In his doctoral thesis
titled Spatial Analysis in Support of Physical Planning (2003), Eric Koomen emphasizes the important role of
spatial analysis in the formulation and evaluation of physical planning
initiatives. To carry out this role, one must be knowledgeable about the
application of different approaches in spatial analysis in such a way that it
will correspond or will respond to the planning issues at hand.
Koomen enumerates
these spatial analysis approaches as follows:
1.
Transformation
Transformation
methods form the basis of data visualisation and essentially change a certain
form of data representation into another to enhance specific features.
Two commonly applied transformation methods are:
classification and filtering.
Classification is
used to diminish the variability in data values and can emphasize a certain
portion of a spatial data set. By adjusting the classification in a visual
representation of a data set specific phenomena can be enhanced or obscured,
indicating that the selection of the appropriate class boundaries is crucial.
Filtering changes the
value at each location in a data set based on the original values at that
location and its surroundings.
2. Aggregation
Spatial
aggregation methods reduce the individual values of a data set to a single
value for a specified region or the whole study area. The latter aggregation
reduced the number of spatial dimension of the data set from 2 to 0, creating a
non-spatial indicator or index value. This loss of spatial information is
compensated by the delivery of a clear, unequivocal summary of the original
content. Aggregation can also be performed at a regional level, producing a new
much coarser representation of the original data. Spatial aggregation methods
either deliver spatial or non-spatial indicator values depending on the use of
the spatial character of the original data. Aggregations based on general
averages or total values are non-spatial as these are independent of the
original spatial configuration of the data. The average size of certain types
of interconnected areas (average size of all urban areas) is considered a
spatial indicator value as this depends on how the urban areas are connected.
3. Combination
Combination
of different spatial data layers is one of the key functions of GIS and it
offers a powerful tool to provide an overview on many different data sets in
one new integrated representation. By overlaying different data layers it is
also possible to create a new data layer instead of merely visualising a
result. The overlay operation is thus a typical spatial analysis operation
available in any proper GIS. A classic example of this type of analysis is to
define the area of overlap of two or more separate data layers indicating, for
example, the area where new developments are not permitted following a large
set of zoning regulations. Overlays are well suited to compare several data
layers in a structured manner. Basically three different comparison options can
be distinguished (Muehrcke, 1973):
1.
a data set with another data set that represents the truth as is common in, for
example, validation exercises;
2.
a data set with another data set, for example, to compare the development over
time of a specific phenomenon or to study spatial patterns of related spatial
phenomena;
3.
a data set with a theoretical data set, to test assumed relations.
4.
Valuation
Valuation
is an appropriate tool to help interpret the results of spatial analysis
operations. By applying a normative and consequently subjective classification
operation to analysis outcomes their value is better understood. In essence,
this is not a spatial analysis method since it, generally, only applies to
non-spatial valuation functions. The main aim of valuation is to make the
content of related data sets comparable. It is a common tool in environmental
impact assessments and decision support systems that aim to provide clear,
easily interpreted outcomes to policy makers and stakeholders. Simple valuation
exercises result in a limited number of categories distinguishing, for example,
positive, negative or neutral outcomes in relation to a reference value.
Monetary valuation that is common in, for example, cost benefit analyses is an
example of a more elaborate valuation method.
5.
Proximity
analysis
A
classic type of GIS-assisted analysis deals with the assessment of distance, normally
expressed as proximity. Buffer analyses that create zones of influence (e.g.
noise contours around roads) surrounding different types of shapes are typical
examples of proximity analysis. Plain distance maps that describe the Euclidian
or other type of distance to a specified object (e.g. railroad, city centre)
offer useful input to various forms of spatial statistical analysis that, for
example, aim to explain specific spatial phenomena.
6.
Simulation
By
describing the relevant relations of a system it is possible to simulate its
future state. A common form of simulation (or modelling) is applied in impact
assessments that describe the possible consequences of a specific event or
policy. Such assessments follow predefined cause-effect relations that are made
operational by one or more of the spatial data analysis methods described
before. More complex examples of simulation are offered by the models that
simulate, for example, the groundwater or land-use system.
In
addition to these Koomen’s approaches is the Hot Spots Analysis as described
below.
7. Hot
Spots Analysis
This tool identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots). It automatically aggregates incident data, identifies an appropriate scale of analysis, and corrects for both multiple testing and spatial dependence. This tool interrogates your data in order to determine settings that will produce optimal hot spot analysis results. If you want full control over these settings, use the Hot Spot Analysis tool instead. (http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/optimized-hot-spot-analysis.)
Source:
Koomen,
Eric Spatial Analysis in Support of Physical Planning (2003)
