Wednesday, July 24, 2024
Tuesday, June 18, 2024
A1
What is a map? Explain the various components of a map?
A map is a visual representation of the Earth's surface or a part of it, typically drawn on a flat surface such as paper or displayed digitally. Maps are used to convey spatial information, including the location, distribution, and characteristics of geographical features, natural phenomena, and human activities. Listed below are the various components of a map:
1. Title:
Description: The title provides a brief description of the map's subject or purpose.
Purpose: Helps users understand what the map is about at a glance.
Example: "Topographic Map of XYZ Region", "Population Density Map of ABC City".
2. Legend or Key:
Description: The legend explains the symbols, colors, and patterns used on the map and their meanings.
Purpose: Helps users interpret the map symbols and understand the information presented.
Example: Symbols for roads, rivers, parks, colors indicating different land cover types.
3. Scale:
Description: Scale represents the ratio between distances on the map and actual distances on the Earth's surface.
Purpose: Helps users understand the relationship between map distances and real-world distances.
Example: 1:10,000 scale means 1 unit on the map represents 10,000 units on the ground.
4. North Arrow or Compass Rose:
Description: Indicates the orientation of the map, typically pointing towards the geographic north.
Purpose: Helps users understand the direction of the map relative to the real world.
Example: Arrow pointing towards the geographic north or a compass rose with cardinal directions.
5. Datum and Coordinate Grid:
Description: Datum specifies the reference point and coordinate system used on the map. Coordinate grid lines provide coordinates for locating features.
Purpose: Allows users to accurately locate features on the map using coordinates.
Example: Latitude and longitude lines, UTM grid, State Plane Coordinate System.
6. Borders and Boundaries:
Description: Borders and boundaries delineate political or administrative divisions such as countries, states, counties, or municipalities.
Purpose: Provides context and helps users understand the spatial extent of the area depicted.
Example: International borders, state boundaries, city limits.
7. Labels:
Description: Text labels identify and describe features such as cities, towns, rivers, mountains, and other geographic landmarks.
Purpose: Provides additional information and context to the map features.
Example: Names of cities, rivers, mountains, and other geographical features.
8. Symbols and Icons:
Description: Symbols and icons represent various features such as roads, buildings, parks, airports, etc.
Purpose: Helps users visually distinguish different types of features on the map.
Example: Lines for roads, dots for cities, polygons for parks, airports.
9. Insets:
Description: Insets are smaller maps included within the main map to provide additional detail or context.
Purpose: Offers enlarged views of specific areas or regions of interest.
Example: Zoomed-in map insets of downtown areas or detailed maps of national parks.
10. Graticule:
Description: Graticule is a network of lines representing parallels of latitude and meridians of longitude.
Purpose: Helps users measure distances and angles on the map.
Example: Grid lines showing latitude and longitude.
11. Scale Bar:
Description: A graphical bar that represents a distance on the ground, corresponding to a certain length on the map.
Purpose: Provides an alternative way to understand distances on the map.
Example: A bar indicating 1 kilometer, 1 mile, etc.
Maps are essential tools for navigation, spatial analysis, planning, and communication of geographic information, and understanding their components helps users interpret and utilize them effectively.
Wednesday, February 7, 2024
Miscellaneous topics of further interest to the GIS professional
First GIS in Canada in 1960s.
MIDAS was used in the USA for processing data on natural resources
Nystuen fundamental spatial concepts - distance, orientation, connectivity
Tobler computer algorithms for map projections, computer cartography
Bunge-theoretical geography-basis for geographic points, lines and areas
Berry's geographical matrix of places by characteristics-regional studies by overlaying maps of different themes
Boost to GISdevelopment by 1960 by CGIS
Second burst of activity by US census in late 1960s = DIME led to ODESSEY GIS in late 1970s
WWW was developed in CERN (European Organization for Nuclear Research) in Switzerland in 1990
Virtual reality and GIS have many features in common and are becoming more and more integrated.
