A new age (?)

• Characterized by:– New Media, ‘Big Data’, volumes of real-time,

micro-level information. More stories, more participation, more voices.

– Challenges with ethics, security, privacy, validity of the data…

For Today….

• How does this new era of big data, volumes of complex, real-time information impact our research, the kinds of questions we can ask, and how we go about methods for collecting and analysis?

• How do these changes impact our research agendas in particular?

• Can we brainstorm new ways to take advantage of the availability and scope of “big data” (and all of these changes collectively considered) in order to conduct interesting and creative new research or modes of inquiry?

The availability & scope of new data impact:

– Kinds of questions we ask, new puzzles arise– What it means to know, or think we know,

something– Hypothesis testing & research design– Operationalization: How to collect data on different scales• What is the relevant unit or level of analysis?• Language, culture, perception & meaning… & many

more!

A workflow• Inquiry, Interest, Hypothesis

• Data Collection

• Automated and human translation, coding, verification & reliability

• Visualization, sonification, and understanding

• Analysis & reflection, detecting the signal in the noise

• Predictive modeling & forecasting

Let passion be your guide

• Here’s what makes me intellectually exuberant:

Endogeneity

• As it applies to patterns of conflict

The question

What explains the escalation of violence against civilians in civil war? Why do combatants sometimes target civilians while at other times they refrain from doing so?

The deliberate targeting of civilians is NOT a ubiquitous feature in war. What explains patterns, variation and change?

The Angolan War

• Losing a battle makes a massacre more likely in the next period, in a neighboring area

• Battle events are endogenous to proximate space-time events

The irony of recent conflicts, and maps

• At this very historical moment, for the first time in history, we are precisely able to track, in real-time, the lat, long, location, direction of wars, troop movements, battles, and event incidents in ongoing wars and conflict zones….

• It is also true that geography/space matters less than ever before as relevant visualizations of these complex datasets. i.e. To ‘understand’ or extract a ‘why’ or meaning from a war you actually would use maps less and less.

• At least when considering the counterinsurgency wars in Iraq and Afghanistan today, wars are not about battle lines, moving fronts, and uniformed soldiers taking territories.

• Instead, visualizing relationships, networks, and funding patterns between insurgents seems more relevant in terms of analysis than where on a map an IED exploded, for example.

• Is this fact coincidental? Or did the availability of real-time info somehow contribute to the changed nature of these conflicts themselves!? Hmm..

What about maps?

• Maps are only one way to visualize data.• And visualizing your data on a map might not

tell you anything meaningful or relevant at all.• Maybe the relevant dimension is: changes

over time, stories and meaning woven between the lines, or networked analysis

Complex systems approach

• Viewing data and interactions as complex systems may be an increasingly useful approach in this environment.

• Scott Page’s Course

Dancing landscapes

• Certain systems are so complex, that what is an optimal solution today may not be tomorrow

• A landscape dances when the optimization depends on the time and place, &

the analysis itself

Complex visualization & sonification. Detecting patterns in the data:

Summary: It gets wild

• Sonification– Higgs-Boson at LHC in Geneva– Radioactive orchestra– The weather

• AntZ: http://openantz.com/ http://openantz.com/gallery.html

•

 

Did we lose the story?

• Maybe we obscure the story when we drill into the data too far.

• Handout from most recent New Yorker for discussion…

Interdisciplinary

• We all need everyone, for everything– Statisticians to analyze the data, artists and designers to

visualize it, software engineers to write the programs, area specialists to understand the context, linguists to translate the data, and on and on

• But the incentives for sharing and working together are not uniform across disciplines, schools, individuals.