Presentation and Interpretation
Aim is to make clear presentation of the data you have collected. Presentation should be graphic so that patterns and changes in the data collected can be understood as spatial changes (i.e. from the centre of the study area to its edge).
Each method of data presentation must show your data in such a way that each of your hypotheses can be tested.
Additionally, you need to demonstrate to the examiners that you are able to select, employ accurately and appropriately, a range of data presentation techniques. At least one of these should use iCt and two should be more complex in nature.
Overall, six methods of presentation are required, but this can include maps and diagrams used in other sections of the report.
Preparation for presenting your data:
1. Be clear in your own mind about what your hypotheses are and which pieces of data will be used to test each of them. If you have four hypotheses, you should have at least four sets of data and each will need to be presented in graphs or maps to show how the values change along the sampled transects or as distance from the centre of the study area increases.
2. For most hypotheses, it will be most appropriate to combine the data from each of the transects surveyed by averaging the values for each sample point or segment (sample point or segment will depend on the data being collected).
3. To achieve 2 above, your data sets will need to be processed. In some cases this will mean that you have to calculate the average (arithmetic mean) value for the sample points, for others you may need to work out the mode or process the data further to obtain a value that can be graphed.
You are advised to approach the graphing work and data processing for each hypothesis one at a time. Once a suitable presentation has been completed, write a brief description of what patterns are revealed, suggest reasons for this (linked to the theory) and draw a conclusion (i.e. does it conform to expectation or not, does it prove or disprove the hypothesis?).
4. As you progress through the presentation and interpretation of your various data, you should begin to spot whether or not different data appear to show the same patterns to some degree. If you can see this, it is worth making a note of the fact in your interpretive writing. When one piece of data tells the same "story" as another, this is called corroboration. It is a good finding as it increases the likelihood that your findings are accurate/true.
In this example, data for PEDESTRIAN FLOWS is being used.
The associated hypothesis is that:
This hypothesis is based on the expectation that more people will be at the centre (expected CBD) because this area attracts people to both work and to take advantage of the services available... it being a COMMERCIAL CENTRE. Linked to this is the idea that the CBD would be the most accessible place in the area since it is likely to be a TRANSPORT NODE.
The table below shows that the pedestrian data has been averaged so that a single (average) figure is available for each sample point (this equates to distance from the centre/start point for the survey). The average figures are shown in purple in the table.
Having averaged the data, it can then be plotted onto a single graph as shown right.
A line graph is employed as the data is viewed as continuous. A line also shows very clearly the declining numbers of people as the surveys extended toward the edge of the study area.
The approach to the interpretation of the graph is outlined below... notice that there are two elements... describing what is shown (patterns, rates, changes etc and suggesting why these features might be present.
In the following narrative... a possible interpretation of the above graph is provided to model the approach...
The line graph shows the average number of pedestrians recorded at sample points from the centre of the study area to its outskirts. Data from all the surveyed transects have been combined and averaged to allow a single graph to be drawn to show the data.
It is clear that the number of people recorded declines as distance from the centre increases. This negative correlation between pedestrians and distance conforms to expectations and the hypothesis appears to be proved.
The pattern, however, is more complex and perhaps suggests a transition between CBD and the next land use zone. Between sample points (sp) 1 and 8, the number of pedestrians is at its highest (over 15). From about sp 8, the number drops quite dramatically to ten or less. It is at its lowest at the furthest sp, number 21.
These changes seem to indicate that the attractions for people found at the centre (work and services) may no longer be present from around sp 8 onward. This would fit with the idea of land use zoning and the concentration of commercial activity at a CBD, as suggested by Burgess, which attracts significantly greater numbers of visitors to this area.
There does, therefore, appear to be a transition from CBD to another land use zone at around sp 8 (on average).
NOTICE:
The description of the graph begins with the obvious
pattern, delves into more detailed patterns and relationships and provides evidence (from the graph) to support the statements.
The explanation references back to the theoretical framework.
As well as being graphed, data can be mapped. In the following example, a similar process is applied to that above...
The map left shows a street map of the study area and its surroundings. Notice that a title, direction arrow, scale and key for the colour-coding have been included... although these are not very clear in this copy.
The information on the map shows the MODAL land use recorded along each transect.
Purple - Commercial
Red - Residential
Brown - Public Buildings
Yellow - Open Space
The table below, derived from the raw data table, shows the modal land use for each segment of each transect. Transects are labeled A, B, C... etc The key to the land use codes is given right.
The modal land uses are simply plotted (to scale) onto the street map following the transects surveyed. Whole segments are coloured in and provide quite a clear impression of the changes in land use... When writing the interpretation you should once again describe the obvious (i.e. concentration of commercial activity toward the centre - give the distances along the transects) and transition into residential at varying distances along each transect etc. The explanation for the patterns will should then reference back to the expectations and the theory...
Deviation from the expected pattern is related to the fact that, firstly, the CBD seems to transition into a residential zone, rather than old industrial as suggested by Burgess. This is worthy of explanation. Secondly, the land use zones are not actually circular as Burgess' model suggests... in fact, they appear to be distorted along the main route ways in and out of the centre...
