Geographic Information Systems (GIS) Fundamentals

• Geographic Information System course code CS497 aims to introduce GIS fundamentals and spatial data handling
• The course learning outcomes include understanding GIS fundamentals, data models, software, and data handling

Fundamentals of GIS

• A Geographic Information System serves as a set of tools for collecting, storing, manipulating, retrieving, and displaying spatial data from the real world
• Geographic implies known locations of items based on coordinates
• Information implies data is organized for useful knowledge in maps, images, tables, and queries
• System implies interconnected components with functions for data capture, manipulation, transformation, visualization, and more
• GIS is used to link databases, manage location-based information, and answer questions regarding location and characteristics, useful as technology with hardware and software
• GIS is used for information handling for strategy, to improve decision-making through data analysis
• Formally, GISis a system for capturing, storing, checking, integrating, processing, manipulating, analyzing, and displaying spatially referenced data
• GIS handles spatial information referencing its location in space and makes connections based on spatial proximity

Applications and Historical Context

• GIS can be used in urban planning, environmental sciences, utility management, business, education, real estate and health
• GIS automates geographic data activities like map production, calculations, slope measurements, vehicle management, and analysis
• GIS allows complex spatial pattern integration and enables spatial queries and modeling
• The technology evolved from digital cartography, CAD, and database management systems

GIS System Architecture, Data Models and Representation

• The core GIS architecture includes data input, query input, a geographic database, output display, and transformation and analysis
• GIS data models digitally represent geographic features in real-world locations, storing them in a database for presentation and problem-solving
• Data is organized by layers representing common features
• Layers are integrated via explicit location on Earth
• Layers have 2 data types, spatial and attribute
• Spatial data describes location
• Attribute data specifies what, how much, and when
• Layers can be represented in vector format as points/lines, or in raster format (pixels)
• Geographic data has 4 4 properties, projection, scale, accuracy, and resolution

GIS Data Types and Models

• Spatial data indicates where
• Attribute data answers what, how much and when
• Vector data uses geometry and coordinates to represent real-world features
• Points are 0-dimensional with location
• Lines represent linear features using ordered coordinates
• Polygons represent areas
• The vector data advantages include accurately representing shapes and sizes and non-continuous data
• Disadvantages of vector data include explicit location storage and topological structure conversion
• Raster data the covers an area by a grid with square cells, where attributes are assigned a single value and image data is often called pixels
• Raster is strongest at storing continuous values
• Raster data may also represent discrete data
• Cell dimension specifies the length and width of the cell in surface units

Raster Data Details

• Raster data models represent continuous phenomena/ spatial features as a uniform grid
• Cells are square and evenly spaced, representing attribute values and cell locations
• In raster, a pixel value is assigned from 0 to 255
• Detail depends on spatial resolution
• Smaller the cell equals higher resolution
• The three raster data types include thematic, imagery, and spectral
• In raster, the grid cell is the basic building block character is created by cell grouping

Data Structure and Analysis

• Data structures reconstruct the spatial data model in digital form using raster or vector construction
• Raster data is cell or grid data structures
• Cell-by-cell encoding is the simplest structure
• Run-length Encoding (RLE) was developed to handle missing data
• In RLE adjacent cells along a row with the same value are a run
• Quadtree encoding reduces storage requirements and improves access speed for homogeneous regions
• Vector files have simply lines with a starting and ending point
• The key vector data structures include topological and non-topological configurations
• Topology is the arrangement of point/line/polygon features
• Topological data constrains how features share geometry
• Topological Integrated Geographic Encoding and Referencing is an early topology application for geospatial data
• Coverage data structure is a georelational vector data model

GIS Database and SDBMS

• Spatial database (SDBMS) is a general-purpose database that is a collection of spatial data types, etc.
• Spatial databases represent geometric objects and allow simple geometric objects with more complex structure
• A SDBMS is a software module that can work with an underlying DBMS supports spatial data
• SDBMS models spatial abstract data types (ADTs) and a query language
• SDBMS has spatial data types and is designed for query optimization
• The GIS tools visualize and analyze spatial data, uses the spatial analysis functions
• GIS performs thematic search, location, terrain and flow analysis, distribution, and measurement
• GIS uses SDBMS to store, search, query, and share spatial data sets
• SDBMS provides simpler set-based query operations and spatial indices for query optimization
• SDBMS can also be used by Astronomy, Genomics, and/or Multimedia
• SDBMS includes a spatial data model, query language, and query processing

Spatial Analysis

• Spatial analysis studies entities using their topological, geometric, or geographic properties with various techniques
• GIS is used to interact with processes to answer questions, support decisions, and reveal patterns
• Transformations, manipulations, and methods are applied to the geographic data to turn them into useful information
• Types of spatial analysis vary from descriptive summaries to sophisticated hypothesis testing
• GIS is used in analysis operations to answer to queries and support reasoning
• Measurements describe aspects of spatial numerical values, or properties
• Transformations change data sets, geometric/arithmetic/logical rules
• Transformations convert raster data to vector data or vice versa.
• Descriptive summaries capture datasets in one or two numbers
• Optimization techniques are used to assign ideal locations to objects given well-defined criteria
• Hypothesis testing uses results from samples the make generalizations about a whole population

Spatial Data Acquisition

• Spatial data can be obtained from data acquisition using various sources in primary and secondary collection technique
• Direct data acquisition includes surveying, photogrammetry, GNSS (global navigation satellite system), remote sensing, scanning and digitizing
• Surverying is a feature mapping technique that uses instruments
• Photogrammetry maps features with aerial photographs
• Remote sensing can use Lidar
• Drone photography captures images and video
• Visual interpretation or automatic classification is to follow remote sensing
• Scanning converts existing maps into a Raster digital format
• Digitization converts existing maps into a Vector digital format