Visualizing Public Health Data Anamaria Crisan, MSc PhD Candidate - - PowerPoint PPT Presentation
Visualizing Public Health Data Anamaria Crisan, MSc PhD Candidate University of British Columbia @amcrisan acrisan@cs.ubc.ca http://cs.ubc.ca/~acrisan 1 I will attempt to make two points The differences between clinical medicine and
Anamaria Crisan, MSc PhD Candidate University of British Columbia
@amcrisan http://cs.ubc.ca/~acrisan acrisan@cs.ubc.ca
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implications
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Clinical Medicine Public Health
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Clinical Medicine Public Health
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pharmaceutical interventions
commonly environmental and behavioral interventions
Clinical Medicine Public Health
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pharmaceutical interventions
specialists (doctors, nurses, etc.)
commonly environmental and behavioral interventions
necessarily specialists (community leaders, etc.)
Clinical Medicine Public Health
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pharmaceutical interventions
specialists (doctors, nurses, etc.)
commonly environmental and behavioral interventions
necessarily specialists (community leaders, etc.)
Clinical Medicine Public Health
Treating lung cancer patient Anti-smoking campaign Example: Both use data, even the same data, in different ways
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Clin Clinic icia ians Re Researchers Pa Patients
§ Currently data vis tends to emphasize clinical medicine applications and targets clinicians, researchers, and patients
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Nu Nurses Clin Clinic icia ians
Me Medical He Health Of Officer ers
Re Researchers Co Communit ity Le Lead aders
§ Public Health has much more multidisciplinary decision making teams
§ More data & diverse data types = more informed decision making § BUT – different stakeholder abilities to interpret data & different needs
§ Gap: few vis applications for public health
Pol Politicians Pa Patients
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The Epidemiological Trinity
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Person Place Time
Outcomes Whole Genome Sequences (WGS) Pa Pathogen or Huma man Treatment
Contact & Social networks
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Person Place Time
Contact & Social networks Outcomes Whole Genome Sequences (WGS) Pa Pathogen or Huma man Treatment My My project: Tuberculosis (TB) WGS
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Person Place Time
Location Geographic Context
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Person Place Time
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Via EHRs data are passively collected about entire populations over time
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§ Barriers for creating data visualizations are lowering
§ Many domain specialists (scientists, public servants) routinely create data visualizations
§ Guidance on what makes a good data visualization is absent
§ Domain specialists don’t read the vis literature
§ Lack of guidance = inefficient unsupervised exploration of vis design space
§ “Hit or Miss” ad hoc design solutions
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§ Barriers for creating data visualizations are lowering
§ Many domain specialists (scientists, public servants) routinely create data visualizations
§ Guidance on what makes a good data visualization is absent
§ Domain specialists don’t read the vis literature
§ Lack of guidance = inefficient unsupervised exploration of vis design space
§ “Hit or Miss” ad hoc design solutions
§ Our proposed solution: systematically create an explorable vis design space
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Design spaces are made of visualization design choices or varying utility (+ 0 - )
Source: Sedlmair (2012) “Design Study Methodology” 21
Source: Munzner (2014) “Visualization Analysis and Design”
GOAL – nudge domain specialists toward better design choice solutions
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BUT – how do we sy system stemati tically describe design space to promote good exploration?
Source: Munzner (2014) “Visualization Analysis and Design” 23
§ Our observation: there’s a lot of figures in research papers, let’s study them! § Challenge: methods for systematic assessment of data visualizations don’t exist
§ Systematic matters! Shows the good, the bad, and the common § Existing studies (setvis, treevis, vishealth) are not systematic reviews of specialist's domain
§ We combined methods from epidemiology with infovis to construct a design space
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§ Scope: Infectious Disease Genomic Epidemiology literature § Objective: Identify and enumerate the kinds of visualizations generated for different topics
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Literature Analysis Qualitative Data Visualization Analysis Quantitative Data Visualization Analysis
§ Scope: Infectious Disease Genomic Epidemiology literature § Objective: Identify and enumerate the kinds of visualizations generated for different topics
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Literature Analysis Qualitative Data Visualization Analysis Quantitative Data Visualization Analysis
WHY are researchers visualizing data? HOW are researchers visualizing data, WHAT are they visualizing? HOW MANY examples are there
§ Scope: Infectious Disease Genomic Epidemiology literature § Objective: Identify and enumerate the kinds of visualizations generated for different topics
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Unpublished & still some work to be done so please don’t distribute
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Surpri rise se fi find nding ng: a lot t of f data ta in n data ta visua sualiza zati tions ns wer ere e no not t visua sualized zed!
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Literature Analysis: Understanding the structure of genomic epidemiology papers promotes systematicity via intelligent sampling
Qualit litativ ive Analy lysis is: Developed GEViT, a Genomic Epidemiology Visualization Typology
types, chart combinations, and chart enhancements
Quantit itativ ive Analy lysis is: It’s nearly all phylogenetic trees, across all pathogens and concepts, but there’s also a lot of tables and plain text
Surpri risi sing ng genera eneral conc nclusi usion: n: mo most da data i is these these da data v visualizations a are n not vi visualized
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An overview of our approach
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Text mining analysis of document corpus
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An overview of our approach
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Text mining analysis of document corpus
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Systematically sample papers
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An overview of our approach
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Text mining analysis of document corpus
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Systematically sample papers
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Create a codeset to classify research figures consistently
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An overview of our approach
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Text mining analysis of document corpus
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Systematically sample papers
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Create a codeset to classify research figures consistently
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Apply code set to research figures
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An overview of our approach
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Create a codeset to classify research figures consistently Text mining analysis of document corpus Descriptive statistics (literally count)
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Systematically sample papers
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Apply code set to research figures
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An overview of our approach
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