The mapping of this PRIMARY data leaves gaps in the patterns of land use. It would be acceptable to use SECONDARY data sources to infill and thus create a more complete picture. To do this, you could employ Google maps, satellite view to observe the roofs of the buildings between the transects. The differences between commercial and residential are identifiable. Further differentiating features include the back gardens/yards attached to properties, the nature of the streets and the amount of trees.
Preparation for presenting your data:
1. Be clear in your own mind about what your hypotheses are and which pieces of data will be used to test each of them. If you have four hypotheses, you should have at least four sets of data and each will need to be presented in graphs or maps to show how the values change along the sampled transects or as distance from the centre of the study area increases.
2. For most hypotheses, it will be most appropriate to combine the data from each of the transects surveyed by averaging the values for each sample point or segment (sample point or segment will depend on the data being collected).
3. To achieve 2 above, your data sets will need to be processed. In some cases this will mean that you have to calculate the average (arithmetic mean) value for the sample points, for others you may need to work out the mode or process the data further to obtain a value that can be graphed.
You are advised to approach the graphing work and data processing for each hypothesis one at a time. Once a suitable presentation has been completed, write a brief description of what patterns are revealed, suggest reasons for this (linked to the theory) and draw a conclusion (i.e. does it conform to expectation or not, does it prove or disprove the hypothesis?).
4. As you progress through the presentation and interpretation of your various data, you should begin to spot whether or not different data appear to show the same patterns to some degree. If you can see this, it is worth making a note of the fact in your interpretive writing. When one piece of data tells the same "story" as another, this is called corroboration. It is a good finding as it increases the likelihood that your findings are accurate/true.
Check
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Tick
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Note
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At least six different methods employed
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Two methods are more complex
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Some methods include data handling eg calculating averages, percentages etc
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Methods give a clear impression of the expected changes from the centre to the outskirts
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Have you identified ways that different presentations appear to support each other?
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Can you identify common points or zones where the data changes and may indicate transition from CBD to another land use zone?
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Example of Presentation and Interpretation:
In this example, data for PEDESTRIAN FLOWS is being used.
The associated hypothesis is that:
"The number of pedestrians counted over a given amount of time will be greater nearer the centre of the study area and lower toward the periphery".
This hypothesis is based on the expectation that more people will be at the centre (expected CBD) because this area attracts people to both work and to take advantage of the services available... it being a COMMERCIAL CENTRE. Linked to this is the idea that the CBD would be the most accessible place in the area since it is likely to be a TRANSPORT NODE.
The table below shows that the pedestrian data has been averaged so that a single (average) figure is available for each sample point (this equates to distance from the centre/start point for the survey). The average figures are shown in purple in the table.
A line graph is employed as the data is viewed as continuous. A line also shows very clearly the declining numbers of people as the surveys extended toward the edge of the study area.
The approach to the interpretation of the graph is outlined below... notice that there are two elements... describing what is shown (patterns, rates, changes etc and suggesting why these features might be present.
It is clear that the number of people recorded declines as distance from the centre increases. This negative correlation between pedestrians and distance conforms to expectations and the hypothesis appears to be proved.
The pattern, however, is more complex and perhaps suggests a transition between CBD and the next land use zone. Between sample points (sp) 1 and 8, the number of pedestrians is at its highest (over 15). From about sp 8, the number drops quite dramatically to ten or less. It is at its lowest at the furthest sp, number 21.
These changes seem to indicate that the attractions for people found at the centre (work and services) may no longer be present from around sp 8 onward. This would fit with the idea of land use zoning and the concentration of commercial activity at a CBD, as suggested by Burgess, which attracts significantly greater numbers of visitors to this area.
There does, therefore, appear to be a transition from CBD to another land use zone at around sp 8 (on average).
NOTICE:
The description of the graph begins with the obvious
pattern, delves into more detailed patterns and relationships and provides evidence (from the graph) to support the statements.
The explanation references back to the theoretical framework.
As well as being graphed, data can be mapped. In the following example, a similar process is applied to that above...
The map left shows a street map of the study area and its surroundings. Notice that a title, direction arrow, scale and key for the colour-coding have been included... although these are not very clear in this copy.The information on the map shows the MODAL land use recorded along each transect.
Purple - Commercial
Red - Residential
Brown - Public Buildings
Yellow - Open Space
The table below, derived from the raw data table, shows the modal land use for each segment of each transect. Transects are labeled A, B, C... etc The key to the land use codes is given right.
The modal land uses are simply plotted (to scale) onto the street map following the transects surveyed. Whole segments are coloured in and provide quite a clear impression of the changes in land use... When writing the interpretation you should once again describe the obvious (i.e. concentration of commercial activity toward the centre - give the distances along the transects) and transition into residential at varying distances along each transect etc. The explanation for the patterns will should then reference back to the expectations and the theory...
Deviation from the expected pattern is related to the fact that, firstly, the CBD seems to transition into a residential zone, rather than old industrial as suggested by Burgess. This is worthy of explanation. Secondly, the land use zones are not actually circular as Burgess' model suggests... in fact, they appear to be distorted along the main route ways in and out of the centre...
The mapping of this PRIMARY data leaves gaps in the patterns of land use. It would be acceptable to use SECONDARY data sources to infill and thus create a more complete picture. To do this, you could employ Google maps, satellite view to observe the roofs of the buildings between the transects. The differences between commercial and residential are identifiable. Further differentiating features include the back gardens/yards attached to properties, the nature of the streets and the amount of trees.
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