ARTICLE GIS PART 2

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GEOREFERENCE
Georefencing is crucial to making aerial and satellite imagery, usually raster images, useful
for mapping as it explains how other data, such as the above GPS points, relate to imagery.
Very essential information may be contained in data or images that were produced at a
difference point of time. It may be desired either to combine or compare this data with
currently available. The latter can be used to analysis the changes in the features under study
over a period of time.
Different maps may use different projection system. Georefencing tool contain methods to
combine and overlay these map with minimum distortion. Using georeferencing methods,
data obtained from surveying tools like total stations may be given a point of reference from
topographic maps already available. It may be required to establish the relationship between
social survey result which have been code with postal codes or street addresses and other
geographic area such as census zones or other areas used in public administration or service
planning.
Georeference means to associate with location in physical space. The term is commonly used
in the geographic information system field to describe the process of associating a physical
map or raster image of a map with spatial locations. Georeferencing may be applied to any
kind of object or structure that can be related to a geographical location, such as points of
interest, road, places, bridges or building. Geographic location are most commonly
represented using a coordinate reference system, which in turn can be related to a geodetic
reference system such as WGS-84.
Method of Geoferencing
1. Aligning the raster with control points
Generally, georeferencing will raster data using existing spatial data (target data) such
as georeferenced rasters or a vector feature class – that resides in the desired map
coordinate system. The process involves identifying a series of ground control points
– known x,y coordinates that link locations on the raster dataset with location in the
spatially referenced data (target data). Control points are locations that can be
accurately identified on the raster dataset and in real-world coordinates. Many
different types of features can be used as identifiable location, such as road or stream

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intersections, the mouth of a stream, rock outcrops, the end of a jetty of land, the
corner od an established field. Street corners, or the intersection of two hedgerows.
The control points are used to build a polynominal transformation that will shift the
raster dataset from its existing location to the spatially correct location. The
connection between one control point on the raster dataset (from the point) and the
corresponding control point on the aligned target data (to the point) as a link.
The example below shows a from control point (yellow cross) placed on the vector
target data at a street crossing and the associated control point (green cross) placed on
the raster dataset. The associated link is represented by the blue line joining the
control points as shown in figure 1.1.
Generally, the greater the overlap between the raster dataset and target data, the better
the alignment result, because have more widely spaced points with which
georeferancing the raster dataset. For example if data only accupies one-quarter of the
area of your raster dataset, the points could be use to align the raster dataset would be
confined to that area on overlap. Thus, the area outside the overlap area are not likely
to be properly aligned.
2. Transforming the raster
When already created enough link. Its can transform-or warp-the raster dataset to
permanently match the map coordinates of the target data. Then using a polynomial, s
spline, an adjust, or a projective transformation to determine the correct map
coordinate location for each cell in the raster.
The polynomial transformation yields two formulas, one for computing the output x-
coordinate for an input (x,y) location and one for computing the y-coordinate for an
input (x,y) location. The goal of the least-squares fitting algorithm is to derive a

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general formula that can be applied to all points, usually at the expense of slight
movement of the to positions of the control points. The number of noncorrelated
control points required for this method must be 1 for a zero-order shift, 3 for a first
order affine, 6 for a second order, and 10 for a third order. The lower order
polynomials tend to give a random type error, while the higher order polynomials tend
to give an extrapolation error.
The first-order polynomial transformation is commonly used to georeference an
image. Below is the equation to transform a raster dataset using the affine (first order)
polynomial transformation. The formula is like shown in figure 1.2.
A zero-order polynomial is used to shift your data. This is commonly used when the
data is already georeferenced, but a small shift will better line up the data. Only one
link is required to perform a zero-order polynomial shift. It may be a good idea to
create a few links, then choose the one that looks the most accurate. Use a first-order
or affine transformation to shift, scale, and rotate a raster dataset. This generally
results in straight lines on the raster dataset mapped as straight lines in the warped
raster dataset. Thus, squares and rectangles on the raster dataset are commonly
changed into parallelograms of arbitrary scaling and angle orientation.

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With a minimum of three links, the mathematical equation used with a first-order
transformation can exactly map each raster point to the target location. Any more than
three links introduces errors, or residuals, that are distributed throughout all the links.
However, you should add more than three links, because if one link is positionally
wrong, it has a much greater impact on the transformation. Thus, even though the
mathematical transformation error may increase as you create more links, the overall
accuracy of the transformation will increase as well. The higher the transformation
order, the more complex the distortion that can be corrected. However,
transformations higher than third order are rarely needed. Higher-order
transformations require more links and, thus, will involve progressively more
processing time. In general, if your raster dataset needs to be stretched, scaled, and
rotated, use a first-order transformation. If, however, the raster dataset must be bent or
curved, use a second- or third-order transformation.
GEOCODING
Geocoding is the process of back (reverse) coding of the point location (latitude,
longitude) to a readable address or place name. Thos permits the identification of
nearby street addresses, places and or areal subdivisions such as neighbourhoods,
country, state or country. Combined with geocoding and routing services, rerverse
geocoding is a critical component of mobile location-based services and Enhanced
911 to convert a coordinate obtained by GPS to a readable street address which is
easier to understand by the user.

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Reverse geocoding can be carried out systematically by services which process a
coordinate similarly to the geocording process. For instant, when a GPS coordinate is
entered the street address is interpolated from the range assigned to the road segment
in a reference dataset that the point is nearest to. If the user provides a coordinate
near the midpoint of a segment that start with address 1 and end with 100, the
returned street address will be somewhere near 50. This approach to reverse
geocoding does not return actual addresses, only estimates of what should be there
based on the predetermined range. Alternatively, coordinates for reverse geocoding
can also selected on an interactive map, or extracted from static maps by
georeferencing in a GIS with predefined spatial layers to determine the coordinates of
a display point. Many of the same limitation of geocoding are similar with reverse
geocoding,
Geocoding and reverse geocoding have raise potential concerns, especially regarding
the ability to reverse engineer street addresses from published statics maps. By
digityzing published maps it is possible to georeference by the overlaying with the
other spatial layers ad then axtract point location which can be used to identify
individuals or reverse geocoded to obtain a street address of the individual. This has
potential implication to determine the location.
CONCLUSION
The finding from these studies is concern regarding the potential used of
georeferencing and geocoding of publishing maps to elucidate sensitive or private
information on mapped individuals. Guidelines for the display and publication of
potentially information are inconsistently applied.
REFENCES
Getting to Know ArcGIS for Desktop, Second Edition 2004, (Ormsby, Napoleon,
Burke, Groessl and Bowden). Publish: Ingram Publisher Services, United States
of America.
The GIS Book, Fifth Edition Aug 2000. By George B. Korte, P.E. Publish: OnWorld
Press, United State of America.

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