Human cognition of the spatial world
Cognitive maps
Spatial learning and development
Navigation
Spatial language
GIS and spatial cognition
Cartographic abstraction
Types of maps - cartographic, thematic
Mapping software - CAD, GDS(Graphic Design System), DIPS(Digital Image Processing System), GIS
Mapping concepts, features, properties
-Map features
-Scale
-Map resolution
-Map accuracy (absolute, relative, attribute, currency, completeness)
Types of information in a digital map
Shape of the Earth - Datum
Datum types - Horizontal, Vertical, Complete
Reference ellipsoids
Geodetic datums
-Topographic surface
-Sea level
-Gravity models
-Geoids
-Spheroid
-Ellipsoid
Global Systems & Regional Systems
General coordinate system (Plane & Global)
Earth coordinate geometry
-Rotation
-Equator
-Geographic grid
-Latitude
-Meridian
-Degrees, minutes, seconds
Great & Small circles
Latitude & distance
Longitude & distance
Map projection & GIS
-Tissot's indicatrix
-Planar projections
-Conic projections
-Cylindrical projections
-Non-geometric projections
Variations of mercator projection shown as secant
-Equatorial
-Transverse
-Oblique
Projections preserving shape (CONFORMAL)
-Lambert conformal conic
-Mercator
Projections preserving area (EQUIVALENT)
-Albers equal area
-Sinusoidal
Projections preserving neither area norshape (COMPROMISE)
-Goodes Homolosine
-Robinson
Geometric analogy
-Tangent
-Secant
Conformal (Orthomorphic) projection
Equal area projection
Equidistant projection
Universal Transverse Mercator (UTM)
COMMON MAP PROJECTIONS: Properties & Applicarion areas
-Albers equal area
-Azimuth equidistant
-Lambert conformal conic
-Mercator
-Equidistant conical
-Polyconic-conical
-Sinusoidal-cylindrical
-Stereographic-planar
-Transverse mercator-cylindrical
Coordinates & Distortions
WGS - World Geographic Reference System
Regional Systens
-British National Grid (BNG)
-Indian Grid System
-State Plane Coordinates (SPC)
Georeferencing
-Place name
-Postal address
-Postal Code
-Telephone code
-Latitude / Longitude
-UTM
-State Plane Coordinates
Discrete georeferencing
-Street address
-Postal code system
-US Public Land Survey System
Affine & Curvilinear transformations
-Affine transformation primitives (four primitives)
--Translation
--Scaling
--Rotation
--Reflection
-Curvilinear transformations
Complex affine transformations
Affine transformations in GIS
INFORMATION ORGANIZATION & DATA STRUCTURE
Data & Information
-Linguistic expression
-Symbolic expression
-Mathematical expression
-Signals
INFORMATION
-Relevant
-Reliable, accurate, verifiable
-Up-to-date & timely
-Complete
-Intelligible
-Consistent
-Convenient
-Easy to handle
-Adequately protected
INFORMATION SYSTEM
-Conversion
-Organization
-Structuring
-Modeling
Geographic data & Geographic Information
-Temporal
-Thematic
-Spatial
Information Organization
-Data perspective
-Relationship perspective
-Operating System perspective
-Application architecture perspective
One dimensional array is called a VECTOR
Two dimensional array is called a MATRIX
DATA FILES
-Tree
--Binary tree
--Heap
DATABASE
-Permanent
-Transient
Database is a CHANGE in the perception of data, mode of data processing and purposes of using the data RATHER THAN physical storage of data.
Characteristics of a data file
Characteristics of a database
THE DIFFERENT WAYS IN WHICH DATABASES ORGANIZE DATA ARE KNOWN AS DATABASE MODELS.
-Relational
-Hierarchical
-Network
-Object oriented
Information organization of graphical data
-point
-line or arc
-polygon or area
Levels of data abstraction
-data models & database models
Data structure is a higher level of data abstraction than information organization.
