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E-grāmata: Analyzing US Census Data: Methods, Maps, and Models in R [Taylor & Francis e-book]

  • Formāts: 352 pages, 76 Tables, black and white; 191 Line drawings, color; 191 Illustrations, color
  • Sērija : Chapman & Hall/CRC The R Series
  • Izdošanas datums: 16-Feb-2023
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-13: 9780203711415
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  • Taylor & Francis e-book
  • Cena: 213,45 €*
  • * this price gives unlimited concurrent access for unlimited time
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Analyzing US Census Data: Methods, Maps, and Models in RPalielināt
  • Formāts: 352 pages, 76 Tables, black and white; 191 Line drawings, color; 191 Illustrations, color
  • Sērija : Chapman & Hall/CRC The R Series
  • Izdošanas datums: 16-Feb-2023
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-13: 9780203711415
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9780203711415
Census data is 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 and shows how to carry out demographic analyses 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 Surveys 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.
Preface xiii
1 The US Census and the R programming language
1(16)
1.1 Census data: an overview
1(1)
1.2 Census hierarchies
2(1)
1.3 How to find US Census data
3(5)
1.3.1 Data downloads from the Census Bureau
5(1)
1.3.2 The Census API
5(2)
1.3.3 Third-party data distributors
7(1)
1.4 What is R?
8(3)
1.4.1 Getting started with R
8(1)
1.4.2 Basic data structures in R
8(1)
1.4.3 Functions and packages
9(1)
1.4.4 Package ecosystems in R
10(1)
1.5 Analyses using R and US Census data
11(6)
1.5.1 Census data packages in R: a brief summary
11(1)
1.5.2 Health resource access
12(1)
1.5.3 COVID-19 and pandemic response
12(1)
1.5.4 Politics and gerrymandering
12(3)
1.5.5 Social equity research
15(1)
1.5.6 Census data visualization
15(2)
2 An introduction to tidycensus
17(22)
2.1 Getting started with tidycensus
17(5)
2.1.1 Decennial Census
18(2)
2.1.2 American Community Survey
20(2)
2.2 Geography and variables in tidycensus
22(5)
2.2.1 Geographic subsets
24(3)
2.3 Searching for variables in tidycensus
27(2)
2.4 Data structure in tidycensus
29(4)
2.4.1 Understanding GEOIDs
30(2)
2.4.2 Renaming variable IDs
32(1)
2.5 Other Census Bureau datasets in tidycensus
33(3)
2.5.1 Using get_estinates ()
33(2)
2.5.2 Using get_flows()
35(1)
2.6 Debugging tidycensus errors
36(1)
2.7 Exercises
37(2)
3 Wrangling Census data with tidyverse tools
39(22)
3.1 The tidyverse
39(1)
3.2 Exploring Census data with tidyverse tools
40(5)
3.2.1 Sorting and filtering data
40(3)
3.2.2 Using summary variables and calculating new columns
43(2)
3.3 Group-wise Census data analysis
45(4)
3.3.1 Making group-wise comparisons
46(1)
3.3.2 Tabulating new groups
47(2)
3.4 Comparing ACS estimates over time
49(7)
3.4.1 Time-series analysis: some cautions
50(2)
3.4.2 Preparing time-series ACS estimates
52(4)
3.5 Handling margins of error in the American Community Survey with tidycensus
56(4)
3.5.1 Calculating derived margins of error in tidycensus
57(2)
3.5.2 Calculating group-wise margins of error
59(1)
3.6 Exercises
60(1)
4 Exploring US Census data with visualization
61(32)
4.1 Basic Census visualization with ggplot2
61(5)
4.1.1 Getting started with ggplot2
62(2)
4.1.2 Visualizing multivariate relationships with scatter plots
64(2)
4.2 Customizing ggplot2 visualizations
66(6)
4.2.1 Improving plot legibility
67(2)
4.2.2 Custom styling of ggplot2 charts
69(2)
4.2.3 Exporting data visualizations from R
71(1)
4.3 Visualizing margins of error
72(4)
4.3.1 Data setup
72(2)
4.3.2 Using error bars for margins of error
74(2)
4.4 Visualizing ACS estimates over time
76(2)
4.5 Exploring age and sex structure with population pyramids
78(4)
4.5.1 Preparing data from the Population Estimates API
78(2)
4.5.2 Designing and styling the population pyramid
80(2)
4.6 Visualizing group-wise comparisons
82(4)
4.7 Advanced visualization with ggplot2 extensions
86(6)
4.7.1 ggridges
86(1)
4.7.2 ggbeeswarm
87(1)
4.7.3 Geofaceted plots
88(3)
4.7.4 Interactive visualization with plotly
91(1)
4.8 Learning more about visualization
92(1)
4.9 Exercises
92(1)
5 Census geographic data and applications in R
93(30)
