Retrotopia draft map

The author John Michael Greer has been publishing a speculative fiction series currently on his blog, The Archdruid Report called 'Retrotopia'. It is set in around 2065, in a North America that has undergone some changes after the breakup of the USA.


Here is a draft map, based on his descriptions in the comments to a recent post, showing the approximate borders of the successor states to the USA and Canada. I have yet to deal with states that are split between more that one nation in 2065.

The nations of the Retrotopia stories, not including at this time states that are split between more than one Retrotopia nation. No account of sea level rise is made, which is assumed to be 6 feet above present day levels.

I used a map of US counties from here, and US state, and Canadian province shapefiles from here. Update - I switched to using this map of Canadian provinces which includes Nunavut.

I have also used the ETOPO1 bedrock relief topographic map as a background, which shows the elevation of Greenland without the ice sheet. It is perhaps a century or two too early to assume complete deglaciation of Greenland. The portion of Greenland below sea level is not actually connected to the sea here, it would be at a +50 metre sea level as assumed in the 25th century setting of Star's Reach.

Update

I have made some updates based on further descriptions based on John Michael Greer's comments, which account for the states that are divided:

The borders of the Retrotopia nations can be seen by the colours, with present-day US states and counties shown as lines.

The Lakeland Republic is not labelled since the Free City of Chicago label has prevented it from appearing.

In this case the Lakeland Republic label wins out.

These are the borders of the western areas, as far as I understand JMGs comments.

Close-up of the north-east former USA and adjacent part of Canada.

Black and white version.

The shapefile itself containing all of the Retrotopia nations in North America I have made available here.

It is also available as a KML file suitable for use with Google Earth.

Showing 2 meters of sea level rise in blue, with 2-4 metres shaded in lighter blue.

Mapping the result of the EU referendum

Since the results of the UK EU referendum were published, a number of maps have been published of the results across the UK.

I have mapped these in QGIS, with the aid of the OS Boundary Line map of local authority boundaries in Great Britain, and Westminster constituencies in Northern Ireland. Both of these sets of boundaries are available as government open data.

I have used data dependent styling in the maps below, which used the following formula:

case when "pc_leave"  >= 50.0 then color_rgba( 127,0,255, scale_linear( "pc_leave" ,50,80,20,255))
else color_rgba( 255,255,0, scale_linear( "pc_remain" ,50,80,20,255)) end

This makes places that had a majority of votes cast for leave shaded in purple, with transparency varied as the percentage who voted leave rises from 50% to 80%. Similarly remain is shaded in yellow under the same scaling. Unlike in General Elections, where parties generally have an established colour scheme, the EU referendum doesn't have a universally accepted colour scheme. I used purple because UKIP uses purple, and yellow because pro-Remain parties LibDems and SNP use it.

Most of my maps retain the geographic rather than a cartogram representation.  It is a point of open debate which is better, but I also add another alternative, which is to represent the absolute size of the majority by crosses, by placing a point at a random place within each polygon for each 1000 votes of majority. The other difficulty with these kind of maps is the varying impact based on the visual impact of different colours.

With a white background

With SRTM elevation overlaid (because I can).

With a black background.

Using QGIS's random dots feature to represent each 1000 voters of majority abs(leave-remain) with a cross. These are not very visible at this scale.

Crosses to represent each 1000 votes of majority, using random dots in QGIS.

Same as above, but remain is now red, which makes it more visible.

With a black background, going back to purple/yellow.

A cartogram with the QGIS cartogram plugin, sized by votes cast (not electorate) since this was the data more easily to hand.

Federation Membership referendum

Here are the arguments for remaining in vs. leaving the Federation to help you decide how to vote in the referendum on Thursday 23rd June 2366:

Remain

Leave

 

Atmospheric correction of Sentinel 2 images

I recently downloaded the Sentinel Application Platform including the Sentinel 2 Toolbox.

This looks to be a fairly fully featured image processing program, but what I was most interested in doing is the atmospheric correction for the Sentinel2 images I recently downloaded.

The Sentinel 2 'sen2cor' plugin accomplishes this, which wasn't too difficult to install, making use of anaconda to manage the various dependencies. Once I managed to get the environment variables set, and have it find all of the libraries it pretty much just worked.

After this, I tried using my own script for stacking the bands, which came out with a non-georeferenced image. I then noticed I could save the layerstacked image as a GeoTIFF/BigTIFF from within SNAP. A single 'granule' of Sentinel2 resampled to 10m produced a 9GB GeoTIFF, so I converted to KEA with gdal_translate.

Sentinel 2 image processed to Level2A, with SNAP and sen2cor. Bands B11/B8/B4

Zooming in on the Truro area. Penryn can be seen at the lower-left.

Using my rsgislib-landexplorer program to juxtapose geotagged ground-level images with Sentinel2

The same as above, but the non-atmosphere corrected version of the Sentinel2 image.

False colour composite of Sentinel 2 - 30.05.16

After using the script in the previous post to create a layerstacked .kea file for each "granule" within the Sentinel 2 images I downloaded, I created a false colour composite mapping band 11 (SWIR 1610nm) to red, band 8 (NIR, broadband centred around 842nm) to green, and band 4 (red 665nm) to blue.

See https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial for a description of the bands available in Sentinel 2.

Here is a mosaic made in Tuiview:

After some issues with opening .kea files in QGIS (due to having reinstalled Ubuntu recently and needing to recompile KEAlib and then finding that due to being compiled against GDAL 2.1 libraries it didn't work in QGIS which was using GDAL 1.11),  I sorted this out, and have a QGIS project file showing the image. I also show some placenames from OpenStreetMap, and hill names from www.hill-bagging.co.uk for context.

