GIS Data Models: Fields vs. Objects

• A model is a simplified, abstract view of a complex reality, representing entities, phenomena, or processes.

Data Model Defined

• This is a computer-based representation of the real world.
• It's a mathematical construct for representing geographic objects or surfaces as data.
• It's also a logical construct for storing and retrieving information.
• It converts real geographical variation into discrete objects using a set of rules.
• Representations are rarely perfect, complete, or universally accepted.

GIS as a Representation of Reality

• GIS involves selecting real-world objects to include in a digital model.
• It represents real-world objects using "virtual" objects within the GIS.
• It quantifies data by storing numeric values assigned to features.
• It is important to recognize the limitations of accuracy in these representations.

Fields vs. Objects

• Fields/Continuous Data: Conceptualizes geographic space with continuous phenomena, where properties vary continuously, and a value can be recorded for every point on the Earth's surface.
• Objects/Discrete Data: Geographic space contains discrete spatial entities with identifiable boundaries, which are represented by graphical elements (points, lines, areas) and attached attributes/characteristics/properties.

GIS Data Types

• Spatial data describes the absolute and relevant location of geographic features, containing their location and shape.
• Attribute data describes characteristics of spatial features, which can be quantitative and/or qualitative.
• Characteristics that define objects include type (unique ID, object class), attributes (qualitative/quantitative data), relations (calculable vs. attributable), geometry (points, lines, area/polygon), and quality (accuracy, resolution, coverage extent, representation).

Geometric (spatial) and Attribute Data

• Geometric data includes points, lines, and areas.
• Attribute data can be qualitative/quantitative, including ordinal, interval, and ratio data.

Spatial Data Models

• Two main types of GIS data models are used for geographic representation:
  • Vector: Utilizes points/nodes and line segments mathematically.
  • Raster: Employs raster cells (pixels).

Raster Data Sources

• This includes satellite imagery, air photos, and scanned maps/documents.

Aerial Photography - Digital Orthophotographs

• Scanned photographs are mathematically rectified to eliminate displacement effects, providing a perpendicular view to the ground.

Raster Images

• These are sets of colored pixels that represent chart information as a picture on a computer screen.
• They are arrays of pixels arranged in rows and columns.
• Pixels are color-coded but don't explicitly represent features.
• Rasters can have values ​​attached.

Vector Data

• These represent real-world objects as points, lines, and polygons.
• Object representation is described by attributes and coordinates.
• Examples include digitized maps and GIS data.

Raster Data Model

• It is an implementation of a field conceptual model.
• It uses a grid-cell data structure.
• Space is divided into a matrix-like series of cells through regular tessellation.
• The process involves dividing the study area into square cells, registering the corners, and representing discrete objects as collections of cells.
• It represents fields by assigning attribute values ​​to cells, suitable for continuous fields rather than discrete objects.

Raster Data Characteristics

• This requires no explicit coding of geographic coordinates, due to the implicit layout of cells.
• Coordinates can be calculated if the origin point and size of grid cells are known.
• Topology is irrelevant.
• Each raster cell (pixel) contains only a single discrete value.
• Each data layer represents only one attribute.
• Raster data structures are useful for sophisticated mathematical modeling and can handle continuous data, but struggle with linear data analysis.

Vector Data Characteristics

• This is an implementation of an object conceptual model.
• It represents the real world with discrete elements: points, lines, and polygons.
• It stores the spatial location of features explicitly, without the space in between objects/features.
• More compact than raster data.
• Commonly used in urban analysis and planning, but less so in natural resource planning where imagery is more common.

Vector Data Structure

• Annotation: Text labeling.
• Polygons: Areas enclosed by arcs.
• Arcs: Line segments that form polygon borders or linear features.
• Points: Single coordinate pairs.
• Nodes: Points at the ends of arcs.
• Vertices: Points along an arc.
• Discrete points: Individual point features, polygon centers, or text positions.

Vector Graphical Primitives

• Points: Zero-dimensional objects (wells, sample locations, trees).
• Lines: One-dimensional, linear features made of interconnected points with nodes at the start and end, and vertices along the line (roads, streams).
• Polygons/Area: Two-dimensional objects made of connected lines, where the starting point is the same as the ending point (fields, lakes, forests).

Examples of Points

• Soil Samples: Include type, pH, contaminants.
• Utility Poles: Include owner, height, attachments.
• Spill Locations: Include accident numbers, type of spill, and extent.
• Parcel Centroids: Include section/block/lot number, address, owner, and assessment data.

Examples of Lines

• Street centerlines.

Examples of Polygons

• Houses, buildings, provinces/cities.

Explore GIS data models, focusing on the distinction between fields and objects. Understand how GIS uses virtual objects to represent real-world entities and the importance of recognizing accuracy limitations in these representations. Data models are simplified, abstract views of complex realities.