Initial commit

.gitignore

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+.ipynb_checkpoints/
+.ipynb_checkpoints/*
+*/.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+raw_data/
+raw_data/*

Dockerfile

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+FROM darribas/gds:2.0
+
+RUN rm -R work/
+
+# Make sure the contents of our repo are in ${HOME}
+# https://mybinder.readthedocs.io/en/latest/tutorials/dockerfile.html#preparing-your-dockerfile
+COPY ./data ${HOME}/data
+COPY ./figs ${HOME}/figs
+COPY ./notebooks ${HOME}/notebooks
+COPY ./spec.md ${HOME}/spec.md
+COPY ./LICENSE ${HOME}/LICENSE
+# Fix permissions
+USER root
+RUN chown -R ${NB_UID} ${HOME}
+USER ${NB_USER}

LICENSE

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+Geographic Data Science with Python - UFMG'19 by Dani Arribas-Bel is licensed under a Creative Commons Attribution 4.0 International License.
+
+To view a copy of this license, visit:
+
+> https://creativecommons.org/licenses/by/4.0/

Makefile

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+test:
+	cd notebooks && jupyter nbconvert --to pdf *.ipynb
+readme: README.md
+	pandoc  -V mainfont='Palatino' \
+			-V fontsize=12pt \
+			-V geometry="margin=1in" \
+			-s README.md -o readme.pdf
+pack: readme
+	rm -f pack.zip
+	mkdir pack
+	cd notebooks && jupyter nbconvert --to pdf *.ipynb
+	mkdir pack/pdf
+	mv notebooks/*.pdf pack/pdf/
+	cd notebooks && rm -fR -- */
+	mkdir pack/html
+	cd notebooks && jupyter nbconvert --to html *.ipynb
+	mv notebooks/*.html pack/html
+	mkdir pack/notebooks
+	cp notebooks/*.ipynb pack/notebooks
+	cp -r data pack/data
+	cp -r figs pack/figs
+	cp readme.pdf pack/readme.pdf
+	cp LICENSE pack/LICENSE
+	zip -r pack_new.zip pack
+	mv pack_new.zip pack.zip
+	rm -r pack
+	rm readme.pdf
+run:
+	jupyter nbconvert --to notebook False --execute notebooks/*.ipynb
+	rm notebooks/*.nbconvert.ipynb

