Background: This tutorial uses any csv file with latitude and longitude columns in order to plot points on the web map. Make sure your csv file is saved in the same folder this notebook is saved (makes things easier).
I recommend downloading the Anaconda Distribution which comes with jupyter notebook.
There are 3 main important python libraries that are used in this tutorial
Pandas: Pandas is a python library that is used for data analysis and manipulation.
kepler.gl: This a FREE open-source web-based application that is capable of handling large scale geospatial data to create beautiful visualizations.
GeoPandas: Essentially, geopandas is an extension of Pandas; fully capable of handling and processing of geospatial data.
The first step is to navigate to the folder where you want this notebook to be saved from the main directory when juypter notebook is launched. Then click ‘new’ -> Python 3, a tab will open up with your notebook (See image below).
Next, using the terminal it is important to have these libraries installed to ensure that this tutorial works and everything runs smoothly.
For more information on jupyter notebook see: https://jupyter.org/
Navigate back to the directory and open a terminal prompt via the ‘new’ Tab’.
A new tab will open up, this will function very similarly to the command prompt on windows. Next type “pip install pandas keplergl geopandas” (do not include quotes). This process will help install these libraries.
Below you will find what my data looks like the map before styling
With some options
KeplerGL also allows for 3D visualizations. Here is my final map:
Lastly, if you wish to save off your web map as an HTML file to host somewhere like GitHub or AWS this command will do that for you:
by Luke Johnson Geovis Project Assignment @RyersonGeo, SA8905, Fall 2017
I’ve been a Toronto Maple Leaf and enthusiastic hockey fan my whole life, and I’ve never been able to intersect my passion for the sport with my love of geography. As a geographer, I’ve been looking for ways to blend the two together for a few years now, and this geovis project finally provided me the opportunity! I’ve always been interested in the debate about how teams located on the west cost travel more than teams located centrally or on the east coast, and that they had a way tougher schedule because of the increased travel time. For this project, I decided to put that argument to rest, and allow anybody to compare two teams for the 2016/2017 NHL season, and visualize all the flights that they take throughout the year, as well as view the accumulated number of kilometres traveled at any point during the season and display the final tally. I thought this would be a neat way to show hockey fans the grueling schedule the players endure throughout the year, without the fan having to look at a boring table!
It all started with the mockup above. I had brainstormed and created a few different interfaces, but this is what I came up with to best illustrate travel routes and cumulative kilometres traveled throughout the year. The next step was deciding on the what data to use and which technology would work best to put it all together!
First of all, all NHL teams were compiled along with the name of their arena and the arena location (lat/long). Next, a pre-compiled csv of the NHL schedule was downloaded from left wing lock, which saved me a lot of time not having the scrape the NHL website, and compile the schedule myself. Believe it or not, that’s all the data I needed to figure out the travel route and kilometres traveled for each team!
All of this data mentioned above was put into a SQLite database with 3 tables – a Team table, Arena table, and a Schedule table. The Arena table could be joined with the Team table, to get information on which team played at what arena, and where that arena is located. The Team table can also be joined with the Schedule table, to get information regarding which teams play on what day, and the location of the arena that they are playing.
Because I wanted to allow the user to select any unique combination of teams, it would have been very difficult to pre-process all of the unique combinations (435 unique combinations to be exact). For this reason, I decided to build a very lightweight Application Programming Interface (API) that would act as a mediator between the database and the web application. API’s are a great resource for controlling how the data from the database is delivered, and simplifies the combination process. This API was programmed in Python using the Flask framework. The following screenshot shows a small exert from the Flask python code, where a resource is set up to allow the web application to query all of the arenas, and will get back a geojson which can be displayed on the map.
To create the animation, the web application sends a request to the API to query the database for each team chosen to compare. The web application then loops through the schedule for each team, refreshing the page rapidly to make a seamless animation of the 2 airplane’s moving. At the same time the distance between two NHL arenas is calculated, and a running total is appended to the chart, and refreshed after each game in the schedule. The following snippet of code shows how the team 1 drop down menu is created.
After everything was compiled, it was time to demo the web app! The video below shows a demo of the capability of the web application, comparing the Toronto Maple Leafs to the Edmonton Oilers, and visualizing their flights throughout the year, as well as their total kilometres traveled.
To get a more in depth understanding of how the web application was put together, please visit my Github page where you can download the code and build the application yourself!