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…
Road accidents — a leading cause of all deaths globally — is a major problem around the globe. Can a Machine Learning model help us understand, classify and predict crash severity based on spatial data?
This will be a three-part series article. In this first article, I will train a baseline model for one city. We will use Pycaret, a convenient Low-code machine learning library.
In the next article, we will use Geospatial Feature Engineering to improve our base model. The final part will scale the process to train a model for the whole of USA country car accidents.
Machine learning(ML) is hard to learn; especially it’s algorithms, data preprocessing and training models.
It is not the case anymore!
With the rise and availability of both no-code and low-code machine learning libraries and platforms, there are fewer barriers to use and apply machine learning models on your applications.
Low-code/No-code platforms and libraries enable users to run machine learning models easily by providing a ready-to-use code and functions. You can access these functions either through a web interface or writing minimal code.
While no-code platforms are the easiest way to train a Machine Learning model through drag and drop interface…
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…
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.
Your wish granted. GEE is all in one package. Google Earth Engine(GEE) is by far the complete…
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.
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…
Podcasts are a goldmine. It is one of my preferred ways to learn new things and keep up with the latest development in the geospatial world. Luckily, we are in for a treat as a significant number of new geospatial podcasts are launched recently.
I have listed some of these wonderful geospatial data science and earth observation podcasts over the past years. In this article, I will share some of my favorites, including the newest ones.
A podcast for the geospatial community
If Statement is a primary logic method in Python and we can find them everywhere in Python code. As a beginner, you often write long lines of code to check if something is the case and execute something based on the result of the condition.
This article will show you three different ways you can write more pythonic, short, and concise If/Elif/Else statements without writing long lines of code.
You will learn how to benefit from the boolean data types to write more concise code and create efficient switch statements for different choices.
As Geospatial data scientists, the examples I…
Data preparation and Exploratory data analysis take a lot of time and effort from data professionals. Wouldn’t it be nice to have a package(s) that enable you to explore your data quickly? In one line of code?
This article will show the best four packages in Python that can automate your data exploration and analysis. I will go through each one of them, what it does and how you can use it.
DataPrep lets you prepare your data using a single library with a few lines of code.
The DataPrep ecosystem currently consists of three…
Have you ever thought about interactive plotting with Geopandas, just like using Folium/Leaflet libraries? Would it be nice to have an interactive plot with Geopandas?
If so? Try Geopandas-View.
Geographic data science