.. _spatial_analysys:
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Spatial Analysis (Interpolation)
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| |gentleLogo| | Objectives: | Understanding of interpolation as part of spatial analysis |
+ +-------------+---------------------------------------------------------------------------------------------+
| | Keywords: | Point data, interpolation method, Inverse Distance Weighted, Triangulated Irregular Network |
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Overview
========
**Spatial analysis** is the process of manipulating spatial information to extract
new information and meaning from the original data. Usually spatial analysis is
carried out with a Geographic Information System (GIS). A GIS usually provides
spatial analysis tools for calculating feature statistics and carrying out
geoprocessing activities as data interpolation. In hydrology, users will likely
emphasize the importance of terrain analysis and hydrological modelling (modelling
the movement of water over and in the earth). In wildlife management, users are
interested in analytical functions dealing with wildlife point locations and their
relationship to the environment. Each user will have different things they are
interested in depending on the kind of work they do.
Spatial interpolation in detail
===============================
Spatial interpolation is the process of using points with known values to estimate
values at other unknown points. For example, to make a precipitation (rainfall)
map for your country, you will not find enough evenly spread weather stations to
cover the entire region. Spatial interpolation can estimate the temperatures at
locations without recorded data by using known temperature readings at nearby
weather stations (see figure_temperature_map_). This type of interpolated surface
is often called a **statistical surface**. Elevation data, precipitation, snow
accumulation, water table and population density are other types of data that can
be computed using interpolation.
.. _figure_temperature_map:
.. figure:: img/temperature_map.png
:align: center
:width: 30em
Temperature map interpolated from South African Weather Stations.
Because of high cost and limited resources, data collection is usually conducted
only in a limited number of selected point locations. In GIS, spatial
interpolation of these points can be applied to create a raster surface with
estimates made for all raster cells.
In order to generate a continuous map, for example, a digital elevation map from
elevation points measured with a GPS device, a suitable interpolation method has
to be used to optimally estimate the values at those locations where no samples
or measurements were taken. The results of the interpolation analysis can then be
used for analyses that cover the whole area and for modelling.
There are many interpolation methods. In this introduction we will present two
widely used interpolation methods called **Inverse Distance Weighting** (IDW) and
**Triangulated Irregular Networks** (TIN). If you are looking for additional
interpolation methods, please refer to the 'Further Reading' section at the end
of this topic.
Inverse Distance Weighted (IDW)
===============================
In the IDW interpolation method, the sample points are weighted during
interpolation such that the influence of one point relative to another declines
with distance from the unknown point you want to create (see
figure_idw_interpolation_).
.. _figure_idw_interpolation:
.. figure:: img/idw_interpolation.png
:align: center
:width: 30em
Inverse Distance Weighted interpolation based on weighted sample point distance
(left). Interpolated IDW surface from elevation vector points (right). Image
Source: Mitas, L., Mitasova, H. (1999).
Weighting is assigned to sample points through the use of a weighting coefficient
that controls how the weighting influence will drop off as the distance from new
point increases. The greater the weighting coefficient, the less the effect points
will have if they are far from the unknown point during the interpolation process.
As the coefficient increases, the value of the unknown point approaches the value
of the nearest observational point.
It is important to notice that the IDW interpolation method also has some
disadvantages: the quality of the interpolation result can decrease, if the
distribution of sample data points is uneven. Furthermore, maximum and minimum
values in the interpolated surface can only occur at sample data points. This
often results in small peaks and pits around the sample data points as shown in
figure_idw_interpolation_.
In GIS, interpolation results are usually shown as a 2 dimensional raster layer.
In figure_idw_result_, you can see a typical IDW interpolation result, based on
elevation sample points collected in the field with a GPS device.
.. _figure_idw_result:
.. figure:: img/idw_result.png
:align: center
:width: 30em
IDW interpolation result from irregularly collected elevation sample points
(shown as black crosses).
Triangulated Irregular Network (TIN)
====================================
TIN interpolation is another popular tool in GIS. A common TIN algorithm is called
**Delaunay triangulation**. It tries to create a surface formed by triangles of
nearest neighbour points. To do this, circumcircles around selected sample points
are created and their intersections are connected to a network of non overlapping
and as compact as possible triangles (see figure_tin_interpolation_).
