GIS Fundamentals: Raster/Vector Data, Functions, DEM, and Georeferencing

Geographic Information System (GIS) is a powerful tool that allows us to capture, store, analyze, manage, and present spatial or geographic data. It integrates hardware, software, and data to gain insights into complex problems and make informed decisions. Understanding the fundamental concepts of GIS is crucial for any geographer, especially when dealing with spatial analysis and data manipulation.

At the core of GIS are two primary ways of representing spatial data: vector and raster. Each has its strengths and weaknesses, making them suitable for different types of geographic features and analyses.

Vector Data Model

The vector data model represents geographic features as discrete geometric objects with defined shapes and locations. These objects are defined by coordinates in a Cartesian coordinate system. The three basic geometric primitives used in the vector model are:

• Points: Represent locations with no dimension, such as cities, wells, or specific landmarks. They are defined by a single pair of X, Y coordinates.
• Lines (or Polylines): Represent linear features with one dimension, such as roads, rivers, or pipelines. They are composed of two or more connected points (vertices).
• Polygons: Represent area features with two dimensions, such as lakes, countries, or buildings. They are closed shapes defined by a sequence of connected points where the start and end points are the same.

Each vector feature can be associated with attributes stored in a table. For instance, a polygon representing a country might have attributes like its name, population, and area. This attribute information is linked to the spatial geometry, allowing for detailed querying and analysis.

Advantages of Vector Data:

• Accurate representation of discrete features.
• Efficient storage for sparse data (e.g., a few points on a large map).
• Well-suited for network analysis (e.g., finding the shortest route).
• Precise representation of boundaries and shapes.

Disadvantages of Vector Data:

• Can be computationally intensive for complex analyses involving large numbers of features.
• Not ideal for representing continuous phenomena like temperature or elevation.
• Storage can become large for dense datasets with many vertices.

Raster Data Model

The raster data model represents geographic space as a grid of cells, also known as pixels. Each cell has a specific size (resolution) and is located in a specific row and column within the grid. Every cell in the grid contains a value representing a characteristic of that location, such as elevation, temperature, land cover type, or precipitation.

Raster data is particularly well-suited for representing continuous phenomena where values change smoothly across space. Examples include digital elevation models (DEMs), satellite imagery, and aerial photographs. The resolution of the raster dataset determines the level of detail. A higher resolution means smaller cell sizes and thus more detailed representation, but also larger file sizes.

Advantages of Raster Data:

• Simple data structure, making it easy to perform spatial operations.
• Excellent for representing continuous surfaces and phenomena.
• Efficient for image processing and analysis.
• Can easily integrate data from sources like satellite imagery and aerial photos.

Disadvantages of Raster Data:

• Can result in large file sizes, especially at high resolutions.
• Less precise representation of discrete features and boundaries compared to vector data.
• Attribute representation is limited to the value within each cell.
• Shape representation can be blocky, especially at lower resolutions.

GIS Functions

GIS software provides a wide array of functions to manipulate, analyze, and visualize geographic data. These functions can be broadly categorized into data conversion, data editing, and spatial analysis.

Data Conversion Functions

These functions are used to transform data from one format or structure to another, enabling interoperability between different GIS software and data types.

• Vector to Raster: Converts vector features (points, lines, polygons) into a raster grid. For example, converting a layer of roads (vector) into a raster where each cell is classified as either "road" or "not road".
• Raster to Vector: Converts raster data into vector features. For instance, converting a land cover raster into polygons representing different land cover types. This is often used to create clean boundaries for vector analysis.
• Format Conversion: Changing data from one file format to another (e.g., Shapefile to GeoJSON, GeoTIFF to JPEG). This is crucial for sharing data or using it in different applications.

Data Editing Functions

Data editing involves modifying existing spatial data to correct errors, update information, or refine features.

• Attribute Editing: Modifying the non-spatial information associated with geographic features (e.g., changing the name of a city, updating population figures).
• Geometry Editing: Modifying the spatial shapes of features. This includes:
  • Creating new features: Digitizing new points, lines, or polygons.
  • Deleting features: Removing unwanted features.
  • Modifying vertices: Adding, deleting, or moving vertices to reshape features.
  • Splitting/Merging features: Dividing a polygon into two or more or combining adjacent features.
  • Affixing/Un-affixing: Attaching or detaching parts of features.
• Topology Editing: Ensuring the spatial relationships between features are correctly represented. This is particularly important for vector data and involves maintaining rules like "no overlapping polygons" or "all line segments connect at endpoints."

Spatial Analysis Functions

Spatial analysis is the heart of GIS, allowing users to extract meaningful information from spatial data. It involves applying analytical techniques to understand spatial patterns, relationships, and processes.

• Overlay Analysis: Combining multiple layers of spatial data to create a new layer that represents the spatial relationships between the input layers. Common types include:
  • Union: Combines all features from two or more layers, preserving the attributes of all input features.
  • Intersect: Creates a new layer containing only the features that are common to all input layers.
  • Symmetric Difference: Creates a new layer containing features that are in one layer but not the other.
  • Identity: Overlays a layer onto another, keeping all features from the base layer and adding attributes from the overlay layer where they intersect.
• Proximity Analysis (Buffering): Creating a zone of a specified distance around a feature. For example, creating a 100-meter buffer zone around a river to identify areas that might be affected by flooding.
• Measurement: Calculating distances, perimeters, and areas of geographic features.
• Network Analysis: Analyzing linear networks (like roads) to find optimal routes, service areas, or nearest facilities.
• Spatial Statistics: Identifying patterns, trends, and outliers in spatial data (e.g., clustering, spatial autocorrelation).
• Surface Analysis: Analyzing elevation data (DEMs) to derive slope, aspect, and hillshade, or to perform hydrological modeling.