README.md

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+# Geographic Data Science in Python
+
+## Description
+
+This course will introduce you to the nascent field of
+Geographic Data Science using the industry standard, the Python programming
+language. We will cover key steps involved in solving practical
+problems with spatial data, from manipulation and processing, to exploration.
+These topics will be explored from a “hands-on” perspective
+using a modern Python stack (e.g. geopandas, seaborn, scikit-learn, PySAL),
+and examples using real-world spatial data.
+
+We will start with an overview of the main ways to manipulate and visualise
+data in Python. Then we will move on to spatial data, learning to perform
+traditional GIS operations and to visualise data in a geographic context (e.g.
+choropleths). The course will then move into explicitly spatial analysis,
+delving into spatial weights matrices, which will give way to exploratory
+spatial data analysis. In this context, we will cover both global and local
+approaches. We will finish the course covering more advanced topics such as
+the analysis of points and spatially constrained clustering.
+
+## Agenda
+
+**NOTE**: bring your own data!!!
+
+### Day 1 - Monday 
+
+- **9:00/10:00**: the Scientific Python stack: Jupyter and the Notebook [[`d1n1_intro`]](notebooks/d1n1_intro.ipynb)
+- **10:00/11:00**: manipulating tabular data [[`d1n2_tabular`]](notebooks/d1n1_tabular.ipynb)
+- **11:00/12:00**: visualising tabular data [[`d1n3_tabular_viz`]](notebooks/d1n3_tabular_viz.ipynb)
+
+### Day 2 - Tuesday
+
+- **9:00/11:00**: Introduction to spatial data [[`d2n1_geodata`]](notebooks/d2n1_geodata.ipynb)
+- **11:00/12:00**: Visualizing spatial data [[`d1n1_geoviz`]](notebooks/d2n2_geoviz.ipynb)
+
+### Day 3 - Wednesday
+
+- **9:00/10:30**: Spatial weights [[`d3n1_esda`]](d3n1_esda.ipynb)
+- **10:30/12:00**: Spatial autocorrelation [[`d3n1_esda`]](d3n1_esda.ipynb)
+
+### Day 4 - Thursday
+
+- **9:00/10:00**: interacting with raster data [[`d4n1_raster`]](d4n1_raster.ipynb)
+- **10:00/11:00**: combining raster and vector data visually [[`d4n1_raster`]](d4n1_raster.ipynb)
+- **11:00/12:00**: analysis combining raster and vector [[`d4n1_raster`]](d4n1_raster.ipynb)
+
+### Day 5 - Friday
+
+- **9:00/10:00**: reading and manipulating satellite imagery [[`d5n1_satellite`]](d5n1_satellite.ipynb)
+- **10:00/12:00**: creating a land-cover classification [[`d5n1_satellite`]](d5n1_satellite.ipynb)
+
+## Data
+
+**BONUS**: data preparation in [`zzz_data_prep`](notebooks/zzz_data_prep.ipynb).
+
+## Installation instructions: `docker`
+
+This course can be run using the [`GDS` Docker container](https://github.com/darribas/gds_env).
+
+Before starting the installation, make sure you have access to a reliable and reasonably fast internet connection. The software required takes up quite a bit of space and hence involves heavy downloads.
+
+If you have administration rights on the machine you will use for the course, Docker is the safest and most stable route. 
+
+**Note**: that you will need at least 10GB of space in your machine.
+
+* Install [`docker CE`](https://www.docker.com/community-edition) (follow instructions for [macOS](https://store.docker.com/editions/community/docker-ce-desktop-mac), [Windows 10](https://store.docker.com/editions/community/docker-ce-desktop-windows) or [earlier](https://docs.docker.com/toolbox/overview/), [Ubuntu](https://store.docker.com/editions/community/docker-ce-server-ubuntu))
+* Open a command prompt (ie. `Terminal.app` on macOS, `Powershell` on Windows)
+* Run the following command. This will download about 10GB of data, so it might take a while (particularly on slow connections). Be patient :-)
+
+    ```shell
+    > docker pull darribas/gds:2.0
+
+    ```
+* Installation is ready!
+* To test that it is correctly installed, you can run from the same command line, the following command:
+
+    ```shell
+    > docker image ls
+
+    ```
+
+* This should list, at least, the following:
+
+    ```shell
+    PS C:\Users\danie> docker image ls
+    REPOSITORY                 TAG                 IMAGE ID            CREATED             SIZE
+    darribas/gds               2.0                 ed2515e065b2        2 weeks ago         8.17GB
+    ```
+    [Note the `IMAGE ID` may vary]
+
+Once installed, you can run the Docker container with the following command:
+
+    ```
+    docker run --rm -ti -p 8888:8888 -v ${PWD}:/home/jovyan/host darribas/gds:2.0
+    ```
+
+This will print out something similar to:
+
+    ```
+    /usr/local/bin/start.sh: ignoring /usr/local/bin/start-notebook.d/*
+
+    Container must be run with group "root" to update passwd file
+    Executing the command: jupyter lab
+    [I 21:38:25.287 LabApp] Writing notebook server cookie secret to /home/jovyan/.local/share/jupyter/runtime/notebook_cookie_secret
+    [W 21:38:26.210 LabApp] WARNING: The notebook server is listening on all IP addresses and not using encryption. This is not recommended.
+    [I 21:38:26.251 LabApp] JupyterLab extension loaded from /opt/conda/lib/python3.6/site-packages/jupyterlab
+    [I 21:38:26.251 LabApp] JupyterLab application directory is /opt/conda/share/jupyter/lab
+    [W 21:38:26.622 LabApp] JupyterLab server extension not enabled, manually loading...
+    [I 21:38:26.623 LabApp] JupyterLab extension loaded from /opt/conda/lib/python3.6/site-packages/jupyterlab
+    [I 21:38:26.624 LabApp] JupyterLab application directory is /opt/conda/share/jupyter/lab
+    [I 21:38:26.700 LabApp] Serving notebooks from local directory: /home/jovyan
+    [I 21:38:26.700 LabApp] The Jupyter Notebook is running at:
+    [I 21:38:26.700 LabApp] http://(267dac20e049 or 127.0.0.1):8888/?token=950b3cd4c304a112e1554cafe67c1973efbed00bfd65b087
+    [I 21:38:26.700 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
+    [C 21:38:26.701 LabApp]
+
+    Copy/paste this URL into your browser when you connect for the first time,
+    to login with a token:
+        http://(267dac20e049 or 127.0.0.1):8888/?token=950b3cd4c304a112e1554cafe67c1973efbed00bfd65b087
+    ```
+
+Then you can go to a browser and access `http://localhost:8888/?token=mytoken`, replacing
+`mytoken` by the token printed in the command line (in the case above, `950b3cd4c304a112e1554cafe67c1973efbed00bfd65b087`).
+
+---
+
+
+<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">Geographic Data Science with Python - UFMG'19</span> by <a xmlns:cc="http://creativecommons.org/ns#" href="https://github.com/darribas/gds_ufmg19" property="cc:attributionName" rel="cc:attributionURL">Dani Arribas-Bel</a> is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.