5.1 Basic usage of tigris
93(8)
5.1.1 Understanding tigris and simple features
97(3)
5.1.2 Data availability in tigris
100(1)
5.2 Plotting geographic data
101(4)
5.2.1 ggplot2 and geom_sf ()
101(2)
5.2.2 Interactive viewing with mapview
103(2)
5.3 Tigris workflows
105(4)
5.3.1 TIGER/Line and cartographic boundary shapefiles
105(1)
5.3.2 Caching tigris data
106(1)
5.3.3 Understanding yearly differences in TIGER/Line files
107(1)
5.3.4 Combining tigris datasets
108(1)
5.4 Coordinate reference systems
109(6)
5.4.1 Using the crsuggest package
110(3)
5.4.2 Plotting with coord_sf()
113(2)
5.5 Working with geometries
115(7)
5.5.1 Shifting and rescaling geometry for national US mapping
115(2)
5.5.2 Converting polygons to points
117(2)
5.5.3 Exploding multipolygon geometries to single parts
119(3)
5.6 Exercises
122(1)
6 Mapping Census data with R
123(44)
6.1 Using geometry in tidycensus
123(3)
6.1.1 Basic mapping of sf objects with plot()
125(1)
6.2 Map-making with ggplot2 and geom_sf
126(2)
6.2.1 Choropleth mapping
126(1)
6.2.2 Customizing ggplot2 maps
127(1)
6.3 Map-making with tmap
128(13)
6.3.1 Choropleth maps with tmap
129(4)
6.3.2 Adding reference elements to a map
133(3)
6.3.3 Choosing a color palette
136(1)
6.3.4 Alternative map types with tmap
137(4)
6.4 Cartographic workflows with non-Census data
141(6)
6.4.1 National election mapping with tigris shapes
142(1)
6.4.2 Understanding and working with ZCTAs
143(4)
6.5 Interactive mapping
147(7)
6.5.1 Interactive mapping with Leaflet
147(4)
6.5.2 Alternative approaches to interactive mapping
151(3)
6.6 Advanced examples
154(7)
6.6.1 Mapping migration flows
155(1)
6.6.2 Linking maps and charts
156(2)
6.6.3 Reactive mapping with Shiny
158(3)
6.7 Working with software outside of R for cartographic projects
161(4)
6.7.1 Exporting maps from R
162(1)
6.7.2 Interoperability with other visualization software
163(2)
6.8 Exercises
165(2)
7 Spatial analysis with US Census data
167(46)
7.1 Spatial overlay
167(4)
7.1.1 Note: aligning coordinate reference systems
168(1)
7.1.2 Identifying geometries within a metropolitan area
169(1)
7.1.3 Spatial subsets and spatial predicates
170(1)
7.2 Spatial joins
171(9)
7.2.1 Point-in-polygon spatial joins
172(4)
7.2.2 Spatial joins and group-wise spatial analysis
176(4)
7.3 Small area time-series analysis
180(7)
7.3.1 Area-weighted areal interpolation
182(1)
7.3.2 Population-weighted areal interpolation
183(2)
7.3.3 Making small-area comparisons
185(2)
7.4 Distance and proximity analysis
187(9)
7.4.1 Calculating distances
188(2)
7.4.2 Calculating travel times
190(2)
7.4.3 Catchment areas with buffers and isochrones
192(2)
7.4.4 Computing demographic estimates for zones with areal interpolation
194(2)
7.5 Better cartography with spatial overlay
196(2)
7.5.1 "Erasing" areas from Census polygons
196(2)
7.6 Spatial neighborhoods and spatial weights matrices
198(4)
7.6.1 Understanding spatial neighborhoods
199(2)
7.6.2 Generating the spatial weights matrix
201(1)
7.7 Global and local spatial autocorrelation
202(9)
7.7.1 Spatial lags and Moran's I
203(1)
7.7.2 Local spatial autocorrelation
204(2)
7.7.3 Identifying clusters and spatial outliers with local indicators of spatial association (LISA)
206(5)
7.8 Exercises
211(2)
8 Modeling US Census data
213(42)
8.1 Indices of segregation and diversity
213(7)
8.1.1 Data setup with spatial analysis
213(2)
8.1.2 The dissimilarity index
215(2)
8.1.3 Multi-group segregation indices
217(1)
8.1.4 Visualizing the diversity gradient
218(2)
8.2 Regression modeling with US Census data
220(14)
8.2.1 Data setup and exploratory data analysis
222(1)
8.2.2 Inspecting the outcome variable with visualization
223(2)
8.2.3 "Feature engineering"
225(1)
8.2.4 A first regression model
225(5)
8.2.5 Dimension reduction with principal components analysis
230(4)
8.3 Spatial regression
234(7)
8.3.1 Methods for spatial regression
236(3)
8.3.2 Choosing between spatial lag and spatial error models
239(2)
8.4 Geographically weighted regression
241(6)
8.4.1 Choosing a bandwidth for GWR
242(1)
8.4.2 Fitting and evaluating the GWR model
243(3)
8.4.3 Limitations of GWR
246(1)
8.5 Classification and clustering of ACS data
247(6)
8.5.1 Geodemographic classification
248(2)
8.5.2 Spatial clustering & regionalization
250(3)
8.6 Exercises
253(2)
9 Introduction to Census microdata
255(14)
