Scope of national mapping agencies is undergoing a seminal transformation, from a basic mapping mandate to providing digital services and solutions to all stakeholders.
The early history of GIS
Some of the early mapmakers were from the ancient civilizations of the Greeks and Romans. They used basic symbols and drawings to describe geographical elements while drawing maps for navigation and defining territorial boundaries.
These early maps were largely utilized for attaining practical objectives such as commerce, taxes, and wars. The ancient Greeks made maps of their city-states and colonies, while the ancient Romans employed maps for military operations and land surveying and measurement for the collection of taxes.
Hand-drawn and elaborately painted, Middle Ages maps primarily served military objectives. These maps were used for strategic planning and were not as precise as those made by the ancient Greeks and Romans.
The widespread availability of maps for use in navigation and commerce may be attributed to the use of the printing press in the 15th century. This advancement aided in the dissemination of information. This is when maps were first used for scientific and instructional purposes.
In the 16th and 17th centuries, cartography (the science and practice of drawing maps) evolved with the use of triangulation to calculate distances and the introduction of the Mercator projection, which enhanced the portrayal of the earth’s curving surface on flat maps.
Additionally, the introduction of aerial photography in the late 19th century and the development of photogrammetry technology in the early 20th century allowed more precise mapping of broad regions. Modern GIS, however, has its roots in the 1960s.
The first GIS
The Canadian government initiated a program in the 1960s to better control the country’s natural resources. The Canada GIS project was the pioneering effort in the history of geographic information systems. In the 1970s, the first GIS software, the Computer-Generated Imagery System (CGIS), was created for the express purpose of storing, manipulating, and analyzing geographic data.
While early GIS initiatives did a good job of managing data and creating maps, they lacked the sophisticated analytical tools seen in current GIS. The emergence of computer technology in the 1960s and 1970s allowed for the storage and processing of massive volumes of data. The invention of relational databases in the 1970s made it easier to link and analyze diverse data sets.
Vector GIS
Significant developments in vector-based GIS technology first appeared in the 1980s, making that decade a watershed moment in the development of geographic information systems. Before the 1980s, GIS largely employed raster data models, which represented geographic characteristics as a grid of cells, each storing a value.
In contrast to their suitability for presenting and evaluating continuous phenomena like elevation, temperature, and precipitation, raster data models struggled when it came to displaying and analyzing discrete phenomena like political borders, land use, and transportation networks.
However, geographic characteristics are represented as points, lines, and polygons in vector data models. Such data models not only improved the accuracy and precision of capturing spatial connections, but they were also more suited for representing discrete phenomena. Users of vector-based GIS had a more refined capability to design, alter, and examine geographical characteristics.
The creation of vector data models in the 1980s was an important achievement in GIS technology because it enabled GIS to represent and analyze a larger range of geographic characteristics, including some that were previously impossible to describe using raster data models. This expanded the potential fields of usage for GIS, including urban planning, transportation, and engineering.
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Topological data models, which may express intricate spatial connections between features, were made possible by vector data models. This opened the door for more complex GIS applications such as spatial analysis and modeling.
Although other more specialized GIS firms existed in the 1980s, Esri was the first to offer GIS software for personal computers. Among the earliest GIS software programs designed for PCs, the firm published ArcInfo in 1982. Since its inception, ArcInfo has been the tool of choice for professional GIS users, enabling them to do complex data manipulations and analyses as well as develop custom maps and other GIS products.
Web GIS
Esri developed ArcIMS (Arc Internet Map Server) in the 1990s as a web-based GIS programme that facilitated the creation and distribution of interactive maps and geographic data. This dramatically enlarged GIS’s reach and capabilities by allowing collaboration and data exchange across many organizations and individuals. Furthermore, ArcIMS enabled the development of web-based GIS applications that could be used for a variety of purposes such as land-use planning, emergency management, and natural resource management. Although it went unnoticed, it paved the way for the development of other web-based GIS software and services, such as ArcGIS Server, Google Maps, and OpenLayers, which are now widely used in the GIS and geospatial industries.
