Leaflet, Framework7 and Beyond

When I began my MA in Digital Humanities in Maynooth I was a traditional historian, a historian with some experience with XML yes, but that’s as far as it went.  This course has introduced me to Javascript, TEI and all sorts of other technological challenges, from server issues to database nightmares. However, nothing on this course has presented a bigger challenge than developing my own app. As part of the course I am creating a walking tour mobile application of ‘Little Jerusalem’ for my internship. The aim is to construct a user-friendly, informative and immersive experience to bring to light the history of Dublin’s Jewish hub in the early twentieth century. So far my journey has encompassed numerous challenges, of all varieties, culminating in the determination that a historic walking tour app is most definitely a job for a digital humanist.

Up to this point my journey has largely been defined by technical triumphs and tribulations. However, before delving into the complicated world of map construction and Framework7, it is important to acknowledge the aspects of this project which I did not anticipate when beginning my project. Initially, I believed this project to have two strands: technology and history. Both these strands could have separate creation processes which would be merged together in the final stages to develop a finished product. What I failed to identify was the intermediate discipline, the skill of merging these two aspects in a flawless sequence. For example, I initially planned to have an overarching narrative guiding my tour. This plan was quickly scrapped when it became obvious that in order to do this I would face a myriad of questions, such as, how do you predict a user’s walking speed? Which direction will they take? Would they have to do the whole tour and if so how would this affect those whom may not be able to or may not want to? What if they get lost? And so I was forced to take a third aspect into consideration, best practice. Through research of existing apps and cultural heritage literature it became clear that while there is no right or wrong answer there are decisions to be made and that the best time to make these decisions is while designing your application’s structure.

LJ2Therefore, after constructing an idea of how the app should function, in line with best practice guidelines and keeping the historical narrative in mind, it was time to start app development. For the map itself I am using Leaflet, an open-source JavaScript library for mobile-friendly interactive maps. I chose leaflet purely because of its open-source nature, meaning that the original code is open to all. I found Leaflet incredibly easy to use and their documentation is really helpful, as well as the online community who answer all and any question asked. I would describe my experience with Leaflet as painless.

Unfortunately, Framework7 is a little more complicated. Framework7 was chosen, again because it is open-source, but also, because it provided templates with an existing navigational structure. Owing to this I did not need to create my app from scratch but ‘clean out’ the existing code and replace the features I did not need with my own. The clear out was easy but navigating Framework7’s structure has not. There was a crossroads point in my project where I could have chosen to programme using Javascript or to go down the line of Framework7, and while I occasionally curse my choice, Framework7 was designed for mobile experiences. I chose to work with it in the hope that my finished product would be specifically geared towards its intended use and would therefore, hopefully, produce an enjoyable and easy user experience.

While there is plenty documentation online to try and LJsupport Framework7 users it is pretty daunting for a novice coder. I still face many challenges in implementing my code and achieving the features I want – content popups are my latest struggle- but the greatest lesson I have learned is to have confidence in what you do know. The application is difficult, increasingly so as the app develops and features become more technical, and while the functions do not appear to resemble anything like those on my JQuery crash course, there is little to separate them. The moral of the story when approaching a mammoth task like this: you know more than you think you do.

I do not think my technological struggle is near its completion. I have no doubt there are many days of anguish ahead but I can confirm that the joy of perfecting that code and accomplishing a task is greater than any error Framework7 can throw at you – and trust me, there are many!

Data Visualisations: Knowledge or not?


As a visual learner, I can see the immediate appeal of data visualisations. Large quantities of information can be represented in succinct forms, highlighted by colour and emphasised through shapes. This kind of display is engaging and provides a nice alternative for what can sometimes be a monotonous world of stagnant text. However, as with any source these visualisations must be subjected to scrutiny. Without delving too far into the deep realm of epistemology it is important to assess what transforms data into knowledge and where do these visualisations fall on our triangle.

I am not an epistemologist, nor am I an expert in data analysis, but in the study of history we are trained to see that while we may never know the past in absolute certainty we can employ strategies to obtain as much knowledge as is within our power. My aim with this post is to highlight how humanities research techniques can be applied to data visualisations so that we can estimate the place of these visual products both on the triangle and within research.

To truly understand the risks in using data visualisations we need to look at the process behind their fncreation. The first is data collation.  During a recent lecture Dr Vinayak Das Gupta posited to our class that data was fact regardless of our state of knowing. Building on this, if we can accept that data is fact, and therefore non-negotiable, our emphasis must shift to the researcher who gathers this data. For any form of data collation, the researcher must set parameters to define their data by. These parameters decide which data is included and which is left behind. Coming from the humanities perspective this selection process is equal to the choice of selecting primary resources. A good researcher will aim to gather large quantities using clear and unbiased parameters in the same way that historians aim to use a wide variety of primary source material. Inherent in both disciplines is the possibility of biased selections. Therefore, when using data visualisations, as with secondary texts, it is essential to interrogate which how the data was collected and which parameters were applied.

Following on from data source selection is contextual support. In the same way that primary source material must be placed within its historical background so to must data visualisations be placed in context. In his TED talk on the subject David McCandless exhibits how visualisations without context can be dangerous and misleading (McCandless). McCandless begins with a visualisation comparing American military spending to other countries, as expected the visualisation returns a large red sector for America which dominates the screen. However, McCandless follows this nvisualisation with another placing American military spending within the context of American GDP. The new data context reveals a new side to the data in general and alters our view of military spending. Therefore, context is integral to understanding whether these visualisations are providing us with knowledge or a skewed reflection of manipulated data sets.

Therefore, data visualisations are very similar to a historian’s traditional realm of benefits. However, they come with added benefits. Thanks to data processing researchers can assess large quantities of data which would overwhelm if not outreach the traditional research. In addition to bringing a visually pleasing product to the reader visualisations can accomplish tasks that would be near impossible without the technology and allows us as researchers to see trends and patterns that we may have never noticed without this technology.

So where does this leave visualisations on the data-knowledge triangle. Despite their benefits there is no doubt that as with any other source visualisations need to be subjected to scrutiny and placed within context to be of true value. They, in themselves, are not enough to constitute knowledge and we cannot automatically assume that they are justified even though the data may be true. As a consequence, visualisations lie firmly in the information category. With added information and justification, they may assist us in our pursuit of knowledge but they in themselves are not enough to constitute it.

Further Reading:

‘Recorded Crime Offences by Type of Offence and Quarter’ cso.ie. Date accessed: 15 February 2017.

McCandless, David, ‘The Beauty of Data Visualisations’ TED Talks. https://www.youtube.com/watch?v=5Zg-C8AAIGg. Date accessed: 15 February 2017