.. _figure_tin_interpolation:
.. figure:: img/tin_interpolation.png
:align: center
:width: 30em
Delaunay triangulation with circumcircles around the red sample data. The
resulting interpolated TIN surface created from elevation vector points is
shown on the right. Image Source: Mitas, L., Mitasova, H. (1999).
The main disadvantage of the TIN interpolation is that the surfaces are not smooth
and may give a jagged appearance. This is caused by discontinuous slopes at the
triangle edges and sample data points. In addition, triangulation is generally
not suitable for extrapolation beyond the area with collected sample data points
(see figure_tin_result_ ).
.. _figure_tin_result:
.. figure:: img/tin_result.png
:align: center
:width: 30em
Delaunay TIN interpolation result from irregularly collected rainfall sample
points (blue circles)
Common problems / things to be aware of
=======================================
It is important to remember that there is no single interpolation method that can
be applied to all situations. Some are more exact and useful than others but take
longer to calculate. They all have advantages and disadvantages. In practice,
selection of a particular interpolation method should depend upon the sample data,
the type of surfaces to be generated and tolerance of estimation errors.
Generally, a three step procedure is recommended:
#. Evaluate the sample data. Do this to get an idea on how data are distributed
in the area, as this may provide hints on which interpolation method to use.
#. Apply an interpolation method which is most suitable to both the sample data
and the study objectives. When you are in doubt, try several methods, if
available.
#. Compare the results and find the best result and the most suitable method.
This may look like a time consuming process at the beginning. However, as you
gain experience and knowledge of different interpolation methods, the time
required for generating the most suitable surface will be greatly reduced.
Other interpolation methods
===========================
Although we concentrated on IDW and TIN interpolation methods in this worksheet,
there are more spatial interpolation methods provided in GIS, such as Regularized
Splines with Tension (RST), Kriging or Trend Surface interpolation. See the
additional reading section below for a web link.
What have we learned?
=====================
Let's wrap up what we covered in this worksheet:
* **Interpolation** uses vector points with known values to estimate values at
unknown locations to create a raster surface covering an entire area.
* The interpolation result is typically a **raster** layer.
* It is important to **find a suitable interpolation method** to optimally
estimate values for unknown locations.
* **IDW interpolation** gives weights to sample points, such that the influence
of one point on another declines with distance from the new point being
estimated.
* **TIN interpolation** uses sample points to create a surface formed by triangles
based on nearest neighbour point information.
Now you try!
============
Here are some ideas for you to try with your learners:
* The Department of Agriculture plans to cultivate new land in your area but apart
from the character of the soils, they want to know if the rainfall is sufficient
for a good harvest. All the information they have available comes from a few
weather stations around the area. Create an interpolated surface with your
learners that shows which areas are likely to receive the highest rainfall.
* The tourist office wants to publish information about the weather conditions
in January and February. They have temperature, rainfall and wind strength data
and ask you to interpolate their data to estimate places where tourists will
probably have optimal weather conditions with mild temperatures, no rainfall
and little wind strength. Can you identify the areas in your region that meet
these criteria?
Something to think about
========================
If you don't have a computer available, you can use a toposheet and a ruler to
estimate elevation values between contour lines or rainfall values between
fictional weather stations. For example, if rainfall at weather station A is 50
mm per month and at weather station B it is 90 mm, you can estimate, that the
rainfall at half the distance between weather station A and B is 70 mm.
Further reading
===============
**Books**:
* Chang, Kang-Tsung (2006). Introduction to Geographic Information Systems. 3rd
Edition. McGraw Hill. ISBN: 0070658986
* DeMers, Michael N. (2005): Fundamentals of Geographic Information Systems. 3rd
Edition. Wiley. ISBN: 9814126195
* Mitas, L., Mitasova, H. (1999). Spatial Interpolation. In: P.Longley, M.F.
Goodchild, D.J. Maguire, D.W.Rhind (Eds.), Geographical Information Systems:
Principles, Techniques, Management and Applications, Wiley.
**Websites**:
* https://en.wikipedia.org/wiki/Interpolation
* https://en.wikipedia.org/wiki/Delaunay_triangulation
* https://www.agt.bme.hu/public_e/funcint/funcint.html
The QGIS User Guide also has more detailed information on interpolation tools
provided in QGIS.
What's next?
============
This is the final worksheet in this series. We encourage you to explore QGIS and
use the accompanying QGIS manual to discover all the other things you can do with
GIS software!
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