Open-Source GIS
The new millennium 2000 saw the release of open-source GIS software that had been discussed throughout the 1990s, such as the US Army Corps GIS system known as GRASS, the US Geological Survey ETL (Extraction, Translation, and Load) tool known as GDAL, and the University of Minnesota web mapping platform known as Mapserver, as well as a GIS created by Gary Sherman known as Quantum GIS (QGIS). There were more than three open-source GIS and several open-source GIS tools available between the end of 1999 and the beginning of 2002.
Storage and analysis advancements
In 1990, the average personal computer had just 40MB of memory, which was insufficient to hold a shapefile of the United States outline today, however by 2002, computers were being made with 60GB hard drives that operated at double the speed.
By the early 2000s, wireless technology and the rise of mobile phones meant that bathymetry, forestry, and aerial images were becoming more precise and widespread at high resolution, with 1m or 5m gridded data not being unusual.
This meant that far more intricate analysis could be completed much faster with considerably larger data sets The surveying sector welcomed the increase in storage because of the growing use of GPS to improve data accuracy. Although it was originally employed in the 1990s, it wasn’t until the convergence of greater storage and more competent GIS for analysis that it became widely available and accessible to industry.
The resolution of global imaging data acquired by NASA satellites increased from 1km x 1km to 90m x 90m, allowing for additional information to be retrieved and used for worldwide analysis. By the early 2000s, wireless technology and the rise of mobile phones meant that bathymetry, forestry, and aerial images were becoming more precise and widespread at high resolution, with 1m or 5m gridded data not being unusual.
GPS was also utilized to track assets in transportation and logistics: To optimize operations and enhance efficiency, companies in this business deployed GPS-enabled asset monitoring systems to track the whereabouts of cars, trailers, and goods in real-time.
Manufacturing companies employed GPS-enabled asset monitoring systems to track the whereabouts of equipment and tools to boost production and decrease losses due to theft or downtime.
GPS-enabled asset monitoring systems are used in the construction sector to track the whereabouts of heavy equipment and vehicles to boost production and decrease losses due to theft or equipment downtime. To optimize operations and improve efficiency, the public utility industry used GPS-enabled asset tracking systems to track the location of vehicles and other equipment. To optimize inventory management and reduce theft losses, the retail and wholesale industries began using GPS-enabled asset tracking systems to track the location of merchandise.
All of this was controlled using more complicated GIS, which began to assume multiple forms and move in different directions. Whereas in the beginning, GIS was seen as a “one-size-fits-all” solution, there were now multiple strands emerging for different sectors of business to serve different tasks. This may be observed in Esri software, where new specialty extensions such as “Military Analyst,” “Tracking Analyst,” “Network Analyst,” and “Business Analyst” would arise virtually biannually.
GIS in 3D & 4D
Cloud-based GIS services became popular in 2010, allowing storing and distribution of geographic data on distant servers, making GIS more accessible and scalable to users. It also saw the widespread use of Google Earth, which displayed 3D and 4D information as well as street views. It wasn’t long until GIS demonstrated practical and useable 3D GIS as well.
Although some GIS could handle and analyze 3D vector data, it was sluggish and difficult, and could only display rudimentary 3D data. By 2010, it was possible to use GIS to create basic 3D scenes such as offshore wind farms, where depth and bathymetry, along with other data, could be drawn with some accuracy. For the first time, vertical coordinate systems and ISO time and date formats were employed in GIS for both 3D and 4D items.
The integration of GIS with other geospatial technologies such as remote sensing, LiDAR, and drone photography expanded as a result. Only a few years later, in 2010, did technologies such as Pix4D, Drone2Map, and other mapping tools become available for converting this survey data into shareable forms via GIS.
Precision agriculture and farming industries grew as it became possible to map and manage the agricultural estate using a GIS-based system, along with crop, yield, and meteorological information, as well as new IoT (Internet of things) sensors that could provide real-time feedback for areas such as pest control.
