Tropict is a set of python and R scripts that adjust the globe to make land masses in the tropics fill up more visual real estate. It does this by exploiting the ways continents naturally “fit into” each other, splicing out wide areas of empty ocean and nestling the continents closer together.

All Tropict scripts are designed to show the region between 30°S and 30°N. In an equirectangular projection, that looks like this:

It is almost impossible to see what is happening on land: the oceans dominate. By removing open ocean and applying the Gall-Peters projection, we get a clearer picture:

There’s even a nice spot for a legend in the lower-left! Whether for convenience or lack of time, the tools I’ve made to allow you to make these maps are divided between R and Python. Here’s a handy guide for which tool to use:

(1) Supported image formats are listed in the Pillow documentation.
(2) A TSR file is a Tropict Shapefile Reinterpretation file, and includes the longitudinal shifts for each hemisphere.

Let’s say you find yourself with a NetCDF file in need of Tropiction, called bio-2.nc4. It’s already clipped to between 30°S and 30°N. The first step is to call splice_grid.py to create a Tropicted NetCDF:

python ../splice_grid.py subjects/bio-2.nc4 ../bio-2b.nc4

But that NetCDF doesn’t show country boundaries. To show country boundaries, you can follow the example for using draw_map.R:

library(ncdf4)
library(RColorBrewer)

## Open the Tropicted NetCDF
database <- nc_open("bio-2b.nc4")
## Extract one variable
map <- ncvar_get(database, "change")

## Identify the range of values there
maxmap <- max(abs(map), na.rm=T)

## Set up colors centered on 0
colors <- rev(brewer.pal(11,"RdYlBu"))
breaks <- seq(-maxmap, maxmap, length.out=12)

## Draw the NetCDF image as a background
splicerImage(map, colors, breaks=breaks)
## Add country boundaries
addMap(border="#00000060")
## Add seams where Tropict knits the map together
addSeams(col="#00000040")

Here’s an example of the final result, for a bit of my coffee work:

For more details, check out the documentation at the GitHub page!

And just for fun, here were two previous attempts of re-hashing the globe:

I admit that moving Australia and Hawaii into the India Ocean was over-zealous, but they fill up the space so well!

Here I can still use the slick division between Indonesian and Papua New Guinea and Hawaii fits right on the edge, but Australia gets split in two.

Enjoy the tropics!

The Global Climate Prospectus will describe impacts across the globe, at high resolution. That means choosing administrative regions that people care about, and representing impacts within countries. However, choosing relevant regions is tough work. We want to represent more regions where there are more people, but we also want to have more regions where spatial climate variability will produce different impacts.

We now have an intelligent way to do just that, presented this week at the meeting of the American Geophysical Union. It is generalizable, allowing the relative role of population, area, climate, and other factors to be adjusted while making hard decisions about what administrative units to combine.  See the poster here.

Below is the successive agglomeration of regions in the United States, balancing the effects of population, area, temperature and precipitation ranges, and compactness. The map progresses from 200 regions to ten.

Across the globe, some countries are maintained at the resolution of their highest available administrative unit, while others are subjected to high levels of agglomeration.

The tool is generalizable, and able to take any mechanism for proposing regions and scoring them. That means that it can also be used outside of the GCP, and we welcome anyone who wants to construct regions appropriate for their analysis to contact us.

This is another installment of my top 500 journal articles: the papers that I keep coming back to and recommending to others.

Few papers have had a larger impact on my thinking and goals as Donella Meadows’s article Leverage Points: Places to Intervene in a System:

Folks who do systems analysis have a great belief in “leverage points.” These are places within a complex system (a corporation, an economy, a living body, a city, an ecosystem) where a small shift in one thing can produce big changes in everything.

She then explains how to understand them and where to find them, with fantastic examples from across the systems literature: global trade, ecology, urban planning, energy policy, and more. Reading it makes you feel like a kid in a candy shop, with so many leverage points to choose from. Shamelessly stealing a punch-line graphic, here are the leverage points:

I have a small example of this, which you can try out. Go to my Thermostat Experiment and try to stabilize the temperature at 4 °C without clicking the “Show Graph” button until at least 30 “game minutes”. Then read on.

I’ve had people get very mad at me after playing this game. Some people find it impossible, get frustrated, and want to lash out. It’s a very simple system, but you are part of the system and you’re only allowed to use the weakest level of leverage point: the parameter behind the thermostat knob. What would each of the other leverage points look like?

