Everything You Need to Know About Spatial Data Science
Global Tech Outlook has got information about Spatial Data Science to solve all the puzzles about this topic in your head.
Spatial Data Science (SDS) is something that is often a topic of misconception and not everyone including expert scientists is totally aware of SDS. In this article, you will be able to understand the core elements of SDS and its applications from the very foundation.
To begin with, Spatial Data Science is often confused with GIS (Geographic Information System) and Data Science. But in reality, both are entirety different, structurally.
A Geographic Information System (GIS) is a framework for gathering, managing, and analysing data. Rooted in the science of geography, GIS integrates many types of data. It analyses spatial location and organizes layers of information into visualizations using maps and 3D scenes. With this unique capability, GIS reveals deeper insights into data, such as patterns, relationships, and situations, helping users make smarter decisions.
GIS due to its one of its kind abilities to relate geographical elements to science and tech has broadened its application to various fields like, health, transportation, education, insurance, natural resources, petroleum, water, telecommunications etc.
With new types of users such as Data Scientists, GIS is starting to happen more outside of traditional GIS tools, allowing more sophisticated spatial analyses to take place in connection with new Data Science & Big Data solutions. This shift is allowing Spatial Data Science to emerge as a discipline with greater interactivity with Open -source and Cloud Technologies.
Whereas, data science on the other hand, is the study of data. It involves developing methods of recording, storing, and analysing data to effectively extract useful information. The central theme of data science is to gain insights and knowledge from any type of data — both structured and unstructured.
Spatial Data Science treats location, distance & spatial interactions as core aspects of the data using specialized methods & software to analyse, visualize & apply learnings to spatial use cases.
SDS has various numbers of formats but to understand the language in and out, certain key terminologies like Vector, Rooster would make one’s job easy.
Vector data is basically a graphical representation of the real world. There are three main types of vector data: points, lines, and polygons. Connecting points create lines and connecting lines that create an enclosed area create polygons. Vectors are best used to present generalizations of objects or features on the Earth’s surface. Vector data and the file format known as shapefiles (.shp) are sometimes used interchangeably since vector data is most often stored in .shp files.
Raster data is presented in a grid of pixels. Each pixel within a raster has a value; it has a colour or unit of measurement that is used to communicate information about the element in question. Rasters typically refer to imagery. However, in the spatial world, this may specifically refer to orthoimage, which are photos taken from satellites or other aerial devices. Raster data quality varies depending on resolution and your task at hand.
Using Spatial Data for Graphics
Maps are a common practice of representing spatial data as it is extremely easy to communicate complex topics. They can help validate or provide evidence for decision making, teach others about historical events in an area, or help provide an understanding of natural and human-made phenomena.
Using Spatial Data for Statistics
As it is with any data, to truly make sense of spatial data and understand what it is saying, you must perform some level of statistical analysis. These processes will help you uncover answers and lead you to make better decisions for your organization. The major difference between spatial data and all other types of data when it comes to statistical analysis is the need to account for factors like elevation, distance, and area in your analytical process.
Python & R are the most commonly used programming languages in the community. Python’s main libraries for data science are well-known for being better centralized and organized, but some within the community say that R still has a more complete offering for specific geospatial libraries (vs Data Science more generally).
Typically, Spatial Data Science workflows follow 5 key steps to take those analysing spatial data all the way from data gathering to the final step of delivering business insights.
Data ingestion & Management
Solutions & Visualization
A common example of Spatial Data can be seen in a road map. A road map is a two-dimensional object that contains points, lines, and polygons that can represent cities, roads and political boundaries such as states or provinces. A road map is a visualization of geographic information.
To conclude with, this is all a beginner needs to know about Spatial Data Science and its applications.