Some screenshots of QGIS are shown below:

Aberystwyth area

Bodmin Moor, Cornwall. Road construction can be seen at the lower left.

Falmouth and surrounding area.

Snowdon

Truro area.

Plymouth, Saltash and Torpoint and the Rame peninsula

A clear day for Sentinel 2 satellite image of Cornwall

The ESA Copernicus programme includes the twin satellites Sentinel 2A and Sentinel 2B which have a multiband sensor. Details can be found here: https://sentinel.esa.int/web/sentinel/missions/sentinel-2.

Sentinel 2A has already been launched and is taking data, and Sentinel 2B will be launched later in 2016.

The Scientific Data Hub provides access to the data, although there is also access via an API, and via Amazon Web Services, and USGS Earth Explorer (currently some time behind).

Until recently, the images of Cornwall had been mostly heavily affected by cloud, although there was a better one taken on 30th May 2016.

A composite of bands 2, 3, and 4 (not atmospherically corrected) showing west Cornwall.

A composite of bands 2, 3 and 4 (not atmospherically corrected) around Aberystwyth in mid-Wales.

A Python script, using RSGISlib to stack Sentinel2 images into a band-stacked .kea file:

# David Trethewey 01-06-2016
#
# Sentinel2 Bands Stacker
# Uses visible, NIR, SWIR bands
# 
# Assumptions:
#
# the .jp2 files are in the current directory
# that this script is being run from
#
# there is only one Sentinel2 scene in the directory
# and no other jp2 files
#  
# Converts jp2 files of each band to single stacked file
#  
# imports
import rsgislib
import rsgislib.imageutils
import os.path
import sys

# image list
# find all *.jp2 files in the current directory
directory = os.getcwd()
dirFileList = os.listdir(directory)
# print dirFileList

jp2FileList = [f for f in dirFileList if (f[-4:].lower()=='.jp2')]

bands = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '8A']
# bands that are already at 10m resolution
bands_10m = ['02', '03', '04', '08']

# resample other bands to resolution of blue image (B02)
bands_toberesam = [b for b in bands if b not in bands_10m]

# identify the band number by counting backwards from the end in the filename
Bands_VIS_NIR_SWIR_FileList = [f for f in jp2FileList if (f[-6:-4] in bands)and(f[-7]=='B')]

# list of bands to be resampled
Bands_resam_FileList = [f for f in jp2FileList if (f[-6:-4] in bands_toberesam)and(f[-7]=='B')]

# find the filename of the blue image
blue_image = [f for f in jp2FileList if (f[-6:-4] == '02')and(f[-7]=='B')][0]

# bands to be resampled 20m --> 10m
# 05, 06, 07, 8b, 11, 12
# bands to be resampled 60m --> 10m
# 01, 09, 10

for b in Bands_resam_FileList:
    print("resampling band {q} to 10m".format(q=b[-6:-4]))
    outFile = b[:-4]+'_10m.kea'
    rsgislib.imageutils.resampleImage2Match(blue_image, b, outFile, 'KEA', 'cubic')
    Bands_VIS_NIR_SWIR_FileList.remove(b)
    Bands_VIS_NIR_SWIR_FileList.append(outFile)

Bands_VIS_NIR_SWIR_FileList = sorted(Bands_VIS_NIR_SWIR_FileList)

fileNameBase = blue_image[:-7]

# Sentinel2 bands
bandNamesList = ["B1Coastal443nm", "B2Blue490nm", "B3Green560nm", "B4Red665nm", "B5NIR705nm", "B6NIR740nm",
                 "B7NIR783nm", "B8NIR_broad842nm", "B9NIR940nm", "B10_1375nm", "B11_SWIR1610nm",
                 "B12_SWIR2190nm", "B8A_NIR865nm"]

#output file name
outputImage = fileNameBase + 'B'+''.join(bands)+'_stack.kea'

#output format (GDAL code)
outFormat = 'KEA'
outType = rsgislib.TYPE_32UINT

# stack bands using rsgislib 
rsgislib.imageutils.stackImageBands(Bands_VIS_NIR_SWIR_FileList, bandNamesList, outputImage, None, 0, outFormat, outType)
# stats and pyramids
rsgislib.imageutils.popImageStats(outputImage,True,0.,True)

# remove individual resampled 10m files
print("removing intermediate resampled files")
for b in Bands_resam_FileList:
    outFile = b[:-4]+'_10m.kea'
    os.remove(outFile)

Some GUI interfaces to Cornish language Python programs

I have recently used Tkinter to code graphical frontends to a number of my programs that I have developed in Python for processing Cornish text.

They are all on my Bitbucket account, and there are a few screenshots below:

mutatya.py is a script for giving it a Cornish word, and returning the mutated form of it, here is the graphical front-end.

This uses espeak to speak Cornish text, by first Cymricising the orthography with a series of string replaces, and then calling espeak -vcy at the command line to speak it as if it were Welsh. The 'Gorhemmyn' button calls a module that decides on an appropriate greeting depending on the time of day according to the system clock.

Niverow renders numbers into Cornish words.

The current version of niverowGUI.py has the option of specifying a plural noun to be used in cases of large and complex numbers, where the pattern of + 'a' + is used.

This is the syllable segmentation module, showing the number of syllables in each word, and the whole line in this case. There are several different modes, 'long' mode, 'short' mode and 'line' mode and segmentation can be done either starting from the beginning or the end.

The long-form output from syllable segmentation.

Transliteration from Kernewek Kemmyn to the Standard Written Form (main).