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Visualizing disease incidence in the context of socioeconomic factors

Authors:

Alark JoshiAuthors Info & Claims

VINCI '12: Proceedings of the 5th International Symposium on Visual Information Communication and Interaction

Pages 29 - 38

https://doi.org/10.1145/2397696.2397701

Published: 27 September 2012 Publication History

Abstract

Certain biological factors such as genetics, physical fitness, and lifestyle have been shown to influence an individual's risk of acquiring disease. But are there are other socioeconomic factors that influence disease incidence as well? In this paper, we introduce a visualization tool called Disease Trends that explores the associations and possible correlations between specific economic (personal income per capita), educational (percentage of adult population with a four year college degree), and environmental (air pollution level) factors with diabetes prevalence and cancer incidence rates across counties throughout the United States. It is structured as an interactive geographical visualization that displays disease incidence data as an interactive choropleth map and connects it with coordinated views of the socioeconomic variables for each county as the user scrolls over it. Additionally, the ability to compare and contrast counties as well as to interactively specify a region for comparison allows further examination of the data. This results in an informative overview of disease incidence trends that allows users to spot areas of interest and potentially pursue these areas further with more scientific research.

References

[1]

Centers for disease control and prevention: National diabetes surveillance system. county level estimates of diagnosed diabetes. http://www.cdc.gov/diabetes/statistics/index.htm.

[2]

National cancer institute. state cancer profiles: Incidence rates report. http://statecancerprofiles.cancer.gov/incidencerates/index.php.

[3]

United states department of agriculture: Economic research service. county-level education data. http://www.ers.usda.gov/Data/Education/#list, 2005.

[4]

Bureau of economic analysis: U. S. department of commerce. regional economic analysis: Local area personal income. http://www.bea.gov/regional/reis/default.cfm#step2, 2009.

[5]

United states environmental protection agency. technology transfer network: National scale air toxics assessment. http://www.epa.gov/ttn/atw/nata2002/tables.html#table1., 2009.

[6]

US census bureau small area income and poverty estimates (saipe). http://www.census.gov/did/www/saipe/data/maps/index.html/, 2012.

[7]

R. Amar, J. Eagan, and J. Stasko. Low-level components of analytic activity in information visualization. In IEEE Symposium on Information Visualization, 2005, pages 111--117, 2005.

Digital Library

[8]

G. Andrienko, N. Andrienko, U. Demsar, D. Dransch, J. Dykes, S. Fabrikant, M. Jern, M. Kraak, H. Schumann, and C. Tominski. Space, time and visual analytics. International Journal of Geographical Information Science, 24(10):1577--1600, 2010.

Digital Library

[9]

N. Andrienko and G. Andrienko. Exploratory analysis of spatial and temporal data: a systematic approach. Springer Verlag, 2006.

Digital Library

[10]

L. E. Barker, K. A. Kirtland, E. W. Gregg, L. S. Geiss, and T. J. Thompson. Geographic distribution of diagnosed diabetes in the U. S. A. diabetes belt. American Journal of Preventive Medicine, 40(4):434--439, April 2011.

[11]

D. B. Carr. Designing linked micromap plots for states with many counties. Statistics in Medicine, 20(9-10):1331--1339, 2001.

[12]

J. Chen, R. E. Roth, A. T. Naito, E. J. Lengerich, and A. M. MacEachren. Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U. S. cervical cancer mortality. International journal of health geographics, 7:57, Jan. 2008.

[13]

S. Colagiuri. Epidemiology of prediabetes. Medical Clinics of North America, 95(2):299--307, 2011. Prediabetes and Diabetes Prevention.

[14]

E. K. Cromley and S. L. McLafferty. GIS and Public Health. The Guilford Press, 2002.

[15]

B. Davenhall. Your health depends on where you live. http://www.ted.com/talks/bill_davenhall_your_health_depends_on_where_you_live.html, 2010.

[16]

J. Dykes, A. MacEachren, and M. Kraak. Exploring geovisualization, volume 1. Pergamon, 2005.

[17]

B. K. Edwards, E. J. Feuer, and L. W. Pickle. US predicted cancer incidence, 1999: Complete maps by county and state from spatial projection models. In NCI Cancer Surveillance Monograph Series, 2003.

[18]

S. Few. Bullet graph design specification. In Perceptual Edge - White Paper, 2010.

[19]

A. Fredrikson, C. North, C. Plaisant, and B. Shneiderman. Temporal, geographical and categorical aggregations viewed through coordinated displays: A case study with highway incident data. In Proceedings of the Workshop on New Paradigms in Information Visualization and Manipulation, pages 26--34. ACM Press, 1999.

Digital Library

[20]

M. Harrower and C. A. Brewer. Colorbrewer.org: An online tool for selecting colour schemes for maps. Cartographic Journal, 40(1):27--37, Jun 2003.

[21]

B. Herrera and C. Lindgren. The genetics of obesity. Current Diabetes Reports, 10:498--505, 2010.

[22]

Q. Ho, P. Lundblad, T. Åström, and M. Jern. A web-enabled visualization toolkit for geovisual analytics. SPIE: Electronic Imaging Science and Technology, Visualization and Data Analysis, Proceedings of SPIE, San Francisco, 2011.

[23]

M. Jern, T. Astrom, and S. Johansson. Geoanalytics tools applied to large geospatial datasets. In Information Visualisation, 2008. IV'08. 12th International Conference, pages 362--372. IEEE, 2008.

