workshop b5 data visualization techniques
DESCRIPTION
WORKSHOP B5 Data visualization techniques. WHAT IS VISUALIZATION?. More than GIS… …MORE THAN YOU THINK. EXAMPLES. WHY VISUALIZE. Get it “ at-a-glance ” Normalizes / Focuses Translates Enhances Quality Accelerate Learning “Discovery” Scenarios of Future “ Enjoy Your Data ”. - PowerPoint PPT PresentationTRANSCRIPT
WORKSHOP B5DATA VISUALIZATION TECHNIQUES
WHAT IS VISUALIZATION?
More than GIS… …MORE THAN YOU
THINK.
EXAMPLES
WHY VISUALIZE
Get it “at-a-glance” Normalizes /
Focuses Translates Enhances Quality Accelerate Learning “Discovery” Scenarios of Future “Enjoy Your Data”
WHERE DOES IT FIT?
• BUSINESS CASE
• DATA STRATEGY
• SAMPLING FRAME
• RECRUITMENT
•DATA COLLECTION
• DATA QUALITY
• DATA ANALYSIS
• DISSEMINATION & PRESERVATION
BUSINESS CASE- Who’s the audience
- What’s the problem
- What’s been done
- ETHICS
- NORMALIZATION
- HARMONISATION
• BUSINESS CASE
• DATA STRATEGY
• SAMPLING FRAME
• RECRUITMENT
•DATA COLLECTION
• DATA QUALITY
• DATA ANALYSIS
• DISSEMINATION & PRESERVATION
• BUSINESS CASE
• DATA STRATEGY
• SAMPLING FRAME
• RECRUITMENT
•DATA COLLECTION
• DATA QUALITY
• DATA ANALYSIS
• DISSEMINATION & PRESERVATION
RECRUITMENT- Show how they fit in
survey
- Increase Response Rates
- INTRODUCE BIAS
- GUIDELINES
-Participant Training
• BUSINESS CASE
• DATA STRATEGY
• SAMPLING FRAME
• RECRUITMENT
•DATA COLLECTION
• DATA QUALITY
• DATA ANALYSIS
• DISSEMINATION & PRESERVATION
DATA QUALITY- Real Time
- Post Processing
- Cleaning
- Inference
- Imputation
- Understanding Quality
- Transparency
WATCH OUT FOR:
Time Consumption Ethics / Misrepresentation Visual Overload Introduction of “bias” Privacy Superficiality
(dazzle vs. inform)
RESEARCH NEEDS “Stable” Funding For:
Reliable Base-Data Resources Operating budget for “maintenance &
preservation” How Visualization can Improve “Response
Rates” Engaging “Hard-to-Reach” groups
Identifying & Quantifying Value-added by using visualization New Risks (i.e. biases)
Privacy Thresholds Impacts of visualizing
RESEARCH NEEDS Framing
In context of traditional surveys In Stated Preference & Other Surveys
Developing Templates (tools) & Guidelines Harmonized, High-Quality Data Bases
Education & Training Computer Science MEETS Transportation SYNTHESIS (what’s out there) Teach the Possibilities Define the skills needed to develop/utilize
Visualization
BUSINESS CASE
- Who’s the audience- What’s the problem- What’s been done
- ETHICS- NORMALIZATION- HARMONIZATION
DATA STRATEGY- Graphic Literature Review
- What we know / Don’t know- Knowledge Accelerometer
- THOROUGHNESS- VISUAL OVERLOAD- APPROPRIATENESS
- GUIDELINES
DATA COLLECTION- Monitoring Progress- - Monitoring Quality
- Monitoring Process & Workforce- Reduce Respondent Burden
- INTRODUCE BIAS- IMPROVE QUALITY
- IMPROVE CATI PROCESS
EST. SAMPLING FRAME
- Review “official’ data- Ensure geospatial compatibility- Encourage “mix-mode” surveys- FUNDING to get spatial data
up-to-date- DEVELOP VIS. TEMPLATES
RECRUITMENT- Show how they fit in survey- Increase Response
Rates- INTRODUCE BIAS
- GUIDELINES- Participant Training
DATA QUALITY- Real Time
- Post Processing- Cleaning- Inference
- Imputation- Understanding Quality
- Transparency
DATA ANALYSIS- Extract Patterns
- Data Fusion- Identify Relationships
- Does not compensate for “POOR ANALYSIS”
- POSSIBILITIES FOR INNOVATION- MISREPRESENTATION- FUNDING FOR TEMPLATES
DISSEMINATION & PRESERVATION
- Sustainability- “Get To The Knowledge”
- PRIVACY (Show / Keep)- TOOLS & GUIDELINES