Explore the expansive world of geospatial data science in 2024
Introduction
Data science has been one of the most sought-after and lucrative fields in recent years. Data scientists are experts in extracting insights from large and complex datasets, using various tools and techniques such as programming, statistics, machine learning, and visualization. As such a powerful tool, data science has applications in almost every industry and sector, from healthcare to finance, from education to entertainment, and from e-commerce to social media.
I don’t know if that’s because I work within this tech bubble, but sometimes I feel that if I miss a new ML algorithm or a fancy DL transformer that just came out, I’m being left behind. The bottom line is that data science is on hype, and it will probably continue to be for the next few years.
But what about the Geospatial aspect?
Data science is not a static field. It is constantly evolving and adapting to new challenges and opportunities. One important trend that is shaping the future of data science is geospatial data science. As the urban population increases, the demand for geospatial analysis rises to support urban planning, infrastructure development and efficient resource allocation.
Another driver for adopting geospatial data science is the increasing awareness and demand for environmental, social, and governance (ESG). ESG cover three broad domains: environmental (such as pollution, resource depletion, biodiversity loss, and climate change), social (such as human rights, labour standards, diversity, health, and safety), and governance (such as corporate ethics, transparency, accountability, and stakeholder engagement). ESG is becoming more important for businesses and investors, as it can affect their reputation, performance, risk, and value. According to a survey by PwC [1], 77% of institutional investors said they would stop buying non-ESG products by 2022. Moreover, ESG investing has outperformed traditional investing in recent years [2], showing that sustainability is not only good for the planet but also from an investment point of view.
What is Geospatial Data Science
Geospatial data science is a complex field that requires transversal knowledge from several other fields such as Geographic Information Systems (GIS), Remote Sensing, Earth Sciences and Computer Science fields.
Geospatial analysis can help businesses and investors by providing spatial insights and solutions to solve complex problems that involve spatial dimensions, such as climate change, urban planning disaster management, transportation, agriculture and more.
Here are just some examples of subjects that Geospatial analysis may help to address (not limited to):
Reduce greenhouse gas emissions by tracking sources and sinks of carbon dioxide;
Optimize energy efficiency by mapping renewable energy potential;
Enhance biodiversity conservation by identifying habitat fragmentation and restoration opportunities;
Improve social welfare by measuring access to health care, education, and other services;
promote good governance by detecting corruption and fraud, and much more.
Geospatial data science can also help create new opportunities for innovation and value creation by leveraging location-based services, geospatial analytics, spatial modelling, and visualization.
In terms of opportunities, according to Fortune Business Insights [3], the global geospatial analytics market size is projected to grow from 80 billion dollars in 2023 to more than 200 billion by 2030, representing a robust compound annual growth rate (CAGR) of almost 15% during the forecast period.
Conclusion
Learning geospatial data science is essential for anyone who wants to stay ahead of the curve in the data-driven world. Geospatial data science can help you gain a competitive edge in the job market and make a positive difference. Perhaps this is the most rewarding aspect.
You have to remember that Geospatial data science is not only a skill but also a mindset that can help you see the big picture and find hidden patterns and (spatial) connections in data. If you are interested in learning geospatial data science, many resources are available online to help you get started. And don’t forget to follow our posts at GeoCorner (the post How to Learn Geospatial Data Science in 2024 is a great starting point), as we will be here to help you through the journey with articles, tutorials, and lots of written codes to use in your projects.