My 30-Day Map Challenge 2023

An overview of selected map topics and algorithms

Image by the Author. Collage of selected self-authored maps.

Introduction

This blog post is an overview of the map design I have been through and a brief comment on the selected daily topic. I made it simple. I believe a well-crafted map should give a clear visual message at first glance and more detailed information for experimented map readers.

Let’s take a look at the maps I have created with quick comments.

Points

The challenge of this map was to attribute the distances from every node to the closest Fire Station. The Nearest Neighbor algorithm was a quick approach in terms of computation. The point size attribute helped make the map attractive and appealing.

Image by the Author. Points.

Lines

The challenge in this map was to process the highest amount of Shortest Paths. Thankfully, I could use HPC at work so this made it handy.

Image by the Author. Lines

Polygons

I changed this map from the original post. I decided to keep the original limit of the Isochrones using Valhalla API.

Image by the Author. Polygons

Navigation

I wanted to compare how different the navigation can be if I use Dijskstra’s algorithm (Shortest Path) weighted by time and distance. This example was done on a long-distance trip in Morocco.

Code in this Blog Post.

Image by the Author. Navigation.

Hexagons

I thought of the Kontur population dataset immediately when I heard Hexagons. The challenge of this map was to add the Spanish islands to the layout.

Image by the Author. Hexagons

North America

This map was selected area of a global map. QGIS did the work. The challenge here was to process the Cellular Antenna density using H3-pandas. It was nothing difficult that Python could not do.

Image by the Author. North America.

South America

Every time that South America is mentioned I think of the warm water of the coastline of my home country. Ecuador. I wanted to show how attractive the SST can be, by MODIS.

Image by the Author. South America.

Europe

I have been working on trajectory analysis of bike-sharing system data from Tartu (Estonia). Here is a quick visualization I did using processed GPS data. The challenge of this map was to process the distance in trajectories but nicely it was done using Movingpandas in Python.

Image by the Author. Europe.

Flow

I was working with data on Bird Migration in my project GIS4 Wildlife. The challenge was to process the large amount of GPS data and make it simple for visualization. KeplerGl is the best tool I have used for spatial-temporal visualization.

Image by the Author. Flow.

3D

The 3D was not a challenge but funny. I put time backward in the height value :), only for visualization, but the color matched as it should. Processed with routingpy using Valhalla API for Isochrone creation.

Image by the Author. 3D

Antarctica

The first thing to my mind when listening to Antarctica is the marine fauna. This map shows the distribution areas of marine-terrestrial mammals. The challenge of this map was to find a good overlay of areas. QGIS did the work.

Image by the Author. Antarctica.

Dots

Following the inspiration of marine fauna I thought of the Galapagos Islands when listening to Dots. I realized that fishing vessels and their activity can be represented as dots in a shiny and point-sized view.

Image by the Author. Dots

Experimental

I was experimenting with visualization and I couldn't finish this last map. When color and shape are attractive in results I want to find a balanced view that can give a message. This one, sadly, does not provide any message. This is a non-published map.

If I remember correctly, I wanted to represent Proximity to Libraries in Helsinki using a very colorful palette.

Image by the Author. Experimental.