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Linked Micromap Plots for South America – General Design Considerations and Specific Adjustments
Gráficas de micromapas enlazados para Suramérica – Consideraciones generales de diseño y ajustes específicos
DOI:
https://doi.org/10.15446/rce.v37n2spe.47949Keywords:
Geographic Data, LM Plots, Map Visualization, R, Statistical Maps, Visualization Software (en)Datos geográficos, Gráficos LM, Mapas estadísticos, R, Software de visualización, Visualización de mapas (es)
merica (USA) since their introduction in 1996 as an effective way to display statistical summaries associated with regional spatial units. However, LM plots were always hard to create by non–experts. The introduction of the micromap R package has simplified the construction of LM plots for arbitrary geographic regions by facilitating the use of external Geographic Information System (GIS) features (such as shapefiles) as the basis for the maps. In this article, we will introduce LM plots for countries from South America. However, spatial representations of features are often not immediately suitable for LM plots, even after some automated simplification of the boundaries of the map regions. A common problem is that relatively small geographic regions are often not visible when plotted in LM plots. Thus, it is necessary to enlarge small regions and display them on the outside of the main map. We introduce some algorithmic guidelines on how small regions can be addressed in LM plots for South America. Moreover, we will provide recommendations how to include areas into LM plots that are far away from the main geographic region.
Las gráficas de micromapas enlazados (LM por sus siglas en inglés) han sido usados en Estados Unidos desde su introducción en 1996 como una forma efectiva de presentar resúmenes estadísticos asociados con unidades espaciales regionales. Sin embargo, las gráficas LM son difíciles de crear por no expertos. La introducción al paquete R micromap ha simplificado la contrucción de gráficos LM para regiones geográficas arbitrarias al facilitar el uso de Sistemas de Información Geográficos (GIS por sus siglas en inglés) como la base para los mapas. En este artículo, se presentan gráficos LM para los países de Suramérica. Sin embargo, las representaciones espaciales están a menudo no disponibles para los gráficos LM, incluso después de simplificaciones automatizadas de los límites de las regiones. Un problema común es que regiones geográficamente pequeñas a menuso no son visibles en los gráficos LM. Entonces, se hace necesario ampliar estas regiones pequeñas y mostrarlas por fuera del mapa principal. Se introducen algunas guías algoritmicas de cómo considerar regiones pequeñas en los gráficos LM de Suramérica. Adicionalmente, se dan recomendaciones de cómo incluir áreas que se encuentran lejanas de la principal región geográfica en los graficos LM.
https://doi.org/10.15446/rce.v37n2spe.47949
1Utah State University, Department of Mathematics and Statistics, Logan, Utah, USA. Professor. Email: symanzik@math.usu.edu
2Utah State University, Department of Mathematics and Statistics, Logan, Utah, USA. PhD Student. Email: xiaotian.dai@aggiemail.usu.edu
3U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Office of Research and Development, Corvallis, Oregon, USA. Geographer. Email: Weber.Marc@epa.gov
4Oregon State University, Department of Statistics, Corvallis, Oregon, USA. PhD Student. Email: paytonq@science.oregonstate.edu
5U.S. Environmental Protection Agency, National Center for Environmental Assessment, Office of Research and Development, Cincinnati, Ohio, USA. Ecologist. Email: McManus.Michael@epa.gov
Linked micromap (LM) plots have been in use in the United States of America (USA) since their introduction in 1996 as an effective way to display statistical summaries associated with regional spatial units. However, LM plots were always hard to create by non--experts. The introduction of the R package has simplified the construction of LM plots for arbitrary geographic regions by facilitating the use of external Geographic Information System (GIS) features (such as shapefiles) as the basis for the maps. In this article, we will introduce LM plots for countries from South America. However, spatial representations of features are often not immediately suitable for LM plots, even after some automated simplification of the boundaries of the map regions. A common problem is that relatively small geographic regions are often not visible when plotted in LM plots. Thus, it is necessary to enlarge small regions and display them on the outside of the main map. We introduce some algorithmic guidelines on how small regions can be addressed in LM plots for South America. Moreover, we will provide recommendations how to include areas into LM plots that are far away from the main geographic region.
Key words: Geographic Data, LM Plots, Map Visualization, R, Statistical Maps, Visualization Software.
