A sequence of four workshops for the Graduate Quantitative Methods Center (aka GradQuant) at the University of California, Riverside (UCR), presented by Dr. Sergio Rey, Founding Director of the UCR Center for Geospatial Sciences.
This workshop series provides a gentle introduction to Python for geospatial data analysis. Through hands-on instruction, the workhops cover the open source Python Spatial Analysis Library (PySAL) and the related Python ecosystem of libraries to facilitate common tasks for scientists and researchers working with geospatial data, and to provide an introduction to methods of exploratory spatial data analysis and spatial econometrics.
The series is organized into four workshops that provide computationally based introductions to the processing, visualization, exploration, and modeling of geospatial data.
(All workshops are from 9:10-11:00 a.m. on the dates listed.)
This workshop covers the core features of the Python language and serves as a primer on using Python for Spatial Data Analysis. Users are introduced to Jupyter Notebooks as a computational environment, followed by a series of Jupyter notebooks that provide introductions to data structures, functions, conditional execution, file processing, and plotting in Python.
Geospatial data comes in a rich variety of formats and from many different sources. Wrangling geospatial data is often a major component of any applied research project, and becoming efficient in geoprocessing is thus vital. This first workshop provides an overview of methods for dealing with the processing of geospatial data. This includes importing, and integrating, data in different formats (e.g. shapefile, GeoJSON, WKT, WKB), and the treatment of different spatial coordinate reference systems. Methods of deterministic spatial analysis involving topological relationships and spatial operations including buffering, dissolving, clipping, splitting and others, are covered.
- Introduction to Shapely
- Introduction to Geopandas
- Geoprocessing with Geopandas
This third workshop covers the visualization and exploration of spatial data. Here we explore a number of Python libraries for scientific visualization in general, and geovisualization in particular. Basic cartographic concepts involving choropleth mapping, color schemes, and attribute classification are covered. Methods for exploratory visualization where the focus is on quickly generating graphical depictions and gather insights from geospatial are also examined. Libraries supporting publication quality visualizations are presented. Finally, the development of interactive geovisualizations is introduced. This workshop also provides and introduction to methods that support the exploration of spatial data. The goal of these methods is to uncover and detect interesting geographical patterns. The formal representation of neighbor relationships via spatial weights matrices are discussed. Global tests for spatial autocorrelation are covered, followed by local autocorrelation methods designed to detect hot-spots in geospatial data patterns. Methods for the exploration of spatial-temporal data are also introduced.
The final workshop in this series provides an introduction to the modeling of relationships in spatial data using methods of modern spatial econometrics. The complications that arise due to spatial dependence and spatial heterogeneity in empirical data sets are explored. Methodological topics include an overview of model specifications for regression with spatial data, methods of estimation, model diagnostics, and validation issues.
- Basic knowledge of Python is assumed.
- Familiarity with geospatial data and methods will be helpful, but not required.
Participants are expected to bring their own laptops and to follow the instructions below for installing the required packages prior to the workshop.
We will be using a number of Python packages for geospatial analysis, and the easiest way to install these is to use the Anaconda Python Distribution.
- Download Anaconda version 3.6 for your operating system.
- Once you have downloaded and installed Anaconda start a terminal
- Download the workshop archive.
- Extract the archive
unzip gdapy18-master.zip cd gdapy18-master/conda-env create -f workshop.yml
That last step downloads a number of packages and could take up to 10 minutes if you have a slow connection.
If using conda version 4.4 or later:
conda activate workshop
If using an older version of conda:
- On Windows:
activate workshop - On Mac/Linux:
source activate workshop
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
This will generate a file check_workshop.html in the local directory.
If you open this up in a browser you should see something like the
following inside the file:
If you do hit any snags, just email the instructor at
sergio.rey at ucr.edu for help.