Using fitness landscapes to visualise evolution in action
Cool visualisation by students of Lada Adamic’s lab.
On climate change responsibility
Today NewScientist reports “The seven deadly sinners of global warming“. What captured my attention was the visualization map telling us that US, China, Russia, Brazil, India, Germany and the UK are responsible for more than 60% of the global warming between 1906 and 2005. Their source is the paper “National contributions to observed global warming” by Matthews, Graham and Keverian (freely available Environmental Research Letters, DOI: 10.1088/1748-9326/9/1/014010).
The top countries change depending how you make the accounting but they can group in two: industrialized countries and deforestation countries. The review from NewScientist put Matthews on my to-read list.
From NewScientist
Mapping ideas: combining text mining and networks
Mapping ideas and how they flow is one of my favorite topics. First I was captured by the concept of misperception of feedbacks. In a nutshell, it refers to how humans have difficulties to understand the complex and sometimes counterintuitive nature of reality. I created models that tried to capture non-linear dynamics of fishermen and lobsters, and see how incomplete understanding of reality often leads to sub-optimal non-sustainable scenarios both in terms of lobster population viability, fishermen profitability and cooperation, as well as traditional knowledge being eroded.
Second I jumped into mapping how scientist explain phenomena of my interest: regime shifts. I use causal loop diagrams to collect the current understanding of such dynamics. In other words, I collect our knowledge about their key drivers and feedbacks. Each link on my CLDs is an hypothesis with different levels of certainty and uneven level of attention on the academic literature. Yet, the diagram capture what I’ve found reported by scientist, and I think of them as the state of the art when it comes to regime shifts. It does not mean it’s right, complete or 100% correct. But it maps how much we know, and what are we thinking is explaining the phenomena we observe.
Lately I’ve been working on a parallel project but with a different perspective. This time I’m looking at ecosystem services: the benefits human receive from nature. I’ve been reading about regime shifts during the past 4 yrs, and it is still really hard to pin down what are their impacts on ecosystem services. Most of the papers on regime shifts do not state what are the potential impacts, and when they do, they often report the easy-to-study / measure short term consequences: e.g. there is less fish, or there is an x% expected decrease on tourism industry revenue. Then most of the interpretation about impacts is subjective and depends on the “reader’s logic”.
To partially circumvent this problem, I’ve been working with Robin Wikström applying topic models – a type of machine learning technique for text mining- in order to discover which ecosystem services are referred to in the scientific literature about regime shifts. The logic behind is more or less the same as the TED video above, where Eric Berlow and Sean Gourley analyse TED videos by translating speech to text and studying the similarity of topics across TED. I find it inspiring because this data rich environment allow them to reduce complexity and see patterns that otherwise couldn’t be pick up just by watching the videos. In the same way, I want to discover patterns of ecosystem services bundles that are hard to grasp in reality, and even harder to see when reading hundreds of papers about regime shifts. Robin and me are re-running our analysis and tuning up the algorithm, hopefully leading to some exciting results about an old question from a different approach: what are the main ecosystem services impacted by regime shifts?
The beauty of data visualization
“Let the dataset change your mindset … and if you can do that, maybe you can also change your behaviour” – David McCandless <- Hans Rosling
Publications in the US 1690-2011
Bill Lane Center from Stanford University has been creating really nice visualizations of publications in the United States. It helps me to visualize the great acceleration in the Anthropocene also in the way we write and publish about the world. You can learn more about the project in Data Visualization: Journalism’s Voyage West
The Growth of US Newspapers, 1690-2011 from Geoff McGhee on Vimeo.
Anthropocene mapping | Video by Globaïa
Nice visualization of the anthropocene b Globaïa. It shows several features of our global civilization: cities, built environment, transmission lines, pipelines, main paved and unpaved roads and railways.
Anthropocene Mapping from Globaïa on Vimeo.
Visualize This: recent book on data visualization
I have to confess, Flowing Data is one of the blogs I check out often. His author, Nathan Yau recently publish the book Visualize This. He address the problem that many of us have in a daily basis: how to explore and visualize data on a meaningful way. Yesterday SmartPlanet published an interesting note with an interview to the author. In case you’re interested, here is the link:
The secrets to successful data visualization | SmartPlanet
In the meanwhile, I’ll order my copy and hopefully learn something about visualization of my network and models data. Hope you enjoy it too.
Desertification in Inner Mongolia, China – in pictures
The Guardian publish some days ago a set of pictures documenting the efforts of NGO’s and local communities to fight desertification in Mongolia. They wrote:
Inner Mongolia, China’s third largest province, is battling severe desertification. Over-grazing, logging, expanding farms and population pressure, as well as droughts, have turned once fertile grasslands into sandy plains. As part of China’s efforts to stop the land degradation, NGOs have been helping with reforestation
See all pictures here: Desertification in Inner Mongolia, China – in pictures | Global development
Interactive Map of Eutrophication & Hypoxia | World Resources Institute
The World Resource Institute recently launched an interactive map of Eutrophication and Hypoxia based on the database collected by professors Robert Diaz and Rutger Rosenberg. It presents a nice visualization of where hypoxia happens the most and the different degrees of severity of the regime shift. This is the link to the WRI page where you can play with the map:
Interactive Map of Eutrophication & Hypoxia | World Resources Institute.
You can actually download the dataset here; and for the interested reader, here is the paper and our description in the Regime Shifts Database.
Full-text visualization
In a recent post I start thinking on data mining and network visualization to further study regime shifts. Applications has been found in medicine. Verbal autopsy is used to assess probable death causes based on interviews where medical appointments are not possible given weak health systems in poor countries.
I just found another application to journalism. Jonathan Stray and colleges have been working on visualizing Iraq war reports using networks and a series of algorithms to quickly extract meaning from text documents. In his blogpost he explain how they did it, and discuss pros and cons of their method.
I’d love to apply such mining methods to the analysis of change in Ecosystem Services for regime shifts. Although I’m not sure if such a thing is possible… yet.
Jigsaw is one of the available softwares.
Jonathan Stray » A full-text visualization of the Iraq War Logs.
Similarities between PhD dissertations
Stanford is developing an interesting network visualization tool to explore similarity between PhD dissertations under the lead of Daniel Ramage and Janson Chuang.
Here is the link to the Stanford Dissertation Browser, it worth to play around a little bit with different disciplines. Below you’ll find the blog where I found it.
Critical Transitions 