The Internet of Things & cloud computing
The next geographical development was triggered by a marketing effort and advancements in an online capacity. The Guardian newspaper launched the “free our data” campaign in 2006, to hold the UK government accountable and make public data available. In 2010, the UK government stated that all public data will be made free and accessible (within reason), as well as data from the UK national mapping agency (Ordnance Survey), just like the US had done.
To be able to distribute data over the web, infrastructure modifications were required, and individuals all over the world realized the possibility of using the cloud for data storage and sharing. While many large businesses enabled their on-premises servers to share data, a company called Amazon Web Services (AWS) was providing cloud PostGres databases at an hourly cost, eliminating the need for businesses to invest in their infrastructure up front, as well as pay for IT engineers and electricians.
The advent of cloud computing enabled massive sharing and interaction potential. Transport and asset tracking businesses might upload real-time data to the cloud, which could then be analyzed instantaneously. Farming and agriculture might supply real-time information about crops and yields, which retail and supermarket corporations could utilize to deliver micro efficiency. Construction companies were now able to provide data straight from the site so that it could be quickly analyzed against the designs, which led to the boom of Building Information Modelling (BIM) and later GeoBIM, in which GIS is utilized to manage various BIM projects.
AI & ML
Machine learning (ML) and artificial intelligence (AI) have been the most disruptive technologies in the last decade. Initially used to extract patterns in data and geometry, it evolved to be capable of detecting characteristics in aerial photography and LiDAR data, as well as identifying faces and the area where they were collected. Other methods include:
Data analysis automation: AI and machine learning techniques may be used to automate the analysis of massive volumes of geographic data, such as satellite images, LIDAR data, and other forms of geospatial data. This enables faster and more accurate data analysis, as well as the ability to extract insights that humans would find difficult or impossible to discern.
Predictive modeling: Using AI and ML algorithms, predictive models may be created that can be used to forecast future trends and patterns in geographical data. This may be used to forecast future occurrences such as natural disasters as well as to optimize resource management and planning.
AI and ML algorithms may be used to handle and evaluate enormous volumes of picture and signal data, such as satellite imaging and LIDAR data. This may be used to extract environmental information, such as changes in land usage, as well as to spot patterns and anomalies that people would find difficult or impossible to notice.
Location-based services: Using AI and ML algorithms, location-based services such as tailored recommendations and real-time traffic updates may be created based on a user’s location and other data. This can enhance GIS application functionality and user experience, making it more accessible and valuable to a broader variety of users.
Smart Mapping: AI and ML algorithms can be used to generate, update, and analyze smart maps, which can then be used to provide real-time information and insights about various aspects of a location, such as traffic, weather, and crime rate.
Emergency Response: Using demographic and deep learning data, AI and ML can anticipate the shortest routes combined with the most efficient ways, as well as offer accurate predictions regarding regions of developing concern.
Indoor mapping
Indoor Mapping has been popular in recent years. Although Google demonstrated it nearly a decade ago as a potential way to navigate around a store, it never caught on due to the difficulty of mapping indoor areas and the inability to guarantee a precise location both horizontally and vertically. However, advances in mobile navigation, such as the introduction of VPS (Visual Positioning System) algorithms, more precise SLAM (Simultaneous Localization and Mapping) algorithms, WIFI triangulation, and mobile communication cell location (General Packet Radio Services- GPRS), have made it a more accurate and reliable solution.
Quantum computers are much faster than classical computers at calculations, allowing faster and more accurate analysis of large amounts of geospatial data. This may be used to build more detailed and precise maps as well as real-time data analysis.
Businesses are beginning to include indoor navigation into their meeting room request systems and hot desk systems to provide the shortest paths and clear information about where rooms are situated. One of the fastest expanding sectors is on educational campuses, where you may not only route yourself to the next class but also acquire vital health and safety information such as the location of the nearest fire assembly point from where you are.
Quantum computing & navigation
The phrase on everyone’s lips right now is quantum; in the previous 10 years, it has progressed from something that may be feasible to real-world testing of quantum navigation by the Navy aboard the HMS Prince of Wales. Although it is yet to gain traction, the potential is enormous and will revolutionize the face of GIS as we know it.
