Blog 15 – Map Outputs Part 2
The following map shows how the road network of Dorchester is constructed calling upon data from
the ‘Road Classification’ attributes. Both the A and B roads are considered ‘Main Roads’ and are
represented with a thicker line to represent their relative size. The presence of the minor roads is
also shown here, using a thinner blue line.
Blog 16 – Location Location Location “Search_1”
To find settlements which satisfy the requirements of the couple we can input a query in ‘Select by
Location/Attributes’.
By using the query:
"Services.PUBLIC_HOU" >=1 AND "Services.POST_OFFIC" >=1
we are able to view settlements which only adhere to the conditions provided. Below (Fig 1) is a map
representing the settlements in the Dorchester area which would be suitable for the couple to
consider.
Figure 1 not only uses the map layer created using the query but also contains the original
settlements layer. This allows us to see that there are four settlements which would be unsuitable.
Furthermore, I have added the flood risk layer to because the couple will be moving house and it
may therefore interest them to observe areas which are at risk to flooding. There is a noticeably
large area of flood risk in the northern areas of Dorchester and in the settlement of Charminster.
This may or may not affect the decision of the couple, but if I were suggesting a home for them I
would try to avoid these areas.
I have also included the main roads layer. Transport is a very important aspect when considering
moving home and links to Dorchester itself are helpful especially when running a business. Out of
the 3 settlements (outside of Dorchester itself) only Charminster and Broadmyne have main road
connections to Dorchester itself.
The couple do not mention having any children, but this may be a future development, so I also
added a schools layer so that they could become aware of locals schools and the transport links
available to them.
Blog 17 – Location Location Location “Search_2”
For this search I used the query:
"Services.PUBLIC_HOU" >=1 AND Settlements.Population <500 AND "Services.RECREATION" = 'Yes'
to see which areas in the study area would be suitable for the couple to move into, with the
following results.
The village highlighted in green is West Knighton, which meets all the criteria that the couple put
forward. The population is 415, which is under the 500 mark set by the wife, it has one pub and also
has available recreation ground. In the map above I have also included the main road network to
show, that despite not being connected to Dorchester by a Main Road West Knighton is close a
direct route into Dorchester. Furthermore, the village is out of a flood risk area which will allow for
lower home insurance for the couple.
Blog 18 – Location, Location, Location “Search_3”
The last ‘Location, Location, Location’ query I constructed used more complex methods to produce a
map showing suitable properties. To do so I had to:
1. Within ‘select by location’ I had to select features from the Listed Buildings layer that were
‘completely within’ the settlements layers.
2. Then in ‘select by attributes’ I had to ‘create a new selection’ and input the settlements layer
and input “Services.PUBLIC.HOU”>=1 to eliminate settlements which did not contain a pub.
3. Lastly I had to change ‘create new selection’ to ‘select from current location’ and change the
input layer to ‘Listed Buildings’ then enter the query "GRADE" = 'II' AND "NEAR_DIST"<500 to
get the buildings which were grade II listed and 500m away from a main road.
These buildings are shown using red diamonds below.
Blog 19 – Visualising Population Data for “Settlements”
The following, in a screen shot of data related to population in settlements. It shows 3 important
piece of information. First, is a graph showing the populations of all of the settlements in a bar
format, which is layered on top of the settlements layer, and the attribute table is also visible.
Drawbacks of using screen shots is that it is difficult to get all of the information to be shown clearly,
for example the settlements layer below is slightly difficult to see.
Figure 1 - Screen Shot of Population Data in Settlements
Blog 20 – Distance Analysis in ArcGIS
The following maps will make up an interim report for a forthcoming meeting with Dorset County
Council; the first four figures are used to make up a ‘tranquillity assessment’ relating to the minor
road network in Dorset. The fifth will use data regarding disturbance in relation to the listed
buildings within the area of study.
The tranquillity assessment will call upon information I have gathered in relation to distance analysis.
These four maps employ the same symbology as the work I have previously carried out on when
analysing distance in relation to main roads. Each one offers a different interpretation of the effect
of Minor Roads in the area which are invaluable to our tranquillity assessment. The maps (Fig1-4)
can be used in relation to other disturbance maps to give a wider understanding of disturbance
levels in the Dorchester area.