-It represents the human implementation-oriented view of data and expressed in terms of database models
-Data structure is software-dependent
-It forms the next level of data abstraction in information system: FILE STRUCTURE OR FILE FORMAT
-File structure is hardware-dependent
Descriptive data structures
-Relational data structure
-Object-oriented data structure
Graphical data structure
-Raster data structure
--Picture Element (PixEl)
--Triangulated Irregular Network (TIN)
--Hierarchical tessellation (Ex: Quad trees)
--Scan-line
-Vector data structure
--Spaghetti
--Hierarchical
--Topological
-Georelational data structure
Relationship perspective of information organization
-Categorical
-Spatial
Scales of measurement
-Nominal
-Ordinal
-Interval
-Ratio
Spatial relationships
-Topological
--Adjacency
--Connectivity
--Containment
-Proximal
Data
Spatial & Non-spatial data
-Spatial data are generally multi-dimensional & auto-correlated
-Non-dimensional data are one-dimensional & independent
Databases for spatial data
-Total DBMS solution
-Mixed solution
Repository is a structure that stores and protects data.Functionality provided by repositories
-Add/insert data
-Retrieve data (find, select)
-Delete data
Repositories are like a bank vault. They exist to protect their contents from theft and accidental destruction
Advantages of a database approach
-Reduction in data redudancy
-Shared
-Maintenance, quality & data integrity
-Data is self-documented and descriptive
-Avoidance of inconsistencies. Imposition of PRESCRIBED MODELS, RULES & STANDARDS
-Reduced cost of software development
-Security restrictions
Database Management Systems (DBMS)
Queries
-Data Definition Language (DDL)
-Data Manipulation Language (DML)
Query Language implements DDL or DML or BOTH. (Ex: SQL = Structured Qurey Language, QUEL, ISBL, Query-by-Example)
Data models
-A mathematical formalism consisting of:
--Notation for describing data
--Set of operations to manipulate data
Data model is a way of organizing a collection of facts pertaining to a system under investigation
Data models provide a way of thinking about the world and a method of organizing the phenomena that interest us.
A data model is an abstract language
The theoretical foundation of the model helps to:
-Perform analysis
-Enables extraction of inferences &
-Create deductions that emerge from raw data
The THREE levels of abstraction in DBMS are:
-Physical
-Conceptual
-View
Steps in Data modelling:
-Conceptual data modeling
-Logical data modelling &
-Physical data modelling
LEVELS OF DATA ABATRACTION IN DATABASE DESIGN:
Conceptual data modelling -> Data model
Logical data modelling -> Data Structure
Physical data modelling -> File structure
CONCEPTUAL DATA MODELLING
-Identifying entities
-Identifying attributes
-Determining relationships
-Drawing Entity-Relatioinship (ER) diagrams
LOGICAL DATA MODELLING
-Comprehensive process to consolidate & refine conceptual data model.
-Proposed database is reviewed to identify potential problems like:
--irrelevent data
--ommitted or missing data
--inappropriate representation of entities
--lack of integration between various parts of database
--unsupported applications
--potential additional cost to revise database
END PRODUCT of logical data modelling is a LOGICAL SCHEMA developed by mapping the ER diagram to SOFTWARE DEPENDENT DESIGN DOCUMENT
PHYSICAL DATA MODELLING involves:
-Data format
-Storage requirements
-Physical location of data
END PRODUCT OF PHYSICAL DATA MODELLING IS A PHYSICAL SCHEMA also called:
-Data dictionary
-Item definition table
-Data specific table or
-Physical database definition
-IT IS BOTH SOFTWARE & HARDWARE SPECIFIC
PROCESS MODELLING
-It is a process-oriented approach
-It identifies processes that the information system will perform
-It also identifies how information is transformed from one process to another
-END PRODUCT IS a Data Flow Diagram (DFD)
-Process modelling is concerned with processes, information organization and data structure.
-Data Flow Diagram is the principal modelling tool constructed with:
-PROCESS
-ENTITY
-DATA STORE &
-DATA FLOW in a business function
SPATIAL DATA FORMATS
-Raster files are used for:
--Digital representations of aerial photographs, satellite images, scanned paper maps and other applications with very detailed images
--To reduce costs
--When map does not require analysis of individual map features
--When 'backdrop' maps are required
--The method of representing geographic eatures by pixels is called raster data model and data is described as raster data
--The raster method is also called 'tessellation method'
--Raster method is used for resource and environmental oriented applications
Relationship between "cell size" and "number of cells" is known as RESOLUTION of the raster
FINE RESOLUTION gives more accurate and better quality image
Vector files are used for:
-Highly precise applications
-When file size is important
-WHEN INDIVIDUAL MAP FEATURE REQUIRES ANALYSIS
-When descriptive information must be stored
-The method pf representing geographic features by basic graphic elements of points, lines and polygons is called vector data model
-Vector data is always organized by themes referred to as layers or coverages
-Themes covering very large geographic area are always divided into tiles
-A tile is the digital equivalent of an individual map in a map series
-A collection of themes of vector data covering the same geographic area and serving the common needs of a many users constitutes the "spatial component of a geographic database"
-Vector method of representing geographic features is based on identifying features as discrete entities
-It is the 'object view' of information organization in conventional mapping and cartography.
