Tag Archives: statistics

Resilience Science in 2010: looking back

How was 2010 for this blog?

Google Analytics can be used to find out what happens on a website, and according to Google Analytics, in 2010 Resilience Science had about 240,000 page views, 190,000 unique visitors, and over 500 feed subscribers (according to Google Reader).

The most common search term for Resilience Science was “resilience science”, but less expected frequent searches included “haiti earthquake sociology“, “adaptive architecture“, “biological art“, “Hunza landslide“, and “financial instability hypothesis“.

Visitors came from all over the world.  Our visitors are frequently from the USA (39%).  The ten countries with the most visitors, after the US, were the UK (9%), Canada (9%), Australia (5%), Sweden (4%), India (3%), Germany (3%), Netherlands (2%), France (1%), and Spain (1%).   Below is a map showing the top locations of visitors by city.  London, Stockholm, New York, Sydney, and Melbourne sent the most visitors.

Most visitors went to the home page.  Some posts are popular because they link to an interesting talk or graphic.  These posts are frequently accessed but only glanced at.  For example, the most popular post overall is a 2006 post on economy distorted cartograms.  Other longer posts are read for a while and provide something more analytical.  The five most popular longer posts from 2010 were:

  1. Haiti, disaster sociology, elite panic and looting
  2. A history of Stommel diagrams
  3. The growth of the ecosystem service concept
  4. Resilience meets architecture and urban planning by Matteo Giusti
  5. Undermine Nature/Culture dichotomy – Bruno Latour visits Stockholm by Henrik Ernstson.

About 45% of visitors came from search engines (>95% google), 40% from referring sites – The Resilience Alliance (35%), Facebook (6%), Twitter (4%), development economist’s Chris Blatman’s blog (4%), and Greenpeace (3%) were the top five referring sites.  Direct visitors made up 15% of the traffic.

Hopefully Resilience Science will continue to be interesting in 2011, and that 2011 will be a good year for our readers and writers.

Hans Rosling animates 200 years of human development

Hans Rosling shows how visualizing public health statistics can communicate development and inequality on the BBC show the Joy of stats.  The BBC writes:

Despite its light and witty touch, the film nonetheless has a serious message – without statistics we are cast adrift on an ocean of confusion, but armed with stats we can take control of our lives, hold our rulers to account and see the world as it really is. What’s more, Hans concludes, we can now collect and analyse such huge quantities of data and at such speeds that scientific method itself seems to be changing.

Resilience Science has featured Hans Rosling’s great work with Gapminder many times before : Hans Rosling at TED, Google gapminder, has the world become a better place?, and visualizing development.  Furthermore, this visualization of the huge growth in health and wealth over the past centuries illustrates the point that my co-authors and I made in our Environmentalist’s paradox paper.

Andrew Gelman’s statistical lexicon

On his group’s weblog, influential Bayesian statistican Andrew Gelman proposes a statistical lexicon to make important methods and concepts related to statistics better know:

The Secret Weapon: Fitting a statistical model repeatedly on several different datasets and then displaying all these estimates together.

The Superplot: Line plot of estimates in an interaction, with circles showing group sizes and a line showing the regression of the aggregate averages.

The Folk Theorem: When you have computational problems, often there’s a problem with your model. …

Alabama First: Howard Wainer’s term for the common error of plotting in alphabetical order rather than based on some more informative variable.

The Taxonomy of Confusion: What to do when you’re stuck.

The Blessing of Dimensionality: It’s good to have more data, even if you label this additional information as “dimensions” rather than “data points.”

Scaffolding: Understanding your model by comparing it to related models.

Multiple Comparisons: Generally not an issue if you’re doing things right but can be a big problem if you sloppily model hierarchical structures non-hierarchically.Taking a model too seriously: Really just another way of not taking it seriously at all.

Video Tutorial on Social Network Analysis Using R

From the Complexity and Social Networks Blog links to video of Steve Goodreau and David Hunter running a tutorial on Social Network Analysis Using R.  They recommend some prior knowledge of R and standard network analytic methods as the tutorial covers:

  • use of exponential random graph (ERG or p*) models for representing structural hypotheses,
  • model parameterization, simulation and inference,
  • degeneracy checking, and goodness-of-fit assessment.

For more information, please see the workshop web page, or our project home page .

Goudreau-Hunter Political Networks 2009 1 of 5 from David Lazer on Vimeo.

Goudreau-Hunter Political Networks 2 of 5 from David Lazer on Vimeo.

Goudreau-Hunter Political Networks 2009 3 of 5 from David Lazer on Vimeo.

Goudrieu-Hunter Political Networks 2009 4 of 5 from David Lazer on Vimeo.

Gooudreau-Hunter Political Networks 2009 5 of 5 from David Lazer on Vimeo.