This Executive Summary section portrays the listed buildings which are at risk to all disturbances,
shown in Figure 5 below. The distribution of these buildings is mainly focused within the town of
Dorchester which is to be expected due to the network of minor roads which are central to the
town. A map of Dorchester is used to show that the majority of the other listed buildings affected
are along roads which connect smaller towns and villages to Dorchester itself. This is a good tool for
analysis, but it could be argued that a rather vague picture of disturbance is painted because there is
nothing to differentiate a building which is subject to great disturbance and those which are subject
to relatively negligible issues. To resolve this we could find a way to measure the individual
disturbance for all the buildings and split them into a rank.
Blog 21 – Density Surface Modelling
The following maps are used to aid with the analysis of the point density of listed buildings within
our study area. This is proceeded with a conclusive statement on why this type of mapping is used,
patterns within the maps and limitations which may be considered.
The above are known as Isopleth maps, which are thought to be a more detailed form of the more
the commonly known choropleth maps. They use ‘neighbourhoods’ to define the search radius for
each cell which produces the maps shown above. There is a clear observable pattern to be observed
in the 4 maps shown above which present the number of listed buildings in an area through a roving
window. As the ‘window’ increases (from 100m-1000m) the number of listed buildings within it also
increases which are represented by the greater area shaded.
The criticisms of Isopleth maps is that the search radius used has to be correctly chosen to produce
an effective output, for this reason I think that the maps using 100m and 1000m are too small and
too large respectively. The map which uses a search radius distance of 500m provides the right
about of detail to be able to analyse the point density of listed buildings in the Dorchester area.
However, the wide range of data in the different distances meant that I had to use different class
values for each distance. This may hinder the validity of the analysis which we carry out; figure 5
below shows by using different classes there is a different output.
Blog 22- Vector Overlay in ArcGIS
The following blog entry will again look at noise disturbance in relation to the road networks of
Dorchester. Furthermore, using sieve mapping I will look at development in the study area and
where projects can be executed whilst also taking into consideration other issues which may affect
the ability to develop.
Figure 1 (above) shows the buildings which receive disturbance from the minor road network in
Dorchester. The buildings in the town of Dorchester itself all receive disturbance from the minor
roads, which is comparative to that of the major road network which has little effect on the buildings
in the heart of Dorchester. This is useful in our analysis of disturbance but we must take into account
that not all minor roads are the same and have the different effects of the buildings running
congruent to them.
To create the Sieve Map we first had to merge all of the buffer layers which we had manufactured to
create and exclusion zone. By erasing this layer from the inclusion zone which we had previously
synthesised we are able to generate the suitable sites for development, which we then ‘sieve’ so it
only encompasses the study area. The map clearly favours the development site on the top left
corner, because the majority of the layer falls within the development site. The site in the bottom
right corner is situated within 1000 metres of a school and was therefore placed in an exclusion zone
unsuitable for development. If this was reduced to 500 metres then the development area could be
considered.
If I were to suggest another area where development could be undertaken it would be at the point
marked on the map because there are no major issues with doing so. It does not come into contact
with the roads, settlements or flood risk and is not particularly close to any settlement.
Blog 23 – Comparing Surface Interpolation Methods
The two figures above show similar data sets, which are presented in a similar way through Spline
and Inverse Distance Weighted (IDW) but there are differences which can be detected. It is
‘commonly known’ in the GIS mapping world that there is ‘no single method’ (Anderson N.D) for
data interpolation.
Where the Spline method is similar to a ‘rubber sheeted surface through known points’ (Anderson,
N.D), IDW uses the assumption that nearby values contribute to the interpolated values more than
distant observations. What end up with are maps which have key differences:
1. The Spline data produces a more detailed map because it is able to construct higher quality
from only a small number of sample points; however it does use different maximum and
minimum values which can be a problem of accuracy.
2. IDW produces a smoother surface which is noticeable from the map above in Figure 2. This
is due to the fact that, unlike Spline interpolation, sample points retain their original value.
3. The IDW can sometimes be subject to ‘bull’s-eyes’ due to ‘abrupt changes in values’ (GIS
Tutor, 2011) which can be seen on numerous occasions in Figure 2.
It is important to note that other methods of interpolation are available such a Kring Interpolation
that could possibly looked into before submitting our final decision.
