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REVIEW LECTURE

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Title: REVIEW LECTURE


1
------Using GIS--
Introduction to GIS
  • REVIEW LECTURE
  • By Austin Troy

2
I. What is GIS?
Introduction to GIS
3
Introduction to GIS
  • WHAT IS GIS? Some definitions
  • The complete sequence of components for
    acquiring, processing, storing and managing data
    (Star and Estes, 1990)
  • It is a configuration of computer hardware and
    software specifically designed for the
    acquisition, maintenance and use of cartographic
    data, (Tomlin, 1990)
  • It is a set of computer tools for collecting,
    storing, retrieving at will, transforming and
    displaying spatial data from the real for a
    particular set of purposes, (Borrough et al.
    1998)
  • A system of hardware, software, data, people,
    organizations and institutional arrangements for
    collecting, storing, analyzing and disseminating
    information about areas of the earth.

Source adapted from UC Berkeley GIS Center
4
Introduction to GIS
Limitations of GIS
  • Simple geometry does not describe nature
  • Nature is impossible to quantify and measure
    accurately
  • When we represent nature on paper or on a
    computer we abstract and simplify
  • GIS is fundamentally an exercise in abstraction
    and approximation
  • It is not like physics or math
  • It is complicated by scale
  • It is complicated by time

5
II. Data Structures
Introduction to GIS
6
Entities and Fields
Introduction to GIS
  • There are two general approaches for representing
    things in space
  • Entities objects, with precise location and
    dimensions and discrete boundaries (remember,
    points are abstractions).
  • Fields, or phenomena a Cartesian coordinate
    system where values vary continuously and smoothly

7
Spatial data structures- Raster
Introduction to GIS
  • Spatial features modeled with grids, or pixels
  • Cartesian grid whose cell size is constant
  • Grids identified by row and column number
  • Grid cells are usually square in shape
  • Area of each cell defines the resolution
  • Raster files store only one attribute, in the
    form of a z value, or grid code.
  • Consider the contrary….

8
Spatial data structures-Vector
Introduction to GIS
  • Each point has a unique location
  • Points can create a line
  • Points and lines can scribe a polygon, whose
    angle points are given by nodes
  • Polygons are closed regions whose boundaries are
    made up of line segments connected at many
    angles.
  • Polygons generally define an area of homogenous
    phenomena
  • These phenomena can be described by one or more
    stored attributes

9
Spatial data structures-Vector
Introduction to GIS
  • Vector files in ARC INFO are topologically
    encoded. These are true vector files.
  • that is, each point, line and node is defined in
    relation to every other point, line and node
  • Polygon and line topology
    is encoded using arcs
  • Arcs are vertices and
    nodes connected by lines
  • Topology allows for powerful analysis tools

10
Importance of Topology
Introduction to GIS
  • The computer needs topology to be able to address
    many types of spatial problems
  • With topology, data is stored very efficiently,
    allowing spatial data sets to be processed
    quickly
  • Topology facilitates analyses, such as
  • modeling flow through networks
  • combining adjacent polygons with similar traits
  • determining distances between and among objects
  • surface modeling

11
Spaghetti Data Model
Introduction to GIS
  • Just because feature looks like a point, line or
    polygon does not mean its topological
  • Spaghetti Model is
  • Non-topological data model that looks like vector
  • collections of line segments and points with no
    real connection or topology
  • Only stores features coordinates there are no
    relative relationships encoded in this model
  • each feature has no knowledge of other features
    that it intersects, is adjacent to, contiguous
    with or near

12
------Using GIS--
Introduction to GIS
  • TIN Triangulated Irregular Network
  • What is a TIN? Alternative model for representing
    terrain (from lecture 16)
  • A TIN is a data structure that defines geographic
    space as a set of contiguous, non-overlapping
    triangles, which vary in size and angular
    proportion.
  • Like grids, TINs are used to represent elevation
    surfaces, and can be created directly from files
    of elevation sample points, but with TINs these
    sample points are irregularly distributed.

13
------Using GIS--
Introduction to GIS
Three Dimensional data TIN
  • Note the triangular facets defined by points

14
------Using GIS--
Introduction to GIS
TINs
  • TIN triangles vary in size with the complexity
    of the terrain
  • How are the sample points that define the
    triangles determined?
  • Several algorithms exist for choosing significant
    sample points from a DEM to make a TIN
  • Very Important Points method (VIP)
  • Maximum z tolerance method

15
------Using GIS--
Introduction to GIS
TINs
  • Maximim z tolerance like VIP, an offset is
    determined, but this uses a specified maximum
    z-tolerance to decide which points are selected.
    The z-tolerance is selected through an iterative
    process. First it constructs a candidate TIN, and
    for each triangle in the TIN it calculates the
    elevation difference of the grid cells bounded by
    it. If the cell with the largest difference to
    triangle is greater than the z tolerance, that
    point is flagged for addition to the TIN model.

