GEOGRAPHY INTERNAL ASSESSMENT
Wordcount: around 2700 words
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Criteria A: Geographical Context
1.1 Focused Research Question
Field work Question: To what extent does urban stress vary from edge in the CBD in
Changshu to the PLVI?
Introduction
1.2 Definition of Urban Stress
Urban stress is defined as tension arising from environmental and social challenges in
urban settings, such as pollution, violence, and social isolation. It can lead to adverse
psychological outcomes, including depression and PTSD. Addressing urban stress is
essential for fostering healthier urban environments and supporting the mental health of
marginalized groups. This definition is referenced in Option G Urban environments part 3
(Urban environmental and social stresses, page 360).
Link:
https://pmc.ncbi.nlm.nih.gov/articles/PMC7432521/#:~:text=The%20tension%20that%20r
esults%20from,use%2C%20racism%2C%20and%20discrimination.
Location
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Data collection occurred
in Changshu, China
(Fangta Street) on Friday,
21st February 2025, from
9 AM to 4 PM. The
morning transect
spanned from the CBD
edge to the PLVI (Fangta
Street), while the
afternoon transect
reversed this route (PLVI
to CBD edge). Sites were
named based on their
distance from the
starting point during data
collection.
Figure 1.1 China Map, highlighting Suzhou
https://commons.wikimedia.org/wiki/File:Suzhou-location-MAP-in-Jiangsu-ProvinceChina-2.jpg
Figure 1. 2 Changshu Map
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https://www.google.com.hk/maps/place/Changshu,+Suzhou,+Jiangsu,+China/@31.66664
51,120.82217,11z/data=!3m1!4b1!4m6!3m5!1s0x35b3c8c42ab320c9:0x36345e8bc09888
be!8m2!3d31.6538099!4d120.75224!16zL20vMDNfczR5?entry=ttu&g_ep=EgoyMDI1MDIyN
S4wIKXMDSoASAFQAw%3D%3D
Theory
Bid-Rent Theory
Bid-Rent Theory explains how the price and demand for real estate change as the
distance from the central business district (CBD) increases. It highlights how
different land users compete for space in urban areas. This affects urban stress
significantly, such as:
1. Competition for Space: Higher land values in the CBD motivate developers to
maximize profit through dense, vertical construction leading to overcrowding,
congestion, noise, and UHI.
2. High Rents: Businesses and residents in the CBD face steep rents, stressing
budgets.
3. Congestion: High demand for CBD access increases traffic and pollution.
REFERENCES:
1. https://www.sciencedirect.com/science/article/abs/pii/S0360132321007903
2. https://library.fiveable.me/key-terms/ap-hug/bid-rent-theory
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3. https://www.kstate.edu/economics/about/staff/websites/babcock/congtax.pdf://testbook.co
m/question-answer/which-one-of-the-following-categories-ofurba-6017d03f554d501df603ba53
Figure 1.2 Bid-Rent Theory
Core Frame Model
Core Frame Model divides the CBD into a core and a frame, focusing on movement and
transportation considerations. The core represents the most crucial commercial and retail
activities. Dense development and high traffic volume lead to air pollution, congestion, and
intense competition for space, which increases rents. The frame represents transitional
zones influenced by accessibility. Transportation pressure and urban renewal
displacement, displacing existing populations.
Both models highlight spatial disparities in urban stress. In Changshu, the CBD edge
exemplifies Bid-Rent-driven pressures, while the PLVI reflects frame-like deprivation.
https://www.jstor.org/stable/24042266?seq=2
Pic: https://commons.wikimedia.org/wiki/File:Core_frame1.png
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Hypothesis
1. Noise pollution
decreases with distance
from the PLVI towards the
edge of the CBD (writing
more paraph)
2. Air pollution decreases
with distance from the PLVI
towards the edge of the
CBD
Figure 1.3 Core Frame Model
3. Traffic congestion
decreases with distance
from the PLVI towards the
edge of the CBD
Statistical Tools:
1. Spearman's rank
This tool is used to show the strength of correlation between two sets of data
Spearman's rank can only be used if data is
•
Linear
•
Is independent from each other
•
The pair of data be between 10 and 30
Spearman Rank formula
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Rs = Spearman's Rank
correlation coefficient
•
D= differences between
ranks
•
n = number of pairs of
measurements
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Criteria B: Methodology
Introduction
This investigation was
conducted on 21st of February,
with 2 Transects.
