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