. No prior programming skills are required.

The U.S. census, conducted every 10 years since 1790, faces dramatic new challenges as the country begins its third century. Critics of the 1990 census cited problems of increasingly high costs, continued racial differences in counting the population, and declining public confidence. This volume provides a major review of the traditional U.S. census. Starting from the most basic questions of how data are used and whether they are needed, the volume examines the data that future censuses should provide. It evaluates several radical proposals that have been made for changing the census, as well as other proposals for redesigning the year 2000 census. The book also considers in detail the much-criticized long form, the role of race and ethnic data, and the need for and ways to obtain small-area data between censuses.

This book provides an up-to-date overview of demographic analysis and methods, including recent developments in demography. Concepts and methods, from the nature of demographic information through data collection and the basics of statistical measures and on to demographic analysis itself are succinctly explained. Measures and analyses of fertility, mortality, life tables, migration and demographic events such as marriage, education and labour force are described while later chapters cover multiple decrement tables, population projections, the importance of testing and smoothing demographic data, the stable population model and demographic software. An emphasis on practical aspects and the use of real-life examples based on data from around the globe make this book accessible, whilst comprehensive references and links to data and other resources on the internet help readers to explore further. The text is concise and well written, making it ideally suited to a wider audience from students to academics and teachers. Students of demography, geography, sociology, economics, as well as professionals, academics and students of marketing, human resource management, and public health who have an interest in population issues will all find this book useful.

Census workers need to capture and analyze information at the finest geographic level with mobile and geospatial-based technology. GIS and the 2020 Census: Modernizing Official Statistics provides statistical organizations with the most recent GIS methodologies and technological tools to support census workers' needs at all the stages of a census. Learn how to plan and carry out census work with GIS using new technologies for field data collection and operations management. After planning and collecting data, apply innovative solutions for performing statistical analysis, data integration and dissemination. Additional topics cover cloud computing, big data, Location as a Service (LaaS), and emerging data sources. While GIS and the 2020 Census focuses on using GIS and other geospatial technology in support of census planning and operations, it also offers guidelines for building a statistical-geospatial information infrastructure in support of the 2020 Round of Censuses, evidence-based decision making, and sustainable development. Case studies illustrate concepts in practice.

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges. Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (i.e. the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a ‘learning by doing’ textbook, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.