Spatial (map) is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies such as Audi, BMW, and Mercedes. Consequently, they are bound to hire more and more spatial data scientists. Based on such business trend, this course is designed to present a firm understanding of spatial data science to the learners, who would have a basic knowledge of data science and data analysis, and eventually to make their expertise differentiated from other nominal data scientists and data analysts. Additionally, this course could make learners realize the value of spatial big data and the power of open source software's to deal with spatial data science problems. This course will start with defining spatial data science and answering why spatial is special from three different perspectives - business, technology, and data in the first week. In the second week, four disciplines related to spatial data science - GIS, DBMS, Data Analytics, and Big Data Systems, and the related open source software's - QGIS, PostgreSQL, PostGIS, R, and Hadoop tools are introduced together. During the third, fourth, and fifth weeks, you will learn the four disciplines one by one from the principle to applications. In the final week, five real world problems and the corresponding solutions are presented with step-by-step procedures in environment of open source software's.
Spatial Reference Framework
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From the course by Yonsei University
Spatial Data Science and Applications
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From the lesson
Geographic Information System (GIS)
The third module is "Geographic Information System (GIS)", which is one of the four disciplines for spatial data science. GIS has five layers, which are spatial reference framework, spatial data model, spatial data acquisition systems, spatial data analysis, and geo-visualization. This module is composed of six lecture. The first lecture "Five Layers of GIS" is an introduction to the third module. The rest of the lectures will cover the five layers of GIS, one by one. The second lecture "Spatial Reference Framework" will make learners understand, first, a series of formulation steps of physical earth, geoid, ellipsoid, datum, and map projections, second, coordinate transformation between different map projections. The third lecture "Spatial Data Models" will teach learners how to represent spatial reality in two spatial data models - vector model and raster model. The fourth lecture "Spatial Data Acquisition Systems" will cover topics on how and where to acquire spatial data and how to produce your own spatial data. The fifth lecture "Spatial Data Analysis", will make learners to have brief taste of how to extract useful and valuable information from spatial data. More advanced algorithms for spatial analysis will be covered in the fifth module. In the sixth lecture "Geovisualization and Information Delivery", learners will understand powerful aspects as well as negative potentials of cartographic representations as a communication media of spatial phenomenon.
Meet the Instructors
Joon HeoProfessor
School of Civil and Environmental Engineering
In the last lecture, we reviewed the five layers of GIS,
which are spatial reference frameworks,
spatial data models, spatial data acquisition system,
spatial data analysis and geo-visualization and information delivery.
We will discuss the very first layer,
spatial reference framework in detail.
The contents of this lecture will cover, first of all,
importance of spatial reference framework,
second, how coordinate systems can be formed.
A series of formulations steps from physical Earth to map projection.
Third, coordinate transformations between different coordinate systems,
which is essential to integrate
different spatial datasets based on different map projections.
Now, you're looking at some lines in red and blue color.
What are they?
They are basically set of geometric data: points,
lines, curves, polygons and so on.
However, if two components are added to the geometric data,
they become spatial data.
The two components are coordinate and quality.
In fact, the data set is DLG,
data line graph, which are extracted from topographic maps.
The reddish lines are road networks and the blue lines are water bodies.
Truly, the coordinate information is the key component that
makes meaningless geometry become spatial data.
Now, you are looking at an aerial image.
Is it just the image or a map?
In other words, image or spatial data?
Just like the previous geometry example,
if the two components are added,
the image becomes spatial data.
Again, coordinate systems and data quality.
For example, positional accuracy make the image becomes spatial data.
That is the reason why spatial reference framework,
which is eventually represented by coordinate system,
is the basis of a GIS and spatial data science and application.
Now, let us discuss spatial reference framework in more detail.
Spatial reference framework is about how to
represent location of spatial object,
basically referring to coordinate systems.
There are 3D, 2D and even one-dimensional coordinate system.
In fact, one-dimensional coordinate system is
actively used in transportation application of GIS.
Mile post, engineering stations are the examples.
2D coordinate is typically expressed by x, y.
And 3D coordinate is by x,
y,z or latitude or longitude and ellipsoidal height.
Major lssues of spatial reference frameworks are,
number one, how to define each spatial reference framework,
in other words, how to define origin and orientation of coordinate systems in 2D and 3D,
and, second, how to convert difference spatial reference framework to each other,
in other words, coordinate transformation.
Now, let's discuss how to define 3D coordinates system in the red box,
either geographic in terms of latitude Phi,
longtitude Lambda, and height from ellipsoid, small h,
or geocentric in terms of x, y, and z.
For the 3D coordinate,
can you use the real shape of the Earth, the physical Earth?
Probably not. Why? Think about the reality of the Earth.
The shape of the earth continues to change, for example,
due to tide effect and some undulated mountains.
In this highly undulated and rugged,
think about mountain Everest.
Basically, we cannot mathematically define the dynamic shape of the physical Earth.
For defining the shape of the Earth, Geoid can be used.
