geo paper 3

question, location and risk

question, location and risk

• physical enquiry

  • relationship between biotic and abiotic factors in an ecosystem

  • Epping Forest, Essex

    • very accessible and as its a protected area we got access to more useful equipment

  • risks

    • avoid trip hazards by wearing appropriate footwear

    • avoid stinging plants by wearing suitable clothes

    • mountain bikes - be vigilant

• human enquiry

  • is Porthmadog in need of regeneration?

  • Porthmadog, Wales

    • small town so we could easily collect a lot of data and was very close to our youth hostel

  • risks

    • don’t talk to strangers

    • stay in groups of 4 to avoid getting lost

    • wear appropriate clothing due to extreme weather

measuring, recording and collecting data

• physical enquiry

  • transects allowed us to collect multiple sets of data safely and minimised variables, making our collection more reliable

  • used a random number generator for coordinates to reduce bias

  • quadrants were used as they were fast and accurate

  • we placed the luxmeter the same distance off the ground to ensure accuracy

• human enquiry

  • RICEPOTS method during the land use survey allowed us to quickly categorise and note observations, and see if the town was providing for the residents needs

  • we categorised every building increasing accuracy and reliability

  • bipolar scale for the EQS allowed us to quantatively compare parts of the town

  • reduced subjectiveness by everyone doing their own and averaging it afterwards

data presentation

• physical enquiry

  • divided pie chart allows us to see the proportion of biodiversity in both areas and easily compare it, see what was dominant and be able to easily explain with my a/biotic findings; quadrat on y, amount on x

  • scatter graph allows us to identify correlation in the areas by a line of best fit between biodiversity and light intensity; both on one graph for easy comparison by using different colours; intensity on x, biodiversity on y

• human enquiry

  • radar graph allows us to see how environmentally friendly an area is (bigger circle is better); can easily see how different aspects contribute to the quality of different areas; plot the mean of each area e.g. the low or high traffic scores

  • open street mapping allows a visual representation of each part of the town and look for spatial patterns to see if regelation was effective; we could then colour code the areas and see if any parts needed more regeneration due to a lack of local amenities

describing, analysis and explaining

• physical enquiry

  • rushy plains has more biodiversity

    • more quadrants had more biodiversity, perhaps due to the diversity in tree species

  • more light intensity = lower biodiversity

    • when light intensity was halved, the majority of squares had moss, and some even contained grass; expected more biodiversity as more sunlight would mean less competition

  • more canopy cover, less light intensity

    • not unexpected however there were a lot of anomalies so this isn’t a definite conclusion

• human enquiry

  • land dominated by commercial activity saw more pedestrians

    • transect a saw more people and double the commercial shops compared to b

  • further from harbour = more for residents

    • we saw more shops like supermarkets and pharmacies on transect a compared to tourist areas like a pottery workshop

  • most of the high street was independent

    • independent shops dominated (60%) although there were some chains such as Costa

confident conclusions based on aims

• physical enquiry

  • variations exist because of the tree types (birch and beech)

• human enquiry

  • there are areas of tourism and of localism, but the vast variations in land use and environmental quality require more targeted regeneration, but we can’t really say cos it was one day with horrendous weather

evaluation human

• over a longer period of time

• days with different types of weather so we can see the effects of that

• pedestrian counts weren’t accurate as it could’ve been the same person twice

• economic data might be useful but it could be difficult to collect that

• conclusions only somewhat reliable

evaluation physical

• accuracy was low

• it wasn’t blooming season so we couldn’t get an accurate read on everything

• mixed results