Transect A: 9:00AM/12:00 PM,
from zhujiang Road/North
Yushan Road intersection to Fangta street with 3.85 Kilometers. Transect B:
1:00PM/3:30PM, from Fangta Street/East Huancheng Road intersection to China
Menswerat Center with 2.7 Kilometers. The weather was cloudy with temperatures (MIN
3 °C, MAX 8 °C )
Figure 2.1 Transect A&B with check points
“3-8 °C DOES NOT include the Max temperature of the whole day, only the highest of
collecting data time)”
Our group is 5 people; therefore, we separate the Roles to:
1. Navigate with a map and keep track of the distance and time
2. Measures air and noise pollution with device and phone
3. Records data for role 2 and take 2 photos and videos of the data collection process
4. Count and record traffic
5. Record data for 2 bipolar surveys and take corresponding photos
We used several apps for measuring our data collection:
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1.
Distance:
IOS app called (Keep)
2.
Noise:
Android app ( Decibel X)
3.
Congestion:
Recording videos by our phones at
intersections for 3-5m and counting
numbers of vehicles passed during this
period.
4.
Air pollution:
Utilized Air quality monitor borrowed
from our geography teacher. which was held at
approximately 1.5 meters above ground
level with orienting the monitor at 160 degrees to
capture a representative sample of the surrounding air
quality.
Hypothesis
This investigation aims to explore the environmental
factors surrounding the (PLVI) in the context of urban
geography. The Hypothesis of 3 aspects:
- Noise pollution decreases with increasing distance
from the (PLVI) toward the edge of the (CBD).
Figure 2.4 Measuring Noise pollution
- Air pollution decreases as the distance from the PLVI
increases toward the CBD's edge.
- Traffic congestion diminishes with greater distance
from the PLVI to the CBD.
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SECTION C
Quality & treatment of information collected
Hypothesis 1: Noise Pollution Decreases with Distance from the PLVI Towards the Edge of
the CBD.
Statistical Findings
Findings of transect 1 shows p=0.01 (99% statistical significance level), Which means my
hypothesis is null because there is a 1% probability of it to be correct. Therefore, this
correlation does not imply causation. One variable may not cause the other.
This graph exhibits the relationship between noise pollution levels and distance from the
edge of the (CBD) to the (PLVI) reveals significant insights into urban stress. Noise levels
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measured in (dB) at various areas but consistent distances. Data was taken every 150m
until it reached 3,450 meters.
As the graph shows, noise levels are stable most of the time in the transect, changing
between 55 dB and 65 dB. These readings assume the further the area from the CBD edge
the more moderate noise pollution indicating of less stressful environment.
The noise levels near the CBD edge peaked around 60 dB, aligning with expectations due to
higher traffic and commercial activities, and more loud activities intended to happen.
The more distance increases towards the PLVI, the more the fluctuations would show but
remain within a similar range. This indicates that urban stress exists, however it does not
increase dramatically in this transect.
The most notable peaks occurred around 1,500 meters and 2,400 meters, when noise
levels reached approximately 65 dB. However, these spikes were not consistent, which
refers to other factors that might be included in this, such as temporary events (for
example, a car horn) or localized constructions, may have influenced the data.
In conclusion, transect 1 suggests that urban stress according to what has been measured
of noise pollution, noise pollution levels do not vary significantly from the CBD edge to the
PLVI.
Statistical findings:
The findings is p=0.10 (90% statistical significance level), which indicates There is a 10%
probability that my null hypothesis is correct.
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In Transect 2, the noise pollution levels across distances from the CBD edge to the PLVI
provides a more dynamic perspective and imbalance on urban stress. The graph exhibits
noise levels ranging from 54 dB to 66 dB with measurements taken every 150 meters to
2,850 meters. Readings near the CBD edge began at around 65 dB, referring to a high level
of urban stress due to commercial activity and dense traffic or other urban stress factors.
