Spatial Data Analysis with PySAL @NARSC2017
This repository contains the materials and instructions for the PySAL workshop at NARSC 2017.
- Overview of PySAL and workshop
- Jupyter notebooks
- Python primer
- Coffee Break
- Spatial data processing
- Choropleth mapping and geovisualization
- Spatial weights
- Global spatial autocorrelation
- Local spatial autocorrelation
- Spatial inequality analysis
- Coffee Break
- Geodemographics and regionalization
- Spatial dynamics
- Spatial regression
Obtaining Workshop Materials
If you are familiar with GitHub, you should clone or fork this GitHub repository to a specific directory. Cloning can be done by:
git clone https://github.com/sjsrey/pysalnarsc17.git
If you are not using git, you can grab the workshop materials as a zip file by pointing your browser to (https://github.com/sjsrey/pysalnarsc17.git) and clicking on the green Clone or download button in the upper right.
Extract the downloaded zip file to a working directory.
We will be using a number of Python packages for geospatial analysis.
An easy way to install all of these packages is to use a Python distribution such as Anaconda. In this workshop we will be using Python 2.7 so please download that version of Anaconda.
Once you have downloaded Anaconda, start a terminal and navigate to the directory of the downloaded/ cloned materials. For example, if the materials now live in the directory
/Users/weikang/Downloads/pysalnarsc17-master, you need to navigate to that directory from the terminal (using command
Once we have done that, run:
conda-env create -f workshop.yml
This will build a conda environment that sandboxes the installation of the required packages for this workshop so we don't break anything in your computer's system Python (if it has one).
This may take 10-15 minutes to complete depending on the speed of your network connection.
Once this completes, you can activate the workshop environment with:
- on Mac, Linux
source activate workshop
- on Windows:
Next, you will want to test your installation with:
jupyter-nbconvert --execute --ExecutePreprocessor.timeout=120 check_workshop.ipynb
You should see something like:
[NbConvertApp] Converting notebook check_workshop.ipynb to html [NbConvertApp] Executing notebook with kernel: python2 [NbConvertApp] Writing 435635 bytes to check_workshop.html
Open check_workshop.html in a browser, and scroll all the way down, you should see something like:
If you do see the above, you are ready for the tutorial. If not, please contact either of us for help.