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DR PRASENJIT DAS
How do we describe geographical features?
 by recognizing two types of data:
◦ Spatial data which describes location (where)
◦ Attribute data which specifies characteristics at that location
(what, how much, and when)
How do we represent these digitally in a GIS?
 by grouping into layers based on similar characteristics (e.g
hydrography, elevation, water lines, sewer lines, grocery sales)
and using either:
◦ vector data model (coverage in ARC/INFO, shapefile in
ArcView)
◦ raster data model (GRID or Image in ARC/INFO & ArcView)
DATA USED IN GIS
Community Resource
Mapping
 continuous: elevation, rainfall, ocean salinity
 areas:
◦ unbounded: landuse, market areas, soils, rock type
◦ bounded: city/county/state boundaries, ownership
parcels, zoning
◦ moving: air masses, animal herds, schools of fish
 networks: roads, transmission lines, streams
 points:
◦ fixed: wells, street lamps, addresses
◦ moving: cars, fish, deer
Categorical (name):
◦ nominal
 no inherent ordering
 land use types, county names
◦ ordinal
 inherent order
 road class; stream class
 often coded to numbers eg
SSN but can’t do arithmetic
Numerical
Known difference between values
◦ interval
 No natural zero
 can’t say ‘twice as much’
 temperature (Celsius or
Fahrenheit)
◦ ratio
 natural zero
 ratios make sense (e.g. twice as
much)
 income, age, rainfall
 may be expressed as integer
[whole number] or floating
point [decimal fraction]
Attribute data tables can contain locational information, such as addresses
or a list of X,Y coordinates.
Attribute table
“Flat File” with columns and rows
Row = geographic feature record
Column = attribute field (item of information about a feature)
Attribute Data Structure
Contain Tables or feature classes in which:
◦ rows: entities, records, observations, features:
 ‘all’ information about one occurrence of a feature
◦ columns: attributes, fields, data elements, variables,
items (ArcInfo)
 one type of information for all features
The key field is an attribute whose values uniquely
identify each row
Parcel Table
Parcel # Address Block $ Value
8 501 N Hi 1 105,450
9 590 N Hi 2 89,780
36 1001 W. Main 4 101,500
75 1175 W. 1st 12 98,000
entity
Attribute
Key field
 Raster data model
◦ location is referenced by a grid
cell in a rectangular array
(matrix)
◦ attribute is represented as a
single value for that cell
◦ much data comes in this form
 images from remote sensing
(LANDSAT, SPOT)
 scanned maps
 elevation data from USGS
◦ best for continuous features:
 elevation
 temperature
 soil type
 land use
 Vector data model
◦ location referenced by x,y
coordinates, which can be
linked to form lines and
polygons
◦ attributes referenced through
unique ID number to tables
◦ much data comes in this form
 DIME and TIGER files from US
Census
 DLG from USGS for streams,
roads, etc
 census data (tabular)
◦ best for features with discrete
boundaries
 property lines
 political boundaries
 transportation
Vector Data Structure
lines
polygons
In vector data layers, the feature layer is
linked to an attribute table. Every individual
feature corresponds to one record (row) in
the attribute table.
Vector Data Structure
Raster Data Structure (Grid)
A raster grid can store values that represent categories, for example,
vegetation type
The basic grid attribute table has a value
and count field
The value field has a code or some real
number representing information about
the grid cell. In this case it is a code for
vegetation.
The count field shows how many grid cells
have that same value.
Raster Data Structure
Grids can also store continuous values like elevation
Raster Data Structure
Elevation grid for area north of Kirkuk, Iraq
From space shuttle radar topography mission (SRTM)
Zoom in and you see the grid cells
These are called:
Digital Elevation Models (DEM)
Raster Data Structure
So 2 ways of representing elevation:
Vector contour lines Raster grid
Raster Data Structure
Manas National Park
Satellite Imagery of Manas National Park
IRS LISS III Image
Bansbari Range Office
Mathanguri I.B.
