Maps: A Statistical View

Literature Cited

1. Andrienko N, Andrienko G. 2006.. Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Berlin:: Springer-Verlag
   [Google Scholar]
2. Andrienko N, Andrienko G, Fuchs G, Slingsby A, Turkay C, Wrobel S. 2020.. Visual Analytics for Data Scientists. Cham, Switz.:: Springer Nature
   [Google Scholar]
3. Anselin L. 1995.. Local indicators of spatial association: LISA. . Geogr. Anal. 27::93–116
   [Crossref] [Google Scholar]
4. Armstrong MP, Rushton G, and Zimmerman DL. 1999.. Geographically masking health data to preserve confidentiality. . Stat. Med. 18::497–525
   [Crossref] [Google Scholar]
5. Ascher M. 1995.. Models and maps from the Marshall Islands: a case in ethnomathematics. . Hist. Math. 22::347–70
   [Crossref] [Google Scholar]
6. Baddeley A, Rubak E, Turner R. 2016.. Spatial Point Patterns: Methodology and Applications with R. Boca Raton, FL:: CRC
   [Google Scholar]
7. Baddeley AJ, Silverman BW. 1984.. A cautionary example for the use of second order methods for analyzing point patterns. . Biometrics 40::1089–93
   [Crossref] [Google Scholar]
8. Banerjee S, Carlin BP, Gelfand AE. 2015.. Hierarchical Modeling and Analysis for Spatial Data. Boca Raton, FL:: CRC. , 2nd ed..
   [Google Scholar]
9. Bartlett MS. 1964.. The spectral analysis of two-dimensional point processes. . Biometrika 51::299–311
   [Crossref] [Google Scholar]
10. Bertin J. 1983.. Semiology of Graphics: Diagrams, Networks, Maps. Madison:: Univ. Wisc. Press
    [Google Scholar]
11. Besag J, York J, Mollié A. 1991.. Bayesian image restoration, with two applications in spatial statistics (with discussion). . Ann. Inst. Stat. Math. 43::1–59
    [Crossref] [Google Scholar]
12. Blangiardo M, Cameletti M. 2015.. Spatial and Spatio-temporal Bayesian Models with R-INLA. Boca Raton, FL:: CRC
    [Google Scholar]
13. Breiman L. 2001.. Statistical modeling: the two cultures (with comments and rejoinder). . Stat. Sci. 16::199–231
    [Crossref] [Google Scholar]
14. Box GEP, Hunter WG, Hunter JA. 1978.. Statistics for Experimenters. New York:: Wiley
    [Google Scholar]
15. Brewer CA. 1994.. Color use guidelines for mapping and visualization. . In Visualization in Modern Cartography, ed. AM MacEachren, DRF Taylor , pp. 123–47. Oxford, UK:: Pergamon
    [Google Scholar]
16. Brewer CA 1996.. Guidelines for selecting colors for diverging schemes on maps. . Cartographic J. 33::79–86
    [Crossref] [Google Scholar]
17. Carroll L. 1982.. The Complete Illustrated Works. New York:: Gramercy
    [Google Scholar]
18. Chiu SN, Stoyan D, Kendall W, Menke J. 2013.. Stochastic Geometry and its Applications. New York:: Wiley. , 3rd ed..
    [Google Scholar]
19. Clayton DG, Kaldor J. 1987.. Empirical Bayes estimates of age-standardized relative risk for use in disease mapping. . Biometrics 43::671–82
    [Crossref] [Google Scholar]
20. Cliff AD, Ord JK. 1973.. Spatial Autocorrelation. London:: Pion
    [Google Scholar]
21. Cowan J. 1996.. A Mapmaker's Dream: The Meditations of Fra Mauro, Cartographer to the Court of Venice. Boston:: Shambhala
    [Google Scholar]
22. Cressie NAC. 1993.. Statistics for Spatial Data. New York:: Wiley
    [Google Scholar]
23. Cressie NAC, Wikle CK. 2011.. Statistics for Spatio-Temporal Data. New York:: Wiley
    [Google Scholar]
24. Diggle PJ. 2014.. Statistical Analysis of Spatial and Spatio-Temporal Point Patterns. Boca Raton, FL:: CRC. , 3rd ed..
