RGS-IBG Online CPD course in GIS Analysing Data in ArcGIS

1. RGS-IBG Online CPD course in GISAnalysing Data in ArcGIS Session 6

2. 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.

3. Objectives • Learn how to classify data • Learn how to query data • Learn how to present data • Become more familiar with exploring data in ArcMap

4. 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.

5. Open ArcMap and create a new map. • Add the LonLSOAimdshapefile 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. • 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.

6. Classifying Data

7. 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.

8. Finally, add the LonLAshapefile (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?

9. Select Data by Attributes

10. 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.

11. 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.

12. 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.

13. Next we will see how many schools in Camden are in, or are near to one of London’s top 20% most deprived LSOAs. • 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. • 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.

14. Select Data by Location

15. 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? • Save the project in your GIS folder as CamdenIMD.mxd • 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. • Finally, experiment with data classification. Notice how different methods of classification and different numbers of classes alter the map.

16. 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?