(FCC)
Dense Forest
Open Forest
Agriculture
River
Fallow Land
Settlement
Water Body
Typical Tone and Texture of Common Features
Sources of raster data
Interpreted
satellite
imagery, e.g.,
land cover
Conversion of vector to raster data
Raster Data Structure
Raster Data Structure
Point
Line
Polygon
Vector Raster
Raster data are described by a cell grid, one value per cell
Zone of cells
 Advantages
 It is a simple data structure
 Overlay operations are
easily and efficiently
implemented
 High spatial variability is
efficiently represented in a
raster format
 The raster model is more or
less required for efficient
manipulation and
enhancement of digital
images
 Disadvantages
 The raster data structure is
less compact data
compression techniques (an
often overcome this
problem)
 Topological relationships
are more difficult to
represent
 The output of graphics is
less aesthetically pleasing
because appearancerather
than the smooth lines of
hand-drawn maps. This can
be overcome by using a
very large number of cells,
but may result in
unacceptably large files
 Advantages
 It provides a more compact
data structure than the
raster model
 It provides efficient
encoding of topology and
as a result more efficient
implementation of
operations that require
topological information,
such as network analysis
 The vector model is better
suited to supporting
graphics that closely
approximate hand-drawn
maps
 Disadvantages
 It is a more complex data
structure than a simple
raster
 Overlay operations are
more difficult to implement
 The representation of high
spatial variability is
inefficient
 Manipulation and
enhancement of digital
images cannot be
effectively done in the
vector domain
0 1 2 3 4 5 6 7 8 9
0 R T
1 R T
2 H R
3 R
4 R R
5 R
6 R T T H
7 R T T
8 R
9 R
Real World
Vector Representation
Raster Representation
Concept of
Vector and Raster
line
polygon
point
Each color represents a different
value of an integer variable denoting
land cover class
Raster representation
Vectorization of Kazironga National Park Boundary
Camp Location -Point Feature
Drainage - Line Feature
Boundary - Polygon Feature
Satellite Imagery – Raster Data
,
UG6thSem_major_GIS Data Structures.pptx DR P DAS.1.pptx
 a sampled array of elevations (z) that
are at regularly spaced intervals in the
x and y directions.
 two approaches for determining the
surface z value of a location between
sample points.
◦ In a lattice, each mesh point represents
a value on the surface only at the center
of the grid cell. The z-value is
approximated by interpolation between
adjacent sample points; it does not
imply an area of constant value.
◦ A surface grid considers each sample as
a square cell with a constant surface
value.
Advantages
• Simple conceptual model
• Data cheap to obtain
• Easy to relate to other
raster data
• Irregularly spaced set of
points can be converted to
regular spacing by
interpolation
Disadvantages
• Does not conform to
variability of the terrain
• Linear features not well
represented
Geographic Watershed Information System/ArcHydro – 1 February 2008
What is a Digital Elevation Model?
Digital Elevation Models, con’t.
Digital terrain analyses –
DEM principles
Digital Terrain Model (DTM)
Digital Elevation Model (DEM)
Digital Surface Model (DSM)
Three terms (DEM,DSM,DTM) for the same thing?
…DIFFERENCE BETWEEN DSM, DTM AND DEM
Digital Surface Model (DSM) is a first surface view of the
earth containing both location and elevation information.
Digital Terrain Model (DTM), aka "bare earth" as it is
often referred, is created by digitally removing all of the
cultural features inherent to a DSM by exposing the
underlying terrain.
A Digital Elevation Model (DEM) is any DIGITAL
representation of ground surface
topography or terrain.
)
Digital Elevation Models, con’t.
10/8/2022
GLY560: GIS and RS
 Conversion of contour lines
 Photogrammetry
 Satellite Stereo
 Radar Stereo
 Radar Inferometry
 Laser Altimetry
DEM principles
UG6thSem_major_GIS Data Structures.pptx DR P DAS.1.pptx
 Determine aspects of terrain
 Slope, aspect, spot elevations
 Source for contour lines
 Finding terrain features
 Watersheds,drainage networks, stream channels
 Modeling of hydrologic functions
A 3rd data structure for representing
surfaces:
Triangulated Irregular Network (TIN)
TIN Data Structure
3 GIS Spatial Data Structure Types
 Advantages
◦ Can capture significant
slope features (ridges,
etc)
◦ Efficient since require
few triangles in flat
areas
◦ Easy for certain
analyses: slope, aspect,
volume
 Disadvantages
◦ Analysis involving
comparison with other
layers difficult
a set of adjacent, non-
overlapping triangles
computed from irregularly
spaced points, with x, y
horizontal coordinates and
z vertical elevations.
