Skip to content
This is the Intermediate Data Science Workshop for the 2019 Scientific Python conference in Austin, TX.
Jupyter Notebook
Branch: master
Clone or download
Latest commit 7e02aab Jul 31, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data add final problemset Jun 18, 2019
docs add built stuff Jul 31, 2019
notebooks wording Jul 8, 2019
LICENSE add license Jun 18, 2019
PROPOSAL.md move readme to proposal Jun 18, 2019
README.md Update README.md Jul 8, 2019
check_environment.py update the check_environment script for the correct data Jun 25, 2019
download.png add image to download button Jun 18, 2019
environment.yml Update environment.yml Jul 8, 2019

README.md

Scipy 2019 Tutorial - Intermediate Methods for Geospatial Data Analysis

Binder

Instructors

This tutorial introduces intermediate concepts in geospatial data analysis in Python, bridging tabular vector data and raster data. The first component of the workshop focuses on intermediate topics in spatial data analysis, such as spatial joins, nearest neighbor queries, regionalization, and zonal statistics. The second component of the workshop focuses on exploratory spatial statistical methods for understanding the structure of spatial data.

Outline of the Workshop

  • 1:10-1:30: Installation & setup

    If you cannot complete the installation instructions ahead of time, please come slightly early so we can work on ensuring everyone can get the required packages installed and so that you can run the workshop material & exercises

  • 1:30-2:45: Relations

    Working with spatial joins, nearest neighbor joins, geocoding, basemapping, and naive regionalization methods. (problem set planned for the final 15 minutes)

  • 2:30-3:30: Rasters

    Working with raster data, and working between raster and vector data. (problem set planned for the final 15 minutes)

  • 3:30-3:45: Break

  • 3:45-4:30: Exploratory spatial data analysis

    Examining spatial relationships in mathematical terms, including how to represent spatial relations in mathematical forms.

  • 4:30-5:30: Clustering & Anomaly Detection

    Understanding and examining the strange and novel spatial structures in data (problem set planned for the final 15 minutes)

Installation notes

Following this tutorial will require recent installations of:

  • Python >= 3.5
  • pandas
  • geopandas >= 0.3.0
  • rasterio
  • python-rasterstats
  • geopy
  • contextily
  • matplotlib
  • rtree
  • libpysal
  • scikit-learn
  • Jupyter Notebook

If you do not yet have these packages installed, we recommend to use the conda package manager to install all the requirements (you can install miniconda or install the (larger) Anaconda distribution, found at https://www.anaconda.com/download/).

Once this is installed, the following command will install all required packages in your Python environment:

conda env create -f environment.yml

Downloading the tutorial materials

NOTE: We may update the materials up until the workshop. So, please make sure that, if you download the materials, you refresh the downloaded material close to the workshop.

If you have git installed, you can get the tutorial materials by cloning this repo:

git clone https://github.com/pysal/scipy2019-intermediate-gds

Otherwise, you can download the repository as a .zip file by heading over to the GitHub repository (https://github.com/pysal/scipy2019-intermediate-gds) in your browser, click the green "Download" button in the upper right, and then click the "Download Zip" link:

Test the tutorial environment

To make sure everything was installed correctly, open a terminal, and change its directory (cd) so that your working directory is the tutorial materials you downloaded in the step above. Then enter the following:

python check_environment.py

Make sure that this scripts prints "All good. Enjoy the tutorial!"

You can’t perform that action at this time.