Introducing our latest recruit, Mr. Computer Program.

Unless you have been living on Mars for the last couple of months (oh wait…) you’ll know that the UK will soon be voting on whether to stay in or leave the European Union. Different countries, with different social, cultural, economic and political histories, and languages, which came together – despite their differences – for common interests. Without getting political, I think it’s uncontroversial to say that the world has changed enough since it was set up for us to debate whether it remains fit-for-purpose.

Like many I cannot name my Member of the European Parliament. Whoever they are, though, I have lately been feeling some sympathy for them. Interdisciplinary research can often feel like diplomatic work. Different fields don’t always want or see the need to speak to each other; distinct cultural norms and languages have evolved within each field, with funding and publication systems to support them, which can be in conflict with and contradict each other. However, the world has moved on and, like it or not, now faces complex challenges that require expertise from different fields to tackle together. It’s a battle I believe is worth fighting, and even find rewarding to fight, but my goodness interdisciplinary research is a battle, at the end of which, like my MEP, I have no expectation that people will remember my name either. It’s hard to make your name in one field, let alone two or three.

I have recently been crawling through a labyrinth of literature for two papers, like a mole in a rabbit warren, to find anything at all that speaks to me. To add extra irony, I am also currently supervising a postgraduate student on a literature review assignment! Honestly, you couldn’t make it up. Short story: this mole finally found some light last week shining from three articles. HURRAH!!! Success in even the most everyday academic activities is especially rewarding and motivating in interdisciplinary research.

So this got me thinking about what differentiates my research from all the other research out there. Clearly, it’s different if it takes me so long to find anything similar! But what is my academic identity? This is honestly not intended as a sales pitch. If it’s a sales pitch, it’s directed at myself, to guide me towards the end of the iMars project and beyond, and answers to my existential questions. So, I present to you, for your Friday afternoon entertainment, my  Venn Diagram self-portrait:

Venn_Jess

The Venn Diagram of my Research Interests

  1. Geographical Information Systems and spatial thinking.
  2. Human Factors: the design of automated systems to work for us, not against us, and lately with us…
  3. Data-Driven Discovery: a term that I shamelessly steal from the University of Nottingham’s research priority area. I’m talking about the construction of knowledge from the largest, most dynamic datasets. With special interest reserved for images, which I firmly believe always speak a thousand words (with apologies to my sister the English teacher).

As a geographer I am inherently curious about the connections between people and places (however you choose to define “place”), and the field itself helps me to explore them through its unique combination of relevant aspects from other fields of study. It’s a ‘discipline of disciplines’, so that I am, by nature, too curious to study anything within a black box. For my love of Geography I blame some very fine and funny documentaries starring Michael Palin and a couple of beautiful atlases that allowed me to explore the world from my childhood bedroom. And my good fortune to grow up in Dorset.

My interest in Big Data is founded upon a love of numbers, images of the Earth – Picasso isn’t a patch on Mother Nature – and the patterns they hide.Consider the articles by my good friend Raechel Bianchetti and, more recently, Kevin Schawinski on The Conversation. It’s a long time since my thoughts turned to the search for Donald Rumsfeld’s unknown unknowns, but the collaboration of man and machine is already constructing knowledge on projects like Space Warps and the latest version of Galaxy Zoo. I blame my Maths teacher Miss Johnson for turning me into a number nerd. Maths was my comfort blanket at school; it was a pool of clear water within a murky, grey ocean, where answers were right or wrong.

My recent paddling in the waters of User-Centred Design has given me a new, much more personal frame-of-reference and reignited a fascination I have for us as humans. If Geography and Maths are my ‘British’ and ‘English’ identities then User-Centred Design is my European one. It’s the habitat where my other interests have evolved to reach their potential.

Despite the technological advances for capturing images, whether it’s remote sensing imagery, medical imaging, or security scans, humans still hold the key to meaningful use of imagery. At the most basic level, humans have a capacity for experiential learning and flexible, creative thinking that computers cannot match without significant resources; computers, by contrast, are built to follow the rules we define efficiently. But the volume and variety of data now captured and stored is testing our capacity to extract meaningful information and construct knowledge from it.

In reading and thinking about this, I discovered discussions about the future of work, and human-computer collaboration, also known as human computation, in many areas of life. (Autonomous cars, anyone?) Crowd-sourcing, the approach that iMars exemplifies, is one of the more widely-known incarnations of human side of the human-computation equation, but its fame has come at somewhat of a price to its reputation as it’s come under increasing scrutiny, as if it’s purported to be some silver bullet, which I don’t think even its most ardent proponents would actually claim. The focus of the field, like my literature search, has thus shifted to figuring out how we can get machines and humans to work together and try to answer questions like: What role(s) will the crowd play and what role will the algorithm play in the resulting workflow? How will they work together? Is the Fitts List I described before going to evolve? How do we explore a collaborative workflow’s “Fitt“ness for purpose?

For me the answer lies that last question. Collaborative workflows. Founded upon humility and reflection. Between humans and machines. And researchers from different disciplines. If I’m lucky enough to take this interest further, all my middle-child mediation skills I developed in my doctoral work (as an interdisciplinary researcher and a spokesperson for the end-users at the developers’ table) would come to the fore again, to see how computers and humans (and researchers from different disciplines!) can work together.

I would be lying if I declared now that I am where I am because of a master plan. But whichever way I look at it, however crazy the combination of social and technical ingredients I add to my research, I have never felt more at home at work than I do now.