Geoid is imaginary 3D surface of mean sea level,
on which every point has the same gravitational potential.
Though it is complicated,
it can be theoretically well-defined.
Geoid can be well approximated with harmonic functions.
However one problem is that it requires many thousands parameters for the approximation.
It is much better than physical Earth but still way too complicated.
From Geoid, we can take one more step to
define the shape of the earth that is an ellipsoid of revolution,
simply an ellipsoid, which can be defined by
only two parameters: semi-major axis and semi-minor axis.
And it can be applied to the earth.
With respect to the Geoid,
local fitting or global fitting is applied to estimate the two parameter.
The figure illustrates the concept of local fitting
of ellipsoid with respect to Geoid in black line.
The red ellipsoid would be the best fit to North America,
and the blue ellipsoid would be the best fit to Europe.
In the past, when each Earth observations are very limited,
such locally-fitted ellipsoids were used to define the shape of the Earth.
Clarke1866, Bessel, Hayford are the examples.
As more and more Earth observations became available,
globally-fitted ellipsoid became possible.
Such efforts came up with world standard ellipsoids such as international, GRS80 and WGS84.
The red ellipsoid would be conceptualize the globally-fitted ellipsoid,
which is the best fit to the global Geoid.
Now, we can define the shape of the Earth using ellipsoid with only two parameters.
However, for building 3D coordinates system,
we need to fix the location of ellipsoid. There are few methods.
First, one fixing point and one azimuth from the point;
second, two fixing points;
third, fixing the center of the ellipsoid and fixing the direction of two axes.
In such ways, an ellipsoid can be used for reference framework
of 3D coordinate system, we call it datum.
One ellipsoid can make many datums only if we could move around the location of ellipsoid,
and they could be all different datum.
The given table shows the examples.
In the table, the Clarke1866 produces four different datums; Hayford makes three.
They are notable ellipsoids,
datum's, and their relationships.
WGS84 is very important because it is used for GPS.
It should be noted that it refers to a datum, as well as an ellipsoid.
ITRF stands for International Terrestrial Reference Framework,
which is a world standard datum based on GRS80 ellipsoid.
NAD83 is currently the standard datum for the US,
also based on GRS80.
One more thing worth remember is that
all three datum's are practically identical, though theoretically different.
Now, we have a solid idea about how
to build the 3D spatial reference framework with datum.
The next issue is how to convert 3D
to 2D spatial reference framework; 2D plane coordinate system,
on which most spatial data applications are developed.
The process is called map projection.
There are numerous map projections.
Among them, we'll focus on
conformal map projections in which the shape of space objects is preserved.
And they are considered standard map projection for large scale maps.
For conformal projections, there are three main types depend on the projection surface,
which are cylindrical, conical and planar.
Though the mathematical formulations are very
complicated and rooted from differential geometry,
which is a branch of mathematics,
the concept is rather simple in which the source
of the light is located at the center of the Earth,
spatial objects are projected from the surface of the Earth
to the projection surface: cylinder, cone or plane.
The shape on the projection surface is the outcome of the conform projection.
Now, you are looking at examples of cylindrical projection.
If projection surface is upright and meets the Earth on a single line,
we call it normal or standard projection.
If the projection surface cuts through the Earth,
consequently, it meets on two lines,
we call it secant projection.
When projection surface is inclined, it is called oblique projection.
When projection surface laid down,
it is called transverse projection.
The parameters to define the projections are tangent lines,
the original projection, and scale factor if it is secant projection.
Similarly to a cylindrical projection,
conical projection are also categorized into normal,
secant, oblique, which depends on how conical projection surface meets the Earth.
The parameters of the projections are again
the tangent line between projection surface in the Earth and projection center.
You are looking at different types of planar projections again which
depends on the way how projection surface and the Earth are related.
Let's take a look at a popular prediction, transverse Mercator shortly,
TM projection, which is
transverse cylindrical projection presented in the previous slide.
It is widely used for national and international mapping system,
particularly with Universal Transverse Mercator, UTM.
For example, the US topographic maps are based on UTM.
TM basically inherits mathematical foundation and many traits from normal Mercator,
the cylindrical projection, except for the orientations.
In TM, the axis of the cylinder lies on the equatorial plane,
and the tangent line is located on any selected meridian.
So it is called central meridian.
The figures shows a secant version in which the scale is reduced at the center, at the central meridian.
Because cylinder slices through the Earth,
the reduced scale at the central meridian in secant projection is called scale factor.
Now, let me ask a question,
where do you think is the most accurate TM projection?
In other words, where do you expect no distortion in the projection surface?
Of course, on the line where the cylinder and earth meet each other.
The scale becomes drastically larger,
while it is farther from the center meridian in any projection.
So TM is suitable for the areas with north and south long extent.
As briefly discussed, shapes,
size and location of mapping area are important for selection of map projection.
If the extent is north-south long,
transverse Mercator would be a good choice.