Blog 24 – Spatial Analysis to Identify Potential Solar Farm Sites
Figure 1 (shown below) shows all of the sites which I believe would be suitable for the development
of a solar farm. These areas do not intersect with Areas of Natural Beauty, Roads, Settlements or
Flood Risk areas. I chose to avoid these areas for relatively obvious reasons namely damage to the
environment and potential damage to the solar farm itself.
To add further detail this search I would have used other criteria that may be needed to narrow my
search further:
1. Woodland
2. Listed Buildings
3. Railways
However, if I add too many fields of exclusion I may be left with a map where there are no suitable
sites so it is important to prioritise the layers.
Blog Entry 25 – Noise Impact and Assessment Modelling
The map output below is used to predict the noise level associated with the wind turbine proposal.
Using data from the British Wind Energy Association (BWEA) I will be able to analyse the effect that
these noise levels will have on the surrounding area.
To create this map I had to first create a distance map similar to ‘Main_Roads_Dist’ which we used
when constructing similar analysis with main roads. Then using the raster calculator I was able to
input the query:
85+20*(Log10(1/Turbine_Dist)
which created the map above showing the noise disturbance which the turbines will create.
My findings:
The map above, to me, communicates that the noise disturbance to the surrounding areas is
minimal. The British Wind Association suggest that noise levels of over 45 dB(A) cause significant
disturbance which only occurs at a small area directly around the turbine base. As the distance from
the turbine doubles (e.g. 1,2,4,8) the noises levels decrease by 6dB(A) which is a rather considerable
amount. This is why there is also only a small area which is subject to ‘medium disturbance’ at 40.145 dB(A) and the rest of the area is only subject to minimal noise.
To enhance this study further we could change the symbology of the data by putting more classes in
so that we have a more detailed understanding of the distribution of noise disturbance. This would
also allow us to note areas which receive no noise disturbance at all, rather than grouping all areas
which receive less than 40 dB(A).
Entry 26 – Viewshed Analysis: Zone of Visual Impact Modelling
The following two maps use view-shed analysis to decipher which areas within the study area would
be in view of the wind turbines. The turbines alone are 25m high which is what Figure 1 presents,
however the blades add another 10m, Figure 2 evaluates the effect that this increase has in the
viewing of the turbines.
Summary of Findings:
The extra 10 metres which the blades add has a noticeable contribution to the visibility of the wind
turbines. The count of the area which can view the turbines with the blades is 676377 compared to
a count of 542723 without them. This shows a considerable increase in visual disturbance which will
have to be taken into account when deciding the location of the wind turbines.
This view-shed analysis is a very useful source to evaluate the visual disturbance of the proposed
wind turbine developments. The drawbacks which may be considered include the idea put forward
by Kim (et al, 2004) that this analysis is unable to identify an optimal set of viewpoints. Furthermore,
this type of analysis does not take into account the curvature of the Earth which over a large
distance is actually a factor which I have to take into account. If this had been taken into
consideration we may see that the turbines are visible to fewer locations in the Dorchester area.
Entry 27 – 3D Visualisation: Virtual Dorchester
Figure 1 - 2D Model of Dorchester
Figure 2 - Close up of Solar Farm and Wind Turbines
Using ArcCreate I was able to create a 3D model of the Dorchester area which uses the layers I had
previously created in ArcMap. This model includes 3 new 3D layers: Plantation, Solar Farm and Wind
Turbines which have not been seen in my previous work. This model combines a lot of work which I
have done and visually is particularly pleasing but also puts into context the analysis which I have
done on the location of the Solar Farm and the Wind Turbines.
References
Anderson, Sharolyn. (N.D). An Evaluation of Spatial Interpolation Methods.Available:
http://www.cobblestoneconcepts.com/ucgis2summer/anderson/anderson.htm. Last
accessed 2/12/13.
GIS Tutor. (2011). Inverse Distance Weighting (IDW) Interpolation using GIS. Available:
http://www.gistutor.com/quantum-gis/20-intermediate-quantum-gis-tutorials/51inverse-distance-weighting-idw-interpolation-using-qgis.html. Last accessed 4/12/13.
Young-Hoon K, Ranab S and Wisea S . (2004). Exploring multiple viewshed analysis
using terrain features and optimisation techniques.Computers & Geosciences. 30 (9-10),
1019-1032.
Solar Trade. (2013). Solar Farms. Available: http://www.solartrade.org.uk/solarFarms.cfm. Last accessed 2/12/13.