Basics of Mapping for the Digital Humanities – IDRE Sandbox To start, navigate to this page via the URL below: “What is a map? In the Humanities, mapping can be defined in so many different ways, there is no easy answer to these questions. “Everything is related to everything else, but near things are more related than distant things.” Waldo Tobler’s statement defines his first law of geography, in ways stating the obvious correlation between objects in space, paving the foundation behind spatial dependencies. As mentioned at the top of the syllabus for this course, space and time are dimensions that are forever intertwined due to their ontological nature. Minard The representation of 3 dimensional space onto a flat, 2 dimensional platform–a map–brings with it many challenges and compromises (e.g. projection systems). Hans Hypercities I turn off Google Maps and start to drive. Google Earth The LA Times uses the SIMILE timeline to effectively show crime over time. CartoDB
The Architecture of a Data Visualization — Accurat studio 1. Composing the main architecture of the visualizationComposing the main architecture: this acts as the formalized base through which the main story will be mapped and displayed, upon this, one will see the most relevant patterns emerging from the story: the essential “map” that conceptually identifies where we are. This base is essentially a matrix or pattern that will serve as our organizer. 2. 3. 4. 5. 6. 7. 8. The final fine-tuning of the piece is the necessary effortrequired to please readers’ eyes:a well-balanced image where negative space and light elementsplay their role aesthetically. Is the process always so linear?
DH Press | Digital Humanities Toolkit How to Start Thinking Like a Data Scientist Slowly but steadily, data are forcing their way into every nook and cranny of every industry, company, and job. Managers who aren’t data savvy, who can’t conduct basic analyses, interpret more complex ones, and interact with data scientists are already at a disadvantage. Companies without a large and growing cadre of data-savvy managers are similarly disadvantaged. Fortunately, you don’t have to be a data scientist or a Bayesian statistician to tease useful insights from data. While the exercise is very much a how-to, each step also illustrates an important concept in analytics — from understanding variation to visualization. First, start with something that interests, even bothers, you at work, like consistently late-starting meetings. Next, think through the data that can help answer your question, and develop a plan for creating them. Now collect the data. Sooner than you think, you’ll be ready to start drawing some pictures. But don’t stop there. So where do you go from here?