Stage of investigation
Describe
Explain
Justify
Evaluate
Identify a suitable geographical question or hypothesis for investigation.
Question:
Which town is the most culturally diverse, Huntingdon or St Neots?
Suitable scale:
Huntingdon was in walking distance. St Neots work was online
Readily researched:
Easy to collect data – shops/restaurants
Clearly defined:
Clear location
Comparison between two towns clear
Clear geographical nature:
– spatial variation
– cultural diversity
– links to migration
– urban structure
Based upon geographical theories:
Similar services cluster together
Clear aim:
to measure various points along the course of the river and see whether discharge increases downstream
- The question has clearly defined terms and sets out the parameters of the investigation
- Problems with defining ‘cultural’, potentially being too broad to focus
- How do you define ‘cultural’
Develop a plan and strategy for conducting the investigation.
Sampling
Linear sample – through the town centre
Systematic sample – every building
Primary data
Record the land use
Secondary data:
Use Goggle Earth street view to record the function of each building along the St Neots High Street transect.
Use Office for National Statistics (ONS) website to find ethic origin of people in Huntingdon and St Neots
Equipment
Huntingdon High Street base map
Clipboard and pen
iPhone
Risk Assessment
- Road crossings
- Conflict with public
Sampling
Provides a sample of commercial buildings in the centre of the two towns, enabling to see the percentage that are ‘culturally diverse’
Primary Data
On a base map, mark on the commercial function of each building along the Huntingdon High Street transect. Use a code (i.e. FR = foreign restaurant)
Secondary Data
Worked along St Neots High Street using the app on iPhone, recording onto paper the commercial function of the buildings.
Ethnic origin will help support the question; the higher the percentage and variety of ethnic origin, the more ethnically divers
Equipment
Base map, clipboard and pen make it simple to collect primary data.
iPhone and websites enable us to access ‘big data’
Risk Assessment
- Crossing roads presents a problem; getting run over!
- Public can potentially present problems
Sampling
Taking a sample of the buildings in the CBD will give a clear view of the cultural diversity of the commercial functions in the two towns.
Primary data
Method is a straightforward way of collecting the required data. No technical equipment needed.
Simple to see what the function of a commercial building is.
Secondary data
Google Earth is easy to use; zooming in makes it clear what the commercial building is used for.
Easy to obtain data and will look beyond the commercial services. This will enhance the conclusion, potentially making it more valid
Equipment
Primary collection is easy to record and up-to-date
Collecting secondary census data will help support primary data to make conclusion more valid
Risk Assessment
- Only use pedestrian crossings
- Stay in groups and carry a mobile phone
Sampling
WWW
Enabled a large amount of commercial buildings to be sampled in a relatively short timescale.
EBI
Needed to include commercial services beyond the high streets, often where culturally diverse services might be found.
Primary data
WWW
Easy to collect data, which was spatial (showed the location of the different services)
EBI
Looked at more than just the High Street, since many ‘foreign’ restaurants/shops may not be found on the High Street
Secondary data
WWW
Easy to use app.
ONS data is reliable (based on census)
EBI
Secondary data may be out of date (last census 2011)
Equipment
WWW
All equipment was easy to use. Deciding on cultural heritage of a commercial service was easy.
EBI
Secondary data was out of date
Risk Assessment
All risks were identified
Collect and record data appropriate to the geographical question or hypothesis.
Primary
Huntingdon land use
Walk along the High Street recording land use
Secondary
St Neots
Use Google Earth Street View to record land use
Office of National Statistics census data
Get the ethnic origin for the two towns.
Primary
Huntingdon land use
Record the land use using the following criteria:
UK shop
Foreign shop (inc. origin) using a key
UK restaurant
Foreign restaurant (inc. origin)
Mark on to a base map
Secondary
St Neots
Use Google Street View to repeat the Huntingdon procedure
Office of National Statistics census data
Use the internet to search for the ethnic diversity of the two towns. Huntingdon had three ‘wards’, whilst St Neots had four ‘wards’.
Primary
Huntingdon land use
This will help us find out the percentage of shops/restaurants that are from a different country/culture. It is also beneficial to record the origin to see the diversity of different cultures.
Secondary
St Neots
Relatively easy to use.
Enabled us to get data from ‘distant’ place
Office of National Statistics census data
Will give a broader picture of the people who live in the two towns, enabling us to get a more accurate view of the cultural diversity
Primary
Huntingdon land use
WWW
Easy method to record building use
Shops/restaurants were clearly ‘labelled’ with the origin
Secondary
St Neots
WWW
Quick to record
Easy to see different buildings
EBI
The size of the transect was much larger in St Neots
Some buildings in St Neots may have been missed.
The Google Earth data was out of date and there was no way of seeing when the data was recorded
Office of National Statistics census data
WWW
Gave a lot of data, giving an overall view of the two towns.
Data is reliable (government collected)
EBI
Data was from the last census (2011), so was out of date.