Elevation points
connected by
lines to form
polygons that
contain
topographic
information
TIN Data Structure
Continuous fields –
TIN
(Triangular irregular network)
TIN Data Structure
TIN Data Structure

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UG6thSem_major_GIS Data Structures.pptx DR P DAS.1.pptx

  • 2. How do we describe geographical features?  by recognizing two types of data: ◦ Spatial data which describes location (where) ◦ Attribute data which specifies characteristics at that location (what, how much, and when) How do we represent these digitally in a GIS?  by grouping into layers based on similar characteristics (e.g hydrography, elevation, water lines, sewer lines, grocery sales) and using either: ◦ vector data model (coverage in ARC/INFO, shapefile in ArcView) ◦ raster data model (GRID or Image in ARC/INFO & ArcView)
  • 3. DATA USED IN GIS Community Resource Mapping
  • 4.  continuous: elevation, rainfall, ocean salinity  areas: ◦ unbounded: landuse, market areas, soils, rock type ◦ bounded: city/county/state boundaries, ownership parcels, zoning ◦ moving: air masses, animal herds, schools of fish  networks: roads, transmission lines, streams  points: ◦ fixed: wells, street lamps, addresses ◦ moving: cars, fish, deer
  • 5. Categorical (name): ◦ nominal  no inherent ordering  land use types, county names ◦ ordinal  inherent order  road class; stream class  often coded to numbers eg SSN but can’t do arithmetic Numerical Known difference between values ◦ interval  No natural zero  can’t say ‘twice as much’  temperature (Celsius or Fahrenheit) ◦ ratio  natural zero  ratios make sense (e.g. twice as much)  income, age, rainfall  may be expressed as integer [whole number] or floating point [decimal fraction] Attribute data tables can contain locational information, such as addresses or a list of X,Y coordinates.
  • 6. Attribute table “Flat File” with columns and rows Row = geographic feature record Column = attribute field (item of information about a feature) Attribute Data Structure
  • 7. Contain Tables or feature classes in which: ◦ rows: entities, records, observations, features:  ‘all’ information about one occurrence of a feature ◦ columns: attributes, fields, data elements, variables, items (ArcInfo)  one type of information for all features The key field is an attribute whose values uniquely identify each row Parcel Table Parcel # Address Block $ Value 8 501 N Hi 1 105,450 9 590 N Hi 2 89,780 36 1001 W. Main 4 101,500 75 1175 W. 1st 12 98,000 entity Attribute Key field
  • 8.  Raster data model ◦ location is referenced by a grid cell in a rectangular array (matrix) ◦ attribute is represented as a single value for that cell ◦ much data comes in this form  images from remote sensing (LANDSAT, SPOT)  scanned maps  elevation data from USGS ◦ best for continuous features:  elevation  temperature  soil type  land use  Vector data model ◦ location referenced by x,y coordinates, which can be linked to form lines and polygons ◦ attributes referenced through unique ID number to tables ◦ much data comes in this form  DIME and TIGER files from US Census  DLG from USGS for streams, roads, etc  census data (tabular) ◦ best for features with discrete boundaries  property lines  political boundaries  transportation
  • 10. In vector data layers, the feature layer is linked to an attribute table. Every individual feature corresponds to one record (row) in the attribute table. Vector Data Structure
  • 12. A raster grid can store values that represent categories, for example, vegetation type The basic grid attribute table has a value and count field The value field has a code or some real number representing information about the grid cell. In this case it is a code for vegetation. The count field shows how many grid cells have that same value. Raster Data Structure
  • 13. Grids can also store continuous values like elevation Raster Data Structure
  • 14. Elevation grid for area north of Kirkuk, Iraq From space shuttle radar topography mission (SRTM) Zoom in and you see the grid cells These are called: Digital Elevation Models (DEM) Raster Data Structure
  • 15. So 2 ways of representing elevation: Vector contour lines Raster grid Raster Data Structure
  • 16. Manas National Park Satellite Imagery of Manas National Park IRS LISS III Image Bansbari Range Office Mathanguri I.B. (FCC)
  • 17. Dense Forest Open Forest Agriculture River Fallow Land Settlement Water Body Typical Tone and Texture of Common Features