Digital Library

[24]

M. Jern, J. Rogstadius, and T. Astrom. Treemaps and choropleth maps applied to regional hierarchical statistical data. In Information Visualisation, 2009 13th International Conference, pages 403--410. IEEE, 2009.

Digital Library

[25]

S. Labs. Clearmaps {mapping framework}. http://github.com/sunlightlabs/clearmaps/, 2010.

[26]

Q. Li, X. Bao, C. Song, J. Zhang, and C. North. Dynamic query sliders vs. brushing histograms. In CHI '03 extended abstracts on Human factors in computing systems, CHI EA '03, pages 834--835, New York, NY, USA, 2003. ACM.

Digital Library

[27]

A. M. MacEachren. How Maps Work: Representation, Visualization, and Design. The Guilford Press, New York, 2nd ed. edition, 2004.

[28]

A. M. MacEachren, S. Crawford, M. Akella, and G. Lengerich. Design and implementation of a model, web-based, gis-enabled cancer atlas. Cartographic Journal, The, 45(4):246--260, Nov. 2008.

[29]

A. M. MacEachren and M.-J. Kraak. Research challenges in geovisualization. Cartography and Geographic Information Science, 28(1):3--12, Jan. 2001.

[30]

J. E. Mersey. Color and thematic map design: The role of colour scheme and map complexity in chloropleth map communication. Carotographica, 27(3):1--167, 1990.

[31]

E. Nelson. The impact of bivariate symbol design on task performance in a map setting. Cartographica: The International Journal for Geographic Information and Geovisualization, 37(4):61--78, 2000.

[32]

T. Ormsby, E. Napoleon, and R. Burke. Getting to Know ArcGIS Desktop: The Basics of ArcView, ArcEditor, and ArcInfo Updated for ArcGIS 9. Esri Press, 2004.

Digital Library

[33]

C. Plaisant and V. Jain. Dynamaps: Dynamic queries on a health statistics atlas. In CHI '94 Video Program, ACM CHI '94 Conference Companion, pages 439--440. ACM, 1994.

Digital Library

[34]

J. Symanzik, G. Klinke, S. Klinke, S. Schmelzer, D. Cook, and N. Lewin. The arcview/xgobi/xplore environment: Technical details and applications for spatial data analysis. In ASA Proceedings of the Section on Statistical Graphics, pages 73--78. American Statistical Association, 1997.

[35]

Tableau. Tableau software. http://tableausoftware.com/, 2012.

[36]

TIBCO. Tibco spotfire - business intelligence analytics software. http://spotfire.tibco.com/, 2012.

[37]

B. Tomaszewski, A. Robinson, C. Weaver, M. Stryker, and A. MacEachren. Geovisual analytics and crisis management. In Proc. 4th International Information Systems for Crisis Response and Management (ISCRAM), pages 173--179, 2007.

[38]

K. Webb. Clearmaps: A mapping framework for data visualization. http://sunlightlabs.com/blog/2010/clearmaps-mapping-framework/, 2010.

[39]

G. Wessel, R. Chang, and E. Sauda. Visualizing GIS: Urban Form and Data Structure. In 96th Annual Conference of Association of Collegiate Schools of Architecture (ACSA), pages 378--384, 2008.

Cited By

Goh KChen YChow C(2014)Location-Based Data Visualisation Tool for Tuberculosis and DengueEmerging Research and Trends in Interactivity and the Human-Computer Interface10.4018/978-1-4666-4623-0.ch013(260-282)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-4623-0.ch013
(2013)Compilation of ReferencesEmerging Research and Trends in Interactivity and the Human-Computer Interface10.4018/978-1-4666-4623-0.chcrf(0-0)Online publication date: 31-Oct-2013
https://doi.org/10.4018/978-1-4666-4623-0.chcrf

Index Terms

Visualizing disease incidence in the context of socioeconomic factors
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Graphical user interfaces
  2. Interaction design
    1. Interaction design process and methods
      1. User centered design

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Information

Published In

cover image ACM Other conferences

VINCI '12: Proceedings of the 5th International Symposium on Visual Information Communication and Interaction

September 2012

122 pages

ISBN:9781450317825

DOI:10.1145/2397696

General Chairs:
Huamin Qu
Hong Kong University of Science and Technology
,
Wei Chen
Zhejiang University
,
Program Chairs:
Philip T. Cox
Dalhousie University
,
Shixia Liu
Microsoft Research Asia

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

State Key Lab of CAD & CG: State Key Lab of CAD & CG, Zhejiang University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2012

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VINCI '12

Sponsor:

State Key Lab of CAD & CG

VINCI '12: The International Symposium on Visual Information Communication and Interaction

September 27 - 28, 2012

Hangzhou, China

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Overall Acceptance Rate 71 of 193 submissions, 37%

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Cited By

Goh KChen YChow C(2014)Location-Based Data Visualisation Tool for Tuberculosis and DengueEmerging Research and Trends in Interactivity and the Human-Computer Interface10.4018/978-1-4666-4623-0.ch013(260-282)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-4623-0.ch013
(2013)Compilation of ReferencesEmerging Research and Trends in Interactivity and the Human-Computer Interface10.4018/978-1-4666-4623-0.chcrf(0-0)Online publication date: 31-Oct-2013
https://doi.org/10.4018/978-1-4666-4623-0.chcrf

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