-The vector method is based on the 'object view of the real world'. It is the method of information organization in conventional mapping and cartography
CHOICE BETWEEN RASTER AND VECTOR DATA
-DATA COLLECTION = Rapid for raster and slow for vector
-DATA VOLUME = Large for raster and small for vector
-DATA STRUCTURE = Simple for raster and complex for vector
-GEOMETRICAL ACCURACY = Low for raster and high for vector
-GRAPHIC TREATMENT = Average for raster and high for vector
-AREA ANALYSIS = Good for raster and average for vector
-NETWORK ANALYSIS = Poor for raster and Good for vector
-GENERALIZATION = Simple for raster and complex for vector
GIS DATA STREAM
-Data (in the form of maps, satellite data, digital data, tabular data and soft ideas) are input into a GIS by
--Digitizing
--Scanning
--Data transfer &
--Key coding
-Data capture
-Editing/Cleaning
-Reprojection
-Generalization
-Edge matching & Rubber sheeting
-Layering
GEOGRAPHIC DATA FORMATS
-Vector
--Automated Mapping System (AMS)
--ESRI coverage
--Computer Graphics Metafile (CGM)
--Digital Feature Analysis Data (DFAD)
--Encapsulated Postscript (EPS)
--Microstation Drawing file format (DGN)
--Dual Independent Map Encoding (DIME)
--Digital Line Graph (DLG)
--AutoCAD Drawing Exchange FFormat (DXF)
--AutoCAD drawing format (DWG)
--MapBase file (ETAK)
--ESRI geodatabase
--Land Use Land Cover Data (GIRAS)
--Interactive Graphic Design Software (IGDS)
--Initial Graphics Exchange Standard (IGES)
--Map Information Assembly Display System (MIDAS)
--MOSS Export File (MOSS)
--Topologically Integrated Geographic Encoding and Referencing (TIGER/line file)
--Spatial Data Transfer Standard/Topological
--Vector profile (SDTS/TVP)
-Raster (Image)
--Arc Digitized Raster Graphics (ARDG)
--Band Interleaved by Line (BIL)
--Band Interleaved by Pixel (BIP)
--Band Sequential (BS)
--Windows Bitmap (BMP)
--Device Independent Bitmap (DIP)
--Compressed Arc Digitized Raster Graphics (CADRG)
--Compressed Image Base (CIB)
--Digital Terrain Elevation Data (DTED)
--ER mapper
--Graphics Interchange Format (GIF)
--ERDAS IMAGINE (IMG)
--ERDAS 7.5 (GIS)
--ESRI GRID file (GRID)
--JPEG File Interchange Format (JFIF)
--Multi-resolution Seamless Image Database (MrSID)
--Tag Image File Format (TIFF; GeoTIFF)
--Portable Network Graphics (PNG)
DATA ENTRY
Attributes can be directly entered into GIS by:
-Direct data loggers
-Manual Keyboard entry
-Optical Character Recognition (OCR)
-Voice regnition
DATA EDITING
-Errors in data may be due to original source data or during encoding process
-Errors in coordinate data or inaccuracies and uncertainities in attribute data
-Data errors should be intercepted before they contaminate the GIS database and propogate to higher levels of information.
-This process is called "Data editing" or "CLEANING". This involves:
--Detection and correction of errors
--Re-projection
--Transformation and generalization
--Edge matching and rubber-sheeting
Detecting and correcting errors
-Main sources are:
--Errors in source data
--Errors introduced during encoding
--Errors propogated during data transfer and conversion
Common spatial errors:
-Missing entities
-Duplicate entities
-Mislocated entities
-Missing labels
-Duplicate labels
-Artifacts of digitizing (Ex: Undershoots, Overshoots, Loops, Spikes)
-Noise
Reprojection, Transformation and generalization
--After encoding and editing spatial and attribute data, it is necessary to process the data geometrically in order to provide a common framework of reference.
--Scale and resolution of source data should be taken into account when combining data from a range of sources to form an integrated database.
--Data derived from different sources should be transformed and projected into a common coordinate system.
--It is of absolute importance to transform the coordinates of each of the input data sets into a common coordinate system.
--The above can be done using linear mathematical transformations.
-In case of translation and scaling:
--In this case, the coordinates are multiplied by a dataset factor depending on the scale
-In case a common origin is to be created:
--The origin of one of the data set is shifted in line with the other by adding the difference between the two origins
-In case of rotation:
--Map coordinates may be rotated using simple trigonometry to more datasets onto a grid of common orientation
-The accuracy of output from a GIS analysis is ONLY AS GOOD AS the WORST INPUT DATA.
