Fundamental of GIS: Definitions, Basic Components and tools of GIS

DEFINITIONS

1. GIS (GEOGRAPHIC INFORMATION SYSTEM)
   
   • A GIS is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data.
   • It integrates hardware, software, data, and people to collect, store, process, analyze, and visualize spatial information.
2. SPATIAL DATA
   
   • Spatial data refers to information that has a geographic or locational component.
   • It includes features such as points, lines, polygons, and raster images, along with associated attributes.
3. GEODATABASE
   
   • A geodatabase is a database designed to store, manage, and query spatial data in a GIS environment.
   • It supports the storage of vector and raster data, topology, relationships, and metadata.

BASIC COMPONENTS

1. HARDWARE
   
   • Computers, servers, storage devices, GPS receivers, digitizers, and other physical devices used to acquire, process, and display spatial data. 
2. SOFTWARE
   
   • GIS software provides tools and capabilities for data input, editing, analysis, visualization, and output.
   • Examples include ArcGIS, QGIS, GRASS GIS, and Google Earth.
3. DATA
   
   • Spatial data includes both vector and raster data representing geographic features, attributes, and phenomena.
   • Vector data consist of discrete geometric objects such as points, lines, and polygons, while raster data represent continuous surfaces as grids of cells or pixels.
   • Data may be obtained from various sources such as satellite imagery, aerial photography, surveys, maps, and databases.
4. PEOPLE
   
   • GIS professionals, analysts, technicians, and users who collect, manage, analyze, interpret, and communicate spatial information. 

TOOLS

1. DATA COLLECTION TOOLS
   
   • GPS receivers, surveying equipment, drones, and satellite imagery are used to collect spatial data.
2. DATA INPUT AND EDITING TOOLS
   
   • Digitisers, scanners, GPS units, and software tools for data capture and editing, such as digitizing tablets and GPS data loggers.
3. DATA ANALYSIS TOOLS
   
   • Spatial analysis tools perform operations such as buffering, overlay, proximity analysis, interpolation, and network analysis to derive insights from spatial data.
4. DATA VISUALISATION TOOLS
   
   • Cartographic tools for creating maps, graphs, and charts to visualize spatial patterns and relationships.
   • GIS software provides capabilities for thematic mapping, symbolisation, labeling, and annotation.
5. DATA QUERY AND MANAGEMENT TOOLS
   
   • Query and database management tools for retrieving, organising, and managing spatial data stored in geodatabases.
6. GEOPROCESSING TOOLS
   
   • Tools for processing, transforming, and analyzing spatial data, including spatial joins, clip, merge, and dissolve operations.

These components and tools collectively enable GIS users to perform a wide range of tasks, from basic mapping and visualization to advanced spatial analysis and decision-making.

GIS Data Structure: Spatial and Attribute Data

GIS data structure encompasses both spatial and attribute data, each playing a crucial role in representing geographic features and their associated attributes within a Geographic Information System (GIS). Here's an explanation of spatial and attribute data in GIS: 

SPATIAL DATA

Spatial data represent the geometric location and shape of geographic features on the Earth's surface. There are two primary types of spatial data in GIS:

1. VECTOR DATA
   
   • Vector data represent geographic features as discrete points, lines, and polygons.
   • POINTS: Represent specific locations on the Earth's surface, such as landmarks, cities, or sampling sites.
   • LINES (POLY LINES): Represent linear features such as roads, rivers, or pipelines.
   • POLYGONS: Represent areas or regions bounded by closed lines, such as countries, administrative boundaries, or land parcels.
   • Vector data can store additional attributes associated with each feature, such as population density for a city point, road name for a road segment, or land use type for a polygon.

2. RASTER DATA:

   • Raster data represent geographic features as a grid of regularly spaced cells or pixels, where each cell has a value representing a certain attribute or property.
   • Each cell in a raster grid corresponds to a specific geographic location and has an associated value, such as elevation, temperature, or land cover classification.
   • Raster data are particularly suitable for representing continuous phenomena, such as elevation surfaces, satellite imagery, or environmental variables.

ATTRIBUTE DATA

Attribute data, also known as tabular data or non-spatial data, represent the descriptive information or attributes associated with spatial features. Attributes provide additional context and characteristics about the geographic features represented by spatial data. Attribute data are typically stored in tabular format, where each row corresponds to a feature, and each column represents a specific attribute or property. Examples of attribute data include:

• FEATURE IDs: Unique identifiers for spatial features, used for linking spatial and attribute data. 
• FEATURE PROPERTIES: Descriptive attributes such as name, population, area, length, elevation, land use type, or any other characteristic associated with the features.
• CATEGORICAL VARIABLES: Nominal or ordinal variables representing categories or classes, such as land cover types, land use categories, or administrative divisions.
• NUMERICAL VARIABLES: Continuous or discrete numerical values representing measurements or counts, such as temperature, population density, or income levels.
• DATE AND TIME: Temporal attributes representing dates, timestamps, or time intervals associated with the features.

