Although d3.js is intended to visualise data (d3 stands for Data Driven Documents), it can also be used to render interesting visual effects when you decide to generate your own data. I was looking for a way to add something visual to the layout of this website, and decided to try something I had been thinking about for a while now: using d3.js to build patterns by repeating geometric shapes.
In the end I thought the result is too busy and distracting to have running in the page constantly. I do like the it looks and how it animates, so I decided to add it as a part of my portfolio. The animating result can be viewed at http://squares.andra.nl.
The main project for the Interface & User Experience Design minor consisted of a assignment from a design firm for an actual client. In our case the assignment came from Fabrique. We were asked to design a mobile application for the nursing staff of University Medical Center (UMC) Utrecht, to improve their daily workflow.
Patient data is stored in a centralized database: the Electronic Hospital Information System, or Electronisch Ziekenhuis Informatie Systeem (EZIS) in Dutch. At the moment of this writing, this information can only be accessed through a desktop or laptop computer. However, not all departments have Computers on Wheels (COWs) available, and in many cases they’re not even practical. So during rounds, most nurses rely on pen and paper to have an overview of their patients and keep track of all measurements they do throughout their shift.
Because this is impractical and error prone, the brief called for an application that would allow a nurse to enter new measurements directly into EZIS. It should also display previous measurements, so the nurse can also see trends for all measurements. During our research at the start of the project, it became clear that nurses not only write down measurements but also a lot of other patient data, like their history and medication. An app that focusses merely on measurements would only add to the tools used during the day, instead of actually simplifying a nurse’s workflow.
So we expanded the app’s functionality to include all data that nurses currently access during a shift. We present this data in several forms, depending on the nurse’s current activity. For instance, at the end of a shift a nurse discusses the patients with a nurse whose shift has just started. The app offers a brief overview of all patients, including measurements that have been marked as noteworthy.
The original title of the project was NurseMapp, short for Nurse Measure App. We decided to go with a different name: EZIS Mobile. Though not extremely original, it communicates our intentions: ultimately this app should feel as a true and complete extension of the central EZIS. The brief described this app as the first of many. We believe a centralized but modular approach would better serve all staff within the hospital.
A “Making of” was designed for this project in the form of a one-page website, in Dutch. It describes the structure of the project and the decisions that were made along the way. You can find the Making of, including a working prototype of the final design, at http://umc.andra.nl.
During the minor Interface and User Experience Design, we were asked to pick a method out of the HCI Toolkit at random, research the method and write a paper on the subject. The method I drew was the Funnel Diagram. A lot has been said about how to interpret funnel diagrams and what can be done to improve the conversion they depict. But during my research I discovered there really isn’t that much to say about why a funnel diagram should be used and how it should be designed. In Communicating the User Experience the authors Richard Caddick and Steve Cable give thorough and well substantiated instructions on how to design a funnel diagram.
Although this tool gave me something to demo during the workshop, I still really didn’t know what to write about. After showing the tool to my teacher Jasper Schelling, he showed me some of the work and ideas of Victor Bret. In particular, Jasper pointed out Bret’s ideas about what he calls explorable explanations and reactive documents. These offered extra ideas to implement in the tool, but also a direction I could take with the paper I was about to write. We came upon the idea of building more tools for the methods in the HCI Toolkit.
The Funnel Diagram is one of those methods where the finished artifact associated with the method offers insight into the design of a product. However the process of creating the artifact doesn’t add anything at all, even though the process takes up significant amounts of time and attention. Contrast this with the method Mind Mapping, where the process of creating the artifact (i.e. the mind map) provides the insight or ideas. By building tools that make light work of creating artifacts, the cost of implementing the associated methods would become significantly lower. This could lead to methods being applied more often, which should ultimately lead to better designed and developed solutions.
I’ve written the paper about these ideas and posted it on this site. If you’re interested, please read Let’s put the “Tool” into “HCI Toolkit”.
During de course INFLAB Maarten van den Hoek and I were challenged to explore potential applications of open data made available by the city of Rotterdam. After some experimentation we decided to utilise Python, MongoDB and the lessons we had learned so far in the Data Mining course to develop web-application that calculates the correlation between different types of objects and their attributes in a given area. For instance: what is the correlation between small trees and red playground equipment? Given the right data, this could be expanded with locations of events. For example: what is the correlation between the presence (or absence) of traffic signs and accidents. In some cases, the tool can even be used to check the quality of the different datasets. If, for instance, the type of soil in which a tree is planted is labeled differently from the traffic sign that stands right next to it, at least one of the data points is probably wrong.
Then we took it a step further: we divided the area in smaller sections and calculated correlations for each subsection. Then the deviations from the global correlation were projected on a map. This way both a pattern and the exceptions to that pattern can be examined. You could discover that one particular subsection of an area has a significantly more desirable correlation between two variables, for instance playgrounds and traffic accidents, than it’s surrounding area. This discovery could then lead to an investigation of the subsection, to see what can be learned from it to improve other areas.
The web-application is available for download at https://github.com/averaart/INFLAB01-Object-Heatmap/tree/NewInterface.