COURSE
STAE 2352 REMOTE SENSING AND ADVANCED GIS
TUGASAN 5
LECTURER
PROF. MADYA DR. TUKIMAT BIN LIHAN
PREPARED BY
KAM CAI SEE, A175735
TOTAL PAGES: 7
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Huraikan operasi-operasi yang boleh digunakan di dalam kaedah tindan lapis (Overlay)
peta menggunakan Sistem Maklumat Geografi (GIS). (10 markah)
An analytical technique for finding the spatial coincidence of geographic characteristics
is known as overlay. "Light-table gymnastics" can be used to describe the general category of
overlay operations. The value assigned to each point, or collection of points, is a function of the
independent values associated with that place on two or more existing map layers. By merging
different data sets, the overlay function output can create composite maps. By combining data
from two or more input data layers, new spatial data sets are produced in the overlay analysis.
This is one of the most frequent and powerful GIS techniques. It determines what is on the top
layer by analyzing several layers with common coordinate systems.
The procedures entail making a new map on which the value given to each place or group
of locations is a function of the independent values associated with that location on two or more
existing map layers. Overlaying operations maps may be done in three ways: location-specific
(point-by-point or cell-by-cell), region-wide, and map-wide.
Location-specific: value assigned based on the present maps' point-by-point coincidence
Region-wide: values allocated to the whole thematic region as a function of values on other
overlays that are connected to the category
Map-wide: Spatial statistics and more complicated statistical approaches
1.0 LOCATION - SPECIFIC (point-by-point)
According to the nature of the data being processed and the specific application of that
data within a modeling context, the most basic set of Location-specific overlay procedures
computes new map values from those of existing map layers. Many environmental assessments
require manipulating quantitative values to produce new quantitative values. The basic arithmetic
operations, such as addition, subtraction, multiplication, division, roots, and exponentiation are
amongst the types of values and functions involved in this operation. Simple statistical
parameters such as maximum, minimum, median, mode, majority, standard deviation, and
weighted average can also be used using functions. The mathematical or statistical method
employed must be appropriate for the type of data being modified. Adding qualitative maps like
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soils and land use would result in mathematically nonsensical sums because the thematic values
have no numerical link. There are numerous more map overlay techniques that may be used to
process quantitative or qualitative input and create values that can be in either form. Masking,
comparison, diversity computation, and intersections of map types are among the overlay
techniques.
2.0 REGION-WIDE
The summation of values over a region-wide is a whole different method to overlaying
maps. This set of procedures summarizes the geographic coincidence of whole categories of two
or more maps, rather than merging data point by point. The total, average, maximum, minimum,
median, mode, or minority value; the standard deviation, variance, or diversity of values; and the
correlation, deviation, or uniqueness of certain value combinations are all summary statistics that
may be utilized in this way. For example, by superimposing a map of county boundaries on a
map of land usage and estimating the ratio of undeveloped land to total land area for each county,
a map displaying the fraction of undeveloped land inside each of many counties might be
created. On the other hand, a map of zip code borders may be overlaid over demographic data
maps to estimate the average income, age, and dominating ethnic community within each zip
code.
3.0 MAP-WIDE
If the underlying dependency of geographic data can be taken into account, more complicated
statistical approaches can be used in this way. Each map is treated as a “variable”, each point as a
“case”, and each value as a“measurement” in this method. For each site, a predictive statistical
model may then be assessed resulting in a spatially continuous surface of estimated values. This
is because traditional non-spatial methods lack information like direct consideration of
coincidence among regression variables and the capacity to physically pinpoint areas with a
particular degree of prediction, however, mapped predictions have the ability to do so.
SPATIAL COINCIDENCE
Because GIS maps are numbers, it is possible to maintain a rigorous, quantitative
approach to map study. However, the majority of our previous map experience has been with
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non-quantitative paper maps with inked lines, shadings and symbol. Map uncertainty and error
propagation are rarely considered. And we surely wouldn't expect such features from our GIS
software.
The ability to overlay maps is a GIS's "bread and butter," as everyone knows. To a
human, map overlay entails "light-table gymnastics," in which we go through a stack of acetate
sheets and evaluate the various hues of the colours that arise. When we require a GIS to
determine the condition from each map layer for every place in a project region, we’re actually
asking the computer to do so. However, from the computer's perspective, this is just one of
several ways to describe the spatial coincidence.
Vector:
Line segments defining polygon borders are checked to see if they cross. A new combinatorial
pattern is signaled when a line on one map meets a line on another map. The X,Y coordinate of
the junction of the lines is calculated using trigonometry. The two line segments are divided into
four segments, each of which is assigned a value that identifies the combined map conditions.
The set of polygonal shapes we laborious defined by hand (manual) is the product of all this
crossing and splitting.
