This past year has led us to reflect on how fast the world is changing. Geospatial analytics is not an exception. “The global geospatial analytics market size was valued at $58.35 billion in 2019 and is projected to reach $158.84 billion by 2027, growing at a CAGR of 14.2% from 2020 to 2027.” – Valuates Reports.
Many industries started to focus on spatial data analysis as part of their data science research and development. Therefore the requirements for data completeness and validation are growing. The main requirements of data scientists towards the geospatial data are its continuity, real or near real-time flow, and availability of multiple alternative sources. However, there are other criteria of data assessment. Let’s explore some of the trends that will drive geospatial analytics in 2021.
Geospatial data is generated in massive volumes from millions of cell phones, sensors, and other sources every day. At the same time, raw data doesn’t have any value. The data has to be cleaned, analyzed, and visualized faster than ever before. In many cases, this process requires the development of complex methodologies and constant monitoring by experienced data scientists.
Data democratization makes big data accessible not only to data analysts and engineers but to any sales, marketing, or executive professional with no data science background. Thus, data democratization allows easy access to thousands of datasets from controlled sources, simplifying their licensing process, and provides simple and clear visualization to the end-user. It provides unbeatable benefits to different industries, allowing them to work with high quality and always up-to-date location data.
Managing and storing the enormous amount of data that is produced daily can quickly become complex and costly for any company. It is a common situation that working with Big Data rapidly increases the demand for financial and human resources to analyze and extrapolate usable information. That’s why many companies consider or are already in the process of adopting cloud computing solutions.
An entirely new architecture of cloud computing solutions has been created for geospatial applications, taking full advantage of the web services and elastic computing and data storage capabilities.
Cloud computing SaaS (Software as a Service) is one of the models existing in the market. Such platform-based solutions provide hundreds of analytical and visualization capabilities combined into easy-to-use applications for various implementations. A modern geospatial cloud solution offers ready-to-use demographic datasets and map/image layers that allow users to gain immediate context for applications of all types. Among the leading companies providing these services on the market are Amazon, Google, and Microsoft.
The biggest benefit is that engineers can run applications and services without managing and operating expensive and complex server infrastructures. The expansion of cloud computing services will definitely continue to evolve and grow throughout 2021.
Efficiently managing Big Data merged from different sources to gain business benefits is crucial for company competitiveness. Data warehousing is a key component of this process.
Data warehouse is a combination of technologies and components that provides an environment separate from the operational systems and is completely designed for decision-support, analytical-reporting, ad-hoc queries, and data mining. It is a process of turning data into information and making it available to users in a timely manner.
So, why do we include it among the most relevant trends for spatial analysis? Because businesses that have to manage massive amounts of data can do it quickly and safely with data warehousing.
Data warehouses can ensure data security by using encryption and specific security setups that are intended to protect confidential data. It also allows to look back at historical data to analyze the evolution of trends, derive accurate insights, and, more importantly, make smarter decisions.
Light detection and ranging (LiDAR) technology is a growing source of detailed 3D elevation data that can provide meaningful context over geographic areas.
In synthesis, LiDAR enables collecting a wide range of information for numerous applications and delivers several advantages over traditional aerial mapping methods. LiDAR is changing the paradigm of terrain mapping and attaining popularity in several applications such as floodplain mapping, forest inventory, geomorphology, hydrology, landscape ecology, urban planning, survey assessments, volumetric calculations, and coastal engineering.
In the past, the common way to generate topographic maps was using traditional stereophotogrammetry. It was a manual method that obviously cannot match the quality or accuracy of a lidar-derived digital terrain model.
In fact, the current technology provides users with a complete dataset and a precise 3D visualization of geographic areas, allowing users to create survey maps, update a GIS system, or create digital models of surface, terrain, etc., without having to travel to the site, thus saving time and resources.
These are some trends in Spatial Data Science that will definitely play a key role in 2021 and bring huge benefits to many industries, such as Transportation and Logistics, Retail, Energy, and Agriculture.