 INTEGRATION OF SPATIAL AND ATTRIBUTE DATA

In GIS, spatial and attribute data are integrated to provide a comprehensive representation of geographic features and their associated attributes. Spatial data provide the geometry and location of features, while attribute data provide the descriptive information and characteristics of those features. GIS software allows users to link spatial and attribute data through a unique identifier, enabling spatial analysis, visualisation, querying, and decision-making based on both spatial and attribute information.

Data Base Structures- Raster and Vector Data Structure

Raster and Vector Data  Structure : Certainly! Let's delve into the database structures for raster and vector data in GIS:

RASTER DATA STRUCTURE

Raster data represent geographic features as a grid of regularly spaced cells or pixels, where each cell contains a value representing a specific attribute or property. The database structure for raster data typically includes:

1. RASTER CELLS OR PIXELS:

   • Each cell or pixel in the raster grid corresponds to a specific geographic location and contains a single value representing a particular attribute, such as elevation, temperature, land cover type, or spectral reflectance.
   • The raster cells are organised in rows and columns, forming a regular grid across the study area.

2. COORDINATE SYSTEM AND GEOREFERNCING:

   • Raster data are associated with a coordinate reference system (CRS) that defines the spatial reference and projection of the raster grid.
   • Georeferencing information, including the origin coordinates, cell size or resolution, and rotation angle, is stored to geolocate the raster data accurately on the Earth's surface.

3. ATTRIBUTE TABLE:

   • In addition to the raster grid, raster datasets may include an attribute table containing additional information about each raster cell, such as its row and column indices, cell values, and statistics.
   • The attribute table may store metadata such as the data source, acquisition date, spatial resolution, and units of measurement.

4. PYRAMIDS AND OVERVIEWS:

   • Raster datasets often include pyramids or overviews, which are precomputed reduced-resolution versions of the original raster data.
   • Pyramids improve rendering performance and display quality by providing optimized representations of the raster data at different zoom levels. 

VECTOR DATA STRUCTURE

Vector data represent geographic features as discrete points, lines, and polygons, each with associated attributes. The database structure for vector data typically includes:

1. FEATURE GEOMETRY

   • Vector features are represented by their geometric shapes, including points (coordinates), lines (sequences of connected vertices), and polygons (closed sequences of connected vertices).
   • The geometry of vector features is stored as coordinate pairs (x, y) or vertices, defining the shape and location of each feature on the Earth's surface.

2. ATTRIBUTE TABLE

   • Vector datasets include an attribute table that stores descriptive information or attributes associated with each vector feature.
   • The attribute table is organized as a tabular data structure, with each row representing a feature and each column representing a specific attribute or property.
   • Attributes may include information such as feature IDs, names, classifications, measurements, or any other relevant characteristics.
3. COORDINATE SYSTEM AND GEOREFERNCING
   • Vector data are associated with a coordinate reference system (CRS) that defines the spatial reference and projection of the vector features.
   • Georeferencing information, including the origin coordinates, units of measurement, and projection parameters, is stored to accurately position the vector data on the Earth's surface.

4. TOPOLOGY AND RELATIONSHIPS

   • Vector datasets may include topological relationships between geometric features, such as adjacency, containment, connectivity, and spatial relationships (e.g., intersects, overlaps).
   • Topological data structures facilitate spatial analysis, such as overlay operations, network analysis, and proximity analysis.

Both raster and vector data structures serve as the foundation for storing, managing, analyzing, and visualizing spatial data in GIS. The choice between raster and vector data depends on factors such as the nature of the geographic features, the type of analysis required, and the specific GIS applications.

Database Management Systems (DBMS)

In Geographic Information Systems (GIS), Database Management Systems (DBMS) play a vital role in storing, managing, querying, and analyzing spatial data efficiently. Here's how DBMS are utilised in GIS:

Integration of GIS and DBMS:

1. Spatial Data Storage:

   • DBMS provide a structured framework for storing spatial data, including vector and raster datasets, in a relational or non-relational format.
   • Spatial data is stored alongside attribute data in tables or collections, enabling seamless integration with traditional database operations.

2. Spatial Data Models:

   • DBMS support spatial data models such as the Open Geospatial Consortium (OGC) Simple Features standard, which defines geometries (points, lines, polygons) and spatial relationships (intersects, contains, overlaps).
   • Spatial extensions or modules are often added to DBMS to enhance support for spatial data types, spatial indexing, and spatial operations.