This is what AV asks you for when you make a TIN
16
III. Database Models
Introduction to GIS
17
Introduction to GIS
  • Three Classic Database Models
  • Hierarchical
  • Network
  • Relational -Arc View and Arc Info use this model

18
Introduction to GIS
  • Data Types
  • An individual data point, or datum (singular of
    data), can be of any number of types, including
  • number (must be continuous) below are
    subclasses
  • Currency (has specific decimal behaviors)
  • Byte (0 to 255)
  • Date
  • Integer
  • Long (integers, with a greater range)
  • Single precision (with decimals)
  • Double precision (like single, with a greater
    range)
  • string (text numbers can be represented as text,
    but they have no numerical properties)
  • Boolean (yes, no)
  • Object (holds data objects)

19
Introduction to GIS
  • Hierarchical Database Model
  • A one-to-many method for storing data in a
    database that looks like a family tree with one
    root and a number of branches or subdivisions.
    Problem linkages in the tables must be known
    before

Groovy 70s TV
Action shows
Drama
Sitcoms
Threes company
Love Boat
Dukes of Hazzard
Dallas
Fantasy Island
CHIPs
Gavin McLeod
John Ritter
Larry Hagman
Tom Wopat
Eric Estrada
Larry Wilcox
Ricardo Montalban
Suzanne Somers
20
Introduction to GIS
  • Networked Database Model
  • A database design for storing information by
    linking all records that are related with a list
    of pointers. Problem linkages in the tables
    must be known before. Not adaptable to change.

Action shows
Drama
Sitcoms
Threes company
Love Boat
Dukes of Hazzard
Dallas
Fantasy Island
CHIPs
NBC
CBS
ABC
21
Network and Hierarchical Examples
Introduction to GIS
  • Think about file storage in MS Windowsanalogous
    to a hierarchical database
  • Shortcuts turn this into the equivalent of a
    network database

22
Introduction to GIS
  • Relational (Tabular) Database Model
  • A design used in database systems in which
    relationships are created between one or more
    flat files or tables based on the idea
  • that each pair of tables has a field in common,
    or key. In a relational database, the records
    are generally different in each table
  • The advantages each table can be prepared and
    maintained separately, tables can remain separate
    until a query requires connecting, or relating
    them, relationships can be one to one, one to
    many or many to one

23
Introduction to GIS
Data Tables (flat files)
  • Records are the unit that the data are specific
    to
  • Fields, or columns, are attribute categories
  • Cells are where individual values of a record for
    a field are stored

fields
Headings are the labels for the columns
records
cells
24
Introduction to GIS
Data key
Is a field that is common to two or more flat
files allows a query to be done across multiple
tables or allows two tables to be joined
Flat file professor info
Flat file course info
25
Introduction to GIS
  • Relational database one to many relationship

One-to-many relationship
In this case, several people co-own the same lot,
so no longer one lot, one person
26
Introduction to GIS
Assuming each owner owned several parcels, we
would structure the database differently
One-to-many relationship
Note this table includes data pertinent only to
Flores ownership of these properties
27
IV. Queries
Introduction to GIS
28
Introduction to GIS
  • Queries in Arc View
  • Arc View queries only select (highlight) records
  • When a record is selected, so is its
    corresponding feature
  • To summarize selected values, must use the
    statistics function or summarize button
  • To create new values based on a query, must use
    the calculate tool.

29

Introduction to GIS
  • So what can Arc View do with queries?
  • A query selects records once selected you can
  • Look at the selection
  • Requery the selection
  • Do stats on the selection
  • Create new fields that recategorize the selection
    by an an attribute field
  • Create new fields by doing calculations across
    several fields
  • Create a shapefile from the selection

30

Introduction to GIS
  • Reclassing say we wanted to prioritize inner
    city areas for urban redevelopment projects
  • Lets query based on unemployment and home value
  • Based on these well make High, Medium and Low
    priority areas everything else is not classified
  • Tracts with median home value un-employment 12 are High

31

Introduction to GIS
32
Introduction to GIS
Multi-layer queries
Lets say we want to select all the houses in our
sample database that overlay fire hazard zones
and then run some statistics
33
Introduction to GIS
Now with sample houses active, we click select
by theme and tell it to choose features that
intersect the features of fire hazard zone
34
Introduction to GIS
Those that overlay a hazard zone are selected
35
Introduction to GIS
Now we can run statistics on the selection This
tells us that 2011 houses overlay fire zones
Since we ran stats on the Price field, it also
tells us that the mean price for these properties
is 460,127!
36
Introduction to GIS
Now, say we want to select features from layer A
that are within a distance of features in layer B
37
V. Legend editing
Introduction to GIS
38
Legend Editor
Introduction to GIS
  • Review there are two ways to classify your
    legend
  • Graduated color number data type
  • Unique value primarily for string data
    (categories)

Class. method
Class. field
39
Legend Editor
Introduction to GIS
  • Often categories must be aggregated and
    redefined this land use map had over 110
    categories that were condensed to 30

40
Legend Editor
Introduction to GIS
  • To do this I had to type in what code ranges
    corresponded with my land use designations (e.g.
    Single family residential, commercial) and assign
    each a color. I decided on the classes

The colors had to be chosen strategically so that
important classes stood out and so that similar
classes (e.g. SF and MF residential)were in the
same color family
41
Legend Editor
Introduction to GIS
  • With graduated color (numeric) legends, we must
    decide
  • How many classes
  • By what method will the data be broken up
  • Natural breaks, quantile, equal area, standard
    deviation
  • The default is natural breaks

42
Legend Editor
Introduction to GIS
  • Here we have houses graduated by sales price. We
    have accepted arc views default of 5 classes and
    natural breaks.

