The document discusses vector data models in GIS. Vector data models represent geographic features using points, lines, and polygons. The key vector data models are the spaghetti model, which encodes features as strings of coordinates, and the TIN (triangulated irregular network) model, which creates a network of triangles connecting points. Vector models allow for discrete boundaries but complex algorithms, while raster models divide space into a grid but are simpler.
This document discusses geo-referencing and geo-coding. Geo-referencing is the process of aligning raster images and vector data to real-world coordinates so they can be overlaid and analyzed with other geographic data in a GIS. There are two main types: geo-referencing raster images and geo-referencing vector data. Geo-coding involves assigning coordinates to point data, often by matching addresses. While geo-referencing aligns geographic images, geo-coding specifically matches addresses to latitude and longitude coordinates.
Remote sensing and GIS techniques are useful tools for civil engineering projects. There are several models that can be used to represent the shape of the Earth, including flat, spherical, and ellipsoidal models. The ellipsoidal model is needed for accurate measurements over long distances. A geodetic datum defines the parameters of the reference ellipsoid and the orientation of the coordinate system grid. Common datums include NAD27 and NAD83, and transformations allow conversion between them. Map projections, such as Mercator and UTM, are used to represent the 3D Earth on a 2D surface, inevitably distorting some spatial properties like shape, area, or distance.
This document discusses band ratioing, image differencing, and principal and canonical component analysis techniques in remote sensing. Band ratioing involves dividing pixel values in one band by another band to enhance spectral differences. Image differencing calculates differences between images after alignment. Principal component analysis transforms correlated spectral data into fewer uncorrelated bands retaining most information, while canonical component analysis aims to maximize separability of user-defined features. These techniques can help analyze multispectral and hyperspectral remote sensing data.
The document discusses the key components of a geographic information system (GIS). It describes the main components as hardware, software, data, people, procedures, and networks. It provides details on each component, including how hardware is used to capture, store and display spatial data; common GIS software and their functions; different types of spatial and attribute data; and how procedures and methods ensure quality. Topological relationships and database models used in GIS are also overviewed.
This document provides an overview of geographical information systems (GIS), including definitions of GIS, its basic principles and components, data types used in GIS (vector and raster), advantages and applications of GIS. Specifically, it defines GIS as a computer system for capturing, storing, analyzing and displaying spatially referenced data. It describes the key principles of data capture, management, analysis and visualization. It outlines the typical hardware, software and data components of a GIS, and differentiates between vector and raster data types. Finally, it discusses advantages like accurate representation and analysis, and applications across different domains.
DEFINITION : GIS is a powerful set of tools for collecting, storing , retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes APPLICATION AREAS OF GIS Agriculture Business Electric/Gas utilities Environment Forestry Geology Hydrology Land-use planning Local government Mapping 11. Military 12. Risk management 13. Site planning 14. Transportation 15. Water / Waste water industry COMPONENTS OF GIS DATA INPUT SPATIAL DATA MODEL Data Model: It describes in an abstract way how the data is represented in an information system or in DBMS Spatial Data Model : The models or abstractions of reality that are intended to have some similarity with selected aspects of the real world Creation of analogue and digital spatial data sets involves seven levels of model development and abstraction SPATIAL DATA MODEL Conceptual model : A view of reality Analog model : Human conceptualization leads to analogue abstraction Spatial data models : Formalization of analogue abstractions without any conventions Database model : How the data are recorded in the computer Physical computational model : Particular representation of the data structures in computer memory Data manipulation model : Accepted axioms and rules for handling the data SPATIAL DATA MODEL SPATIAL DATA MODEL Objects on the earth surface are shown as continuous and discrete objects in spatial data models Types of data models Raster data model vector data models RASTER DATA MODEL Basic Elements : Extent Rows Columns Origin Orientation Resolution: pixel = grain = grid cell Ex: Bit Map Image (BMP),Joint Photographic Expert Group (JPEG), Portable Network Graphics(PNG) etc RASTER DATA MODEL VECTOR DATA MODEL Basic Elements: Location (x,y) or (x,y,z) Explicit, i.e. pegged to a coordinate system Different coordinate system (and precision) require different values o e.g. UTM as integer (but large) o Lat, long as two floating point numbers +/- Points are used to build more complex features Ex: Auto CAD Drawing File(DWG), Data Interchange(exchange) File(DXF), Vector Product Format (VPF) etc VECTOR DATA MODEL RASTER vs VECTORRaster is faster but Vector is corrector TESSELLATIONS OF CONTINUOUS FIELDS Triangular Irregular Network: (TIN) TIN is a vector data structure for representing geographical information that is continuous Digital elevation model TIN is generally used to create Digital Elevation Model (DEM) DIGITAL ELEVATION MODEL DATA STRUCTURES Data structure tells about how the data is stored Data organization in raster data structures Each cell is referenced directly Each overlay Is referenced directly Each mapping unit is referenced directly Each overlay is separate file with general header
GIS is a computer system that can assemble, store, manipulate, and display geographic data. It efficiently captures, stores, updates, analyzes and displays geographically referenced information through hardware, software, data and personnel. GIS allows for data capture through various methods, storage of data in both physical and digital forms, manipulation through editing attributes, and analysis to aid in decision making. It has advantages like easily analyzing locations, general purpose problem solving, and mapping. The scope of GIS includes using its functions to find locations of hospitals, schools, businesses, government offices and transportation hubs.
A Geographic Information System (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data.
This document provides an introduction to geographic information systems (GIS). It defines GIS as a collection of hardware, software, and geographic data used to capture, store, analyze and display spatially referenced information. The document gives a brief history of GIS and describes its key components, including spatial data, attribute data, software, and users. It also explains different types of data layers, scales, and four common map types: choropleth, contour, dot, and symbol maps. The overall purpose of the document is to explain the basic concepts and applications of GIS.
Topology refers to the spatial relationships between GIS features or objects. It is important for network routing and maintaining data quality and integrity when features are shared across layers. Geodatabases provide the strongest topological functionality, storing relationships in topology rules and feature classes. The node-arc data model represents the most common topology, with nodes at intersections and endpoints and arcs between nodes forming polygons. Topology allows for analysis without coordinate data but establishing topology is time-consuming.
The document discusses various methods of georeferencing, which is assigning accurate locations to spatial information. The most comprehensive method is using latitude and longitude, which defines locations based on angles from the equator and Greenwich Meridian. However, the Earth's curved surface poses issues for technologies that work with flat maps and data. Therefore, map projections are used to translate locations on the spherical Earth onto flat planes or surfaces, though all projections introduce some distortion. Common projections include cylindrical, conic, and the Universal Transverse Mercator system.
This document discusses remote sensing platforms and sensors. It describes the different types of orbits used by remote sensing satellites, including low Earth orbit, sun synchronous orbit, and geostationary orbit. It also outlines the various platforms that can be used, such as ground-based, airborne, and space-borne. Finally, it examines the characteristics of remote sensing sensors, including spatial, spectral, radiometric, and temporal resolution.