  • 18. Sources of raster data Interpreted satellite imagery, e.g., land cover Conversion of vector to raster data Raster Data Structure
  • 20. Point Line Polygon Vector Raster Raster data are described by a cell grid, one value per cell Zone of cells
  • 21.  Advantages  It is a simple data structure  Overlay operations are easily and efficiently implemented  High spatial variability is efficiently represented in a raster format  The raster model is more or less required for efficient manipulation and enhancement of digital images  Disadvantages  The raster data structure is less compact data compression techniques (an often overcome this problem)  Topological relationships are more difficult to represent  The output of graphics is less aesthetically pleasing because appearancerather than the smooth lines of hand-drawn maps. This can be overcome by using a very large number of cells, but may result in unacceptably large files
  • 22.  Advantages  It provides a more compact data structure than the raster model  It provides efficient encoding of topology and as a result more efficient implementation of operations that require topological information, such as network analysis  The vector model is better suited to supporting graphics that closely approximate hand-drawn maps  Disadvantages  It is a more complex data structure than a simple raster  Overlay operations are more difficult to implement  The representation of high spatial variability is inefficient  Manipulation and enhancement of digital images cannot be effectively done in the vector domain
  • 23. 0 1 2 3 4 5 6 7 8 9 0 R T 1 R T 2 H R 3 R 4 R R 5 R 6 R T T H 7 R T T 8 R 9 R Real World Vector Representation Raster Representation Concept of Vector and Raster line polygon point
  • 24. Each color represents a different value of an integer variable denoting land cover class Raster representation
  • 25. Vectorization of Kazironga National Park Boundary Camp Location -Point Feature Drainage - Line Feature Boundary - Polygon Feature Satellite Imagery – Raster Data ,
  • 27.  a sampled array of elevations (z) that are at regularly spaced intervals in the x and y directions.  two approaches for determining the surface z value of a location between sample points. ◦ In a lattice, each mesh point represents a value on the surface only at the center of the grid cell. The z-value is approximated by interpolation between adjacent sample points; it does not imply an area of constant value. ◦ A surface grid considers each sample as a square cell with a constant surface value. Advantages • Simple conceptual model • Data cheap to obtain • Easy to relate to other raster data • Irregularly spaced set of points can be converted to regular spacing by interpolation Disadvantages • Does not conform to variability of the terrain • Linear features not well represented
  • 28. Geographic Watershed Information System/ArcHydro – 1 February 2008 What is a Digital Elevation Model?
  • 30. Digital terrain analyses – DEM principles
  • 31. Digital Terrain Model (DTM) Digital Elevation Model (DEM) Digital Surface Model (DSM) Three terms (DEM,DSM,DTM) for the same thing?
  • 32. …DIFFERENCE BETWEEN DSM, DTM AND DEM Digital Surface Model (DSM) is a first surface view of the earth containing both location and elevation information. Digital Terrain Model (DTM), aka "bare earth" as it is often referred, is created by digitally removing all of the cultural features inherent to a DSM by exposing the underlying terrain. A Digital Elevation Model (DEM) is any DIGITAL representation of ground surface topography or terrain. )
  • 34. 10/8/2022 GLY560: GIS and RS  Conversion of contour lines  Photogrammetry  Satellite Stereo  Radar Stereo  Radar Inferometry  Laser Altimetry
  • 37.  Determine aspects of terrain  Slope, aspect, spot elevations  Source for contour lines  Finding terrain features  Watersheds,drainage networks, stream channels  Modeling of hydrologic functions
  • 38. A 3rd data structure for representing surfaces: Triangulated Irregular Network (TIN) TIN Data Structure
  • 39. 3 GIS Spatial Data Structure Types
  • 40.  Advantages ◦ Can capture significant slope features (ridges, etc) ◦ Efficient since require few triangles in flat areas ◦ Easy for certain analyses: slope, aspect, volume  Disadvantages ◦ Analysis involving comparison with other layers difficult a set of adjacent, non- overlapping triangles computed from irregularly spaced points, with x, y horizontal coordinates and z vertical elevations.
  • 41. Elevation points connected by lines to form polygons that contain topographic information TIN Data Structure