43
Legend Editor
Introduction to GIS
  • Notice how it changes when we classify by equal
    intervalmost are in the lowest category now
    because outliers make the intervals meaningless

44
Legend Editor
Introduction to GIS
  • Another way to display point data is by the
    graduated symbol methodthe larger the value, the
    larger the symbol

45
VI. Vector geoprocessing
Introduction to GIS
46
Using the Geoprocessing Wizard
Introduction to GIS
  • The GP wizard allows you to dissolve, clip,
    merge, intersect, union and spatial join

47
Spatial Join
Introduction to GIS
  • Assigns data from one location in one layer to
    same location in another
  • Can assign polygon data to a point that overlays
  • Can assign point to point and point to line
    distances between two layers
  • Simple adds attributes to the DBF table

48
Buffering A GIS Classic
Introduction to GIS
  • Arc View 3.2 allows for buffer operations
  • Allows for variable width based on attribute
  • Allow multiple buffers

49
VII. Vector representation
Introduction to GIS
50
Vector RepresentationPoints
Introduction to GIS
  • Simple points
  • Nodes topological junction between line features
  • Vertices define kinks
  • All zero dimensional points dont exist in
    reality

51
Vector Representationpoints
Introduction to GIS
  • Arcview allows the user to represent points as
    symbols of numerous types and at various sizes.
  • Remember graduated symbol from last week where
    point symbol size varies with an attribute.
  • Arcview comes with a bunch of symbol pallettes
    that can be loaded for specific sectors and
    industries

52
Vector Representationlines
Introduction to GIS
  • String a sequence of line segments
  • Chain a directed sequence of non-intersecting
    line segments with nodes at each end
  • Arc some consider any chain or string as an arc
    others consider it a chain or string in which
    segments are smooth curves defined by
    mathematical formulae

53
Vector Representationlines
Introduction to GIS
  • Ring this is a series of line segments (a
    string) that close upon each other
  • It is NOT a polygon!!

54
Vector Representationarea
Introduction to GIS
  • Simple Polygon an area defined by an outer ring
    that may have no inside features or holes
  • Complex polygons like a simple polygon with
    internal features

55
Vector representationScale
Introduction to GIS
  • Scale is the ratio of the map distance to the
    ground distance
  • Hence, 1200,000 means 1 cm on the map 200,000
    cm in the real world
  • The smaller the ratio, the LARGER the scale and
    the smaller the area depicted
  • That area is known as the map extent.

56
Vector representationScale
Introduction to GIS
  • Scale and Vector representation are closely tied
    up
  • On a small scale map (e.g. 12,000,000) a city is
    represented as point, without dimension, while on
    a large scale map (124,000), a city would likely
    be represented as an area with dimensions
  • Think of other examples rivers, roads,
    buildings these
  • The smaller the scale, the more we abstract, and
    the more we use points and lines

57
Introduction to GIS
  • What is Geocoding?
  • Geocoding is the mechanism that allows you to use
    addresses to identify locations on a map.
  • Addresses are the most common form of storing
    geographic data.
  • With geocoding can display the tabular data
    containing addresses as points on a map.
  • An address specifies a location in the same way
    that a geographic coordinate does.
  • But since an address is merely a text string
    containing the information of house number,
    street name, direction, and/or zip codes, an
    address needs a mechanism to calculate the
    geographic coordinate for the address and then
    display the location on a map based on the
    assigned coordinate.

Source UC Berkeley GIS CENTER
58
Introduction to GIS
Geocoding with TIGER Address segments
Match
R-F-ADDR
R-T-ADDR
Main St
1000
1100
1001
1101
L-F-ADDR
L-T-ADDR
Address Style
1060 Main St
Source UC Berkeley GIS CENTER
59
Introduction to GIS
The Problems with Address Geocoding Interpolation
opens geocoding to error
An urban road segment smaller, more precise
A rural area with a long road segment very
imprecise
60
VIII. Advanced Raster Analysis
Introduction to GIS
61
Neighborhood Statistics
Introduction to GIS
  • In Arcview, neighborhood statistics command
    allows you to specify statistic
  • Min, max, mean, standard deviation, range, sum,
    variety

62
Neighborhood Statistics
Introduction to GIS
  • Neighborhood statistics creates a new grid layer
    with the neighborhood values
  • This can be used to
  • Simplify or filter down the features
    represented
  • Emphasize areas of sudden change in values
  • Look at rates of change
  • Look at these at different spatial scales

63
Neighborhood Filters
Introduction to GIS
  • Generating neighborhood means is similar to RS
    technique called low pass filtering
  • Low pass filtering takes tonally rough
    surfaces, with abrupt changes in cell values, and
    makes those values vary more smoothly.
  • The opposite is called a high-pass filter.
  • High pass filtering emphasizes detailed, abrupt
    changes in cell values, deemphasizes areas of
    gradual change.

