GRASS GIS Mini Project 2022: Space-Time Dataset Visualization and Improved Interactive Maps for grass.jupyter

Title:	Space-Time Dataset Visualization and Improved Interactive Maps for grass.jupyter
Student Name:	Caitlin Haedrich, ​North Carolina State University
Mentor Name:	Vaclav Petras, Helena Mitasova, Stephan Blumentrath
GitHub Fork:	​View Repo
Proposal:	​View Proposal
Budget:	1000 € (via GRASS GIS ​Student Grants)

Abstract

During GSoC 2021, we created “grass.jupyter”, a package that improves the integration of GRASS GIS and Jupyter with a set of functions for displaying GRASS data in Jupyter Notebooks. In its current state, “grass.jupyter” allows users to create static visuals and simple interactive maps. However, several additional features are needed to allow Jupyter users to fully and easily access the power GRASS, including space-time dataset visualization and more options for interactive mapping (such as color or vector attribute access).

Goal

This project had three main objectives:

1. create a class for visualizing space time datasets allowing users to create interactive time-sliders and non-interactive animations such as GIFs.

2. improve the integration of grass and folium, the library used for InteractiveMap, so that users can access all of folium’s functionality. Currently, InteractiveMap allows users to add rasters and vectors to folium maps. Users can toggle between layers and export the map in HTML. However, folium allows for much more sophisticated mapping as well: users can control color, vector symbology, create heatmaps (point density maps) and view vector attributes with a click or hover. I propose to create new grass-folium objects that allow users to directly call folium (thus avoiding the problem of needing to wrap the entire folium library and continue to update as folium features change, depreciate and expand).

3. create a function to display vector attribute data in nicely-formatted Pandas or GeoPandas tables (as opposed to text output which is currently possible with “v.db.select”).

Timeline

Time Period	Milestones
	Tasks	Status
January 10th - January 14th Week 1	Project Intro Email Start TimeSeries class	Ok Ok
January 17th - January 21st Week 2	Draft of TimeSlider method	Ok
January 24th - January 28th Week 3	Draft of time_slider and animate	Ok
January 31st - February 4th Break	--	--
February 7th - February 11th Week 4	Docstrings and comments for time_slider and animate Solution for varying timesteps in TimeSeries Started GRASS-folium	Ok Ok Ok
February 14th - February 18th Week 5
February 21st - February 25th Week 6
February 28th - March 4th Week 7
March 7th - March 11th Week 8

Weekly reports

Week 2

Here's a quick update of what I worked on last week for my Mini Project on improving grass.jupyter.

1) What did I accomplish this week? I started on visualization functions for space time datasets (strds and stvds). I created a grass.jupyter class called TimeSeries that will eventually have two methods: TimeSlider for creating an interactive time slider where users can scroll through the rendered maps and animate for creating animations of the space time datasets. I have a draft version of the TimeSlider working - currently, users have to switch through the maps with a drop down menu instead of a slider (or scroll with arrow keys) . You can test it out on Binder ​here and view the PR ​on GitHub.

2) What do I plan to do next week?

• Switch from a dropdown menu to a SelectionSlider for the TimeSlider visualization
• Create more robust ways to add background and overlay layers to the TimeSlider
• Start the TimeSeries.animate() function for animating space time datasets

3) Am I blocked on anything? No, I'm not currently blocked on anything.

Feedback, thoughts and comments welcome!

Week 3

Here's a quick update on my Mini Project for improving grass.jupyter.

1) What did I accomplish this week?

• Switched from a Dropdown menu to a SelectionSlider for the time_slider visualization. I found a bug in ipywidgets which slowed me down a bit this week. I ended up ​opening an issue on GitHub and it was quickly addressed (go ipywidgets!).
• Wrote TimeSeries.animate() function for animating space time datasets You can ​test it out on Binder and ​view the PR.

2) What do I plan to do next week?

• Cleanup and write nice docstrings for TimeSeries visualizations
• The current visualizations are not great for multitemporal datasets (i.e. datasets with varying time steps), since the steps in the animation and time slider are all regularly spaced. Next week, I'll work on a way to address this.
• Maybe start working on GRASS-folium integration for interactive mapping

3) Am I blocked on anything?

• No, I'm not currently blocked on anything.

Feedback, thoughts and comments welcome!

Week 4

Here's an update on what I've been working on over the last two weeks for my Mini Project on improving grass.jupyter.

1) What did I accomplish over the past week?

• Took a week off to catch up on other work
• Made minor edits, cleaned-up and wrote docstrings for TimeSeries visualizations (​view the PR)
• Addressed visualizations issues with datasets that have varying time steps (​view the PR)
• Started improving GRASS-folium integration for interactive mapping by restructuring interact_display.py and adding classes that can be passed to folium (​view the PR)

You can test it out the TimeSeries visualizations (animate and time_slider) ​in Binder

2) What do I plan to do next week?

• Work on cleaning up temporary files created by grass.jupyter
• Region handling for InteractiveMap (as you can see in the ​Binder, the map is no longer automatically zoomed to the computation region)
• Docstrings, comments, cleanup interact_display.py
• Update grass_jupyter Notebook to include GRASS-folium example
• Begin functions for viewing vector attributes in Pandas

3) Am I blocked on anything?

• No, I'm not currently blocked on anything.

Feedback, thoughts and comments welcome!

Week 5

Week 6

Week 7

Week 8

Final Report

Title: Title

Abstract:

The state of integration BEFORE the start of Mini Grant:

The state of integration AFTER Mini Grant:

Conclusion:

Future Work: