Fundamental Geospatial Data Types

Why This Matters

Geospatial data types form the foundation of everything you'll do in geospatial engineering—from building 3D city models to analyzing flood risk across watersheds. You're being tested on more than just definitions; examiners want to see that you understand when to use vector versus raster, why point clouds capture detail that DEMs cannot, and how attribute data transforms raw geometry into actionable intelligence. These concepts appear repeatedly in questions about data selection, analysis workflows, and system design.

The key principle here is fitness for purpose: every data type exists because it solves a specific representation problem. Vector data excels at discrete boundaries; raster data captures continuous phenomena; 3D formats model the vertical dimension that flat maps ignore. Don't just memorize what each format contains—know what problem it solves and when you'd choose it over alternatives.

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Spatial Representation Models

The most fundamental decision in geospatial work is how to represent real-world features digitally. Discrete features with clear boundaries call for vector models; continuous phenomena that vary gradually across space demand raster grids.

Vector Data

• Represents geography through points, lines, and polygons—each feature is a distinct geometric object with precise coordinates
• Ideal for discrete features like road networks, parcel boundaries, and building footprints where edges matter
• Supports rich attribute association—every feature can carry unlimited descriptive data, enabling complex queries and analysis

Raster Data

• Organizes space as a grid of cells (pixels)—each cell stores a single value representing a measurement or classification
• Optimized for continuous phenomena such as elevation, temperature, precipitation, and spectral reflectance from satellite sensors
• Resolution determines detail and file size—smaller cells capture finer variation but exponentially increase storage and processing demands

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Three-Dimensional Surface Models

When the vertical dimension matters—for terrain analysis, volumetric calculations, or line-of-sight modeling—you need data types designed to capture elevation. The choice between these formats depends on whether you prioritize uniform sampling or adaptive detail.

Digital Elevation Models (DEMs)

• Raster grids where each cell value represents surface elevation—typically derived from satellite stereo imagery, radar interferometry, or LiDAR
• Foundation for terrain analysis including slope, aspect, hillshade, and hydrological flow direction calculations
• Regular grid structure simplifies processing but can miss fine details in complex terrain where elevation changes rapidly

Triangulated Irregular Networks (TINs)

• Vector-based surface model using irregularly spaced triangles—vertices concentrate where terrain is complex, spread out where it's uniform
• More efficient than DEMs for variable terrain—captures cliff edges and ridgelines precisely without wasting storage on flat areas
• Preferred for engineering applications like flood modeling, cut-and-fill calculations, and viewshed analysis requiring high local accuracy

Point Clouds

• Dense collections of 3D coordinates typically generated by LiDAR scanning or photogrammetric processing
• Captures unprecedented surface detail—millions of points can represent building facades, vegetation structure, and ground topography simultaneously
• Raw format requiring processing—must be classified, filtered, and often converted to DEMs or TINs before analysis

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Data Description and Context

Spatial geometry alone tells you where something is—but attribute data tells you what it is, and metadata tells you whether you can trust it. These descriptive layers transform raw coordinates into meaningful information.

Attribute Data

• Tabular information linked to spatial features—stored in database tables with a unique identifier connecting each row to its geometry
• Enables querying, classification, and thematic mapping—without attributes, a polygon is just a shape; with attributes, it becomes a land parcel with owner, value, and zoning
• Supports both qualitative and quantitative fields—text categories, numeric measurements, dates, and even hyperlinks to external resources

Metadata

• Documents data lineage, accuracy, and limitations—answers critical questions about source, collection date, coordinate system, and processing history
• Essential for fitness-for-purpose assessment—a 30-meter DEM from 2005 might be useless for a project requiring 1-meter accuracy from current conditions
• Enables interoperability and data sharing—standardized metadata schemas (like ISO 19115) let systems automatically discover and integrate datasets

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Storage and Exchange Formats

Data types must be stored in file formats that balance portability, functionality, and software compatibility. Your format choice affects what analysis is possible and who can use your data.

Shapefiles

• Industry-standard vector format comprising multiple files (.shp for geometry, .dbf for attributes, .prj for projection) that must stay together
• Near-universal GIS compatibility—virtually every mapping application can read and write shapefiles, making them the lingua franca of geospatial exchange
• Legacy limitations persist—10-character field names, 2GB size limit, and no support for topology or complex data types

GeoTIFF

• Georeferenced raster format embedding coordinate system and spatial extent directly in the TIFF file header
• Self-describing and portable—no sidecar files needed; any GIS can place the image correctly on a map without manual georeferencing
• Supports diverse raster content—satellite imagery, scanned maps, classified land cover, and continuous surfaces like elevation or temperature

Geodatabases

• Integrated spatial database systems (like Esri's file geodatabase or enterprise geodatabase) storing vector, raster, and attribute data together
• Supports advanced data structures—topology rules, relationship classes, domains, and subtypes that enforce data integrity
• Enables multi-user editing and versioning—essential for enterprise workflows where teams collaborate on the same datasets

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Quick Reference Table

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Self-Check Questions

1. You need to model both building footprints and land surface temperature for an urban heat island study. Which data types would you use for each, and why?

2. Compare DEMs and TINs: what terrain characteristics would make you choose one over the other for a flood inundation model?

3. A colleague sends you a shapefile with no documentation. What specific metadata would you need before using it in a professional analysis?

4. Point clouds, DEMs, and TINs all represent 3D surfaces. Rank them from rawest to most processed, and explain what information might be lost at each processing step.

5. If you needed to share elevation data with an organization using different GIS software than yours, would you choose a geodatabase or GeoTIFF format? Justify your choice based on the properties of each format.