64
Tabulate Areas
Introduction to GIS
  • This is a command for analysis between two vector
    layers, but it does so by temporarily rasterizing
    these layers, overlaying and then determining
    areas that are coincident, in the measurement
    units specified for the view
  • It summarizes the area for each feature in the
    row theme by each category that exists in the
    column theme
  • So, if I have three categories for the attribute
    field that Im tabulating by, it results in three
    new attributes, which give the area of features
    in layer 1 that overlay features in layer 2 with
    that category value

65
Raster Data Structuring
Introduction to GIS
  • Run-length encoding (RLE)
  • Chain Code
  • Block Code
  • Quad Tree

66
IX. Public Data
Introduction to GIS
67
DLG Summary
Introduction to GIS
68
DLG Layer Availability in CA-124,000
Introduction to GIS
Listings available at the same web site Browse by
state and by layer
69
DEM Summary
Introduction to GIS
70
Other USGS Data
Introduction to GIS
  • National Elevation Data (NED) seamless, fewer
    terrain artifacts
  • National Land Cover Data
  • DOQs-digital ortho quarter quadrangle
  • DRG-digital raster graphic
  • GNIS- Geographic Names information system

71
GNIS
Introduction to GIS
  • Includes location, names and category of features
    such as
  • Schools/universities
  • Churches/cemeteries
  • Airports/ports
  • Parks/recreation centers
  • Shopping centers
  • Stadiums/arenas
  • Theaters/auditoriums/cultural facilities
  • Country clubs/golf courses
  • Marinas/yacht clubs
  • Trailheads (some)
  • Rural fire stations (some)
  • Dams/reservoirs
  • Cities/incorporated areas (as points)

72
Introduction to GIS
  • US Census TIGER line files
  • Line Features
  • 1.Roads
  • 2.Railroads
  • 3.Hydrography
  • 4.Transportation and Utility Lines
  • Boundary Features
  • 1.Statistical boundaries, such as census
    tracts and blocks
  • 2.Local government boundaries, such as
    places and counties
  • 3.Administrative boundaries, such as
    congressional and school districts
  • Landmark Features
  • 1.Point landmarks, such as schools and
    churches
  • 2.Area landmarks, such as parks and
    cemeteries
  • 3.Key geographic locations, such as
    apartment buildings and factories
  • Can be integrated with Census data

73
Transfer Formats SDTS
Introduction to GIS
  • Spatial Data Transfer Standard
  • Newer Standard for USGS data
  • Large scale DLGs only available in this format
  • The Federal Geographic Data Committee has
    mandated that all federal digital geographic data
    go to this standard
  • The Standard allows the exchange of digital
    spatial data between different computer systems.
    It provides a solution to the problem of spatial
    data transfer from the conceptual level to the
    details of physical file encoding.
  • Several software tools have been developed for
    the importing SDTS data, but each data product
    requires a different software tool

74
X. Remote Sensing
Introduction to GIS
75
The Physics of RS
Introduction to GIS
  • An RS sensor can detect spectral responses from
    objects in various wavelength ranges.
  • Each class of objects has a different spectral
    responses across wavelength
  • Spectral reflectance values of an object can be
    plotted on a graph as a function of wavelength,
    known as a spectral reflectance curve.

76
The Physics of RS
Introduction to GIS
  • Each object feature class on the earth has a
    spectral reflectance curve that helps us to
    identify it remotely. This is why we can use RS
    to tell the difference between types of objects
  • A spectral response pattern delivers much more
    information than a single pixel value

77
The Physics of RS
Introduction to GIS
  • A Spectral reflectance curve for two classes of
    similar object conifers and deciduous trees
  • Note how visible band is similar, but near IR
    band is very different means eye could not pick
    this up
  • The shape of an objects curves will determine
    what bands we use to ID it

Source Lillesand and Kiefer 2000
78
The Physics of RS
Introduction to GIS
  • Spatial resolutionsize of pixel
  • Radiometric resolutionnumber of possible z
    values sensors with high radiometric resolution
    can perceive subtler gradations in brightness

79
Panchromatic imaging
Introduction to GIS
  • With panchromatic imaging, the sensor is a single
    channel detector sensitive to radiation in a
    broad wavelength range. Where the wavelength
    range coincides with the visible range, the
    resulting image resembles a "black-and-white"
    photograph. The physical quantity being measured
    is the apparent brightness of the targets. The
    spectral information or "colour" of the targets
    is lost.

80
Multispectral Imaging
Introduction to GIS
  • With multispectral, or multiband data, there are
    several layers, or values for each pixel, each
    representing a different channel or reflectance
    in a wavelength spectrum
  • Each band or channel is sensitive to
    radiation within a different band of wavelength,
    through use of different filters
  • These bands can be combined to make composite
    images, can be looked at separately, or can be
    analyzed using overlay analysis methods

81
Multispectral Imaging
Introduction to GIS
  • True color composite where bands are assigned to
    color channels in such a way that colors in the
    image roughly correspond with the colors in the
    real world. Often assigned red to red, green to
    green and blue to blue can result in this
  • Another is a false color composite, which shows
    colors that dont really exist in that location.
    An example is color infrared composite, where
    green band is assigned to blue display channel,
    red is assigned to green and Near IR is assigned
    to red