Las gráficas de micromapas enlazados (LM por sus siglas en inglés) han sido usados en Estados Unidos desde su introducción en 1996 como una forma efectiva de presentar resúmenes estadísticos asociados con unidades espaciales regionales. Sin embargo, las gráficas LM son difíciles de crear por no expertos. La introducción al paquete R micromap ha simplificado la contrucción de gráficos LM para regiones geográficas arbitrarias al facilitar el uso de Sistemas de Información Geográficos (GIS por sus siglas en inglés) como la base para los mapas. En este artículo, se presentan gráficos LM para los países de Suramérica. Sin embargo, las representaciones espaciales están a menudo no disponibles para los gráficos LM, incluso después de simplificaciones automatizadas de los límites de las regiones. Un problema comón es que regiones geográficamente pequeñas a menuso no son visibles en los gráficos LM. Entonces, se hace necesario ampliar estas regiones pequeñas y mostrarlas por fuera del mapa principal. Se introducen algunas guías algoritmicas de cómo considerar regiones pequeñas en los gráficos LM de Suramérica. Adicionalmente, se dan recomendaciones de cómo incluir áreas que se encuentran lejanas de la principal región geográfica en los graficos LM.
Palabras clave: datos geográficos, gráficos LM, mapas estadísticos, R, Software de visualización, visualización de mapas.
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References
1. Ahn, J. Y. (2013), Visualizing Statistical Information using Korean Linked Micromap Plots, 'Proceedings of IASC-Satellite Conference for the 59th ISI WSC & The 8th Conference of IASC-ARS', Asian Regional Section of the IASC, p. 219-221.
2. Bivand, R. S. & Lewin--Koh, N. (2014), maptools: Tools for Reading and Handling Spatial Objects. R package version 0.8-29. *http://CRAN.R-project.org/packagemaptools
3. Bivand, R. S. & Rundel, C. (2014), rgeos: Interface to Geometry Engine - Open Source (GEOS). R package version 0.3-4. *http://CRAN.R-project.org/packagergeos
4. Bonnal, L., Favard, P., Laurent, T. & Ruiz--Gazen, A. (2011), 'Pourquoi le cout de l'éducation est-il plus élevé en zone rurale? Le cas de la région Midi-Pyrénées', Revue d'Economie Régionale & Urbaine 2011/5, 887-910.
5. Brunsdon, C. & Chen, H. (2014), GISTools: Some further GIS Capabilities for R. R package version 0.7-2. *http://CRAN.R-project.org/packageGISTools
6. Carr, D. B., Bell, B. S., Pickle, L. W., Zhang, Y. & Li, Y. (2003), The State Cancer Profiles Web Site and Extensions of Linked Micromap Plots and Conditioned Choropleth Map Plots, 'Proceedings of the Third National Conference on Digital Government Research', Digital Government Research Center (DGRC), p. 269-273. *http://dl.acm.org/citation.cfm?id1123196
7. Carr, D. B., Chen, J., Bell, B. S., Pickle, L. W. & Zhang, Y. (2002), Interactive Linked Micromap Plots and Dynamically Conditioned Choropleth Maps, 'Proceedings of the Second National Conference on Digital Government Research', Digital Government Research Center (DGRC), p. 61-67. *http://dl.acm.org/citation.cfm?id1123098
8. Carr, D. B. & Pearson, J. B. (2014), micromapST: Linked Micromap Plots for U.S. States. R package version 1.0.3. *http://CRAN.R-project.org/packagemicromapST
9. Carr, D. B. & Pickle, L. W. (2010), Visualizing Data Patterns with Micromaps, Chapman & Hall/CRC, Boca Raton, FL.
10. Carr, D. B. & Pierson, S. M. (1996), 'Emphasizing statistical summaries and showing spatial context with micromaps', Statistical Computing and Statistical Graphics Newsletter 7(3), 16-23.
11. Douglas, D. H. & Peucker, T. K. (1973), 'Algorithms for the reduction of the number of points required to represent a digitized line or its caricature', The Canadian Cartographer 10(2), 112-122.
12. Gebreab, S. Y., Gillies, R. R., Munger, R. G. & Symanzik, J. (2008), 'Visualization and interpretation of birth defects data using linked micromap plots', Birth Defects Research (Part A): Clinical and Molecular Teratology 82, 110-119.
13. Hóhle, M., Meyer, S. & Paul, M. (2013), surveillance: Temporal and SpatioTemporal Modeling and Monitoring of Epidemic Phenomena. R package version 1.70. *http://CRAN.R-project.org/package=surveillance.
14. Han, K. S., Park, S. J., Mun, G. S., Choi, S. H., Symanzik, J., Gebreab, S. & Ahn, J. Y. (2014), 'Linked micromaps for the visualization of geographically referenced data', ICIC Express Letters 8(2), 443-448.
15. Harrower, M. & Bloch, M. (2006), 'MapShaper.org: A map generalization web service', IEEE Computer Graphics and Applications 26(4), 22-27.