By time noise levels fluctuated, showing a simple decline to approximately 60 dB before
rising again at different intervals.
The most significant and notable rise was when the noise levels at 750 meters showed
peaks where levels reached their highest points in the graph at around 66 dB. In addition to
the constant fluctuations between 1350 to 2100, starting with rapid dropping and rising
which highlights urban activity, such as traffic variability, or land use changes.
With suggesting the PLVI experiences heightened stress due to the concentration of
businesses and traffic. This variation contrasts with Transect 1, where noise pollution levels
remained more stable throughout the distance. The presence of many peaks and unstable
noise levels in Transect 2 may indicate to infrastructural changes or specific
events/changes that may contributed to temporary spikes in noise pollution.
The error bars reflect variability in the data, with the consideration of external factors such
as weather conditions, time of day could influence noise levels as well. Ultimately, transect
2 demonstrates a clearer variation in urban stress indicated by noise pollution, particularly
as it approaches the PLVI. This impact of urban design and density on noise levels, referring
that areas closer to commercial areas, activities experience significantly higher urban
stress.
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Hypothesis 2: Air Pollution Decreases with Distance from the PLVI Towards the Edge of the
CBD
statistical tool:
p=0.01, there is a 1% probability that the null hypothesis is correct (no correlation).
In Transect 1, the analysis of air pollution data reflects crucial insights into urban stress
levels as measured by the ratio of air pollution in various distances from the edge of the
(CBD) to (PLVI). The graph exhibits air pollution ratios measured in % from 500 meters to
3,500 meters, the data refers to a possible consistent air pollution ratio, from 70% to 80%.
At 750 meters, the air pollution ratio starts at 75% then increases gradually to a peak of
80% around 2,250 meters. This rise suggests that the more densely the populated areas,
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there is a corresponding cause increase in air pollution, likely due to higher emissions,
especially vehicle and industrial activities concentrated in the CBD.
After 1,500 meters, the air pollution ratio stabilizes, fluctuating a bit but remaining within
the range of 65% to 70%. This stability might indicate effective urban planning measures
that help mitigate pollution away from the CBD. The data points and their corresponding
reflect influences, such as weather conditions. Overall, transect 1 exhibit urban stress,
indicated by air pollution, experiences a medium increase approaching the PLVI.
Statistical findings:
The findings is p=0.20 (80% statistical significance level), which indicates There is a 20%
probability that my null hypothesis is correct.
Transect 2 presents a more view of air pollution levels from the CBD edge to the PLVI. The
graph shows a stable air pollution ratio between 75% and 85%. Measurements taken from
0 to 3,000 meters.
At the start of the transect, the air pollution ratio is approximately 78%, indicating a
significant level of pollution near the CBD. As the distance increases, there are
fluctuations, with the highest ratio reaching about 85% around the 1,000-meter mark. This
peak suggests that the immediate vicinity of the CBD experiences heightened urban stress
due to increased traffic and commercial activities.
However, the opposite of Transect 1, the pollution levels in Transect 2 do not stabilize as
stronger after the peak at the beginning. Readings remain on a higher average and
fluctuating around 80% to 85% throughout the remaining reading. This shows a continuous
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influence of urban activities the entire length of the transect. With considering the fact that
certain areas near the PLVI have higher chance of being at persistent pollution levels due to
ongoing commercial, vehicle congestion.
There are more fluctuations in Transect 2 compared to Transect 1 indicates that urban
stress is more variable in this section. These variations could be influenced by factors such
localized industrial emissions. At the end, Transect 2 highlights a higher, ,more consistent
level of urban stress in terms of air pollution, emphasizing the challenges faced in
managing air quality in populated urban centers.
Hypothesis 3: Traffic Congestion Decreases with Distance from the PLVI Towards the Edge
of the CBD.
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Statistical tool result: p=0.50 therefore There is a 50% probability that my null hypothesis is
correct.