Quantum sensors can detect and quantify signals with far more precision than traditional sensors. This may be used to build more comprehensive and precise maps, as well as discover patterns and abnormalities that people would find difficult or impossible to notice.
Quantum computers are much faster than classical computers at calculations, allowing faster and more accurate analysis of large amounts of geospatial data. This may be used to build more detailed and precise maps as well as real-time data analysis.
Quantum communication offers substantially improved security and dependability in information transfer than traditional communication. This may be used to secure sensitive geospatial data like satellite pictures and LIDAR data.
Quantum positioning systems employ quantum technology to deliver far more exact and accurate positioning data than conventional systems. This may be used to increase the accuracy of GIS data and applications, as well as to add new features to existing technologies like indoor navigation and tracking.
GIS technological advancements and their future possibilities
Gartner predicts that the next advances in GIS technology will be in cloud/edge computing, AR, and IoT, whereas Esri predicts that AI, automation, and 3D mapping will empower urban planning, GeoBIM, and Smart Cities.
It is more likely to be a convergence of cutting-edge technology working together than any of these things. We will witness an average 30-50% energy efficiency and the capacity to make the technology smaller with the new 3nm chips, which have recently gone into production (December 2022). Since the chips are 16-20% smaller, even if they deliver 25% more. When combined with AI and ML, breakthroughs in 5G technology, and mobile LiDAR (as shown in current Apple products), we might see significant advances in indoor mapping, AR, and possibly the metaverse.
GIS is projected to play an increasingly vital role in society as technology advances. GIS integration with other technologies such as artificial intelligence and quantum computing is expected to create new and intriguing capabilities in areas like autonomous cars and smart cities.
If quantum technology advances, it may be integrated with AI and machine learning to tackle global environmental and urban concerns through quicker big data computing and large geographic analysis.
In the short to medium term, smart cities with GIS capable of handling massive, live data could become a reality for every city on the planet; this could even be generated off the back of current information captured for the metaverse.
We are now installing satellites around the moon, so lunar navigation and spatial will be possible. Also, governments globally are investing in green infrastructure, so GIS will drive efficiency in sustainable infrastructure.
Challenges facing continued development & implementation of GIS
Although it is exciting to think about what we can achieve in the future, it is unattainable without some non-technical labour. This means that organizations and rules will need to be in place to guarantee important sectors are governed and empowered.
One of the challenges for GIS is the requirement for precise and high-quality data. GIS must ensure that data is correct and up to date. Although this may be achievable in modest amounts, growing this internationally will necessitate thought.
Another problem for GIS is the requirement to combine data from many sources, formats, and systems. GIS relies on data from several sources, including satellite images, GPS, and survey data. It can be difficult and time-consuming to integrate this data. Standardizing data formats through steering committees and global policy will be critical to ensuring that GIS data can be shared and used effectively across systems.
Concerns around data security and privacy are becoming increasingly serious as GIS technology gets more widely used. GIS data frequently contain sensitive information, such as location data therefore its protection is critical.
GIS development and implementation need a high degree of technical skill. It is a huge difficulty to ensure that there are enough trained experts to create and maintain GIS systems. Additionally, establishing and maintaining GIS systems may be costly, and obtaining financing and resources for these systems can be difficultly Standardizing data formats is critical to ensuring that GIS data can be effectively shared and used across multiple systems.
Summary of GIS evolution & its impact
GIS has had a significant influence on both the planet and society. It has improved our understanding and management of natural resources, as well as our ability to plan and construct cities and infrastructure, as well as respond to emergencies and catastrophes. GIS has also played an important role in assisting organizations to make better decisions by delivering detailed location-based data.
GIS is projected to play an increasingly vital role in society as technology advances. GIS integration with other technologies such as artificial intelligence and quantum computing is expected to create new and intriguing capabilities in areas like autonomous cars and smart cities. However, issues such as data quality and management, data integration, data security and privacy, technical expertise, funding and resources, scalability, and standardization will need to be addressed in the future to ensure that GIS remains effective and impactful.