-Accuracy of large scale maps is good due to low level of generalization and abstraction while the exact opposite is true for small scale maps
-Routines exist in GIS packages for WEEDING OUT unnecessary points from digitized lines so that the basic shape of the line is preserved.Ex: Douglas-Peucker algorithm
-The disadvantage is that the shape of features may not be preserved.
EDGE MATCHING & RUBBER SHEETING:
-If a study area exists across two or more map sheets, differences or misamtches between adjacent sheets should be resolved.
-Each sheet is digitized separately and joined to adjacent sheets after editing, re-projection, transformation and generalization. This process is known as edge matching and involves the following stages:
--Resolving mismatches at sheet boundaries to ensure complete features and topologically correct data
--In order to use data as a vector layer, the topology must be rebuilt as new lines and polygons are created from joining segments across sheets
--redundant map sheet boundary lines are deleted or dissolved
-Geocoding is the process of converting an address into a point location
Data conversion
-Manipulation and analysis of data is possible only when all data is in the SAME FORMAT
-When different layers are used simultaneously, they should ALL be in either VECTOR or RASTER format
-Normally, conversion is from vector to raster since biggest part of analysis is done in the raster domain
-Vector data are transformed to raster data by overlaying the grid with a user-defined cell size.
-Since raster data require huge storage space, data reduction can be achieved by converting raster data to vector data
-Remote sensing images are digital datasets recorded by satellite operating agencies and stored in their image database. These images should be converted into spatial database format before they can be downloaded
GEOGRAPHIC DATA - LINKAGES & MATCHING
-Linkages
-Exact matching
-Hierarchical matching
-Fuzzy matching (Pg 141)
MIDAS was used in the USA for processing data on natural resources
Nystuen fundamental spatial concepts - distance, orientation, connectivity
Tobler computer algorithms for map projections, computer cartography
Bunge-theoretical geography-basis for geographic points, lines and areas
Berry's geographical matrix of places by characteristics-regional studies by overlaying maps of different themes
Boost to GISdevelopment by 1960 by CGIS
Second burst of activity by US census in late 1960s = DIME led to ODESSEY GIS in late 1970s
WWW was developed in CERN (European Organization for Nuclear Research) in Switzerland in 1990
Virtual reality and GIS have many features in common and are becoming more and more integrated.
Human cognition of the spatial world
Cognitive maps
Spatial learning and development
Navigation
Spatial language
GIS and spatial cognition
Cartographic abstraction
Types of maps - cartographic, thematic
Mapping software - CAD, GDS(Graphic Design System), DIPS(Digital Image Processing System), GIS
Mapping concepts, features, properties
-Map features
-Scale
-Map resolution
-Map accuracy (absolute, relative, attribute, currency, completeness)
Types of information in a digital map
Shape of the Earth - Datum
Datum types - Horizontal, Vertical, Complete
Reference ellipsoids
Geodetic datums
-Topographic surface
-Sea level
-Gravity models
-Geoids
-Spheroid
-Ellipsoid
Global Systems & Regional Systems
General coordinate system (Plane & Global)
Earth coordinate geometry
-Rotation
-Equator
-Geographic grid
-Latitude
-Meridian
-Degrees, minutes, seconds
Great & Small circles
Latitude & distance
Longitude & distance
Map projection & GIS
-Tissot's indicatrix
-Planar projections
-Conic projections
-Cylindrical projections
-Non-geometric projections
Variations of mercator projection shown as secant
-Equatorial
-Transverse
-Oblique
Projections preserving shape (CONFORMAL)
-Lambert conformal conic
-Mercator
Projections preserving area (EQUIVALENT)
-Albers equal area
-Sinusoidal
Projections preserving neither area norshape (COMPROMISE)
-Goodes Homolosine
-Robinson
Geometric analogy
-Tangent
-Secant
Conformal (Orthomorphic) projection
Equal area projection
Equidistant projection
Universal Transverse Mercator (UTM)
COMMON MAP PROJECTIONS: Properties & Applicarion areas
-Albers equal area
-Azimuth equidistant
-Lambert conformal conic
-Mercator
-Equidistant conical
-Polyconic-conical
-Sinusoidal-cylindrical
-Stereographic-planar
-Transverse mercator-cylindrical
Coordinates & Distortions
WGS - World Geographic Reference System
Regional Systens
-British National Grid (BNG)
-Indian Grid System
-State Plane Coordinates (SPC)
Georeferencing
-Place name
