Tag Archives: data

Mapping the worlds rivers

Bernhard Lehner my geography colleague from Burnside Hall at McGill has recently released HydroSHEDS a new global map of the worlds rivers.  Maps based upon this data were featured in the March issue of National Geographic.

HydroSHEDS is:

a new hydrographic mapping product that provides river and watershed information for regional and global-scale applications in a consistent format. It offers a suite of geo-referenced data sets (vector and raster) at various scales, including river networks, watershed boundaries, drainage directions, and flow accumulations. HydroSHEDS is based on high-resolution elevation data obtained during a Space Shuttle flight for NASA’s Shuttle Radar Topography Mission (SRTM).

It can be downloaded from USGS at HydroSHEDS Data.

short links: open data, candian census, and merchants of doubt

1) An Open Data Litmus Test: Is There a Download Button from Off the Map

In order for any data to be open you need to be able to download the data so that you can remix, reuse and share the data. Data and the government agency that supplies it are not transparent if you can’t download the raw data. PDF’s and web services don’t count. They can be useful additions to the raw data, but they are not a replacements.

2)  Idiotically the Canadian government is planning to stop collecting detailed census data.  As the Toronto Globe and Mail explains:

For the first time in 35 years, the census will not feature a detailed, long form that Canadians are obliged to send back to the government.

Users of census information, including myself, are not happy and somewhat puzzled as to why this decision was made.

3) Historians of science Naomi Oreskes and Erik Conway’s new book  Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming, describes how politically connected scientists have operated effective campaigns to skew public opinion towards the denial of well-established scientific knowledge over four decades, now has a website – merchantsofdoubt.org – that links to a bunch of the documents supporting the books arguemnet. I linked to a lecture based on the book earlier this year, and they recently wrote an article based on their book for Yale360 Global Warming Deniers and Their Proven Strategy of Doubt.

Short Links: Climate change economics and impacts, dealing with data, and analyzing social networks

  1. Paul Krugman writes a popular article in New York Times Magazine on climate change economics.
  2. Nature reports on how Marine Reserves can be a ‘win–win’ for fish and fishermen.  Our colleague Terry Hughes research is mentioned.
  3. Nature Reports Climate Change has several articles that relate to resilience to sea level rise.  Mark Schrope describes coastal development in Florida (which combines a lack of planning with a lack of memory). Mason Inman reports on ecological engineering to adapt to sea level rise.
  4. An Economist special report on dealing with large volumes of data.
  5. Mathematican Steven Storgatz writes about analysis of social networks in New York Times in the Enemy of My Enemy.

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.