82
RS Sources
Introduction to GIS
  • LANDSAT TM
  • LANDSAT MSS
  • SPOT
  • IKONOS
  • Scanner Types pushbroom vs. whiskbroom
  • Orbit sun-synchronous

83
Preprocessing
Introduction to GIS
  • Geometric correction corrects distortion due to
    parallax, altitude, velocity, curvature of earth,
    atmosphere, terrain, etc
  • These consist of systematic and random sources of
    distortion
  • Radiometric correction distortion in radiance
    measure due to atmosphere, angle of sun, angle of
    view, etc.
  • E.g. haze compensation
  • Noise correction- removal of random noise in
    signal-e.g. destripping

84
Image enhancement
Introduction to GIS
  • Three types of manipulation are
  • Contrast manipulation methods include gray level
    thresholding, level slicing and contrast
    stretching
  • Spatial feature manipulation methods include
    spatial filtering, edge enhancement and Fourrier
    analysis
  • Multi-image manipulation methods include
    multispectral band ratioing and differencing,
    principal components, canonical components,
    vegetative components, decorrelation stretching,
    others

85
Contrast enhancement
Introduction to GIS
  • Most images start with low contrast these
    improve it
  • Level slicing reclasses DNs into fewer classes,
    so differences can be more easily seen colors or
    grayscale values can be assigned. Like resampling
    down radiometric resolution. Often used where
    histogram shows bimodal distribution of
    reflectance values
  • Contrast Streching is the opposite, where a
    smaller number of values are stretched out over
    full DN range

86
Contrast enhancement
Introduction to GIS
  • Gray level thresholing all pixel values below a
    lower threshold are mapped to zero and those
    above an upper threshold are mapped to 255. All
    other pixel values are linearly interpolated to
    lie between 0 and 255

Source http//www.sci-ctr.edu.sg/ssc/publication/
remotesense/process.htm
87
Spatial Feature Enhancement
Introduction to GIS
  • Spatial filtering neighborhood operations (like
    we reviewed for raster analysis), that they
    calculate a new value for the center pixel based
    on the values of its neighbors within a window
    (see lecture 9 for more) includes low-pass
    (emphasizes regional spatial trends, demphasizes
    local variability ) and high-pass (emphasizes
    local spatial variability) filters
  • Convolution a filtering operation where where
    pixels within the window, or kernel are
    weighted, so each pixel coefficient is multiplied
    by DN and they are all summed and assigned to the
    pixel at the middl

88
Spatial Feature Enhancement
Introduction to GIS
  • Edge Enhancement This is a convolution method
    that combines elements of both low and high-pass
    filtering in a way that accentuates linear and
    local contrast features without losing the
    regional patterns
  • First, a high-pass image is made with local
    detail
  • Next, all or some of the gray level of the
    original scene is added back
  • Finally, the composite image is contrast stretched

89
Spectral Classification
Introduction to GIS
  • Two types of classification
  • Supervised the analyst designates on-screen
    training areas known land cover type from which
    an interpretation key is created, describing the
    spectral attributes of each cover class .
    Statistical techniques are then used to assign
    pixel data to a cover class, based on what class
    its spectral pattern resembles.
  • Unsupervisedautomated algorithms produce
    spectral classes based on natural groupings of
    multi-band reflectance values (rather than
    through designation of training areas), and the
    analyst uses references data, such as field
    measurements, DOQs or GIS data layers to assign
    areas to the given classes

90
Spectral Classification
Introduction to GIS
  • Unsupervised
  • Computer groups all pixels according to their
    spectral relationships and looks for natural
    spectral groupings of pixels, called spectral
    classes
  • Assumes that data in different cover class will
    not belong to same grouping
  • Once created, the analyst assesses their utility

Spectral class 1
Spectral class 2
Source F.F. Sabins, Jr., 1987, Remote Sensing
Principles and Interpretation.
91
Spectral Classification
Introduction to GIS
  • Unsupervised
  • After comparing the reclassified image (based on
    spectral classes) to ground reference data, the
    analyst can determine which land cover type the
    spectral class corresponds to
  • Has advantage over supervised classification the
    classifier identifies the distinct spectral
    classes, many of which would not have been
    apparent in supervised classification and, if
    there were many classes, would have been
    difficult to train all of them. Not required to
    make assumptions of what all the cover classes
    are before classification.

92
Spectral Classification
Introduction to GIS
  • Supervised
  • Better for cases where validity of classification
    depends on a priori knowledge of the technician
  • Conventional cover classes are recognized in the
    scene from prior knowledge or other GIS/ imagery
    layers
  • Therefore selection of classes is pre-determined
    and supervised
  • Training sites are chosen for each of those
    classes
  • Each training site class results in a cloud of
    points in n dimensional measurement space,
    representing variability of different pixels
    spectral signatures in that class

93
Spectral Classification
Introduction to GIS
  • Supervised
  • The next step is for the computer to assign each
    pixel to the spectral class is appears to belong
    to, based on the DNs of its constituent bands
  • There are numerous algorithms the computer uses

94
Spectral Classification
Introduction to GIS
  • Supervised
  • These algorithms look at clouds of pixels in
    spectral measurement space from training areas,
    and try to determine which cloud a given
    non-training pixel falls in.
  • The simplest method is minimum distance in
    which a theoretical center point of point cloud
    is plotted, based on mean values, and an unknown
    point is assigned to the nearest of these. That
    point is then assigned that cover class.
  • They get much more complex from there.