-Postal address
-Postal Code
-Telephone code
-Latitude / Longitude
-UTM
-State Plane Coordinates
Discrete georeferencing
-Street address
-Postal code system
-US Public Land Survey System
Affine & Curvilinear transformations
-Affine transformation primitives (four primitives)
--Translation
--Scaling
--Rotation
--Reflection
-Curvilinear transformations
Complex affine transformations
Affine transformations in GIS
INFORMATION ORGANIZATION & DATA STRUCTURE
Data & Information
-Linguistic expression
-Symbolic expression
-Mathematical expression
-Signals
INFORMATION
-Relevant
-Reliable, accurate, verifiable
-Up-to-date & timely
-Complete
-Intelligible
-Consistent
-Convenient
-Easy to handle
-Adequately protected
INFORMATION SYSTEM
-Conversion
-Organization
-Structuring
-Modeling
Geographic data & Geographic Information
-Temporal
-Thematic
-Spatial
Information Organization
-Data perspective
-Relationship perspective
-Operating System perspective
-Application architecture perspective
One dimensional array is called a VECTOR
Two dimensional array is called a MATRIX
DATA FILES
-Tree
--Binary tree
--Heap
DATABASE
-Permanent
-Transient
Database is a CHANGE in the perception of data, mode of data processing and purposes of using the data RATHER THAN physical storage of data.
Characteristics of a data file
Characteristics of a database
THE DIFFERENT WAYS IN WHICH DATABASES ORGANIZE DATA ARE KNOWN AS DATABASE MODELS.
-Relational
-Hierarchical
-Network
-Object oriented
Information organization of graphical data
-point
-line or arc
-polygon or area
Levels of data abstraction
-data models & database models
Data structure is a higher level of data abstraction than information organization.
-It represents the human implementation-oriented view of data and expressed in terms of database models
-Data structure is software-dependent
-It forms the next level of data abstraction in information system: FILE STRUCTURE OR FILE FORMAT
-File structure is hardware-dependent
Descriptive data structures
-Relational data structure
-Object-oriented data structure
Graphical data structure
-Raster data structure
--Picture Element (PixEl)
--Triangulated Irregular Network (TIN)
--Hierarchical tessellation (Ex: Quad trees)
--Scan-line
-Vector data structure
--Spaghetti
--Hierarchical
--Topological
-Georelational data structure
Relationship perspective of information organization
-Categorical
-Spatial
Scales of measurement
-Nominal
-Ordinal
-Interval
-Ratio
Spatial relationships
-Topological
--Adjacency
--Connectivity
--Containment
-Proximal
Data
Spatial & Non-spatial data
-Spatial data are generally multi-dimensional & auto-correlated
-Non-dimensional data are one-dimensional & independent
Databases for spatial data
-Total DBMS solution
-Mixed solution
Repository is a structure that stores and protects data.Functionality provided by repositories
-Add/insert data
-Retrieve data (find, select)
-Delete data
Repositories are like a bank vault. They exist to protect their contents from theft and accidental destruction
Advantages of a database approach
-Reduction in data redudancy
-Shared
-Maintenance, quality & data integrity
-Data is self-documented and descriptive
-Avoidance of inconsistencies. Imposition of PRESCRIBED MODELS, RULES & STANDARDS
-Reduced cost of software development
-Security restrictions
Database Management Systems (DBMS)
Queries
-Data Definition Language (DDL)
-Data Manipulation Language (DML)
Query Language implements DDL or DML or BOTH. (Ex: SQL = Structured Qurey Language, QUEL, ISBL, Query-by-Example)
Data models
-A mathematical formalism consisting of:
--Notation for describing data
--Set of operations to manipulate data
Data model is a way of organizing a collection of facts pertaining to a system under investigation
Data models provide a way of thinking about the world and a method of organizing the phenomena that interest us.
A data model is an abstract language
The theoretical foundation of the model helps to:
-Perform analysis
-Enables extraction of inferences &
-Create deductions that emerge from raw data
The THREE levels of abstraction in DBMS are:
-Physical
-Conceptual
-View
Steps in Data modelling:
-Conceptual data modeling
-Logical data modelling &
-Physical data modelling
LEVELS OF DATA ABATRACTION IN DATABASE DESIGN:
Conceptual data modelling -> Data model
Logical data modelling -> Data Structure
Physical data modelling -> File structure
CONCEPTUAL DATA MODELLING
-Identifying entities
-Identifying attributes
-Determining relationships
-Drawing Entity-Relatioinship (ER) diagrams
LOGICAL DATA MODELLING
-Comprehensive process to consolidate & refine conceptual data model.
