Geospatial Data Science

Definition

Geospatial Data Science is an interdisciplinary field that involves utilizing statistical, computing, and data analysis techniques in the interpretation and comprehension of geographic or spatial patterns and relationships. These methods are applied to data that are geographically or spatially referenced. As such, Geospatial Data Science combines Geographic Information Systems (GIS), advanced analytical techniques (machine learning, data mining, statistics), big data technologies, and data visualization.

What is Geospatial Data Science?

Geospatial Data Science is a branch of data science that focuses on the collection, management, and analysis of geospatial data. Geospatial data refers to data that is associated with a physical location or geographic coordinates. This data can be from various sources, including satellites, UAVs, maps, surveys, and other tech-based geolocation devices.

The objective of Geospatial Data Science is to extract valuable insights, identify trends, make predictions, and uncover patterns from geospatial data. These outcomes help inform decision-making processes in various sectors, such as urban planning, environment management, transportation, public health, and risk management among others. Geospatial data scientists use a variety of software tools, such as R, Python, QGIS, ArcGIS, among others to achieve these objectives.

FAQs

What are the applications of Geospatial Data Science?

Geospatial Data Science is used in various fields such as urban planning, environment management, public health, transportation, and risk management. For example, in the environmental sector, it can be used to monitor and manage natural resources, habitats and biodiversity, and environmental impact assessments. In urban planning, it can assist in land use planning, disaster management, and transportation planning.

What types of data do Geospatial Data Scientists work with?

Geospatial Data Scientists work with various data types, including raster data, vector data, point cloud data, and satellite imagery. Raster data is data composed of pixels, such as images. Vector data is data consisting of points, lines, and polygons, such as maps. Point cloud data represents objects or spaces in 3D, and satellite imagery includes images taken from satellites.

What tools do Geospatial Data Scientists use?

Geospatial Data Scientists use a variety of software tools and platforms to collect, manage, and analyze data. Some of these tools include Geographic Information Systems (GIS) such as QGIS and ArcGIS, programming languages like Python and R, and big data technologies like Hadoop and Apache Spark.

What is the difference between GIS and Geospatial Data Science?

GIS is focused on the collection, storage, manipulation, and presentation of geographic information, while Geospatial Data Science involves using advanced statistical, computing, and data analysis techniques to interpret and comprehend geographic or spatial patterns and relationships. Therefore, while GIS is a tool for dealing with geographic data, Geospatial Data Science is a methodology for interpreting this data.