Transcript Analysing data in ArcGIS

RGS-IBG Online CPD course in GIS
Analysing Data in ArcGIS
Session 6
In this exercise you will become familiar with
some of the functions in ArcMap that help you
analyse data, in particular classifying, querying
and presenting data. The objectives of this
session are to understand the analytical
concepts above, which you will find in most GIS
software.
Objectives
• Learn how to classify data
• Learn how to query data
• Learn how to present data
• Become more familiar with exploring data in
ArcMap
Exercise
In this short exercise you will be creating a map of the spatial
distribution of deprivation across London. You will then
concentrate on the London Borough of Camden and re-create
the map you made using Neighbourhood Statistic’s mapping
tool. Further datasets will be used to gain an understanding of
how they can be used to solve problems. Questions such as
how many schools are in deprived areas and how many
schools are within distance of a location will be addressed.
The data you will use are local authority areas, lower super
output areas and the Index of Multiple Deprivation 2007. A
list of point locations will be added as well as some
background mapping.
1. Open ArcMap and create a new map.
2. Add the LonLSOAimd shapefile from your ‘6 Analysing
Data in ArcGIS Data’ folder. This is a boundary dataset of
Lower Super Output Areas in London integrated with the
Index of Multiple Deprivation for 2007.
3. Save the project in your GIS folder naming it as you wish.
(Continue to do this every now and again). However,
before you do so, go to File > Document Properties > Data
Source Options. Choose the ‘Store relative pathnames to
data sources’ option. Press ok, ok again, and then save.
This allows the computer to find the data when you open
the project even if the name of the drive changes. For this
to work you must keep all your data in the GIS folder.
Classifying Data
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You have just seen how to classify data in order to bring out spatial
patterns. Double click LonLSOAimd and choose the Symbology tab.
Select Quantities > Graduated Colours. In the Fields Value section choose
the overall IMD07 indicator. Change Classification Method to Quantile
and stay with 5 classes. Press ok. Change the colour ramp to yellow (for
lowest values) – brown (for highest values). Press ok. You have already
seen functionality like this in the online mapping tools.
You have created a class map of deprivation in London. Each class
contains 20% of the super output areas. This allows you to look at for
example the top 20% of deprived areas. However, you will notice there is
a lot of grey obscurring the map, these are the super output area
boundaries. For a clearer map we will remove these. Double click
LonLSOAimd and go to Symbology. Click the word ‘Symbol’ (below
Colour Ramp) and then choose ‘Properties for all Symbols’. Change the
Outline Colour to No Colour, and press ok.
6.
Finally, add the LonLA shapefile (this is a local authority boundary set). Using the
layer’s symbology change the Fill Colour to No Colour. Because local authorities
are bigger than SOAs they present a clearer map whilst adding some spatial
reference. You should be left with a map something like below, notice the spatial
pattern, i.e. where are the majority of darker (more deprived) areas?
Select Data by Attributes
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Querying data allows you to highlight, or bring out those features that you are
interested in. We will a selection function to look at an area more closely. Go to
Selection from the top menu and choose Select by Attributes. As in the video, make
sure the Layer you are querying is ‘LonLSOAimd’ and that Method is ‘Create New
Selection’. In the next box scroll down to LA_Name, double click this so it appears in
the bottom box. Press ‘Like’ so it also appears in the box. Finally, press Get Unique
Values, once they have appeared find Camden and add it to the box below. Press ok.
In the Table of Contents right click on LonLSOAimd, go to Selection > Zoom to
Selected Features.
Again, right click LonLSOAimd, go to Selection > Create Layer from Selected Features.
Once ‘LonLSOAimd Selection’ has been added change its name to Camden LSOA. Go
to ‘Selection’ from the top menu bar and press ‘Clear Selected Features’.
You will now give the new layer the same classification as London overall. Right click
Camden LSOA and press Properties. Under Symbology press the Import option. Make
sure the ‘Import Symbology from another layer in the map’ option is selected. Change
Layer to ‘LonLSOAimd’ , press ok. The Value field should be IMD07. Press ok.
Because we have zoomed in to Camden we can put the LSOA boundaries back on.
Press Symbol > Properties for all Symbols. Change the Outline Colour to a medium
grey. Press ok.
12. Press the Display tab at the bottom of the Table of
Contents and move London Boroughs to the top. Turn the
LonLSOAimd layer off. You should now have a clear map of
deprivation in Camden by LSOA. Press save.
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You will now integrate another dataset, this will be used later to query the data
spatially. Add the Schools layer from your GIS folder. This is a point layer of
school locations.
Classify the schools based upon their phase of education (i.e. primary or
secondary) using the technique you learned earlier, however this time classify by
category rather than quantity. Choose Unique Values from the Categories
option. The Value field should be ‘Phase’. Press ‘Add All Values’. Choose two
clear and distinct symbols for each type of school by double clicking on each
existing symbol.
You now have a clear map
showing schools by type
and areas by deprivation
within the borough of
Camden.
16. Next we will see how many schools in Camden are in, or are near
to one of London’s top 20% most deprived LSOAs.
17. Go to Selection from the top menu and choose ‘Select by
Attributes’. The top 20% most deprived LSOAs have a score
greater than 38.7 (look at the LonLSOAimd layer’s classification,
remember you also used this to present Camden). Create a new
selection from Camden LSOA where IMD07 is greater than 38.7.
First you may need to remove any old queries from the ‘Select *
From’ box, simply highlight the old query and delete it. Once you
have created the new query press ok.
18. Now watch the following video about Selecting by Location. Using
both select by attributes combined with select by location allows
for a versatile set of queries.
Select Data by Location
19. Open the Schools table of attributes, how many schools
have been selected (look at the bottom of the table, it states
how many records have been selected)? What proportion of
all schools in Camden are within or are of close proximity to
one of London’s top 20% most deprived areas?
20. Save the project in your GIS folder as CamdenIMD.mxd
21. Use the techniques in this exercise to see if there is a
difference between all, secondary and primary schools. Tip:
there is no one correct method, experiment with the
selection criteria.
22. Finally, experiment with data classification. Notice how
different methods of classification and different numbers of
classes alter the map.
Summary
You should now know;
• How to classify data
• How to query data
• Begin to understand some of the analytical
techniques used in GIS
Further Exercise
You are now able to classify data. You have also seen that different techniques
produce different maps. Experiment by mapping different IMD scores (look in
the table of attributes), use different classification methods as well as varying
the number of classes. What are the impacts of different representations of the
same data?