Present the data collected in appropriate forms.
Land Use
Proportional pie charts to show the land use differences between the two towns
Census data
Divided bar chart to show the census data
Land Use
Work out the total number of shops/restaurants, then take the square root of the value. This figure was the radius (cm) of the proportional circle.
A pie chart was then drawn in the circle to show:
UK shop
UK restaurant
Foreign shop
Foreign restaurant
The foreign shop and restaurant was divided into the different countries of origin.
Census data
Divided bar chart to show the census data was draw 100mm long (1mm = 1%). The divided bar chart was then divided into the different ethnic groups.
Land Use
The proportional circle was drawn to highlight there were more buildings recorded in St Neots. The pie chart was a very visual way of comparing the amount and diversity of foreign shops/restaurant between the two towns
Census data
Visual way of showing the % of ethnic backgrounds and diversity of the two towns.
Land Use
WWW
Easy to compare the two towns
EBI
Difficult to get enough colours to represent different ethnic groups for the pie chart
Census data
WWW
Very visual, not only shows the total % from different ethnic backgrounds, but also the diversity (different places)
EBI
Some ethnic backgrounds had very small %, so were not represented clearly on the divided bar chart (had to be classified as ‘others’). This prevented us from showing the true diversity of ethnic origins in the two towns.
Analyse and interpret the data.
This includes both written and visual techniques which are geographical and statistical.
DATA PRESENTATION: see general pattern of discharge increasing downstream as choropleth gets darker further downstream, however not in perfect order
GRAPH: scatter graph hand drawn, shows positive correlation between distance from source and discharge, line of best fit used to identify trend in data & find anomalies, compared against Bradshaw’s Model – shows it follows it generally with a few anomalies, potentially at sites 6, 8 and 9
STATISTICAL TEST: used Spearman Rank Correlation Coefficient and got a result of 0.9 (strong positive correlation), scored 99% on significance test so is a valid result
INTERPRETATION/EXPLANATION:
-CSA gets bigger downstream due to tributaries joining, adding more water – leads to lateral erosion due to increased hydraulic action
-depth doesn’t change much as rocks get smaller so can’t abrade & vertically erode as much
-less friction downstream so river is faster, combined with bigger CSA = higher discharge
Reasons for anomalies:
- Bridges at some sites
- Straightening & dredging of river, meaning river flows faster
- Drainage ditches from farmers’ fields
Why scatter graph?
Gave clear visual representation whilst incorporating numerical values – more useful in drawing conclusions
Why Spearman’s rank?
Gave quantifiable values as to correlation of 0.9 – strong positive correlation. This enabled me to statistically accept my hypothesis to be true
Scatter graph showed data clearly, however it was harder to find an exact line of best fit due to some extreme anomalies.
Spearman’s rank gave clear result.
Present a summary of the findings and an evaluation of the investigation.
CONCLUSION:
The discharge of the river does increase with distance from the source as a general pattern; however there are a few anomalies. After performing a statistical test – the Spearman Rank Correlation Coefficient – it was made clear that the correlation between the discharge of the river and the distance from the source had a strong positive correlation of 0.9 (which scored 99% on the significance test, so is a valid result). The anomaly at site 8 is most probably due to straightening of the channel and dredging of the river which have taken place there, meaning that the river will have been flowing faster than usual here. The anomaly at site 6 is due to the fact that we took our readings before a bridge, which slows the river down slightly. Despite the anomalies, the general trend of my results follows the trend expected from Bradshaw’s Model. If the investigation was to be repeated, I would use more precise equipment and a systematic sampling of sites to reduce the potential of changes in the river being missed. I would prefer to use a stream flow meter, as using dog biscuits to measure velocity was a very limited method.
Suitable question:
Yes, appropriate scale & defined parameters of investigation
Plan and strategy:
Clear plan that was successful & coped with any issues faced on the day to ensure all results were collected
Record data:
Data clearly recorded & enabled clear synthesis of results – meant accurate and valid conclusions could be drawn
Data presentation:
Using location and colour to visualise trends in the river
Analyse and interpret data:
Data analysis was very useful and clearly proved the hypothesis through the graph and statistical test
Summary and evaluation:
Summary gives clear numerical answer to question posed
- Conclusions from this investigation use a reliable method to collect data
- Tried where possible to reduce risk of human/systematic error – could put at risk accuracy of data recorded through poor method
- However overall collection was accurate & helped to form valid conclusions working with numerical & visual analysis and presentation of data
Timing:
Timings worked successfully – plenty of time at each site
Place:
Locations of sites worked well for investigation
Physical:
Some issues in accessing river due to steep banks, but with ranging poles it was safe and access was possible
Human Error:
Potentially occurred with different groups taking some site readings due to size of sites but still followed method to hopefully avoid any human error
Equipment and Methodology:
Both worked well to gather required results, however would’ve preferred to use stream flow meter to measure velocity in more accurate way