How to display a Choropleth map in Jupyter Notebook

Here is the code:

%matplotlib inline
import geopandas as gpd
import matplotlib as mpl  # make rcParams available (optional)
mpl.rcParams['figure.dpi']= 144  # increase dpi (optional)
 
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
world = world[world.name != 'Antarctica']  # remove Antarctica (optional)
world['gdp_per_person'] = world.gdp_md_est / world.pop_est
g = world.plot(column='gdp_per_person', cmap='OrRd', scheme='quantiles')
g.set_facecolor('#A8C5DD')  # make the ocean blue (optional)

Here is what the map looks like:

Dependencies:

pip install matplotlib
pip install geopandas
pip install pysal  # for scheme option

(Integer) Linear Programming in Python

Step one:

brew install glpk
pip install pulp

Step two:

from pulp import * 
 
prob = LpProblem("test1", LpMinimize) 
 
# Variables 
x = LpVariable("x", 0, 4, cat="Integer") 
y = LpVariable("y", -1, 1, cat="Integer") 
z = LpVariable("z", 0, cat="Integer") 
 
# Objective 
prob += x + 4*y + 9*z 
 
# Constraints 
prob += x+y <= 5 
prob += x+z >= 10 
prob += -y+z == 7 
 
GLPK().solve(prob) 
 
# Solution 
for v in prob.variables():
    print v.name, "=", v.varValue 
 
print "objective=", value(prob.objective)

In the documentation there are further examples, e.g. one to minimise the cost of producing cat food.

Things that are visible from space, the Garzweiler Surface Mine

I was looking at arial photos of north-western Europe in Google Maps when I noticed a big white dot on the map!

I thought, what the hell? To satisfy my curiosity I decided to zoom in for further investigation.

It turns out that the big white dot is a giant surface mine. The 48 km² mine is operated by RWE and used for mining lignite, also known as brown coal.

Fun fact: 50% of Greece's power supply and 27% of Germany's comes from burning lignite. Lignite also has innovative uses in farming and drilling.

Isn't the geometric juxtaposition of farmland, urban area and surface mine quite enchanting? To get a sense of the scale, take a look at the size of cars next to the big heavy machine; then try to find the big heavy machine on the zoomed out image.

Here is a video that displays the grotesque beauty of the place...

Create a European city map with population density

Datasets:

- Urban morphological zones 2000 (EU): https://www.eea.europa.eu/data-and-maps/data/urban-morphological-zones-2000-2
- Population count (World): http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev10/
- Administrative regions (World): http://gadm.org/

The map is European since the "urban" data from the European Environmental Agency (EEA) only covers Europe.

Caveats

The UMZ data ended up in PostGIS with srid 900914. You can use prj2epsg.org to convert the contents of a .prj file to an estimated SRID code. In this case the UMZ .prj file as the contents:

PROJCS["ETRS89_LAEA_Europe",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_origin",52],PARAMETER["central_meridian",10],PARAMETER["false_easting",4321000],PARAMETER["false_northing",3210000],UNIT["Meter",1]]

Which translates to 3035 - ETRS89_LAEA_Europe.

How to create a world-wide PostgreSQL database of administrative regions

The GADM database contains geographical data for administrative regions, e.g. countries, regions and municipalities. As always, once you have the data in the right format, it is easy to import it into a database. The data is available from GADM in several formats. All data has the coordinate reference system in longitude/latitude and theWGS84 datum.

Step-by-step:

  1. Download data for the whole world or by country. For a change, I will use the GeoPackage format.
  2. Create a PostgreSQL database (assumed to exist)
  3. Import the data with ogr2ogr (see instructions below)

Import data instructions

Download data (example for Denmark):

wget http://biogeo.ucdavis.edu/data/gadm2.8/gpkg/DNK_adm_gpkg.zip
unzip DNK_adm_gpkg.zip

Next, create a database called "gadm" on my local PostgreSQL server; of course you can use another name if you prefer. Install the PostGIS extension:

create extension postgis

Finally, use ogr2ogr with the GPKG (GeoPackage) driver to import the data:

ogr2ogr -f PostgreSQL "PG:dbname=gadm" DNK_adm.gpkg

Now the data is imported an ready to be queried.

As a test, we can query the adm2 table (municipalities) with a coordinate inside the municipality of Copenhagen, Denmark.

SELECT name_2, ST_AsText(wkb_geometry)
FROM dnk_adm2
WHERE ST_Intersects(ST_SetSRID(ST_Point(12.563585, 55.690628), 4326), wkb_geometry)
-- AND ST_Point(12.563585, 55.690628) && wkb_geometry

You can view the selected well-known string geometry (WKT) in an online viewer, such as openstreetmap-wkt-playground. Other viewers are listed on stackexchange.

Alternative sources

For this post I really wanted a dataset of populated/urban areas. However, the GADM data I downloaded only contains adm0-adm2, which is a tessellation of the land area, i.e. cannot be used to discriminate between urban and rural areas.