95
Land Cover/ Land Use Mapping
Introduction to GIS
  • Anderson land use and land cover classification
    system for use with remote sensor data (levels 3
    and 4 not shown, because vary locally)
  • Level I Level II
  • 1 Urban or Built-up Land 11 Residential
  • 12 Commercial and Services
  • 13 Industrial
  • 14 Transportation, Communications, and
    Utilities
  • 15 Industrial and Commercial Complexes
  • 16 Mixed Urban or Built-up Land
  • 17 Other Urban or Built-up Land

96
Land Cover/ Land Use Mapping
Introduction to GIS
  • Level I Level II
  • 2 Agricultural Land 21 Cropland and Pasture
  • 22 Orchards, Groves, Vineyards, Nurseries, and
    Ornamental Horticultural Areas 23 Confined
    Feeding Operations
  • 24 Other Agricultural Land
  • 3 Rangeland 31 Herbaceous Rangeland
  • 32 Shrub and Brush Rangeland
  • 33 Mixed Rangeland
  • 4 Forest Land 41 Deciduous Forest Land
  • 42 Evergreen Forest Land
  • 43 Mixed Forest Land
  • 5 Water 51 Streams and Canals
  • 52 Lakes
  • 53 Reservoirs
  • 54 Bays and Estuaries
  • 6 Wetland 61 Forested Wetland
  • 62 Nonforested Wetland

97
XI. Data Input
Introduction to GIS
98
Data input
Introduction to GIS
  • Weve already talked about remote sensing
    imagery, one of the most important sources of
    input data
  • Today well talk about three other sources of
    input
  • GPS
  • Scanning
  • Digitizing

99
How does GPS work?
Introduction to GIS
  • We need at least 3 satellites as reference points
    to triangulate our position.
  • Based on the principle that where we know our
    exact distance from a satellite in space, we know
    we are somewhere on the surface of an imaginary
    sphere with radius equal to the distance to the
    satellite.
  • With two satellites we know we are in the plane
    where the two intersect. With three or more, we
    can get two possible points, and one of those is
    usually impossible from a practical standpoint
    and can be discarded

100
How does GPS work?
Introduction to GIS
  • Heres how the sphere concept works
  • A fourth satellite narrows it from 2 possible
    points to 1 point

Source Trimble Navigation Ltd.
101
How does GPS work?
Introduction to GIS
  • This method assumes we can find exact distance
    from our GPS receiver to a satellite. How does
    that work?
  • Simple answer see how long it takes for a radio
    signal to get from the satellite to the receiver.
  • Since we know speed of light, we can answer this
  • This gets complicated when you think about the
    need to perfectly synchronize satellite and
    receiver.
  • We use pseudo random code to synchronize

102
How does GPS work?
Introduction to GIS
  • 4th Satellite helps make up for fact that ground
    receiver does not have perfect atomic clock.
  • While 3 perfect satellite signals can give a
    perfect location, 3 imperfect signals cant, but
    4 can
  • Imagine time to receiver as distance, with each
    distance from each satellite defining a circle
    around each satellite of that radius
  • If receiver clock is correct, 4 circles should
    meet at one point. If they dont meet, the
    computer knows there is an error in the clock
    They dont add up

103
How does GPS work?
Introduction to GIS
  • So now we know how far we are from the
    satellites, but how do we know where the
    satellites are?? We cant use them as a reference
    otherwise.
  • Because the satellites are 11,000 ft up, they
    operate according to the well understood laws of
    physics, and are subject to few random, unknown
    forces.
  • This allows us to know where a satellite should
    be at any given moment.

104
Selective availability
Introduction to GIS
  • Until May of 2000, the DoD intentionally
    introduced a small amount of error into the
    signal for all civilian users, calling it
    selective availability, so non- US military
    users would not have the same positional accuracy
    as the US military.
  • SA resulted in about 100 m error most of the time
  • Turning off SA reduced error to about 30 m radius

105
Differential GPS
Introduction to GIS
  • This is a way to dramatically increase the
    accuracy of GPS positioning to a matter of a few
    meters, using basic concepts of geometry
  • This was used in the past to overcome SA, but
    with that gone, is now used for reducing the 30m
    error
  • DGPS uses one stationary and one moving receiver
    to help overcome the various errors in the signal
  • By using two receivers that are nearby each
    other, within a few dozen km, they are getting
    essentially the same errors (except receiver
    errors)

106
How does DGPS work?
Introduction to GIS
  • The stationary receiver must be located on a
    control point whose position has been accurately
    surveyed eg. USGS benchmarks
  • The stationary unit works backwardsinstead of
    using timing to calculate position, it uses its
    position to calculate timing
  • It determines what the GPS signal travel time
    should be and compares it with what it actually
    is
  • Can do this because, precise location of
    stationary receiver is known, and hence, so is
    location of satellite

