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Posts tagged mapping
New publication - Using Geotagged Digital Social Data in Geographic Research

This chapter outlines how one might utilize the massive amounts of web-based, geographically-referenced digital social data for geographical research. Because much of these data are user-generated and produced through social media platforms, we also focus on the pitfalls associated with such sources and the benefits of a mixed methods approach to these data. Not only can digital social data be mapped for visual analysis, it is also useful to use a range of quantitative methods to understand relationships between different subsets of the data. In addition, closer, systematic readings via qualitative methods of social data provides insights of particular people’s perceptions and experiences of the world around them. Thus, while making maps is often the starting point for geographers working with this kind of research, it is rarely the end point.

You can see the chapter on Google Books, or download a pre-publication version below.

Poorthuis, A., Zook, M., Shelton, T., Graham, M, and Stephens, M. 2016. Using Geotagged Digital Social Data in Geographic Research. In Key Methods in Geography. eds. Clifford, N., French, S., Cope, M., and Gillespie, T. London: Sage. 248-269.

Geographies of Information Inequality in Sub-Saharan Africa (new publication)

A new publication of ours in now out in  The African Technopolitan. Graham, M., and Foster, C. 2016.  Geographies of Information Inequality in Sub-Saharan AfricaThe African Technopolitan. 5. 78-85.

The piece draws on some of our previous empirical research to reflect on what connectivity means to inclusion in the ‘network society.’ Connectivity certainly isn’t a sufficient condition for inclusion and equity, and we need to ask whether it is a necessary one.

Connectivity, rather, tends to be an amplifier: one that often reinforces rather than reduces inequality. We therefore need to move towards deeper critical socio-economic interrogations of the barriers or structures that limit activity and reproduce digital inequality.  The categorisations developed in the paper offer an empirically-driven and systematic way to understand these barriers in more detail.

Hashtags and Haggis: Mapping the Scottish Referendum

Reposted from our work over at Floatingsheep

The past weeks have been quite eventful in Scotland as a monumental election unfolds. Everyone wants to know, which way will the Scots vote? While we here at Floatingsheep certainly don’t have the answer or power to predict the referendum, we thought it might be interesting to see the geographic dimension of how Scots (and the rest of the world) are tweeting about a fundamentally geographic decision [1].

We pulled data from DOLLY from the last month and a half for a number of hashtags and terms that we thought might be helpful in taking the pulse of Twitter discussion around the independence referendum. Most obviously, we collected the hashtags #VoteYes and #YesBecause due to their association with the pro-independence movement, and the hashtag #NoThanks because of its association with anti-pro-independence sentiment [2].


We started by comparing the prevalence of ‘no’ (i.e., pro-union) hashtags versus 'yes’ (i.e., pro-independence) hashtags the global level. In the map below, orange indicates a greater prevalence of 'yes’ tweets and purple indicates that there are more 'no’ tweets. Perhaps the most interesting thing here is that we can see the United Kingdom swing towards a 'yes’ vote, which has, for the most part, appeared to be the underdog in more conventional polling leading up to the referendum. Then again, most of Western Europe, along with Thailand and Australia, also have a general preference for 'yes’ tweets. Oddly enough, the United States is the staunchest defender of the union, based solely on it’s massive preference for 'no’ tweets. Strange for a country that yearly celebrates its breaking away from Mother England

Comparing 'Yes’ vs. 'No’ Tweets at the Global Scale

Looking closer at the UK, we can see that much of Scotland has a roughly equal number of tweets in support of both the 'yes’ and 'no’ positions – reflecting the contentious and hotly-contested nature of this referendum. But the Central Belt in particular – where a lot of actual votes will be coming from, as it is the most densely populated part of the nation – swings heavily towards 'yes’. The English, on the other hand, seem very much inclined towards pro-union or anti-separation tweeting.

Comparing 'Yes’ vs. 'No’ Tweets in the United Kingdom

To take an alternative look at support for the different positions, we mapped the percentage of each of the three hashtags that originates in each of the administrative sub-regions of both Scotland and the UK as a whole. The Highlands and parts of the Central Belt again show up as strong bastions of 'yes’ votes.