If east-west long and large,
conical projection would be a reasonable choice.
If the area is circular and relatively small,
panel projection would work out.
Basically, the shape, size,
location of the given area are collectively considered for deciding how
to locate the tangent lines between the Earth and projection surface.
The figure would substantiate the argument.
The state of Alaska has a long tail to the south-eastern direction.
For the area, the tangent line is inclined
so that the selection should be an oblique projection.
For example, an oblique cylindrical projection in this case.
So far, we have discussed only conformal mapping generally for a large scale map.
However, for a world wide mapping at a small scale,
conformal mapping would not be a solution.
In that case, equal area projection should be used such as Goode's homolosine,
mollweide, Lambert cylindrical equal area projection and so on.
This issue is related to the subject and proposal of the map,
which should be considered in the first place.
Now, for checking your understanding of spatial reference framework,
let's take a look at a few examples.
The first one is Wisconsin transverse Mercator.
The map projection is a secant transverse cylindrical
because the state of Wisconsin is rather north-south long.
Datum is North American Datum, NAD83 based on GRS80 ellipsoid.
The central meridian of WTM is minus 90 degree,
penetrating the center of Wisconsin.
And 520,000 meters is added to east-west in order to make x coordinate a positive value
on the other hand, a large value over 4 million meters were subtracted
from y-axis because the latitude of the origin is an equator.
So y coordinate could be unreasonably large values without minus false northing.
Finally, the scale factor at the central meridian is 0.996.
Can you visualize the map projection along with the given datum?
If you can say yes, now,
you understand the concept of spatial reference framework very well.
Let's take a look at another example.
Texas statewide mapping system.
This time, the map projection is a secant
conical because state of Texas is rather east-west long and large.
The datum is North American datum 27 based on Clarke 1866 ellipsoid.
The central meridian and the latitude of origin is minus 100 degree,
and 31 degree 10 minutes.
Because it's a secant conical prediction,
it has two standard parallel.
On each projection surface,
slice through the Earth.
They are 27 degree 25 minutes and 34 degrees 55 minutes in latitude.
Also, it has false easting and false northing on x-axis and y-axis to make the coordinate
have a positive and reasonable values.
Now, the last issue in spatial reference framework,
which is coordinate transformation.
In spatial data applications, you come up with these problem very
often when you combine two or more data sets and analyze them together.
In that case, you have to make map projections of each dataset unified for
aligning spatial data on top of each other.
There are mainly four major methods for coordinate transformation,
from coordinate system A to coordinate system B.
The first approach is the direct transformation.
Without consideration of map projection,
coordinate system A can be transformed to B with a heuristic polynomial function,
generally, third order polynomials.
It works well, In case that the given area is relatively small.
However, if it requires a mathematically thorough transformation with high accuracy,
we should consider datum side,
meaning that this approach wouldn't work out very well.
So that's the second case of coordinate transformation.
In each coordinate system A transformed backward to datum A,
and transformed forward to coordinate system B.
This would work when the two coordinate systems A and B share their datum,
meaning that they have the same datum.
For example, on some coordinate system A is
Lambert conformal conic based on NAD83,
and coordinate system B is WTM on NAD83.
They share the same datum so that the method shown on
the figure would give you a mathematical rigid solution.
Now, my question is,
what if two coordinate systems have different datum?
The third method should be applied to the case.
Coordinate system A transformed to backward to datum A. Datum A approximate
to datum B by regression or other 3D to 3D transformation.
And then datum B is projected to coordinate system B.
The example could be a transformation within Lambert conformal conic on
NAD83 and Texas statewide mapping system on NAD27, two different datums.
In the case, two map projections have different datums so that the third method would work out.
Theoretically, there is a more rigid transformation method.
The fourth method is to take another series of
steps to geocentric coordinate systems, meaning x, y,
z in 3D Cartesian coordinate which is only applied to geodetic coordinate transformation,
which requires the most accurate outcomes in transformation.
As mentioned, the third method approximates the datum-to-datum transformation.
So it inevitably comes up with some error,
while the fourth method would include only minimum error in coordinate transformation.
Isn't it somewhat complicated?
Yes, it certainly is.
However, one good news is that, in reality,
the fourth method is never used in GIS applications,
An even better news is that most GIS softwares have function of
so-called on-the-fly projection, with which
users don't have to worry about coordinate transformation,
as long as coordinate systems of your datasets are well-defined in metadata.
GIS softwares automatically align spatial data on top of each other,
even though they are based on different map projection.
In this lecture, you have studied on spatial reference framework.
In detail, you studied how 3D coordinate
on the surface of the Earth can be converted to 2D coordinate.
For that, the concept of Geoid,
ellipsoid and datum were introduced.
Then we discussed a variety of map projections and how to
choose an appropriate one for a given area.
Finally, coordinate transformation were
discussed to align special data of different map projections all together.
All right. This is the end of this lecture,
and hope you have a solid concept of spatial reference framework,
in other words, coordinate systems.
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