-Proposed database is reviewed to identify potential problems like:
--irrelevent data
--ommitted or missing data
--inappropriate representation of entities
--lack of integration between various parts of database
--unsupported applications
--potential additional cost to revise database
END PRODUCT of logical data modelling is a LOGICAL SCHEMA developed by mapping the ER diagram to SOFTWARE DEPENDENT DESIGN DOCUMENT
PHYSICAL DATA MODELLING involves:
-Data format
-Storage requirements
-Physical location of data
END PRODUCT OF PHYSICAL DATA MODELLING IS A PHYSICAL SCHEMA also called:
-Data dictionary
-Item definition table
-Data specific table or
-Physical database definition
-IT IS BOTH SOFTWARE & HARDWARE SPECIFIC
PROCESS MODELLING
-It is a process-oriented approach
-It identifies processes that the information system will perform
-It also identifies how information is transformed from one process to another
-END PRODUCT IS a Data Flow Diagram (DFD)
-Process modelling is concerned with processes, information organization and data structure.
-Data Flow Diagram is the principal modelling tool constructed with:
-PROCESS
-ENTITY
-DATA STORE &
-DATA FLOW in a business function
SPATIAL DATA FORMATS
-Raster files are used for:
--Digital representations of aerial photographs, satellite images, scanned paper maps and other applications with very detailed images
--To reduce costs
--When map does not require analysis of individual map features
--When 'backdrop' maps are required
--The method of representing geographic eatures by pixels is called raster data model and data is described as raster data
--The raster method is also called 'tessellation method'
--Raster method is used for resource and environmental oriented applications
Relationship between "cell size" and "number of cells" is known as RESOLUTION of the raster
FINE RESOLUTION gives more accurate and better quality image
Vector files are used for:
-Highly precise applications
-When file size is important
-WHEN INDIVIDUAL MAP FEATURE REQUIRES ANALYSIS
-When descriptive information must be stored
-The method pf representing geographic features by basic graphic elements of points, lines and polygons is called vector data model
-Vector data is always organized by themes referred to as layers or coverages
-Themes covering very large geographic area are always divided into tiles
-A tile is the digital equivalent of an individual map in a map series
-A collection of themes of vector data covering the same geographic area and serving the common needs of a many users constitutes the "spatial component of a geographic database"
-Vector method of representing geographic features is based on identifying features as discrete entities
-It is the 'object view' of information organization in conventional mapping and cartography.
-The vector method is based on the 'object view of the real world'. It is the method of information organization in conventional mapping and cartography
CHOICE BETWEEN RASTER AND VECTOR DATA
-DATA COLLECTION = Rapid for raster and slow for vector
-DATA VOLUME = Large for raster and small for vector
-DATA STRUCTURE = Simple for raster and complex for vector
-GEOMETRICAL ACCURACY = Low for raster and high for vector
-GRAPHIC TREATMENT = Average for raster and high for vector
-AREA ANALYSIS = Good for raster and average for vector
-NETWORK ANALYSIS = Poor for raster and Good for vector
-GENERALIZATION = Simple for raster and complex for vector
GIS DATA STREAM
-Data (in the form of maps, satellite data, digital data, tabular data and soft ideas) are input into a GIS by
--Digitizing
--Scanning
--Data transfer &
--Key coding
-Data capture
-Editing/Cleaning
-Reprojection
-Generalization
-Edge matching & Rubber sheeting
-Layering
GEOGRAPHIC DATA FORMATS
-Vector
--Automated Mapping System (AMS)
--ESRI coverage
--Computer Graphics Metafile (CGM)
--Digital Feature Analysis Data (DFAD)
--Encapsulated Postscript (EPS)
--Microstation Drawing file format (DGN)
--Dual Independent Map Encoding (DIME)
--Digital Line Graph (DLG)
--AutoCAD Drawing Exchange FFormat (DXF)
--AutoCAD drawing format (DWG)
--MapBase file (ETAK)
--ESRI geodatabase
--Land Use Land Cover Data (GIRAS)
--Interactive Graphic Design Software (IGDS)
--Initial Graphics Exchange Standard (IGES)
--Map Information Assembly Display System (MIDAS)
--MOSS Export File (MOSS)
--Topologically Integrated Geographic Encoding and Referencing (TIGER/line file)
--Spatial Data Transfer Standard/Topological
--Vector profile (SDTS/TVP)
-Raster (Image)
--Arc Digitized Raster Graphics (ARDG)
--Band Interleaved by Line (BIL)
--Band Interleaved by Pixel (BIP)
--Band Sequential (BS)