3. Spatial Indexing:

   • DBMS utilize spatial indexing techniques such as R-tree, Quadtree, or Grid Indexes to accelerate spatial queries and spatial join operations.
   • Spatial indexes improve query performance by organizing spatial data in a hierarchical structure, allowing for efficient retrieval of spatially adjacent or overlapping features.

4. Spatial Query Language:

   • GIS software interfaces with DBMS using a spatial query language, such as SQL with spatial extensions (e.g., PostGIS for PostgreSQL) or proprietary spatial query languages (e.g., Oracle Spatial).
   • Spatial query languages support spatial predicates (e.g., ST_Intersects, ST_Contains) and spatial functions (e.g., buffer, centroid) for performing spatial analysis and spatial data manipulation.

5. Spatial Analysis and Processing:

   • DBMS provide capabilities for performing spatial analysis and processing tasks directly within the database environment.
   • Spatial functions and operators are executed as SQL queries, leveraging the computational power of the DBMS to perform operations such as buffering, overlay, proximity analysis, and raster analysis.

6. Data Integration and Interoperability:

   • DBMS support data integration and interoperability by providing connectors, APIs, and standards-based interfaces for exchanging spatial data with other GIS software, web services, and data formats.
   • DBMS enable seamless integration of spatial data with non-spatial data sources, facilitating holistic analysis and decision-making.

7. Data Management and Administration:

   • DBMS offer tools for managing spatial data, including data loading, indexing, backup, recovery, and security.
   • Database administrators can monitor and optimize database performance, scale database resources, and implement data governance policies to ensure data integrity and availability.

Examples of DBMS commonly used in GIS include:

• PostgreSQL with PostGIS: An open-source relational database management system with spatial extensions for storing and querying spatial data.
• Oracle Spatial: A proprietary spatial extension for Oracle Database, providing advanced spatial data management and analysis capabilities.
• Microsoft SQL Server with SQL Server Spatial: A relational database management system with built-in support for spatial data types and spatial indexing.
• MongoDB with GeoJSON: A NoSQL database that supports geospatial data storage and queries using the GeoJSON format and spatial indexes.

Overall, the integration of GIS and DBMS enables organisations to leverage spatial data effectively for decision-making, resource management, urban planning, environmental analysis, and many other applications.

Concepts and Application of Digital Elevation Model (DEM)

Digital Elevation Models (DEMs) are digital representations of terrain elevations over a specific area, typically represented as a grid of elevation values. DEMs play a crucial role in various fields, including environmental modelling, terrain analysis, hydrology, urban planning, and natural resource management. An overview of the concepts and applications of Digital Elevation Models:

  DIGITAL ELEVATION MODELS

CONCEPTS

1. Grid Representation: DEMs are commonly represented as raster grids, where each cell or pixel in the grid corresponds to a specific geographic location and contains an elevation value representing the height or elevation of the terrain at that location.

   
   

   
   

2. Elevation Data Sources: Elevation data for generating DEMs can be obtained from various sources, including:

   • Remote sensing technologies such as LiDAR (Light Detection and Ranging) and SAR (Synthetic Aperture Radar).
   • Aerial and satellite imagery.
   • Ground-based surveys using GPS (Global Positioning System) or total stations.
   • Existing topographic maps and survey data.

3. Resolution: DEM resolution refers to the size of each grid cell or pixel in the raster grid. Higher resolution DEMs have smaller cell sizes and provide more detailed elevation information, while lower resolution DEMs have larger cell sizes and are suitable for broader-scale analysis.

   
   

   
   

4. Accuracy and Precision: DEM accuracy refers to how closely the elevation values in the DEM represent the true elevations of the terrain. Precision refers to the level of detail or granularity of elevation measurements. High-accuracy DEMs are essential for applications requiring precise elevation information, such as flood modelling or slope analysis.

APPLICATIONS

1. Terrain Analysis:

   • DEMs are used for analyzing terrain characteristics such as slope, aspect, curvature, and visibility.
   • Slope analysis helps in identifying areas prone to landslides, erosion, or flooding.
   • Aspect analysis determines the direction of slope faces, which is crucial for solar radiation modeling and vegetation distribution studies.

2. Hydrological Modeling:

   • DEMs are used to delineate watershed boundaries, calculate flow accumulation, and model surface runoff.
   • Hydrological analysis based on DEMs aids in watershed management, floodplain mapping, and water resources planning.