• 11. Buffer sizes: you can sit at a bad temperature for longer without hurting your supplies
• 10. Material stocks and flows: you can move all the supplies out of the broken refrigerator
• 9. Length of delays: the delay between setting the thermostat and seeing a temperature change is less
• 8. Negative feedback: you’re better at setting the temperature
• 7. Positive feedback: the recovery from a bad temperature is faster
• 6. Information flows: you get to use the “Show Graph” button
• 5. Rules of the system: you can get a new job not working at a refigerator warehouse
• 4. Change system structure: you can modify the Thermostat experiment code
• 3. Goals of the system: you replace the thermostat with a “fresh-o-stat” and just turn that up
• 2. System mindset: you can close the website
• 1. Transcending paradigms: you can close your computer

Resources:
Take a look at this podcast on Leverage Points from Made You Think.

The effects of climate change on migration are a… moving concern. The news usually go under the heading of climate refugees, like the devastated hoards emanating from Syria. But there is already a less conspicuous and more persistent flow of climate migrants: those driven by a million proximate causes related to temperature rise. These migrants are likely to ultimately represent a larger share of human loss, and produce a larger economic impact, than those with a clear crisis to flee.

In most parts of the world, we only have coarse information about where migrants move. The US census might not be representative of the rest of the world, but it’s a pool of light where we can look for our key. I matched up the ACS County-to-County Migration Data with my favorite set of county characteristics, the Area Health Resource Files from the US Department of Health and Human Services. I did not look at migration driven by temperature, because I wanted to know if some of the patterns we were seeing there were a reflection of anything more than the null hypothesis. Here’s what I found.

First, the distribution of the distance that people move is highly skewed. The median distance is about 500 km; the mean is almost 1000. Around 10% of movers don’t move more than 100 km; another 10% move more than 2500 km.

The differences between characteristics of the places where migrants are moving from and where they are moving to reveals an interesting fact: the US has approximate conservation of housing. The distribution of the ratio of incomes in the destination and origin counties is almost symmetric. For everyone who moves to a richer county, someone is abandoning that county for a poorer one. The same for the difference between the share of urban population in the destination and origin counties. These distributions are not perfectly symmetric though. On median, people move to counties 2.2% richer and 1.7% more urban.

The urban share distribution tells us that most people move to a county that has about the same mix of rurality and urbanity as the one they came from. How does that stylized fact change depending on the backwardness of their origins?

The flows in terms of people show the same symmetry as distribution. Note that the colors here are on a log scale, so the blue representing people moving from very rural areas to other very rural areas (lower left) is 0.4% of the light blue representing those moving from cities to cities. More patterns emerge when we condition on the flows coming out of each origin.

City dwellers are least willing to move to less-urban areas. However, people from completely rural counties (< 5% urban) are more likely to move to fully urban areas than those from 10 - 40% urban counties. How far are these people moving? Could the pattern of migrants' urbanization be a reflection of moving to nearby counties, which have fairly similar characteristics?

Just considering the pattern of counties (not their migrants) across different kinds degrees of urbanization, how similar are counties by distance? From the top row, on average, counties within 50 km of very urban counties are only slightly less urban, while those further out are much less urban. Counties near those with 20-40% urban populations are similar to their neighbors and to the national average. More rural areas tend to also be more rural than their neighbors.

What is surprising is that these facts are almost invariant across the distance considered. If anything, rural areas are *more* rural than their immediate neighbors than to counties further away.

So, at least in the US, even if people are inching their way spatially, they can quickly find themselves in the middle of a city. People don’t change the cultural characteristics of their surroundings (in terms of urbanization and income) much, but those it is again the suburbs that are stagnant, with rural people exchanging with big cities almost one-for-one.

I’d like to introduce SEEL, David Anthoff’s nascent lab within the Energy and Resources Group at UC Berkeley. What was initially a ramshackle group of Ph.D. students, associated with David for as little reason that economically minded folk in ERG’s engineering-focused community need to stick together, seems to be growing into a healthy researching machine. Check out the new website for the Society, Environment and Economics Lab.

The current drivers are around FUND, a widely-used integrated assessment model, maintained by David. For a long time, models like this have been black boxes, and FUND is one of the few with open source code. That’s changing with David’s new modeling framework, Mimi, which has allowed him to rewrite FUND as a collection of interconnected components.

I like the vision, and I think it’s implemented in a way that has real legs for shifting the climate impact assessment process into a more open process. But we’ll find out soon. The National Academy of Sciences is meeting soon to discuss the future of the “social cost of carbon”, an influential quantity computed by models like FUND. David is going to try to convince them that the future of impact modeling looks like Mimi. Godspeed.