Digital Elevation Model (DEM)

A Digital Elevation Model (DEM) is a specific type of raster dataset that represents the bare-earth terrain surface. It is a three-dimensional representation of topography, where each cell in the raster grid contains an elevation value. DEMs are fundamental for various geospatial applications, including hydrological modeling, land-use planning, and visualization.

Creation of DEMs:

DEMs are typically generated from various sources:

• Photogrammetry: Using overlapping aerial photographs or satellite imagery to create 3D models.
• LiDAR (Light Detection and Ranging): A remote sensing method that uses laser pulses to measure distances to the Earth's surface, providing highly accurate elevation data.
• Radar Interferometry (InSAR): Using radar signals from satellites to measure surface topography.
• Digitizing contour lines: Converting topographic maps with contour lines into elevation data.

Applications of DEMs:

• Terrain Visualization: Creating shaded relief maps and 3D views of the landscape.
• Hydrological Analysis: Determining flow paths, watershed boundaries, and flood inundation areas.
• Slope and Aspect Calculation: Deriving information about the steepness and direction of slopes, important for agriculture, forestry, and construction.
• Visibility Analysis: Determining areas visible from a certain point.
• Site Suitability Analysis: Assessing land for development based on elevation and slope.

A DEM represents the "bare earth" – meaning vegetation and buildings are typically removed to show the underlying ground surface. A related dataset, the Digital Surface Model (DSM), represents the elevation of the Earth's surface including all objects on it, such as buildings and tree canopies.

Georeferencing

Georeferencing is the process of assigning geographic coordinates (latitude and longitude, or projected coordinates) to data that does not have them. This allows the data to be located, viewed, and analyzed within a GIS environment alongside other spatially referenced data. It essentially "places" a map or image onto the Earth's surface.

Georeferencing involves establishing a relationship between the coordinates of an image or map (e.g., pixel coordinates or coordinates on a scanned map) and real-world coordinates. This is typically done by identifying a set of common control points that exist in both the source data and a known, spatially referenced dataset.

Control Points

Control points are pairs of coordinates:

• Source Coordinates: The coordinates of a point in the original, unreferenced data (e.g., pixel row/column in an image, or coordinates on a scanned map).
• Target Coordinates: The corresponding real-world coordinates of the same point (e.g., latitude/longitude or projected coordinates from a GPS survey, a known map, or another GIS layer).

The more control points used, and the better their distribution across the dataset, the more accurate the georeferencing will be. A minimum of three control points is usually required to define a transformation, but more are recommended for better results.

Transformation

Once control points are identified, a mathematical transformation is applied to warp the source data to match the target coordinate system. Common transformation methods include:

• Affline Transformation: Preserves straight lines and parallelism but can change lengths and angles. It uses translation, scaling, rotation, and shearing. It requires at least two control points.
• Polynomial Transformation: Uses polynomial equations to fit the control points. Higher-order polynomials can achieve a better fit for complex distortions but can also lead to unrealistic warping between control points. First-order (Similarity) preserves shape and direction, second-order uses quadratic equations, and third-order uses cubic equations.
• Spline Transformation: A more flexible method that fits a smooth curve through the control points, suitable for complex, non-linear distortions.

The accuracy of the georeferencing is often assessed using a Root Mean Square Error (RMSE), which measures the difference between the predicted and actual target coordinates for all control points.

Projections and Datums

To accurately represent the Earth's surface on a flat map or in a digital format, we need to understand coordinate systems, datums, and map projections.

Datum

A datum is a reference system that defines the size and shape of the Earth and the origin and orientation of the coordinate systems used on its surface. Since the Earth is not a perfect sphere, but an irregular shape called a geoid, datums are used to approximate its shape.

• Geodetic Datum: Based on an ellipsoid (a mathematical approximation of the Earth's shape). Examples include:
  • WGS 84 (World Geodetic System 1984): The most widely used datum globally, especially for GPS.
  • NAD 83 (North American Datum 1983): Used primarily in North America.
  • NAD 27 (North American Datum 1927): An older datum, still found in some legacy data.
• Vertical Datum: Used to measure elevations, typically referenced to mean sea level.

Different datums use different ellipsoids and may have different origins, meaning the same geographic coordinates can represent different locations depending on the datum used. This is a common source of error in GIS if data from different datums are not properly transformed.

Map Projections

A map projection is a systematic transformation of the latitudes and longitudes of locations from the surface of a spheroid (or ellipsoid) onto a plane. Because the Earth is curved and a map is flat, some distortion is inevitable. Projections attempt to minimize distortion in specific properties: area, shape (conformal), distance (equidistant), or direction (azimuthal).

Projections are classified based on the type of surface they use to project the globe:

• Cylindrical Projections: The globe is projected onto a cylinder. The most famous is the Mercator projection, which is conformal (preserves shape and angles) but greatly distorts area, especially near the poles.
• Conic Projections: The globe is projected onto a cone. These are good for mid-latitude regions and preserve area and distance reasonably well along standard parallels.
• Azimuthal (or Planar) Projections: The globe is projected onto a flat plane. These are useful for showing polar regions or for representing directions from a central point.

Projections also define the coordinate system used for measurement:

• Geographic Coordinate System (GCS): Uses latitude and longitude on a spheroid (e.g., WGS 84). Units are degrees.
• Projected Coordinate System (PCS): Uses Cartesian coordinates (X, Y) on a flat plane, derived from a GCS using a specific map projection. Units are meters or feet. Examples include UTM (Universal Transverse Mercator) and State Plane coordinate systems.

When working with GIS data, it is critical to understand the datum and projection of each layer to ensure accurate analysis and visualization. Reprojecting data (transforming it from one coordinate system to another) is a common task in GIS.