Raster:
Things are a little easier with a raster system. The computer 'goes' to a place, obtains the
information stored for each map layer, and provides a value reflecting the combined map
conditions since all locations are preset as a consistent collection of cells within a matrix. As a
result, a new set of values for the matrix identifies the maps' coincidence. However, the major
difference between manual and computer approaches to map overlay is in how the data is treated,
not so much in methodology. If we have a lot of maps to overlay, researcher will rapidly run out
of different colors, and the entire stack will turn a dark, purple tint. One solution is to divide each
map layer into only two groups: acceptable and unsuitable. One should be clear acetate (good)
and the other should be light grey (bad). The resultant stack eliminates color-combination
problems by depicting the best sections in lighter tones. However, in order to make the approach
work, we had to restrict the data's substance to simply good and bad.
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Binary maps can be used to simulate this approach on a computer. A "0" denotes ideal
conditions, whereas a "1" denotes unfavorable ones. The total of the maps contains the same data
as the brightness scale researchers looking at—the lower the value, the better. It is possible to
compute the two most fundamental types of logical conjunction. For instance, researcher
combine the maps and "Look for places that have decent slopes. AND, excellent soils AND, a
good vegetative cover”. This appears to reseacrhes’ sight as absolutely distinct places. This is
recognized by the computer as the numeric pattern 0-0-0. When researcher wants to "Locate
slopes that are suitable for skiing. OR, excellent soils. OR, excellent vegetative cover." To
manual work, this might be any area that isn't the darkest shade; to the computer, it's any number
pattern with at least one 0. However, if researcher wants to "Identify those areas with good
slopes. OR, excellent soils. AND, good vegetative cover." Reseachers won't be able to discover
them merely by looking through the stack of maps. Researcher woul'd have to sift through the
stack for quite some time and this is just the patterns 0-1-0, 1-0-0, and 0-0-0 to the computer. On
the contrary, from a digital standpoint, it's a piece of cake.
BOOLEAN
EXAMPLE
AND
R1 & R2
OR
R1 | R2
NOT
~R2, !R2
4.0 OPERATIONS
4.1 Composite
By expanding informative scale to include not suitable (0), poor (1), marginal (2), good (3), and
excellent (4) - from terrible to perfect. Then reseachers could operate the computer to
COMPLETELY INTERSECT SLOPES WITH SOILS FOR ALL-COMBINATIONS. The result
is a map that shows all possible combinations between the three maps. This map will be too
complicated for researchers to analyse, but it provides the comprehensive information that many
application models require. This method creates a map that sums up values from one map that
match the categories of another. On one map, the regions identified by category values serve as
Cookie Cutter shapes (Template map) for aggregating data from another map (Data map).
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4.2 Intersect
Continue from the composite operation, a more direct approach is to operate the computer to
INTERSECT SLOPES WITH SOILS WITH COVER FOR EXCELLENT-AREAS
ASSIGNING 1 TO 4. Any combination that isn't given a value is set to 0, resulting in a map with
1 represents the best areas.
4.3 Cover
Assume researchers created two acetate sheets, one with all of the wooded regions opaque green
and the other with all of the highways opaque red. Then place them on a light-table. If
researchers lay the forest sheet down first, the red roads will "cover" the green woods, revealing
the roads that run through them. The red lines will suddenly end at the green forest globs if the
roads map is displayed first. After that in GIS, the colours are converted to numbers, and the
clear acetate is given a value of zero. The command COVER FORESTS WITH ROADS
instructs the computer to locate to a certain place and evaluate the values it discovers. This
method replaces the values on the preceding (bottom) map, or stack of maps, with nonzero
values from the top map the maps are compared "cell by cell," with the value in the top most cell
replacing the previous values unless that value is zero, in which case the top most non-zero value
is kept. To my understanding, this is a really spatial action because there is no standard math
equivalent.
4.4 Calculate and Compute
From this method it creates a map by combining two or more maps into a mathematical or
statistical function. When GIS commands, COMPUTE SLOPES MINIMIZE SOILS MINIMIZE
COVER FOR WEAK-LINK, this is another approach to combine these maps. The numbers on
the generated map represent the minimum coincidence rating for each site. Low numbers suggest
regions to be concerned about, while a 0 indicates areas to disregard as unsuitable based on at
least one map's data. Grid maps can do all of the fundamental mathematical operations that a
conventional pocket calculator can for instance, plus, minus, times, divide, and exponentiation,
are just a few of the various computing processes reseacrhers may use. Functions such as Square,
Square Root, Maximum, Mininum and Trigonometry are used too.
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4.5 Crosstab
A cross-tab table is a statistical method of summarizing map coincidence. When researchers
CROSSTAB FORESTS WITH SOILS, a table appears that shows how frequently each forest
type occurs with each soil type. This is the total area of each forest/soil combination in a vector
system. While this is just a count of all the cell positions for each forest/soil combination in a
raster system. The maps are compared "cell by cell," and the number of shared occurrences
between map categories is tabulated.
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