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Geography Post-Field Work Presentation

Geography Post-Field Work Presentation. Group 4: Ryan Low (25) Eugene Tan (27) Li Yiqun (16) Austin Yu (34). THE INVESTIGATION. On the 16 th and 17 th of January 2014, our team went to 7 locations around the school to record the temperatures within our school’s microclimate.

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Geography Post-Field Work Presentation

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  1. GeographyPost-Field Work Presentation Group 4: Ryan Low (25) Eugene Tan (27) Li Yiqun (16) Austin Yu (34)

  2. THE INVESTIGATION • On the 16th and 17th of January 2014, our team went to 7 locations around the school to record the temperatures within our school’s microclimate. • Before the investigation, we had the following hypothesis: Concrete areas are generally hotter than non-concrete areas.

  3. FINDINGS Location A – Top floor of multi-storey carpark Temperature: DAY 1: Lots of sun, concrete ground | DAY 2: Sunny, cloudless and windy, concrete ground

  4. FINDINGS Location B – Garden outside printing centre Temperature: DAY 1: Trees and grass nearby | DAY 2: No wind close to vegetation and a bit of clouds

  5. FINDINGS Location C – Open space between canteen and block C Temperature: DAY 1: Sun. trees and grass | DAY 2: Sun, no cloud cover, near vegetation

  6. FINDINGS Location D – Terraces Temperature: DAY 1: Windy and sunny with no cloud cover | DAY 2: Sun, no cloud cover, concrete ground

  7. FINDINGS Location E – Field Temperature: DAY 1: Wind, cloud cover, grass | DAY 2: Sun, a bit of cloud cover, grass

  8. FINDINGS Location F– Running track Temperature: DAY 1: Cloud cover, windy | DAY 2: Sun, a bit of cloud cover, grass nearby

  9. FINDINGS Location G – Running track Temperature: DAY 1: Sun, near water, no wind, concrete ground | DAY 2: Little wind, no cloud cover, near water

  10. SUMMARY OF DATA We calculated the average temperatures for the each of the locations and altitudes for the two days.

  11. SUMMARY OF DATA We then arranged the locations in increasing order of temperature: Coolest Hottest

  12. ANALYSIS • The recorded temperatures did not strictly follow the factors of proximity to water, vegetation or concrete. In fact, there was no distinct pattern to tell what caused the temperature differences. EVIDENCE Locations close to water: Fountain – 2nd hottest (not justified) Locations close to vegetation: Garden – 2nd coolest (justified) (continued on next slide)

  13. ANALYSIS (continued from previous slide) Field – 3rd hottest (not justified) Garden – hottest (not justified) Locations close to concrete: MSCP – 3rd coolest (not justified) Terraces – 4th coolest (not justified) Fountain – 2nd hottest (justified)

  14. ANALYSIS 2) The air temperature drops slightly in most cases, at a height of 1.5m as compared to 15cm. EVIDENCE Average temperature of all locations 15cm above ground=29.9˚c (3 s.f.) Average temperature of all locations 1.5m above ground=28.8˚c (3 s.f.) Difference=1.1˚c

  15. ANALYSIS As seen, most of the locations have temperatures we cannot justify. Coming back to our hypothesis:

  16. HYPOTHESIS

  17. WRONG!!!!! Nope, our hypothesis was WRONG. The discrepancies in the information gathered has proved it wrong, without the foggiest doubt. Here’s a recap: Locations close to concrete: MSCP – 3rd coolest (not justified) Terraces – 4th coolest (not justified) Fountain – 2nd hottest (justified) Two of the three locations close to concrete were in the cooler half of the table, contrary to our hypothesis. Furthermore, for the word ‘generally’ to apply, MOST of the results should comply. However, we are very far from that.

  18. THE CULPRIT Factors that could have been the culprits of altering our information: • Body heat. The temperature sensor can pick up our body temperature if our fingers are too close, then mistake it for air temperature. Therefore we have to place our fingers far away from the sensor to avoid such a situation. • Damaged equipment. Any damage done to the temperature sensor can drastically affect the readings taken. Therefore we have to handle the equipment with extreme care. Anyway, we have been reminded that the equipment costs a king’s ransom. • Human error. Someone could have inaccurately recorded the reading on the weather meter. Just be careful!

  19. CREDITS MR LAU – FOR PROVIDING EQUIPMENT AND GUIDANCE RYAN LOW – FOR COMPILING DATA AND PREPARING SLIDES EUGENE TAN – FOR RECORDING DATA LI YIQUN – FOR TIME-KEEPING AND RECORDING DATA AUSTIN YU – FOR TAKING THE PICTURES AND ALL OTHERS WHO HAVE SUPPORTED US ONE WAY OR ANOTHER.

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