16. Payton, Q. C., Weber, M. H., McManus, M. G. & Olsen, A. R. (2012), Linked Micromaps: Statistical Summaries in a Spatial Context, 'Water: One Resource - Shared Effort - Common Future, 8th National Monitoring Conference, April 30-May 4, 2012, Portland, Oregon', National Water Quality Monitoring Council.
17. Pickle, L. W., Pearson, J. & Carr, D. B. (2014), 'MicromapST: Exploring and communicating geospatial patterns in U.S. State data', Journal of Statistical Software, Under Review 15(11). *http://www.jstatsoft.org/v07/i11/
18. X. Wang, J. X. Chen, D. B. Carr, B. S. Bell, & L. W. Pickle, (2002), 'Geographic statistics visualization: Web-based linked micromap plots', Computing in Science & Engineering 4(3), 90-94.
Este artículo se puede citar en LaTeX utilizando la siguiente referencia bibliográfica de BibTeX:
@ARTICLE{RCEv37n2a11,
AUTHOR = {Symanzik, J{\"u}rgen and Dai, XiaoTian and Weber, Marc H. and Payton, Quinn and McManus, Michael G.},
TITLE = {{Linked Micromap Plots for South America -- General Design Considerations and Specific Adjustments}},
JOURNAL = {Revista Colombiana de Estadística},
YEAR = {2014},
volume = {37},
number = {2},
pages = {451-469}
}
References
Ahn, J. Y. (2013), Visualizing Statistical Information using Korean Linked Micromap Plots, in S.-H. Cho, ed., ‘Proceedings of IASC–Satellite Conference for the 59th ISI WSC & The 8th Conference of IASC–ARS’, Asian Regional Section of the IASC, pp. 219–221.
Bivand, R. S. & Lewin-Koh, N. (2014), maptools: Tools for Reading and Handling Spatial Objects. R package version 0.8–29.
*http://CRAN.R-project.org/package=maptools
Bivand, R. S. & Rundel, C. (2014), rgeos: Interface to Geometry Engine — Open Source (GEOS). R package version 0.3–4.
*http://CRAN.R-project.org/package=rgeos
Bonnal, L., Favard, P., Laurent, T. & Ruiz-Gazen, A. (2011), ‘Pourquoi le coût de l’éducation est–il plus élevé en zone rurale? Le cas de la région Midi–Pyrénées’, Revue d’Économie Régionale & Urbaine 2011/5, 887–910.
Brunsdon, C. & Chen, H. (2014), GISTools: Some further GIS Capabilities for R. R package version 0.7–2.
*http://CRAN.R-project.org/package=GISTools
Carr, D. B., Bell, B. S., Pickle, L. W., Zhang, Y. & Li, Y. (2003), The State Cancer Profiles Web Site and Extensions of Linked Micromap Plots and Conditioned Choropleth Map Plots, in ‘Proceedings of the Third National Conference on Digital Government Research’, Digital Government Research Center (DGRC), pp. 269–273.
*http://dl.acm.org/citation.cfm?id=1123196
Carr, D. B., Chen, J., Bell, B. S., Pickle, L. W. & Zhang, Y. (2002), Interactive Linked Micromap Plots and Dynamically Conditioned Choropleth Maps, in ‘Proceedings of the Second National Conference on Digital Government Research’, Digital Government Research Center (DGRC), pp. 61–67.
*http://dl.acm.org/citation.cfm?id=1123098
Carr, D. B. & Pearson, J. B. (2014), micromapST: Linked Micromap Plots for U.S. States. R package version 1.0.3.
*http://CRAN.R-project.org/package=micromapST
Carr, D. B. & Pickle, L. W. (2010), Visualizing Data Patterns with Micromaps, Chapman & Hall/CRC, Boca Raton, FL. Carr, D. B. & Pierson, S. M. (1996), ‘Emphasizing statistical summaries and showing spatial context with micromaps’, Statistical Computing and Statistical Graphics Newsletter 7(3), 16–23.
Douglas, D. H. & Peucker, T. K. (1973), ‘Algorithms for the reduction of the number of points required to represent a digitized line or its caricature’, The Canadian Cartographer 10(2), 112–122.
Gebreab, S. Y., Gillies, R. R., Munger, R. G. & Symanzik, J. (2008), ‘Visualization and interpretation of birth defects data using linked micromap plots’, Birth Defects Research (Part A): Clinical and Molecular Teratology 82, 110–119.
Han, K. S., Park, S. J., Mun, G. S., Choi, S. H., Symanzik, J., Gebreab, S. & Ahn, J. Y. (2014), ‘Linked micromaps for the visualization of geographically referenced data’, ICIC Express Letters 8(2), 443–448.