107
GPS Uses
Introduction to GIS
  • Trimble Navigation Ltd., breaks GPS uses into
    five categories
  • Location positioning things in space
  • Navigation getting from point a to point b
  • Tracking - monitoring movements
  • Mapping creating maps based on those positions
  • Timing precision global timing
  • You can learn about all these applications at
    these web links, but we mainly care about mapping

108
Scanning
Introduction to GIS
  • Encodes a digital raster image of some surface
  • Three types of scanner
  • Flat bed
  • Drum
  • Continuous feed

109
Digitizing
Introduction to GIS
  • Heads upon screen
  • Tablet digitizing

110
XII. Network analysis Wont be on the test
Introduction to GIS
111
XIII. Projections and coordinate systems
Introduction to GIS
112
So, what shape IS the earth?
Introduction to GIS
  • Earth is not a sphere, but an ellipsoid, because
    the centrifugal force of the earths rotation
    flattens it out.

Source ESRI
  • This was finally proven by the French in 1753
  • The earth rotates about its shortest axis, or
    minor axis, and is therefore described as an
    oblate ellipsoid
  • But also called spheroid because so close to
    sphere

113
Spheroids
Introduction to GIS
  • We must use a different spheroid for different
    regions to account for irregularities, or we get
    positional errors
  • The International 1924 and the Bessel 1841
    spheroids are used in Europe while in North
    America the GRS80, and decreasingly, the Clarke
    1866 Spheroid, are used

114
The Geographic Graticule/Grid
Introduction to GIS
  • This is a location reference system for the
    earths surface, consisting of
  • Medians lines of longitude and
  • Parallels lines of latitude
  • Prime meridian is at Greenwich, England (that is
    0º longitude)
  • Equator is at 0º latitude

Source ESRI
115
The Geographic Graticule/Grid
Introduction to GIS
  • This is like a planar coordinate system, with an
    origin at the point where the equator meets the
    prime meridian
  • The difference is that it is not a Grid because
    grid lines must meet at right angles this is why
    its called a graticule instead
  • Each degree of latitude represents about 110 km,
    although, that varies slightly because the earth
    is not a perfect sphere
  • Can use degrees minutes seconds or decimal
    degrees

116
Map Projection-distortion
Introduction to GIS
  • The problem with map projection is that it
    distorts one or several of these properties of a
    surface
  • Shape
  • Area
  • Distance
  • Direction
  • Some projections specialize in preserving one or
    several of these features, but none preserve all

117
Map Projection-distortion
Introduction to GIS
  • Hence, when choosing a projection, one must take
    into account what it is that matters in your
    analysis and what properties you need to preserve
  • Conformal and equal area properties are mutually
    exclusive but some map projections can have more
    than one preserved property. For instance a map
    can be conformal and azimuthal
  • Conformal and equal area properties are global
    (apply to whole map) while equidistant and
    azimuthal properties are local and may be true
    only from or to the center of map

118
Map Projection-distortion
Introduction to GIS
robinson
Mercatorgoes on forever
sinusoidal
119
Map Projection-General Types
Introduction to GIS
  • Cylindrical projection created by wrapping a
    cylinder around a globe and, in theory,
    projecting light out of that globe the meridians
    in cylindrical projections are equally spaced,
    while the spacing between parallel lines of
    latitude increases toward the poles meridians
    never converge so poles cant be shown

Source ESRI
120
Map Projection-General Types
Introduction to GIS
  • Conic Projections projects a globe onto a cone
  • In simplest case, globe touches cone along a
    single latitude line, or tangent, called standard
    parallel
  • Other latitude lines are projected onto cone
  • To flatten the cone, it must be cut along a line
    of longitude (see image)
  • The opposite line of longitude is called the
    central meridian

Source ESRI
121
Map Projection-General Types
Introduction to GIS
  • Conic Projections
  • Projection is most accurate where globe and cone
    meetat the standard parallel
  • Conic projections are typically used for
    mid-latitude zones with east-to-west orientation.

122
Map Projection-General Types
Introduction to GIS
  • Planar or Azimuthal Projections simply project a
    globe onto a flat plane
  • The simplest form is only tangent at one point
  • Any point of contact may be used but the poles
    are most commonly used
  • When another location is used, it is generally to
    make a small map of a specific area
  • When the poles are used, longitude lines look
    like hub and spokes

Source ESRI
123
Map Projection-Specific Types
Introduction to GIS
  • Some of the most important
  • Mercator
  • Transverse Mercator
  • Lambert Conformal Conic
  • Albers Equal Area

124
Datums
Introduction to GIS
  • A datum is realized by a set of referenced survey
    monuments, with known position. These monuments
    are connected by a network of precise
    measurements that enable the computation of a
    position.
  • Datum changes when different spheroid used
  • Datum closely approximates the mean sea-level
    surface for an area of interest
  • Provides surface to which ground control
    measurements can be references

125
Datums
Introduction to GIS
  • Datum closely approximates the mean sea-level
    surface for an area of interestbased on spheroid
  • Provides surface to which ground control
    measurements can be references
  • The North American Datum of 1927 (NAD27) is based
    on Clarke 1866, and has its center in Kansas.
  • A newer, satellite measured spheroid is the World
    Geodetic System 1984 (WGS84) spheroid, which is
    more or less identical to Geodetic Reference
    System 1980 (GRS80)- measured from center of
    spheroid

