GIS Lecture 2a Notes PDF

Process of Representing Geographic Data in GIS: 1. Real-world Entity or Event: ○ The process begins with identifying a real-world spatial entity or event to represent digitally in a computer. 2. Data Model Selection: ○ Based on the characteristics we wish to abst...

Process of Representing Geographic Data in GIS: 1. Real-world Entity or Event: ○ The process begins with identifying a real-world spatial entity or event to represent digitally in a computer. 2. Data Model Selection: ○ Based on the characteristics we wish to abstract, an appropriate data model is chosen. ○ The selected model simplifies the entity or event while maintaining essential attributes and spatial relationships. 3. Data Structure and Format: ○ The chosen data model is translated into a structure and format suitable for computer processing. Note: The choice of data model impacts how real-world complexities are represented and must be made carefully, as each model has specific strengths and weaknesses. Spatial Data Models: 1. Vector Data Model Key Concept: Represents the world as discrete objects with fixed spatial locations. This approach assumes space is empty except where objects exist. Features Represented: ○ Points: Single locations in space with no length or area (e.g., tree locations). ○ Lines: Linear features representing one-dimensional objects with length but no area (e.g., rivers). ○ Polygons: Two-dimensional features with a definable area (e.g., lakes, land parcels). Key Characteristics: ○ Each object is discrete and can be assigned multiple attributes (e.g., a land parcel labeled as "commercial"). ○ Captures topology, the spatial relationships between objects (e.g., a river flows into a lake). Applications: Best for mapping features with clear boundaries, such as census tracts, state borders, or road networks. 2. Raster Data Model Key Concept: Represents the world as a continuous field of variables, divided into a grid of cells (lattice or tessellation). Features Represented:  ○ Continuous phenomena, such as elevation, temperature, or land cover. ○ Each cell is assigned one attribute value (e.g., a specific land cover type). Key Characteristics: ○ Uniform shape: Always rectangular with a fixed number of rows and columns. ○ Spatial resolution: The size of each cell determines the level of detail. ○ Only one attribute per raster dataset; multiple attributes require separate rasters. Applications: Commonly used for land cover classification, elevation models, and temperature maps. 3. Image Data Model Key Concept: Similar to raster data but specifically records electromagnetic reflectance values for each pixel. Features Represented: ○ Pixels (picture elements) store spectral reflectance intensities rather than physical attributes. Key Characteristics: ○ Pixels have spatial properties, such as X, Y coordinates and resolution. ○ Values (digital numbers or brightness) range from 0 to 255 for each band of spectral data. Applications: Used in remote sensing, aerial photography, and creating new data by digitizing features. Unlike raster datasets, images often do not directly describe physical features. Note: This course covers image data briefly, with detailed study reserved for remote sensing courses. Comparisons and Strengths of Data Models: Aspect Vector Data Model Raster Data Model Image Data Model Representation Discrete objects (points, Continuous data Spectral reflectance lines, polygons) (fields) intensities Attributes Multiple per object One per raster None, only dataset reflectance values Spatial Objects with distinct Uniform grid Pixels in a Organization shapes (rows/columns) rectangular lattice Applications Boundaries, roads, Land cover, elevation, Remote sensing, features climate digitizing data Topology Captures relationships N/A N/A Summary of Key Points: 1. Vector Data Model: ○ Ideal for discrete objects with defined boundaries. ○ Attributes and spatial relationships can be represented. 2. Raster Data Model: ○ Best for continuous data and spatial phenomena. ○ Each cell represents one attribute, and spatial resolution affects detail. 3. Image Data Model: ○ Records spectral reflectance for remote sensing. ○ Provides visual context and supports data creation through digitization.