--Windows Bitmap (BMP)
--Device Independent Bitmap (DIP)
--Compressed Arc Digitized Raster Graphics (CADRG)
--Compressed Image Base (CIB)
--Digital Terrain Elevation Data (DTED)
--ER mapper
--Graphics Interchange Format (GIF)
--ERDAS IMAGINE (IMG)
--ERDAS 7.5 (GIS)
--ESRI GRID file (GRID)
--JPEG File Interchange Format (JFIF)
--Multi-resolution Seamless Image Database (MrSID)
--Tag Image File Format (TIFF; GeoTIFF)
--Portable Network Graphics (PNG)
DATA ENTRY
Attributes can be directly entered into GIS by:
-Direct data loggers
-Manual Keyboard entry
-Optical Character Recognition (OCR)
-Voice regnition
DATA EDITING
-Errors in data may be due to original source data or during encoding process
-Errors in coordinate data or inaccuracies and uncertainities in attribute data
-Data errors should be intercepted before they contaminate the GIS database and propogate to higher levels of information.
-This process is called "Data editing" or "CLEANING". This involves:
--Detection and correction of errors
--Re-projection
--Transformation and generalization
--Edge matching and rubber-sheeting
Detecting and correcting errors
-Main sources are:
--Errors in source data
--Errors introduced during encoding
--Errors propogated during data transfer and conversion
Common spatial errors:
-Missing entities
-Duplicate entities
-Mislocated entities
-Missing labels
-Duplicate labels
-Artifacts of digitizing (Ex: Undershoots, Overshoots, Loops, Spikes)
-Noise
Reprojection, Transformation and generalization
--After encoding and editing spatial and attribute data, it is necessary to process the data geometrically in order to provide a common framework of reference.
--Scale and resolution of source data should be taken into account when combining data from a range of sources to form an integrated database.
--Data derived from different sources should be transformed and projected into a common coordinate system.
--It is of absolute importance to transform the coordinates of each of the input data sets into a common coordinate system.
--The above can be done using linear mathematical transformations.
-In case of translation and scaling:
--In this case, the coordinates are multiplied by a dataset factor depending on the scale
-In case a common origin is to be created:
--The origin of one of the data set is shifted in line with the other by adding the difference between the two origins
-In case of rotation:
--Map coordinates may be rotated using simple trigonometry to more datasets onto a grid of common orientation
-The accuracy of output from a GIS analysis is ONLY AS GOOD AS the WORST INPUT DATA.
-Accuracy of large scale maps is good due to low level of generalization and abstraction while the exact opposite is true for small scale maps
-Routines exist in GIS packages for WEEDING OUT unnecessary points from digitized lines so that the basic shape of the line is preserved.Ex: Douglas-Peucker algorithm
-The disadvantage is that the shape of features may not be preserved.
EDGE MATCHING & RUBBER SHEETING:
-If a study area exists across two or more map sheets, differences or misamtches between adjacent sheets should be resolved.
-Each sheet is digitized separately and joined to adjacent sheets after editing, re-projection, transformation and generalization. This process is known as edge matching and involves the following stages:
--Resolving mismatches at sheet boundaries to ensure complete features and topologically correct data
--In order to use data as a vector layer, the topology must be rebuilt as new lines and polygons are created from joining segments across sheets
--redundant map sheet boundary lines are deleted or dissolved
-Geocoding is the process of converting an address into a point location
Data conversion
-Manipulation and analysis of data is possible only when all data is in the SAME FORMAT
-When different layers are used simultaneously, they should ALL be in either VECTOR or RASTER format
-Normally, conversion is from vector to raster since biggest part of analysis is done in the raster domain
-Vector data are transformed to raster data by overlaying the grid with a user-defined cell size.
-Since raster data require huge storage space, data reduction can be achieved by converting raster data to vector data
-Remote sensing images are digital datasets recorded by satellite operating agencies and stored in their image database. These images should be converted into spatial database format before they can be downloaded
GEOGRAPHIC DATA - LINKAGES & MATCHING
-Linkages
-Exact matching
-Hierarchical matching
-Fuzzy matching (Pg 141)
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