I’m always looking for ways to make nicer maps. Here are a number of resources.
- Cartographer’s Toolkit: Colors, Typography, Patterns
- GIS Cartography: A Guide to Effective Map Design
- QGIS Map Design
nice maps
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Category Archives: Online Tools / Apps / Data Sources
dots moving around on a map
This is just dots moving around on a map, but I find these dots very engaging in helping me understand urban planning concepts and results of a simulation.
I found this on R bloggers, which talks about how the simulation and map were created.
Data Scientist Todd Schneider has followed-up on his tour-de-force analysis of Taxi Rides in NYC with a similar analysis of the Citi Bike data. Check out the wonderful animation of bike rides on September 16 below. While the Citi Bike data doesn’t include actual trajectories (just the pick-up and drop-off locations), Todd has “interpolated” these points using Google Maps biking directions. Though these may not match actual routes (and gives extra weight to roads with bike lanes), it’s nonetheless an elegant visualization of bike commuter patterns in the city.
swing the election
Here’s an interesting interactive tool on FiveThirtyEight.com where you can play around with U.S. voter turnout and preferences among various demographic groups.
I ran a few scenarios:
- The default scenario is that each demographic group (educated white, uneducated white, black, hispanic/latino, and Asian) votes for the same party in the same proportions as 2012, and turns out at the same rate, but the absolute size of each group is adjusted for changes between 2012 and 2016.
- electoral votes 332-206 in favor of DEMOCRATS
- Let’s go back to the default, and all the Asian people stay home.
- 332-206 in favor of DEMOCRATS (just not enough people, and maybe already concentrated in democratic states)
- Back to the default, and all the hispanic/latino people stay home.
- 283-255 in favor of DEMOCRATS (perhaps hispanics/latinos are also concentrated in already democratic states?)
- Back to the default, and black turnout falls from 66% to 29%
- 286-252 in favor of REPUBLICANS (perhaps this flips some key midwest swing states like Pennsylvania, Ohio, Michigan, Wisconsin, etc.)
- Back to the default, and uneducated whites swing strongly to the right, from 62% last time to 69% Republican (maybe a terrorist attack? a major incident with China or Russia? I don’t want to say false flag, this is not one of those conspiracy websites…)
- 282-256 in favor of REPUBLICANS (probably those swing states again)
- Stay with the previous scenario, but educated whites swing ever so slightly to the left, from 56% Republican last time to 54% Republican (what would cause this? I don’t know, some crazy right-wing candidate spouting racist nonsense maybe, I’m not naming names…)
- 275-263 in favor of DEMOCRATS
So the bottom line is that the minority groups tend to vote Democrat.The uneducated whites tend to vote Republican. The educated whites are the swing voters who end up being the deciding factor. So it is hard to see how a Republican candidate who appeals strongly to uneducated whites but alienates educated whites could ever stand much of a chance.
on the issues
Ontheissues.org is a little bit junky but it has a lot of information on where the candidates stand, well, on the issues. It then graphs them on an interesting spectrum based on where they stand on government intervention in the social and economic spheres.
Social Questions: Liberals and libertarians agree in choosing the less-government answers, while conservatives and populists agree in choosing the more-restrictive answers.
Nate Silver’s Iowa Caucus Predictions
Political season is data science season! Here is some more on Nate Silver’s forecasting methods. If you are reading this in real time (Sunday January 31), by tomorrow night we will find out what actually happens. I will reproduce some graphics here – these are all from the FiveThirtyEight site, so please thank me for the free advertising and don’t send me to copyright jail.
For Clinton vs. Sanders, here is Nate’s average of polls as of today. He gives more recent polls greater weighting, and also adjusts somehow for bias shown in the same polls in the past.
Average of polls: Clinton 48.0% vs. Sanders 42.7%
Now, this is within the 4-6% “margin of error” reported by most polls. (I find this easier to find on the RealClearPolitics site, although curiously it lists margins of error for Democratic polls but not Republican ones. RealClearPolitics does a straight-up poll average without all the corrections that today is Clinton 47.3% vs. Sanders 44%. So all the corrections don’t make an enormous difference.) I can’t easily and quickly find information on whether the “margin of error” is a standard error or a confidence interval or what, but generally when the polls are within the margin of error the media tends to report it as a “statistical tie” or dead heat. And that is exactly what they are saying in this case.
Nate Silver does a set of simulations – it sounds very complicated, but in essence I assume he takes his adjusted poll average for each candidate, some measure of spread like standard error, then runs a whole bunch of simulations. Which leads to results like this:
http://projects.fivethirtyeight.com/election-2016/primary-forecast/iowa-democratic/
Based on this, Nate Silver gives Clinton an 80% chance of winning Iowa and Sanders only a 20% chance.