126
Coordinate Systems
Introduction to GIS
  • Map projections, like we discussed in last
    lecture provide the means for viewing small-scale
    maps, such as maps of the world or a continent or
    country (11,000,000 or smaller)
  • Plane coordinate systems are typically used for
    much larger-scale mapping (1100,000 or bigger)

127
Coordinate Systems
Introduction to GIS
  • Projections are designed to minimize distortions
    of the four properties we talked about, because
    as scale decreases, error increases
  • Coordinate systems are more about accurate
    positioning (relative and absolute positioning)
  • To maintain their accuracy, coordinate systems
    are generally divided into zones where each zone
    is based on a separate map projection

128
Coordinate Systems
Introduction to GIS
  • The four most commonly used coordinate systems in
    the US
  • Universal Transverse Mercator (UTM) grid system
  • The Universal Polar Stereographic (UPS) grid
    system
  • State Plane Coordinate System (SPC)
  • And the Public Land Survey System (PLSS)

129
XIV. Data quality and error
Introduction to GIS
130
Data quality
Introduction to GIS
  • Components of data quality
  • Accuracy
  • Precision
  • Accuracy and Precision apply to
  • Position
  • attributes

131
Other measures of data quality
Introduction to GIS
  • Logical consistency
  • Completeness
  • Data currency/timeliness
  • Accessibility
  • These apply to both attribute and positional data

132
Random and Systematic error
Introduction to GIS
  • Error can be systematic or random
  • Systematic error can be rectified if discovered,
    because its source is understood
  • Random error cannot be controlled for because its
    source is not understood.
  • Random errors are often introduced in little bits
    at each stage of data collection and processing

133
Random and Systematic error
Introduction to GIS
  • Systematic errors affect accuracy, but are
    usually independent of precision data can use
    highly precise methods but still be inaccurate
    due to systematic error

Accurate and precise no systematic , little
random error
Accurate and imprecise no systematic , but
considerable random error
inaccurate and precise little random error but
significant systematic error
inaccurate and imprecise both types of error
134
Sources of error
Introduction to GIS
  • Obvious sources old maps, lack of cover, etc
  • Measurement error GPS, RS, manual attribute
    classification, temporal measurement, etc
  • Can be systematic or random
  • Processing errors
  • Numerical (rounding off), geocoding, digitizing,
    automated classification
  • Errors can propagate and cascade

135
Documentation and Metadata
Introduction to GIS
  • To avoid many of these errors, good documentation
    of source data is needed
  • Metadata is data documentation, or data about
    data
  • Ideally, the metadata should descrive the data
    according to FGDC metadata standards

136
Documentation and Metadata
Introduction to GIS
  • Critical components usually break down into
  • Dataset identification
  • Data definition
  • Dataset overview
  • Data quality
  • Spatial reference information
  • Administrative information

137
XV. Spatial Interpolation
Introduction to GIS
138
What is interpolation?
Introduction to GIS
  • Process of creating a surface based on values at
    isolated sample points.
  • Sample points are locations where we collect data
    on some phenomenon and record the spatial
    coordinates
  • We use mathematical estimation to guess at what
    the values are in between those points
  • We can create either a raster or vector
    interpolated surface
  • Interpolation is used because field data are
    expensive to collect, and cant be collected
    everywhere

139
How does it work
Introduction to GIS
  • Let say we have our ground water pollution samples

140
What isnt interpolation?
Introduction to GIS
  • Interpolation only works where values are
    spatially dependant, or spatially autocorrelated,
    that is, where the value depends on location
  • Where values across a landscape are
    geographically independent, interpolation does
    not work because value of (x,y) cannot be used to
    predict value of (x1, y1).

141
Where interpolation does not work
Introduction to GIS
  • Cannot use interpolation where values are not
    spatially autocorrelated
  • Say looking at household incomein an
    income-segregated city, you could take a small
    sample of households for income and probably
    interpolate
  • However, in a highly income-integrated city,
    where a given block has rich and poor, this would
    not work

142
Sampling
Introduction to GIS
  • As you can see, the density and spacing of
    samples depends on many things
  • A key component of any study with spatially
    referenced field data is the sampling strategy
  • If the values in your interpolation surface
    (layer A) depend on some factor in layer B, then
    we can design our sample of A based on layer B
  • We can do this by conducting a stratified random
    sample

143
Sampling
Introduction to GIS
  • The number of samples we want within each zone
    depends on the statistical certainty with which
    we want to generate our surface
  • Do we want to be 95 certain that a given pixel
    is classified right, or 90 or 80?
  • Our desired confidence level will determine the
    number of samples we need per strata
  • This is a tradeoff between cost and statistical
    certainty
  • Think of other examples where you could stratify….

144
How does the math work?
Introduction to GIS
  • There are many many methods for interpolation
  • These are the methods used by Arc View
  • IDW, or inverse distance weighted interpolation
    also known as natural neighbor interpolation
  • SPLINE Also known as thin-plate splines
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