    [Google Scholar]
25. Dupont E, Wood SN, Augustin NH. 2022.. Spatial+: a novel approach to spatial confounding. . Biometrics 78::1279–90
    [Crossref] [Google Scholar]
26. Dwork C. 2006.. Differential privacy. . In Automata, Languages, and Programming: 33rd International Colloquium, ICALP 2006, Venice Italy, July 10–14, 2006, Proceedings, Part II 33, ed. M Bugliesi, B Preneel, V Sassone, I Wegener , pp. 1–12. New York:: Springer
    [Google Scholar]
27. Eco U. 1994.. How to Travel with a Salmon and Other Essays. San Diego, CA:: Harcourt
    [Google Scholar]
28. Fay RE, Herriot RA. 1979.. Estimates of income for small places: an application of James-Stein procedures to census data. . J. Am. Stat. Assoc. 74::269–77
    [Crossref] [Google Scholar]
29. Fotheringham AS, Brunsdon C, Charlton M. 2003.. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. New York:: Wiley
    [Google Scholar]
30. Gehlke CE, Biehl K. 1934.. Certain effects of grouping upon the sized of the correlation coefficient in census tract material. . J. Am. Stat. Assoc. Suppl. 29::169–70
    [Google Scholar]
31. Haining R. 2003.. Spatial Data Analysis: Theory and Practice. Cambridge, UK:: Cambridge Univ. Press
    [Google Scholar]
32. Hodges JS, Reich BJ. 2010.. Adding spatially-correlated errors can mess up the fixed effect you love. . Am. Stat. 64::325–34
    [Crossref] [Google Scholar]
33. Koch T. 2011.. Disease Maps: Epidemics on the Ground. Chicago:: Univ. Chicago Press
    [Google Scholar]
34. Koch T. 2017.. Cartographies of Disease: Maps, Mapping, and Medicine. Redlands, CA:: Esri. Expanded ed.
    [Google Scholar]
35. Krieger N. 2021.. Ecosocial Theory, Embodied Truths, and the People's Health. Oxford, UK:: Oxford Univ. Press
    [Google Scholar]
36. Kurz CF, König AN, Emmert-Fees KMF, Allen L. 2022.. The effect of differential privacy on Medicaid participation among racial and ethnic minority groups. . Health Serv. Res. 57::207–13
    [Crossref] [Google Scholar]
37. Lawson AB. 2006.. Statistical Methods in Spatial Epidemiology. New York:: Wiley
    [Google Scholar]
38. Lawson AB. 2018.. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Boca Raton, FL:: CRC. , 3rd ed..
    [Google Scholar]
39. Lewis-Jones H. 2018.. The Writer's Map: An Atlas of Imaginary Lands. Chicago:: Univ. Chicago Press
    [Google Scholar]
40. Lyons H. 1928.. The sailing charts of the Marshall Islanders. . Geogr. J. 72::325–27
    [Crossref] [Google Scholar]
41. MacEachren AM. 1979.. Communication effectiveness of choropleth and isopleth maps: the influence of visual complexity. PhD Diss. , Univ. Kansas, Lawrence, KS:
    [Google Scholar]
42. MacEachren AM. 1994.. Some Truth with Maps: A Primer on Symbolization and Design. Washington, DC:: Assoc. Am. Geogr.
    [Google Scholar]
43. MacEachren AM. 1995.. How Maps Work: Representation, Visualization, and Design. New York:: Guilford
    [Google Scholar]
44. Machanavajjhala A, Kifer D, Abowd J, Gerhrke J, Vilhuber L. 2008.. Privacy: theory meets practice on the map. . In 2008 IEEE 24th International Conference on Data Engineering, pp. 277–86. Piscataway, NJ:: IEEE
    [Google Scholar]
45. Maxcy KF. 1926.. An epidemiological study of endemic typhus (Brill's disease) in the southeastern United States: with special reference to its mode of transmission. . Public Health Rep. 41:(52):2967–95
    [Crossref] [Google Scholar]
46. Møller J, Waagepetersen RP. 2004.. Statistical Inference and Simulation for Spatial Point Processes. Boca Raton, FL:: CRC
    [Google Scholar]
