A comparison — choosing the right option for the right project.

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Numbers do not speak for themselves!. We make them talk.

An essential part of data analysis is communication. We need to arrange information in a comfortable and digestible way to communicate, highlight and visualise critical areas.

Dashboards take your data visualisation to the next level. They connect different visualisation components and make a whole and integrated data visualisation stories. Web application Dashboards also allow users to interact with the data and the visualisation, offering them to see and adjust what they want or need.

It has never been easier to create a dashboard in Python. We have several dashboard tools…


Essential skills to transition from GIS to Spatial Data Science.

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GIS skills and education have changed over the past years. “I’ve been GIS. Now I’m geospatial.” writes Will Cadell in a recent article titled Geospatial Is Not GIS.

As a GIS person typically produces cartographic and analytical products using desktop software, Geospatial data scientist creates code and runs pipelines that produce analytical products and cartographic representations. The difference might seem subtle, but it requires a new set of tools and mindset.

The GIS skills are still relevant, but there are a lot of other skillsets necessary for geospatial data scientists to succeed, some obvious while others are less known. A…


Platforms, Tools and Packages for Geospatial/Earth Observation Data Scientists

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The satellite-based earth observation data is increasing at a rapid base, thanks to technological development in remote sensing platforms, and breakthroughs in data collection and storage. Today, we have more than 768 earth observation satellites in orbit, compared with only 150 in 2018.

As a Geospatial or earth observation data scientist, you have a vast array of tools and resources to choose. In this article, I highlight the best open source tools in the market that are integrated into the data science ecosystem.

1. Google Earth Engine (GEE)

Your wish granted. GEE is all in one package. Google Earth Engine(GEE) is by far the complete…


A tutorial on efficient and quick spatial joining for a large dataset.

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Recently, I thought back to a few years ago, when I tried to process a large geospatial dataset with Python. You can only guess how it ended. My laptop refused to cooperate and froze spectacularly without failing.

Frustration ensued.

Fast forward today, I was experimenting with RAPIDS AI Suite and came across the same dataset. I immediately knew what to do. So I jumped into coding.

The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs.

In this tutorial, I will go through a complete…


What kind of Geospatial Data Scientist are You?

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You are a GIS practitioner? You have taken courses. Maybe you learned the basics of the programming language. Now, What is next?

What do you do to get a job or an internship as a Geospatial Data Scientist?

You can either swim in the tutorial/course trap, stay in your comfort zone and keep that loop forever. Or either jump into the real world and improve your skills with real-world projects.

Doing projects is the best way to learn, showcase your skills, and at the same time differentiate yourself from other competitors.

Getting geospatial data science jobs requires a lot of…


Non-Technical, insightful, and easy to read books about AI, ML, and Data Science.

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Data science books tend to be too technical. While we need to learn and master the technological know-how, we also need to have a conceptual understanding and intuition to get a holistic view of the data science technologies and their broader impact on our societies.

I have enjoyed reading the following three books. They offer a good dose of intuition and stories about AI, ML, data science, and statistics. They are for the layman and minimal on math, so reading them does not feel like reading textbooks but rather an easy-to-read good non-fiction book.

1. AIQ: How artificial intelligence works and how we can harness its power for a better world

Nick Polson & James Scott


The Best VPN with zero configuration for building a secure network.

SSH

I have always struggled to create a Virtual Private Network and access it via SSH. That was until I have discovered and started using Tailscale last year. And since then, it has been a breeze to connect to my Deep Learning workstation.

If you have multiple computers and find it cumbersome to access any of them from anywhere you are, then Tailscale can help you set up a VPN and make your life easier by assigning a static IP address for each of your devices all for free.

Tailscale is helpful if you are a data scientist or learning data…


Understand how to use *args and **kwargs in Python.

Image from Canva.

Why read?

In your Python learning journey, you meet functions, a compelling self-contained programming concept that bundles a set of instructions to carry out specified tasks. As a beginner, you certainly use arguments to supply the function either by position or by name so that arguments are matched to named parameters.

But what happens when we want to capture an argument that we might not know in advance (i.e, datatype/length)?

Variadic parameters (Variable Length argument) are Python’s solution to that problem. A Variadic Parameter accepts arbitrary arguments and collects them into a data structure without raising an error for unmatched parameters numbers.


A new Library for Interactive geospatial data visualization + Geospatial data analysis (both Vector & Raster data)

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Motivation

Do you want to combine the ability to use both Vector and Raster data in Python using one Package? Have you thought about creating interactive geospatial data visualization with few lines of code? or maybe even wanted to be able to use low code GUI for exploring your geospatial data in Jupyter Notebook?

Then Leafmap is a powerful new addition to your geospatial data science arsenal.

In this post, we look at Leafmap, what it is, its features, and code examples to illustrate some of its functionality. …


Microsoft Planetary Computer and the new AI Land Cover Mapping Platform

Microsoft Planetary Computer — PEARL Land Cover Mapping

Many of us in the Geo Comunity was excited to see another revolutionary idea like Google Earth Engine from Microsoft. The Microsoft Planetary Computer promises revolutionary global-scale environmental monitoring tools for scientists, developers, and decision-makers.

Most of us are still waiting for the early access private preview of the Microsoft Planetary Computer API and tools. However, Microsoft launched the PEARL — AI Accelerated Land Cover Mapping Platform on the browser.

The Planetary Computer puts global-scale environmental monitoring capabilities in the hands of scientists, developers, and policy makers, enabling data-driven decision making. Learn about some of the applications our partners are…

Abdishakur

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