Harrower, M. & Bloch, M. (2006), ‘MapShaper.org: A map generalization web service’, IEEE Computer Graphics and Applications 26(4), 22–27.
Höhle, M., Meyer, S. & Paul, M. (2013), surveillance: Temporal and Spatio–Temporal Modeling and Monitoring of Epidemic Phenomena. R package versión 1.7–0.
*http://CRAN.R-project.org/package=surveillance
Mast, B. D. (2013), ‘Visualizing same–sex couple household data with linked micromaps’, Cityscape: A Journal of Policy Development and Research 15(2), 267–271.
Olsen, A. R., Carr, D. B., Courbois, J. P. & Pierson, S. M. (1996), Presentation of Data in Linked Attribute and Geographic Space, in ‘1996 Abstracts, Joint Statistical Meetings, Chicago, Illinois’, American Statistical Association, Alexandria, VA, p. 271.
Payton, Q. C., McManus, M. G., Weber, M. H., Olsen, A. R. & Kincaid, T. M. (2014), ‘micromap: A Package for Linked Micromaps’, Journal of Statistical Software, Under Review 15(11).
*http://www.jstatsoft.org/v07/i11/
Payton, Q. C. & Olsen, A. R. (2014), micromap: Linked Micromap Plots. R package version 1.8.
*http://CRAN.R-project.org/package=micromap
Payton, Q. C., Weber, M. H., McManus, M. G. & Olsen, A. R. (2012), Linked Micromaps: Statistical Summaries in a Spatial Context, in ‘Water: One Resource — Shared Effort — Common Future, 8th National Monitoring Conference, April 30–May 4, 2012, Portland, Oregon’, National Water Quality Monitoring Council.
Pickle, L. W., Pearson, J. & Carr, D. B. (2014), ‘micromapST: Exploring and communicating geospatial patterns in U.S. State data’, Journal of Statistical Software, Under Review 15(11).
*http://www.jstatsoft.org/v07/i11/
R Core Team (2014), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
Stabler, B. (2013), shapefiles: Read and Write ESRI Shapefiles. R package versión 0.7.
*http://CRAN.R-project.org/package=shapefiles
Symanzik, J., Axelrad, D. A., Carr, D. B., Wang, J., Wong, D. & Woodruff, T. J. (1999), HAPs, Micromaps and GPL — Visualization of Geographically Referenced Statistical Summaries on the World Wide Web, in ‘Annual Proceedings (ACSM–WFPS–PLSO–LSAW 1999 Conference CD)’, American Congress on Surveying and Mapping.
Symanzik, J. & Carr, D. B. (2008), Interactive Linked Micromap Plots for the Display of Geographically Referenced Statistical Data, in C. Chen, W. Härdle & A. Unwin, eds, ‘Handbook of Data Visualization’, Springer, Berlin, Heidelberg, pp. 267–294 & 2 Color Plates. Symanzik, J. & Carr, D. B. (2013), Linked Micromap Plots in R, in S.-H. Cho, ed., ‘Proceedings of IASC–Satellite Conference for the 59th ISI WSC & The 8th Conference of IASC–ARS’, Asian Regional Section of the IASC, pp. 213–218.
Symanzik, J., Carr, D. B., Axelrad, D. A., Wang, J., Wong, D. & Woodruff, T. J. (1999), Interactive Tables and Maps — A Glance at EPA’s Cumulative Exposure Project Web Page, in ‘1999 Proceedings of the Section on Statistical Graphics’, American Statistical Association, Alexandria, VA, pp. 94–99.
Symanzik, J., Wong, D., Wang, J., Carr, D. B., Woodruff, T. J. & Axelrad, D. A. (2000), Web–based Access and Visualization of Hazardous Air Pollutants, in ‘Geographic Information Systems in Public Health: Proceedings of the Third National Conference August’, Agency for Toxic Substances and Disease Registry, San Diego, California, pp. 18–20.
Wang, X., Chen, J. X., Carr, D. B., Bell, B. S. & Pickle, L. W. (2002), ‘Geographic statistics visualization: Web–based linked micromap plots’, Computing in Science & Engineering 4(3), 90–94.
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1. Michael G. McManus, Gregory J. Pond, Lou Reynolds, Michael B. Griffith. (2016). Multivariate Condition Assessment of Watersheds with Linked Micromaps. JAWRA Journal of the American Water Resources Association, 52(2), p.494. https://doi.org/10.1111/1752-1688.12399.
2. Chunyang Li, Jürgen Symanzik. (2024). Human Interface and the Management of Information. Lecture Notes in Computer Science. 14690, p.65. https://doi.org/10.1007/978-3-031-60114-9_6.
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