Other data sources are listed below:

- http://www.naturalearthdata.com/downloads/
- https://data.humdata.org
- https://freegisdata.rtwilson.com/

From the rtwilson list, here are some specific datasets that indicate population density and urbanism:

- http://sedac.ciesin.columbia.edu/data/collection/gpw-v4/sets/browse
- https://www.eea.europa.eu/data-and-maps/data/urban-morphological-zones-2000-2
- http://www.worldpop.org.uk/ (does not cover Europe and North America)
- https://nordpil.com/resources/world-database-of-large-cities/

How to assess computers on your local area network

I teach children how to programm and do other things with technology in an organisation called Coding Pirates in Denmark, which aims to be a kind of scout movement for geeks. A best seller among the kids is learning how to hack and I see this as a unique opportunity to convey some basic human values in relation to something that can be potentially harmful.

Yesterday, I and one of the kids played with nmap, the network surveying tool, to investigate our local area network. The aim was to find information about the computers that were attached, such as operating system, system owner's first name (often part of the computer name) and whether any computer had open server ports (SSH, web etc.). We used nmap in combination with Wireshark.

  1. Tell another person about a fun website (any website will do)
  2. Use wireshark to detect the IP address (e.g. 192.168.85.116) of any computer that accesses that website
  3. Use nmap to scan the IP address we found: nmap -vS 192.168.85.116

We also learned how to detect that someone logs into your computer and e.g. kick the person (assume an Ubuntu host):

# Monitor login attempts
tail -f /var/log/auth.log
# See active sessions
who
# List remote sessions
ps fax | grep 'pts/'
# Kill sessions
kill -9 [pid of bash processes connected to session]

Other tricks

List all hosts (ping scan) on your local area network:

nmap -sP 192.168.1.*

Find computers on your local area network that run an SSH server:

nmap -p 22 --open -sV 192.168.1.*

Urban Mining – Platinum from Road-side Dust

These guys found that road-side dust contains 1000 DKK worth of platinum per 1 ton of dust.

Cody explains that this concentration of platinum equals the amount found in good-quality platinum ore.

Does the stretch of road matter?

In a word, yes. The user Jafromobile commented that a car is likely to eject platinum particulates into the exhaust stream when the catalytic converter is the hottest, when the exhaust system is under pressure, and both of these conditions are basically achieved at the same time. In other words, at full throttle.

Where do cars use full throttle? In an urban area it could be at traffic lights. Other sections of road might in a similar manner reveal consistently-high platinum deposits.

Urban Mining – Steel Scraps

While I intuitively like the concept of Urban Mining - we have all these precious raw materials within easy reach of robots - I know nothing about it. I want to remedy that with the best ignorance cure of all: YouTube! Please, see an exteneded list of videos at the bottom of this post.

The rest of the post will focus on steel recycling to understand the problems and potentials (with regards to AI and robotics) in this industry.

ELB in Duisburg:

Hammel metal separation:

The steel scraps produced in the recycling videos go to steel plants. To see how important this raw material is, check out Thyssen-Krupp's product website.

Important problems

Here is a list of problems that are specific to steel recycling -- as shown in the videos from ELG.

Pollution

Perhaps the elephant in the room is that recycling can be a very toxic process. This gives an edge to countries with more relaxed regulation, e.g. certain Asian countries. For example, to extract gold from electronics you may use cyanide. If robotics are more pervasive in robotics, this could eliminate the edge of low-regulation countries as recycling can be done in pure machine environments.

Buying

The scrap buyer at the German company (ELG in Duisburg) must buy deliveries of scrap metal on a daily basis and at the right price. For example, nickel can be particularly expensive. She will sometimes go out of the office to buy even small quantities of steel from scrap dealers, and always carry a magnet in her pocket to test the quality.

The dealer has to sell her the steel at a loss because he is running low on space! However, since many competitors also wants to buy the scraps, the buyer has to offer fair prices and have the dealers interest in mind.

Analysis

The scrap company must examine the quality of a shipment (e.g. steel) before it can be sent to a plant. The process involves cutting out samples, melting the samples and finally determining the alloying elements (e.g. molybdenum) it contains. High-grade steel must have the right mix of non-iron elements. Furthermore, the scrap should not contain toxis elements, such as heavy metals.

Sorting!

A shipment of steel scraps may contain copper or zink parts. These metals would contaminate the steel melt later on, so they have to be picked out of the pile before shipping to the steel plant. Workers at a German plant manually sort through the pile and test the metal with a magnet!

Cutting!

Big pieces of scrap larger than 500 kg have to be cut into smaller pieces, otherwise they won't fit in the furnace of the customer. ELG uses a big crane fitted with a giant claw to cut big pieces of sheet metal (e.g. from dismantled factories) into smaller pieces.

Sometimes ELG uses external companies to reduce the piece size for them.

Transportation

Another problem that is exposed in the video is that of transportation. A delayed shipment of scrap metal to a steel plant can cause critical downstream delays in the supply chain. In the video, the recycling plant receives too few rail cars into their on-sight train yard (how cool is that!). They have to act fast in order to get the shipment out the door, which means they have to get more train cars fast. Perhaps an alternative would be to ship the metal using self-driving train cars that can be summoned on demand or some other sci-fi solution.

Making new steel

The scrap is melted into new steel in an electric arc furnace at the plant. The furnace uses a lot of electricity! At the plant, the composition of the scraps is analysed again using a randomly selected sample of the shipment. The balance of chromium, nickel and phosphorus to iron has to be within certain bounds.

Robotic potential

How could robots solve the problems outlined above?

Additional video material that relates to Urban Mining

Some of these videos are about extracting gold from electronics.