Percentage of Referendum-Related Tweets from Different Regions

But seeing as we’re interested in doing more than just mapping distributions, the next question is how are we to put all of this into context? The only proper place to start is, of course, with the Queen. The map below illustrates those places which also tend to have higher-than-normal levels of tweeting about the Queen (in orange) and those places that are tweeting less about the Queen than might usually be expected (in purple), based on a baseline measure of tweeting activity. Sadly, the whole country seems to be ignoring her. Apart from Glasgow, that is. In the interests of not upsetting an 88 year-old lady, we have chosen not to explore these tweets in any more detail.

Tweets referencing “Queen”

Building on this, we also explored the geography of references (using the same method described above) to something inherent in most people’s definitions of Britishness: tea and crumpets

We see an all-around tea-depression; hardly anywhere is particularly pro-tea at the moment, truly a shocking state of affairs. The British are clearly not being their usual selves, and for their sake we’re glad the referendum will be over soon, regardless of the outcome. Scotland, in particular, has average tea counts that are low by historical standards.

Tweets referencing “tea and crumpets”

This analysis would, of course, all be meaningless unless we mapped the geographies of a range of uniquely Scottish phenomena: haggis [3], kilts and Nessie. Still using the same method as above, the map below shows without a shadow of a doubt that Scotland is destined to become it’s own nation.

Tweets referencing “haggis”, “kilts” or “Nessie” 

The Scots are tweeting about these topics at a greater-than-usual rate, while their southern neighbors remain distinctly uninterested. If ever there were an indication that these nations are divided by more than just a line on a map, we see that manifested in the topic of people’s Twitter conversations. In short, the Scottish referendum is not just simply about “yes” or “no” but seemingly touches on much more fundamental questions of ovis-based cuisine, men’s wear and mythological creatures.

So even if the 'no’ votes win out in and the Kingdom remains united, the geographies of haggis related tweeting (along with a few other things) has revealed that these are two very different nations, indeed.

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[1] In case you don’t know what Twitter, is we refer you to the Scots Wikipedia page on the subject, which states: “Twitter is an online social networkin service an microbloggin service that enables its uisers tae send an read text-based messages o up tae 140 characters, kent as 'tweets’”.
[2] Perhaps we could have simplified this phrasing, but then we would have lost the chance to type “anti-pro-independence”, which is a lot of fun. Anti-pro-independence. Anti-pro-independence.
[3] Normally the Floatingsheep collective avoids conversation about sheep heart, liver, and lungs that are boiled in a sheep stomach. But we made an exception this time.
Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’ (new paper)

Together with my colleagues, Taylor Shelton, Ate Poorthuis, and Matt Zook, I’ve written a new paper about data shadows, and some important sociospatial dimensions of ‘big data.’

Digital social data are now practically ubiquitous, with increasingly large and interconnected databases leading researchers, politicians, and the private sector to focus on how such ‘big data’ can allow potentially unprecedented insights into our world. This paper investigates Twitter activity in the wake of Hurricane Sandy in order to demonstrate the complex relationship between the material world and its digital representations. Through documenting the various spatial patterns of Sandy-related tweeting both within the New York metropolitan region and across the United States, we make a series of broader conceptual and methodological interventions into the nascent geographic literature on big data. Rather than focus on how these massive databases are causing necessary and irreversible shifts in the ways that knowledge is produced, we instead find it more productive to ask how small subsets of big data, especially georeferenced social media information scraped from the internet, can reveal the geographies of a range of social processes and practices. Utilizing both qualitative and quantitative methods, we can uncover broad spatial patterns within this data, as well as understand how this data reflects the lived experiences of the people creating it. We also seek to fill a conceptual lacuna in studies of user-generated geographic information, which have often avoided any explicit theorizing of sociospatial relations, by employing Jessop et al.’s TPSN framework. Through these interventions, we demonstrate that any analysis of user-generated geographic information must take into account the existence of more complex spatialities than the relatively simple spatial ontology implied by latitude and longitude coordinates.

Below you can find the full citation and a link to a publicly available (free) version:

Shelton, T., Poorthuis, A., Graham, M,. and Zook, M. 2014. Mapping the Data Shadows of Hurricana Sandy: Uncovering the Sociospatial Dimensions of 'Big Data’. Geoforum (52) 167-179.  (free pre-publication version available here).

We’d welcome any comments or questions about the paper.