3. 3D Visualization:

   • DEMs are utilized for creating realistic 3D visualizations of terrain features, landscapes, and urban environments.
   • 3D visualization enhances the understanding of topographic features and supports visual impact assessments, urban planning, and landscape design.

4. Landform Classification:

   • DEMs facilitate landform classification and geomorphological analysis by identifying landform types such as valleys, ridges, hills, and depressions.
   • Landform classification aids in geological mapping, soil erosion modeling, and land use planning.

5. Environmental Modeling:

   • DEMs serve as input data for various environmental models, including climate modeling, ecological niche modeling, and habitat suitability modeling.
   • Environmental models use elevation data to simulate processes such as temperature distribution, habitat connectivity, and species distribution.

6. Engineering and Infrastructure Planning:

   • DEMs support engineering applications such as site selection, route planning, and infrastructure design.
   • DEM-based analysis helps in identifying suitable locations for roads, bridges, pipelines, and communication towers, considering factors such as terrain ruggedness, accessibility, and environmental sensitivity.

7. Natural Resource Management:

   • DEMs are used in natural resource management applications such as forestry, agriculture, and wildlife habitat assessment.
   • DEM-derived terrain parameters aid in forest inventory, soil erosion prediction, crop suitability analysis, and wildlife habitat mapping.

Digital Elevation Models are versatile geospatial datasets that provide valuable insights into terrain characteristics and support a wide range of applications across various disciplines. Their accessibility and utility make DEMs indispensable tools for understanding and managing the Earth's surface.

Definitions & Basic Principle of Global Positioning System (GPS)

The Global Positioning System (GPS) is a satellite-based navigation system developed by the United States Department of Defense. It provides location and time information in all weather conditions, anywhere on or near the Earth, where there is an unobstructed line of sight to four or more GPS satellites.

DEFINITION

1. Global Positioning System (GPS):

   • GPS is a satellite-based navigation system that provides accurate positioning and timing information to users worldwide.

2. Satellite Navigation:

   • Satellite navigation is a system that uses signals transmitted from satellites to determine the location, speed, and time of receivers on or near the Earth's surface.

3. Navigation Satellite:

   • A navigation satellite is an artificial satellite equipped with precise atomic clocks and radio transmitters that broadcast signals used by GPS receivers to determine their position.

4. GPS Receiver:

   • A GPS receiver is a device that receives signals from GPS satellites and calculates its position, velocity, and time based on the timing and phase differences of the signals.

BASIC PRINCIPLES

1. Trilateration:

   • Trilateration is the fundamental principle behind GPS positioning. It involves determining the position of a receiver by measuring the time it takes for signals to travel from multiple satellites to the receiver.
   • To calculate its position, a GPS receiver measures the time delay between the transmission of a signal from a satellite and its reception at the receiver. By knowing the speed of light and the time delay, the receiver can calculate the distance (range) to each satellite.
   • With range measurements from at least four satellites, the receiver can determine its position by intersecting spheres centered around each satellite.

2. Satellite Constellation:

   • The GPS constellation consists of a network of 24 operational satellites orbiting the Earth in precise orbits at an altitude of approximately 20,200 kilometers (12,550 miles).
   • The satellites are distributed in six orbital planes with four satellites in each plane, providing global coverage.
   • The satellites continuously broadcast signals that include their precise time and orbital parameters, allowing GPS receivers to calculate their position based on the signals received from multiple satellites.

3. Time Synchronization:

   • Precise time synchronization is critical for GPS operation. Each GPS satellite is equipped with multiple atomic clocks that provide highly accurate time measurements.
   • GPS receivers use the time information transmitted by satellites to calculate the time delay between signal transmission and reception accurately.
   • The accuracy of GPS positioning depends on the synchronization of time between satellites and receivers.

4. Selective Availability (SA) and Augmentation Systems:

   • Selective Availability (SA) was a feature intentionally introduced to degrade the accuracy of civilian GPS signals. However, it was turned off in 2000, resulting in improved civilian GPS accuracy.
   • Augmentation systems, such as Differential GPS (DGPS) and Wide Area Augmentation System (WAAS), enhance the accuracy and reliability of GPS by correcting errors in satellite signals caused by factors like atmospheric conditions and satellite clock inaccuracies.

5. Triangulation vs. Trilateration:

   • While both terms are often used interchangeably, it's essential to note the difference. Triangulation involves measuring angles between known reference points to determine an unknown point's position, while trilateration involves measuring distances to known reference points to determine an unknown point's position. GPS primarily uses trilateration.

GPS technology has revolutionized navigation, surveying, mapping, and a wide range of applications, including transportation, agriculture, defense, and emergency services, by providing precise positioning and timing information worldwide.