17.3 Geographic Information Systems (GIS)

Data Types and Structure

Geographic Information Systems (GIS) are tools for analyzing and visualizing spatial data. They combine maps, databases, and analytical capabilities to reveal geographic relationships and patterns that would be hard to spot otherwise.

In Earth observation, GIS acts as the integration hub. Data from satellites, field surveys, GPS devices, and other sources all get pulled into one system. From there, you can create detailed maps, run spatial analyses, and make evidence-based decisions about environmental management and resource use.

Spatial Data Representation

Spatial data represents geographic features and their locations on Earth's surface. There are two main types, and understanding the difference matters for every GIS task you'll encounter.

Vector data uses points, lines, and polygons to represent discrete features. A city might be a point, a road is a line, and a national park boundary is a polygon. Vector data works best when features have clear, defined edges.

Raster data uses a grid of cells (like pixels in an image), where each cell holds a value. This format works well for continuous data like elevation, temperature, or satellite imagery. The smaller the cells, the higher the resolution, but also the larger the file size.

GIS organizes spatial data into layers, each representing a specific theme or feature type (land use, transportation networks, soil types). These layers can be stacked and combined, which is where the real analytical power comes in. Overlaying a flood risk layer on top of a population density layer, for instance, reveals which communities are most vulnerable.

Attribute Data Management

Every geographic feature in a GIS can carry non-spatial information stored in attribute tables. Think of it like a spreadsheet attached to a map.

• Each row corresponds to a specific feature in the spatial data
• Each column represents a characteristic of that feature (name, population, area, land cover type)

Attribute data is what makes GIS more than just a map. You can use it to symbolize features (coloring counties by population density), perform queries ("show me all cities with populations over 100,000"), and run statistical analyses. The integration of spatial and attribute data is what lets you explore patterns and relationships across geography.

Data Manipulation

Georeferencing and Data Integration

Georeferencing is the process of assigning geographic coordinates to spatial data so it aligns correctly with other datasets. Without it, a scanned historical map or an aerial photo would just float in space with no connection to real-world locations.

The georeferencing process works like this:

1. Identify control points on the un-referenced data that correspond to known real-world coordinates (road intersections, building corners, survey markers)
2. Match each control point to its known coordinate location
3. Apply a mathematical transformation that warps the data to fit the coordinate system
4. Check accuracy and adjust if features don't line up properly

You generally want a minimum of four well-distributed control points for a reliable transformation. Using too few, or clustering them in one area, leads to distortion elsewhere in the dataset.

GIS enables the integration of data from many sources (field surveys, GPS, remote sensing) into a common spatial framework. Proper georeferencing is the foundation of this integration. If your data isn't aligned correctly, every analysis built on top of it will be unreliable.

Coordinate Systems and Map Projections

Coordinate systems provide the framework for defining locations on Earth's surface. There are two main categories:

• Geographic coordinate systems use latitude and longitude to represent locations on a curved surface. These are useful for global datasets but don't work well for measuring distances or areas directly, because the spacing between longitude lines changes with latitude.
• Projected coordinate systems transform those geographic coordinates onto a flat surface, which is necessary for mapping and most spatial analyses that involve measurement.

Map projections are the mathematical methods that make this transformation happen. The catch is that you can't flatten a curved surface without introducing some distortion. Different projections preserve different properties:

• Mercator preserves shape and direction (a property called conformality), making it useful for navigation. However, it badly distorts area near the poles. Greenland appears as large as Africa, when Africa is actually about 14 times bigger.
• Albers Equal Area preserves area, making it a strong choice for thematic maps showing density or distribution across a region.
• Universal Transverse Mercator (UTM) divides Earth into 60 zones, each 6° of longitude wide, and minimizes distortion within each zone. This makes it popular for regional-scale work and is widely used in field surveys and military mapping.

Choosing the right coordinate system and projection depends on your study area and what property matters most for your analysis. Getting this wrong can introduce significant measurement errors. A common mistake is measuring area using a Mercator projection, which will give you wildly inaccurate results at high latitudes.

GIS Applications

Spatial Analysis Techniques

GIS enables a range of spatial analysis techniques for exploring patterns, relationships, and trends in geographic data. Here are the major ones:

Overlay analysis combines multiple layers to identify areas meeting specific criteria. You might overlay slope data, soil type, and proximity to water to find suitable locations for agriculture. Or you could combine fault lines, building density, and soil liquefaction risk to assess earthquake hazards. The key idea is that layering different datasets together reveals relationships that no single dataset could show on its own.

Proximity analysis determines relationships between features based on distance. Buffer zones are the most common tool here. A 500-meter buffer around a river, for example, could define a riparian protection area where development is restricted. Nearest-facility searches (finding the closest hospital to an accident site) are another frequent application.

Network analysis examines connectivity and flow within systems like road or utility networks. Finding the shortest route between two points, optimizing delivery routes, or modeling how a contaminant might spread through a drainage system are classic examples.

Terrain analysis derives information from digital elevation models (DEMs), including:

• Slope (steepness of the terrain)
• Aspect (which direction a slope faces, important for solar exposure)
• Viewshed (what's visible from a given point)
• Watershed delineation (determining drainage boundaries)

These are critical for applications ranging from solar energy siting to wildfire risk assessment to flood modeling.

Map Creation and Visualization

GIS allows you to create maps that communicate spatial information clearly. Good cartographic design follows a few core principles:

• Choose symbology and colors that intuitively represent your data (blue for water, graduated colors for intensity, diverging color schemes for data with a meaningful midpoint)
• Include essential map elements: title, legend, scale bar, north arrow, and data source citation
• Keep the layout clean and focused on the message you're trying to convey

Beyond static maps, interactive maps and web-based GIS platforms (such as ArcGIS Online and Google Earth Engine) let users explore and query spatial data dynamically. You can click on features to see their attributes, toggle layers on and off, and zoom to different scales.

3D visualization techniques add another dimension, providing realistic representations of terrain, urban environments, and landscape change over time. These tools are increasingly important for communicating Earth observation findings to both specialists and the public, since a well-designed 3D flythrough of sea-level rise impacts, for example, conveys urgency in a way that a flat map often can't.