from GSoC import splot

This blog post will introduce you to the splot package, how it is designed and what exactly I will be working on during the Google Summer of Code

What is splot?

The goal of the splot package is to meet the growing demand for a simple to use, lightweight interface that connects PySAL to different popular visualization toolkits like bokeh, matplotlib or folium. The splot package will ultimately provide users with both, static plots ready for publication and interactive visualizations that allow for quick iteration over ideas and data exploration. Please visit the viz module website for more detailed information and first examples for a possible design of such a package.

Design and components of splot

The splot package is ultimately structured into three levels. The highest level directly provides visualization functions for end users. Two lower layers are setting the basis for easy visualization by first, converting PySAL geometries (polygon, line, shape) into Matplotlib geometries and second, allowing for subsetting (e.g. plot only part of a .shp), aligning (e.g. same axes for different layers) and transforming (e.g. classify values to colors) graphical objects. So far the existing Moran plot provides a great example of how such functionality could look like.

GSoC project

Initial visualizations like LISA and choropleth maps were stated to be developed in the splot package but many functions remain to be coded. Besides refining existing plots, common views indicate that the Matplotlib interface needs to be extended by new maps (Join Count BW, regression maps), scatter plots (pairwise regression plots, …) and many more visualizations.

Next to providing these missing functional static plots the GSoC project will leverage more recent visualization technologies of the constantly evolving visualization space. This geovisualization project provides the scope to incorporate interactive visualizations developed in Jupyter within the splot package. It allows for exploration of potential new interfaces for alternative packages like Bokeh (plots with interactivity such as tooltips and zooming) and Folium (for plotting on top of web-sourced base layers, e.g. OpenStreetMap).

In the first phase of this project, we will therefore create different visualizations in both a static version with Matplotlib and an interactive version with Bokeh. Secondly, we will create a common API for easy access to both versions. After adding documentation we will be able to provide a complete and user friendly package. Finally, we will explore how alternative visualization packages, like Vega, could be integrated into the splot package in future.

Additionally, we will refactor the package to ensure all functionality and documentation can be accessed in the splot namespace and work towards its inclusion into the PySAL user guide.

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