9.1 What is "microdata?"
255(2)
9.1.1 Microdata resources: IPUMS
256(1)
9.1.2 Microdata and the Census API
256(1)
9.2 Using microdata in tidycensus
257(3)
9.2.1 Basic usage of get_pums()
257(1)
9.2.2 Understanding default data from get_pums()
258(2)
9.3 Working with PUMS variables
260(3)
9.3.1 Variables available in the ACS PUMS
261(1)
9.3.2 Recoding PUMS variables
261(1)
9.3.3 Using variables filters
262(1)
9.4 Public Use Microdata Areas (PUMAs)
263(4)
9.4.1 What is a PUMA?
263(2)
9.4.2 Working with PUMAs in PUMS data
265(2)
9.5 Exercises
267(2)
10 Analyzing Census microdata
269(16)
10.1 PUMS data and the tidyverse
269(5)
10.1.1 Basic tabulation of weights with tidyverse tools
269(3)
10.1.2 Group-wise data tabulation
272(2)
10.2 Mapping PUMS data
274(1)
10.3 Survey design and the ACS PUMS
275(5)
10.3.1 Getting replicate weights
275(2)
10.3.2 Creating a survey object
277(1)
10.3.3 Calculating estimates and errors with srvyr
278(1)
10.3.4 Converting standard errors to margins of error
279(1)
10.4 Modeling with PUMS data
280(4)
10.4.1 Data preparation
281(1)
10.4.2 Fitting and evaluating the model
282(2)
10.5 Exercises
284(1)
11 Other Census and government data resources
285(32)
11.1 Mapping historical geographies of New York City with NHGIS
285(7)
11.1.1 Getting started with NHGIS
286(1)
11.1.2 Working with NHGIS data in R
287(2)
11.1.3 Mapping NHGIS data in R
289(3)
11.2 Analyzing complete-count historical microdata with IPUMS and R
292(10)
11.2.1 Getting microdata from IPUMS
294(2)
11.2.2 Loading microdata into a database
296(1)
11.2.3 Accessing your microdata database with R
297(3)
11.2.4 Analyzing big Census microdata in R
300(2)
11.3 Other US government datasets
302(10)
11.3.1 Accessing Census data resources with censusapi
302(4)
11.3.2 Analyzing labor markets with lehdr
306(2)
11.3.3 Bureau of Labor Statistics data with blscrapeR
308(2)
11.3.4 Working with agricultural data with tidyUSDA
310(2)
11.4 Getting government data without R packages
312(3)
11.4.1 Making requests to APIs with httr
312(1)
11.4.2 Writing your own data access functions
313(2)
11.5 Exercises
315(2)
12 Working with Census data outside the United States
317(26)
12.1 The International Data Base and the idbr R package
317(8)
12.1.1 Visualizing IDB data
319(3)
12.1.2 Interactive and animated visualization of global demographic data
322(3)
12.2 Country-specific Census data packages
325(16)
12.2.1 Canada: cancensus
325(2)
12.2.2 Kenya: rKenyaCensus
327(4)
12.2.3 Mexico: combining mxmaps and inegiR
331(2)
12.2.4 Brazil: aligning the geobr R package with raw Census data files for spatial analysis
333(8)
12.3 Other international data resources
341(1)
12.4 Exercises
341(2)
Conclusion 343(2)
Bibliography 345(8)
Index 353
Kyle Walker is an associate professor of geography at Texas Christian University, director of TCUs 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.
Permanent link: https://www.kriso.lv/db/9780203711415_pe.html
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