So what’s interesting is that you have the average of polls (48-43 or 47-44 depending on source), which everyone says is a statistical tie. You have Silver’s predicted result (50-43) based on a large number of simulations, and then you have the resulting odds considering both the predicted result and the spread in the predictions (80-20). In other words, the computer is generating random numbers and 80% of simulations end up favoring Clinton. Of course in real life the dice get rolled only once, but these odds seem pretty good for Clinton.
Meanwhile, the Trump-Cruz contest is similarly close in the polls (30-25 in favor of Trump), but the predicted result (26-25 in favor of Trump) and odds (48-41 in favor of Trump) are much closer. From a quick glance, this appears to be because the spreads are much wider. I don’t know why that would be the case – presence of more viable candidates on the Republican side? Or maybe there is just more variability in the polls and nobody actually knows why.
http://projects.fivethirtyeight.com/election-2016/primary-forecast/iowa-republican/
word clouds in R
Here’s a step-by-step guide to word clouds and other ways of analyzing text in R. And here are some examples applying some of those ideas to presidential candidate debates.
where are the refugees from?
Here’s a pretty awesome data analysis on where (legal) refugees who enter the U.S. come from, and where they go. It’s great both for the information, and for the presentation of the information, which is simple yet highly effective. Click on the link, but here are a few facts to whet your appetite:
- The country of origin for the most refugees to the U.S. in 2014 was Iraq, at 19.651.
- Surprisingly (to me at least), next is Burma at 14,577.
- Rounding out the top five are Somalia (9,011), Bhutan (8,316), and D.R. Congo (4,502).
- After Cuba (4,063), the next highest country from Central or South America is Columbia at 243.
I might have guessed Iraq, but I don’t think I would have guessed anything else on this list. In a number of cases, there are groups of essentially stateless people living in various places (Bhutan and Burma, for example) that the U.S. has agreed to resettle in fairly large groups. In other cases, there are just a handful of people from a given country granted refugee status in a given year. It is a little hard to make sense of why one group is allowed and the next is not.
Raspberry Pi
Here are a bunch of resources for learning Raspberry Pi:
To make it easier to find the kind of resource you want, we’ve grouped our resources under the headings of Teach, Learn and Make. In our Teach resources you’ll find individual lesson plans, complete schemes of work and teachers’ guides, including a teachers’ guide to using Raspberry Pi in the classroom to give educators who are new to the device the information they need to get started.
Our Learn resources guide learners through independent activities. One of the newest is Gravity Simulator, in which students learn about the effects of gravity and how to simulate them in Scratch with Mooncake, the official Raspberry Pi Foundation Cat. It’s one of a number of resources that support activities linked to British ESA Astronaut Tim Peake’s upcoming mission aboard the International Space Station.
Our Make resources support physical computing projects. They range from “getting started” activities for beginners and more in-depth standalone projects to fairly substantial, satisfying builds that you might complete over several sessions. One of these resources is a guide to making a Raspberry Pi marble maze using aSense HAT. A Sense HAT is at the heart of each of the two Astro Pi flight units that will soon be flying to the International Space Station; on board the ISS its gyroscope, accelerometer and magnetometer will be able to detect how the station is moving, and this activity uses the same sensors to work out which way a virtual marble will roll.
Givewell
GiveWell is an organization that claims it has found the charities that do the most good, and also need the most funding. They have concluded that “serving the global poor” is the way to do the most good and alleviate the most human suffering today.
GiveWell is a nonprofit dedicated to finding outstanding giving opportunities and publishing the full details of our analysis to help donors decide where to give.
Unlike charity evaluators that focus solely on financials, assessing administrative or fundraising costs, we conduct in-depth research aiming to determine how much good a given program accomplishes (in terms of lives saved, lives improved, etc.) per dollar spent. Rather than try to rate as many charities as possible, we focus on the few charities that stand out most (by our criteria) in order to find and confidently recommend the best giving opportunities possible (our list of top charities).
Our top charities are (in alphabetical order):
- Against Malaria Foundation (AMF)
- Deworm the World Initiative (DtWI), led by Evidence Action
- GiveDirectly
- Schistosomiasis Control Initiative (SCI)
We have recommended all four of these charities in the past.
We have also included four additional organizations on our top charities page as standout charities. They are (in alphabetical order):
seafood
National Geographic has put together an online seafood app. It uses information available elsewhere (Monterey Aquarium, etc.), but what is innovative is that you can easily filter the most sustainable, nutritious and low-mercury species using a tool bar. The only problem being that, if you pick all those options at once, there are only a couple choices left.