Special Session on Geographic Data Science: Boston AAG 2017

This is a call for papers at the AAG 2017 being held in Boston. We are aiming to organise two sessions around the theme of Geographic Data Science, one focusing on methods and the other on applications.

Session organisers: Alex Singleton, Dani Arribas-Bel, Seth Spielman, Nick Malleson, Mark Birkin, Marynia Kolak.

If this is of interest, then please contact Alex Singleton (alex.singleton@liverpool.ac.uk) with an abstract and talk title by Thursday 20th October 2016.

The integration of digital technologies into nearly every aspect of human activity reshapes the landscape of urban and regional geographic research.

New forms of data from social media, open government initiatives, sensors (both mobile and fixed), and many other geo-referenced sources collectively  provide a new perspective on old geographic questions and potentially  raise many new ones.

These new forms of data are just electronic bits sitting in data centers until someone finds meaning within them. In order to transform these bits into actionable insight and/or knowledge that advances our understanding of social processes, new forms of analysis are necessary.

The nascent field of Data Science (DS), at the intersection of mathematics, statistics and computer science provides a set of methods and tools designed from the ground up to tackle such types of data. Since its inception at internal teams of Sillicon Valley startups in the early 2000s to deal with server logs, the field has expanded substantially and today its applications can be found beyond web tracking data into urban management contexts (crime and policing, trash scheduling, bike sharing systems allocation…), smart-crop agriculture, or humanitarian causes (targetting of relief funds, poverty estimation…),  to name just a few. In all of these examples, geographical context and space represent an important part of the set of questions to be asked, and also play a significant role in the best answers to be provided. However, very little of current DS has engaged so far with explicitly spatial methods.

A new field Geographic Data Science (GDS, or G+DS, or DS+G) is emerging at the nexus of data science and geography. We believe this  field requires substantive domain expertise in geography and the  skills of a modern data scientist. The G+DS more than just rebranding of GISc, or GISc w/ different data. G+DS represents a new way of working that is both open, reproducible, and agile.

For these sections, we welcome papers that effectively blend new forms of data and a DS approach with explicitly spatial analysis. We hope these sessions will begin to catalyze a community of like minded Geographers (with expertise/interst in DS). Some examples, of potential topics, although not an exhaustive list, include:

Empirical applications of new forms of data combined with DS approaches to tackle traditional Geographical questions.

Novel methodologies that formally include space into standard DS techniques.

Methodological comparisons of DS techniques that include space formally.

Web tracking data and consumer segmentation

Research using new forms of data combined with DS approaches to influence policy