by Jock Mackinlay
Visual analysis is an iterative process for working with data that exploits the power of the human visual system. The formal core of visual analysis is the mapping of data to appropriate visual representations.
In this talk, you’ll learn: •What years of research by psychologists, statisticians and others have taught us about designing great visualizations •Fundamental principles for designing effective data views for yourself and others •How to systematically analyze data using your visual system
Data visualization is often where people realize the real value in underlying data. Good data visualization tools are therefore vital for many data projects to reach their full potential.
Many companies have realized this and are looking for the best solutions to address their data visualization needs. There is plenty of tools to choose from, but even for relatively simple charting, many have found themselves with limited options. As the requirements pile up, options become limited: Cross-browser compatibility, server-side rendering, iOS support, interactivity, full control of branding, look and feel … and you’ll find yourself compromising, or – worse yet – building your own visualization library!
Building our data publishing platform – DataMarket.com – we’ve certainly been faced with the aforementioned challenges. In this session we’ll share our findings and approach for others to avoid our mistakes and learn from our – sometimes hard – lessons learned.
We’ll also share what we see the future of online data visualization holding: the technologies we’re betting on and how things will become easier, visualizations more effective, code easier to maintain and applications more user friendly as these technologies mature and develop.
Data isn’t just for supporting decisions and creating actionable interfaces. Data can create nuance, giving new understandings that lead to further questioning—rather than just actionable decisions. In particular, curiosity, and creative thinking can be driven by combining different data sets and techniques to develop a narrative around a set of data sets that tells the story of a place—the emotions, history, and change embedded in the experience of the place.
In this session, we’ll see how far we can go in exploring one street in San Francisco, Haight Street, and see how well we can understand it’s geography, ebbs and flows, and behavior by combining as many data sources as possible. We’ll integrate basic public data from the city, street and mapping data from Open Street Maps, real estate and rental listings data, data from social services like Foursquare, Yelp and Instagram, and analyze photographs of streets from mapping services to create a holistic view of one street and see what we can understand from this. We’ll show how you can summarize this data numerically, textually, and visually, using a number of simple techniques.
We’ll cover how traditional data analysis tools like R and NumPy can be combined with tools more often associated with robotics like OpenCV (computer-vision) to create a more complete data set. We’ll also cover how traditional data visualization techniques can be combined with mapping and augmented reality to present a more complete picture of any place, including Haight Street.
by Bitsy Hansen
I am frequently asked for advice about using data visualization to solve communication problems that are better served through improved information architecture. A nicely formatted bar chart won’t rescue you from a poorly planned user interface. When designing meaningful data experiences it’s essential to understand the problems your users are trying to solve.
In this case, I was asked to take a look at a global data-delivery platform with a number of issues. How do we appeal to a broad cross-section of business users? How do we surface information to our clients in a useful way? How do we facilitate action, beyond information sharing? How do we measure success?
A user-centered approach allowed us to weave together a more meaningful experience for our business users and usability testing revealed helpful insights about how information sharing and data analysis flows within large organizations.
Data visualization is a powerful tool for revealing simple answers to complex questions, but context is key. User-centered design methods ensure that your audience receives the information they need in a usable and actionable way. Data visualization and user experience practices are not mutually exclusive. They work best when they work together.
Many options exist when choosing a framework to build a custom data explorer on top of your company’s stack. With a brief nod to out-of-the-box business intelligence solutions, the presenters will offer an overview of the creative coding frameworks that lend themselves to data visualization on and across web browsers and native apps written for Mac OS X, iOS, Windows, and Android. Evaluation of the strengths and weaknesses of libraries such as Processing, OpenFrameworks, Cinder, Polycode, Nodebox, d3.js, PhiloGL, Raphael.js, Protovis, and WebGL will be explored through visual examples and code. The audience should come away with a sense of what investments into education will return a high value product that serves unique design goals.
Since the early days of the data deluge, Lift Lab has been helping many actors of the ‘smart city’ in transforming the accumulation of network data (e.g. cellular network activity, aggregated credit card transactions, real-time traffic information, user-generated content) into products or services. Due to their innovative and transversal incline, our projects generally involve a wide variety of professionals from physicist and engineers to lawyers, decision makers and strategists.
Our innovation methods embark these different stakeholders with fast prototyped tools that promote the processing, recompilation, interpretation, and reinterpretation of insights. For instance, our experience shows that the multiple perspectives extracted from the use of exploratory data visualizations is crucial to quickly answer some basic questions and provoke many better ones. Moreover, the ability to quickly sketch an interactive system or dashboard is a way to develop a common language amongst varied and different stakeholders. It allows them to focus on tangible opportunities of product or service that are hidden within their data. In this form of rapid visual business intelligence, an analysis and its visualization are not the results, but rather the supporting elements of a co-creation process to extract value from data.
We will exemplify our methods with tools that help engage a wide spectrum of professionals to the innovation path in data science. These tools are based on a flexible data platform and visual programming environment that permit to go beyond the limited design possibilities industry standards. Additionally they reduce the prototyping time necessary to sketch interactive visualizations that allow the different stakeholder of an organization to take an active part in the design of services or products.
28th February to 1st March 2012