GIS has come a long way. A few years ago, a lot of industries didn’t know it existed, let alone applying it in their decision-making process. Fast forward and GIS has revolutionized most industries, including Real Estate, Disaster Management, Marketing, and many more. And that’s not all, with the advancements in geospatial technology; GIS will penetrate other industries. The future of GIS depends on how the various practitioners apply it in their line of work. Below are some of the questions that you can address today, to help you understand what the future holds for you in the GIS fields;
- What are the latest trends in GIS?
- How can Geospatial data be used to solve emerging problems?
- What technological advancements are changing how spatial data is collected, visualized, and shared?
Current Trends in GIS
To understand the future, we need to evaluate the current trends and how they are already expanding the horizons of where geospatial technology can be applied.
Data Collection and Analytics
Since real-time data collection was introduced, it has helped industries such as eCommerce and research make data-driven decisions. However, the interaction with the data as its being collected seems to be yielding better results. For instance, an application such as the eLocust3 collects rainfall data, vegetation data, and soil data in real-time. It then sends the data to the headquarters where it’s analyzed and used to create distribution maps of where locusts may hit next. Smallsats (Small satellites) and drones have increased the rate at which remote sensing data is available for analysis.
Besides the collection of satellite data in real-time, there’s the miniaturization of devices. Thanks to the Internet of Things (IoT), phones, cars, and electronic devices are designed to collect data and make it accessible to users. All these technologies have made a lot of geographic data available. As a result, you’ll see heavy usage of GIS in military operations, flight management, fleet management, traffic management, and even in sports. GIS computing capability, such as the use of Python and R, makes it possible to handle bulk data all at once. Other useful computer networks for handling data include CyberGIS and cloud GIS.
There are several open-source software that enables users to interact with spatial data. A good example is OpenStreetMaps that engages locals to share their data by participating in mapping projects. As a result, they have been able to update maps on inaccessible areas, create distribution maps on disaster-stricken areas, and even update the topographic maps for major city centers. OpenStreetMaps relies on field data, satellite imagery, and GPS devices.
As a result of the open-source mapping, and the numerous GIS applications, a lot of people have access to geographic data. This may be a good thing since now; more industries can take advantage of data to make better decisions. However, there’s no effective control over who can access the data and how they can use it. IoT may also lead to the availability of sensitive information, exposing the users to cyber-attacks.
Since the applications of GIS are on the rise, more people are joining the profession. Also, people in different disciplines are taking courses in GIS so that they can leverage the power of spatial data in their industries. The rise in the workforce poses a challenge since most people who are joining the workforce are only knowledgeable in the front-end of GIS. That is, they can use some of the tools, but they have no idea how to explain the data in spatial terms. People who have a background in Geography, Geospatial Engineering, Geomatics, or any other related field are equipped with critical thinking and analytical skills that help them derive more information from Geographic data. To effectively apply GIS in various industries, the workforce will need to learn the theoretical aspect of collecting and analyzing Geographic data.
What does the future hold?
For starters, the advancements in GIS computing have widened the scope in which GIS data can be used. Lots of organizations are already developing GIS apps that are tailored to collecting, analyzing, and visualizing data that are relevant to their line of work. For instance, there was a surge of web applications and dashboards built to track the spread of the Covid-19. Real-time data, web frameworks, and GIS computing made it possible to track this disaster. From the looks of it, there will be an increase in demand for such applications in the next few years.
Platforms like GitHub have also encouraged GIS practitioners to collaborate and share their repositories. It’s also easier to develop apps nowadays since there are several GIS APIs; all you need to do is “call” them within your code.
GIS’s future is most promising when combined with machine learning. An excellent example of this is autonomous vehicles. For them to be successful, they combine a variety of technologies such as LiDar, radar, and cameras. The role of GIS here is to calculate the shortest route, capture traffic, weather, and road conditions, and aid in navigation.
There’s much to be expected from a combination of GIS, AI, machine learning, and deep learning. For instance, they can help in creating predictive models for precision agriculture, fighting crime, predicting extreme weather conditions and their possible effects, and many more applications.
The more GIS is combined with emerging technologies, the more useful it becomes in solving real-world problems. These technologies include AI, Deep Learning, Machine Learning, Augmented reality, cloud computing, IoT, and real-time data collecting and analysis. However, there needs to be some regulation on the data that users should have access to. Also, to make sure that we make the most out of it, the workforce should have some understanding of various geographical aspects, not just the front end mapping and visualizing tools.