What is spatial data? This is what you need to know (in under 5 min)

By
Laura Tolson
October 30, 2023

In this article, we will be exploring the fundamentals of spatial data. At swyvl.io we live and breathe this type of data every day.

Let’s dive right in – the clock is ticking

What is Spatial Data?

Spatial data is any data that is associated with a specific geographical location or area. Essentially, if you can place it on a map, it’s spatial data. This includes information about natural features like rivers and mountains, as well as man-made structures like roads and buildings. Geospatial data is any data that is associated with a specific geographical location or area on the Earth’s surface.

Why is spatial data important?

Spatial data is important because it allows us to visualize and understand patterns, relationships and trends in a geographically referenced space. It provides context, making abstract data more tangible and actionable. Whether it’s for predicting natural disasters, planning city infrastructures or understanding the spread of diseases, spatial data offers valuable insights.

Examples of spatial data

There are many types of spatial data:

  • topographic maps showing elevation changes
  • satellite images of deforestation
  • GPS coordinates of your favorite coffee shop
Map topography, satellite imagery and GPS maps are all examples of spatial data.

The basics of spatial data

Spatial data can be categorized broadly into two types: Vector and Raster

Vector Data

This is composed of discrete geometric locations, defined by coordinates. There are three main types

  • Points: represent specific locations, such as the location of a landmark
  • Lines: represent linear features, like roads or rivers
  • Polygons: define areas, such as the boundary of a city or a lake

Raster Data

Unlike vector data, raster data is comprised of cells or pixels. It’s used to represent spatial variations, like a digital photograph or a satellite image, where each cell has a value representing information.

Attributes

Attributes are the additional information attached to each spatial feature. For instance, a point on a map representing a school might have attributes like the school’s name, number of students, and courses offered.

Additional Types of Spatial Data

Spatial data is multifaceted, with several types beyond the basic vector and raster data. These additional types of spatial data cater to specific needs and applications, enhancing our ability to analyze and interpret the world around us.

3D Spatial Data

3D spatial data adds a third dimension, usually elevation or depth, to the traditional 2D spatial data. This type of data is crucial in various fields such as urban planning, where architects and city planners can create 3D models of buildings and cities to assess their design and impact on the surrounding environment. In geology, 3D spatial data helps in understanding subsurface structures, aiding in resource exploration and hazard assessment.

  • Digital Elevation Models (DEMs): These are raster files representing the terrain’s surface with elevation values assigned to each pixel. DEMs are instrumental in flood modeling, landform classification, and watershed analysis.
  • 3D Building Models: These models provide a realistic representation of building structures, which is vital for urban planning, line of sight analysis, and emergency response planning.
A Digital Elevation Model (DEM) from the Space Shuttle Radar Topography Mission (SRTM)
4D Spatial Data (Spatio-temporal Data)

4D spatial data integrates the temporal dimension with 3D spatial data, allowing for the analysis of changes over time. This type of data is pivotal for monitoring environmental changes, urban development, and studying the movement patterns of animals or humans.

  • Time Series Data: This could include satellite imagery taken over several years to monitor deforestation or urban sprawl.
  • Trajectory Data: This represents the movement of objects over time and space, like tracking the migration patterns of birds or the routes of ships.
Multispectral and Hyperspectral Data

Multispectral and hyperspectral data consist of data captured at different wavelengths across the electromagnetic spectrum. These types of data are primarily used in remote sensing and have applications in agriculture, mineralogy, and environmental monitoring.

  • Satellite Imagery: Satellites equipped with multispectral sensors can capture data in various bands of the electromagnetic spectrum, which can be used to assess vegetation health, soil properties, and water quality.
  • Hyperspectral Imaging: This involves capturing images in hundreds of narrow spectral bands, providing detailed information about the material properties of the objects being imaged.
LiDAR Data

Light Detection and Ranging (LiDAR) data is used to make high-resolution maps, providing precise 3D information about the Earth’s surface. Applications of LiDAR data include forestry, topographic mapping, and flood modeling.

  • Airborne LiDAR: Mounted on aircraft, this LiDAR variant is used for creating detailed terrain models and has applications in forestry for tree height estimation and biomass calculation.
  • Ground-based LiDAR: This is used for detailed mapping of structures and landscapes, aiding in cultural heritage documentation and precision agriculture.
Cadastral Data

Cadastral data refers to information about land ownership and boundaries. This type of data is crucial for land management, urban planning, and legal purposes.

  • Land Parcels: These represent individual lots of land, detailing their boundaries and ownership information.
  • Utility Networks: These show the distribution networks for utilities like water, gas, and electricity, which is vital for urban planning and maintenance.

By exploring these additional types of spatial data, we gain a richer understanding of the spatial dimensions and temporal aspects of our world, enhancing our ability to make informed decisions and predictions. From 3D city models to tracking wildlife migration, the applications are as diverse as they are impactful, showcasing the indispensable role of spatial data in our modern world.

Using Spatial Data

What is GIS?

GIS, or Geographic Information System, is a tool that capture, stores, checks, integrates, manipulates, analyzes, and displays data related to positions on the Earth’s surface. It’s how we make sense of and utilize spatial data.

Using spatial data for graphics

Graphics are a powerful way to represent spatial data. Heat maps, for instance, can visualize the intensity of a particular phenomenon in an area. Choropleth maps can represent data values for specific regions, such as population density.

An example of a choropleth map showing population density in the United States.

Using spatial data for statistics

Statistical analysis of spatial data can identify patterns or anomalies. This could be used in predicting earthquake-prone zones or understanding crime rates in urban areas.

An example of how analysing historical earthquate data on a map can identify patterns.

What industries use spatial data?

Nearly every industry can benefit, but particularly:

  • Urban planning and architecture
  • Healthcare for disease control
  • Environmental monitoring
  • Retail for market analysis
  • Agriculture for crop yield predictions

Useful Definitions

  • Spatial Data: Information that has a specific geographical reference point.
  • Vector Data: Data represented by points, lines or polygons.
  • Raster Data: Data represented by pixels or grid cells.
  • Attributes: Additional information attached to spatial features
  • GIS: A system to capture, analyze, and present geographical data.
  • Georeferencing: Associating a physical map or raster image with locations in physical space.
  • Geocoding: Converting addresses into spatial data (ie getting the latitude and longitude of a specific address)
  • Geographic Coordinate System: A system that determines the location of data points on the Earth’s surface. It’s what allows us to provide accurate latitudinal and longitudinal coordinates at any given point.

Conclusion

By understanding the nuances and applications of spatial data, we can better utilize it to inform our decisions and shape the world around us. Whether you’re a data scientist, a geographer, or just someone curious about the world, spatial data offers a unique lens to view, analyze, and interact with our environment.

Laura Tolson
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