47. Monmonier M. 1996.. How to Lie with Maps. Chicago:: Univ. Chicago Press. , 2nd ed..
    [Google Scholar]
48. Monmonier M. 2001.. Bushmanders and Bullwinkles: How Politicians Manipulate Electronic Maps and Census Data to Win Elections. Chicago:: Univ. Chicago Press
    [Google Scholar]
49. Moran PAP. 1950.. Notes on continuous stochastic phenomena. . Biometrika 37::17–23
    [Crossref] [Google Scholar]
50. NRC (Natl. Res. Counc.). 2006.. Learning to think spatially. Rep. , Natl. Acad. Sci. Eng. Med., Washington, DC:
    [Google Scholar]
51. Neuwirth E. 2022.. RColorBrewer: ColorBrewer palettes. . R Package, version 1.1–3. https://CRAN.R-project.org/package=RColorBrewer
    [Google Scholar]
52. Olson JM, Brewer CA. 1997.. An evaluation of color selections to accommodate map users with color-vision impairments. . Ann. Assoc. Am. Geogr. 87::103–34
    [Crossref] [Google Scholar]
53. Openshaw S, Taylor PJ. 1979.. A million or so correlation coefficients: three experiments on the modifiable areal unit problem. . In Statistical Applications in the Spatial Sciences, ed. N Wrigley , pp. 12744. London:: Pion
    [Google Scholar]
54. Paciorek CJ. 2010.. The importance of scale for spatial-confounding bias and precision of spatial regression estimators. . Stat. Sci. 25::107–25
    [Crossref] [Google Scholar]
55. Quick H, Waller LA. 2018.. Using spatiotemporal models to generate synthetic data for public use. . Spatial Spatiotemporal Epidemiol. 27::37–45
    [Crossref] [Google Scholar]
56. Rao JNK. 2003.. Small Area Estimation. New York:: Wiley
    [Google Scholar]
57. Robinson AH. 1952.. The Look of Maps. Madison:: Univ. Wisc. Press
    [Google Scholar]
58. Rue H, Held L. 2005.. Gaussian Markov Random Fields: Theory and Applications. Boca Raton, FL:: CRC
    [Google Scholar]
59. Salichtchev KA. 1982.. Cartography: A Textbook for Geographical Specialities of Universities. Moscow:: Vysshaya Shkola (in Russian). , 3rd ed..
    [Google Scholar]
60. Snow J. 1855.. On the Mode of Communication of Cholera. London:: John Churchill
    [Google Scholar]
61. Stein M. 1999.. Interpolation of Spatial Data: Some Theory for Kriging. New York:: Springer-Verlag
    [Google Scholar]
62. Tobler W. 1970.. A computer movie simulating urban growth in the Detroit region. . Econ. Geogr. 46:(Suppl.):234–40
    [Crossref] [Google Scholar]
63. Tufte ER. 1990.. Envisioning Information. Cheshire, CT:: Graphics
    [Google Scholar]
64. Tufte ER. 1997.. Visual Explanations. Cheshire, CT:: Graphics
    [Google Scholar]
65. Tufte ER. 2001.. The Visual Display of Quantitative Information. Cheshire, CT:: Graphics. , 2nd ed..
    [Google Scholar]
66. Tufte ER. 2006.. Beautiful Evidence. Cheshire, CT:: Graphics
    [Google Scholar]
67. Tukey J. 1977.. Exploratory Data Analysis. Reading, MA:: Addison-Wesley
    [Google Scholar]
68. Turnbull D. 2000.. Masons, Tricksters, and Cartographers. Abington, UK: OPA Routledge:
    [Google Scholar]
69. Wainer H. 1984.. How to display data badly. . Am. Stat. 38::137–47
    [Crossref] [Google Scholar]
70. Waller LA. 2009.. Detection of clustering in spatial data. . In The Sage Handbook of Spatial Analysis, ed. AS Fotheringham, PA Rogerson , pp. 299–320. London:: SAGE
    [Google Scholar]
71. Waller LA. 2014.. Putting spatial statistics (back) on the map. . Spatial Stat. 9::4–19
    [Crossref] [Google Scholar]
72. Waller LA. 2022.. Global and local impacts of differential privacy on estimates of health care inequity. . Health Serv. Res. 57::204–6
    [Crossref] [Google Scholar]
73. Waller LA, Gotway CA. 2004.. Applied Spatial Statistics for Public Health Data. New York:: Wiley
    [Google Scholar]
74. Waller LA, Zhu L, Gotway CA, Gorman DM, Gruenewald PJ. 2007.. Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models. . Stochastic Environ. Res. Risk Assess. 21::573–88
    [Crossref] [Google Scholar]
75. Weigand T, Moloney KA. 2013.. Handbook of Spatial Point-Pattern Analyses in Ecology. Boca Raton, FL:: CRC
    [Google Scholar]
76. Wheeler DC, Hickson DA, Waller LA. 2010.. Assessing local model adequacy in Bayesian hierarchical models using the partitioned deviance information criterion. . Comput. Stat. Data Anal. 54::1657–71
    [Crossref] [Google Scholar]
77. Wheeler DC, Waller LA. 2008.. Mountains, valleys, and rivers: the transmission of raccoon rabies over a heterogeneous landscape. . J. Agric. Biol. Environ. Stat. 13:(4):388–406
    [Crossref] [Google Scholar]
78. Wilkinson L. 2005.. The Grammar of Graphics. New York:: Springer. , 2nd ed..
    [Google Scholar]
79. Wickham H. 2016.. ggplot2: Elegant Graphics for Data Analysis. New York:: Springer-Verlag
    [Google Scholar]
80. Zhou Y, Dominici F, Louis TA. 2010.. A smoothing approach for masking spatial data. . Ann. Appl. Stat. 4::1451–75
    [Crossref] [Google Scholar]