Spatial data science has revolutionised the IT world with a wide range of applications in diverse fields. It includes healthcare, urban planning, marketing, social networking and more. This has led to a massive demand for data professionals with domain expertise in spatial data science across all industries. This article highlights critical features of spatial data science, its practical applications, and future career prospects.
Data science involves studying and analysing a set of collected data to extract valuable information. It is a broad term that encapsulates all aspects of data processing activities. It deals with data capturing, storage, manipulation, analysis, interpretation, and so on.
The concept of data science has been there for more than 50 years now. In the 60s, mathematical skills and knowledge of statistics were applied to a detailed study of any available data. Today, computer professionals have various tools and technologies along with mathematics and statistical processes to interpret data more efficiently.
As the name suggests, spatial data science is an indispensable part of data science. We can refer to it as a sub-discipline of data science. Here, the primary focus is geospatial data. It resolves data-driven issues with spatial analysis of data collected from GIS (Geographic Information System).
Data is collected in the form of facts and figures. Some critical spatial data sources are the internet, mobile network, GPS tracking, high-resolution satellites, etc. Spatial data has more relevance in several fields of work because of its unique location component. In other words, spatial data carries information related to a specified geographic space.
GIS is a computer-based system used to create, manage, analyse, and map all types of geographical data. GIS is more beneficial than other information systems because the data centres around varied positions on Earth’s surface. The position can be determined based on latitude, longitude, ZIP code, etc. Another significance is that GIS recognises spatial patterns and spatial interactions. For instance, spatial patterns of a city identify the location of schools, parks, factories, water pipelines, etc. Similarly, spatial interactions establish the connection between two phenomena, like the types of soil available in a region and its vegetation.
Following are some crucial tools and techniques that support spatial data science ecosystem and enhance its effectiveness:
Artificial Intelligence (often referred to as AI) and machine learning, a subset of AI, are the two most effective tools that play a significant role in spatial data analysis. Artificial intelligence consists of computer systems with programmed machine-learning models that imitate human intelligence to execute tasks like problem-solving. These algorithms are capable of learning, reasoning and creating visual perception like human minds. It has a special role in processing the translation of natural languages from geospatial.
Machine learning is a set of tools and concepts which is capable of handling data with accuracy but does not require explicit programming. It extracts data and understands its pattern through statistical analysis, creates predictive data that forecasts the future accurately, and recommends possible solutions.
Data exploration is the preliminary inspection of a dataset at a basic level. It gives clues related to the characteristics of raw, unfiltered data. Its primary objective is to decide which data is essential and qualifies for a particular spatial data analysis. Data exploration makes it easier to isolate data as per the area of business interests.
Data visualisation examines data minutely by preparing tables, line graphs, bar graphs, pie charts, etc. These types of visual representations place the data into an easy-to-understand format. Visual elements make the study of data simpler for experts. They are in a better position to draw conclusions and make estimations and projections for the future with ease.
A series of models are built, and machine learning algorithms are scripted to manage repetitive workflows of big data. Modelling and scripting designed for spatial data science are object-oriented that replicate look and feel of the physical world. A model is selected based on the business problem it is meant to solve. The accuracy of each model is checked on a test dataset first. If it is found to be capable of predicting desired outcomes, then it is implemented in actual analytic operations. This automated system reduces manual efforts to a great extent.
In the digital world, data is available in a more concrete and usable form. This is possible with the help of data engineering. It empowers spatial data science with the complex task of designing, developing, and arranging data pipelines for geospatial data. These data pipelines ensure a smooth flow of data from an application to a data warehouse. Robust data pipelines are vital to retrieving good-quality data for analysis.
This tool can facilitate a faster and better decision-making process for any business. With the rapid expansion of the digital world, a massive amount of spatial data is available for analysis. Some are structured data like Excel, some are semi-structured data like e-mail messages, and others are unstructured data like maps, images, etc.
Big data analytics uses advanced analytic techniques to manage big data through data filtering, data extraction, and data integration at a faster pace. Analysis of big geospatial data is crucial for business organisations to understand market demands, consumer behaviour, and customer preferences of a particular region.
Spatial analysis is the process of a proper interpretation of GIS data to meet a specific goal. It is fully automated from start to finish. It resolves real-world problems from an actual geographic location by establishing a relationship between an event and its underlying causes. It is a powerful tool to measure the suitability and capability of solutions that solve complex problems. It also helps in risk assessment and prevention of future losses. The methodology can be defined with these five steps:
Spatial analysis in GIS is used to achieve several objectives. It is useful for scientific purposes, administrative purposes by governments and commercial purposes as well.
Here are five practical uses of geospatial data analysis:
There is tremendous growth opportunity in spatial data science. It is one such sub-discipline of data science which has not yet been explored fully. Business organisations and governments find spatial data science systems reliable for regional growth and expansion of their activities.
However, the scarcity of trained professionals in the field of spatial data science is a matter of concern for employers. They need certified candidates only. StarAgile offers a Data Science Online Course designed for working professionals who are interested in upgrading their skills. This Data Science Certification improves your chance of getting hired by reputed organisations in this industry.
We have been dealing with data for many years now. However, the volume of data has never been so huge. As the volume is growing, the complexity of data is increasing too. Data science provides optimum solutions to manage bulk data systematically. When data science is centred around spatial information, it becomes spatial data science. Here, data engineering techniques and software tools come into play to derive data from a geographic location and process them to provide innovative ideas for business and community development. Companies are ready to invest in spatial data science to tackle their regional business problems.
Signup for StarAgile Data Science Online Course today and become job ready with Data Science Certification. At StarAgile, you develop domain expertise with the latest technology and bridge your knowledge gaps. As a skilled and certified data science professional, you can look forward to a lucrative career ahead!
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