Panama Papers: where the money is hiding
A Cartodb.com visualization by country of corporations, etc., named in the Panama Papers.
As experience with the data set develops, linking this summary to individual documents and named individuals would be an enormous but very useful resource.
Makes one wonder what it will be like if similar leaks develop at other “international” law firms?
Will a leak a day make corruption go away?
Free Your Maps from Web Mercator! by Mamata Akella.
From the post:
Most maps that we see on the web use the Web Mercator projection. Web Mercator gained its popularity because it provides an efficient way for a two-dimensional flat map to be chopped up into seamless 256×256 pixel map tiles that load quickly into the rectangular shape of your browser.
If you asked a cartographer which map projection you should choose for your map, most of the time the answer would not be Web Mercator. What projection you choose depends on your map’s extent, the type of data you are mapping, and as with all maps, the story you want to tell.
Well, get excited because with a few lines of SQL in CartoDB, you can free your maps from Web Mercator!
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Not only can you choose from a variety of projections at CartoDB but you can also define your own projections!
Mamata’s post walks you through these new features and promises that more detailed posts are to follow with “advanced cartographic effects on a variety of maps….”
You are probably already following the CartoDB blog but if not…, well today is a good day to start!
Time Integrated Multi-Altitude Migration Patterns by Wouter Van den Broeck, Jan Klaas Van Den Meersche, Kyle Horton, and Sérgio Branco.
From the webpage:
The Problem
Every year hundreds of millions of birds migrate to and from their wintering and breeding grounds, often traveling hundreds, if not thousands of kilometers twice a year. Many of these individuals make concentrated movements under the cover of darkness, and often at high altitudes, making it exceedingly difficult to precisely monitor the passage of these animals.
However one tool, radar, has the ability to measure the mass flow of migrants both day and night at a temporal and spatial resolution that cannot be matched by any other monitoring tool. Weather surveillance radars such as those of the EUMETNET/OPERA and NEXRAD networks continually monitor and collect data in real-time, monitoring meteorological phenomena, but also biological scatters (birds, bats, and insects). For this reason radar offers a unique tool for collecting large-scale data on biological movements. However, visualizing these data in a comprehensive manner that facilitates insight acquisition, remains a challenge.
Our contribution
To help tackle this challenge, the European Network for the Radar Surveillance of Animal Movement (ENRAM) organized the Bird Migration Visualization Challenge & Hackathon in March 2015 with the support of the European Cooperation in Science and Technology (COST) programme. We participated and explored a particular approach.
Using radar measures of bioscatter (birds, bats, and insects), algorithms can estimate the density, speed, and direction of migration movement at different altitudes around a radar. By interpolating these data both spatially and temporally, and mapping these geographically in the form of flow lines, a visualization might be obtained that offers insights in the migration patterns when applied to a large-scale dataset. The result is an experimental interactive web-based visualization that dynamically loads data from the given case study served by the CartoDB system.
Impressive work with both static and interactive visualizations!
Enjoy!
CartoDB and Plotly Analyze Earthquakes
From the post:
CartoDB lets you easily make web-based maps driven by a PostgreSQL/PostGIS backend, so data management is easy. Plotly is a cloud-based graphing and analytics platform with Python, R, & MATLAB APIs where collaboration is easy. This IPython Notebook shows how to use them together to analyze earthquake data.
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Assuming your data/events have geographic coordinates, this post should enable you to plot that information as easy as earthquakes.
For example, if you had traffic accident locations, delays caused by those accidents and weather conditions, you could plot where the most disruptive accidents happen and the weather conditions in which they occur.
CartoDB makes D3 maps a breeze
From the post:
Anybody who loves maps and data can’t help but notice all the beautiful visualizations people are making with D3 right now. Huge thanks to Mike Bostock for such a cool technology.
We have done a lot of client-side rendering expirements over the past year or so and have to say, D3 is totally awesome. This is why we felt it might be helpful to show you how easy it is to use D3 with CartoDB. In the near future, we’ll be adding a few tutorials for D3 to our developer pages, but for now, let’s have a look.
Very impressive.
But populating a map with data isn’t the same as creating a useful map with data.
Take a look at the earthquake example.
What data would you add to it to make the information actionable?
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