In Transect 1, the analysis of vehicle congestion gives a significant insight into urban stress
from the CBD) to the (PLVI). The graph shows the total number of vehicles at various
distances from 0 meters to 3500 meters.
At the CBD edge (0 meters), congestion is notably high, with approximately 45 vehicles,
mainly the counted numbers are cars and buses. This high vehicle density exhibit an
intense urban stress and high levels of human activities due to increased traffic flow. As we
move to 300 meters, vehicle counts remain getting higher, indicating persistent congestion
in this commercial area.
A critical peak occurs at 1800 meters, where total vehicle counts rise to approximately 55
vehicles. This peak likely correlates with close commercial zones that have heavy traffic.
After 2,100 meters, vehicle numbers decrease significantly, dropping to around 25
vehicles, which indicates a transition to less congested residential areas and human
activities in general.
In conclusion, transect 1 mirrors the challenges associated with managing transportation
in heavily populated urban areas. It also gives a clear pattern of urban stress, with high
vehicle counts concentrated close to the CBD that gradually decline with distance.
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Statistical tool: p = > 0.50 (below 50% statistical significance level) there is a higher than
50% possibility that my hypothesis is correct.
Transect 2 provides a new perspective on vehicle congestion and urban stress moving from
the CBD edge to the PLVI. The graph shows total vehicle counts at distances from 400m to
1300 meters.
At 400 meters from the CBD, vehicle counts are lower, around 15/20, with a notable
presence of bikes. This indicates a presence of awareness of transport that are less
pressure to the environment, which may help reducing urban stress around the CBD. As we
rise to 800 meters, vehicle counts increase to approximately 25/20, with a rise in trucks and
lorries, suggesting a small rise in commercial activity.
The peak congestion occurs at 1000 meters, where total vehicles reach about 20/30. This
increase reflects a transition zone characterized by intensive urban activity. By 1,300
meters, vehicle counts lower rapidly, indicating more stable urban stress, though still lower
than near the CBD.
In summary, Transect 2 gives a more balanced distribution of vehicle types, indicating a
diverse transportation mix. However, congestion remains a significant issue throughout the
whole analysis, especially as one approaches the PLVI, emphasizing the need for effective
urban transport strategies to mitigate urban stress.
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Criteria E: Conclusion
In conclusion, the fieldwork question discuss how urban stress varies from the CBD edge
to the PLVI in Changshu, China, the analysis of the 3 hypothesis, noise pollution, air
pollution and traffic congestion provides more detailed answer for the RQ and here they
are:
Hypothesis 1
Noise Pollution: The data from Transect 1 showed stable noise pollution levels with no
significant decrease with distance increased (p=0.01), referring to persistent urban stress
near the CBD. However, for transect 2, noise levels fluctuated significantly (p=0.10),
particularly near the PLVI, referring to urban activities that contributed to change
constantly stress levels. In summary, noise pollution does not consistently decrease with
distance, highlighting ongoing urban stress throughout.
Hypothesis 2
Air Pollution: For air pollution, transect 1 indicated an increase in pollution levels towards
PLVI (p=0.01). Transect 2 showed consistently high pollution levels (p=0.20) across
distances. This suggests that urban stress related to air quality has not been taken care of
enough moving from the PLVI toward the CBD, contradicting the hypothesis.
Hypothesis 3
Traffic Congestion: Transect 1, congestion was high near the CBD and decreased with
distance (p=0.50), supporting the hypothesis. However, transect 2 was more variability and
included a higher ratio of non-motorized vehicles, indicating that while congestion
decreases, urban stress remains a concern in both areas.
The findings from the analysis of urban stress in Changshu align with the Core Frame Model
and Bid-Rent Theory. According to the analysis and reading of graphs air pollution and high
noise levels near the CBD tend to have more competition for space and development.
Urban stress is higher near the PLVI due to intense commercial activities but then
decreases the closer we get to the frame of the CBD, indicating some mitigate for urban
stress happened. This pattern suggests the need for effective urban planning strategies to
manage urban stress levels and promote sustainable environment.
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Criteria F: Evaluation
This study on urban stress in Changshu used surveys to assess air and noise pollution
levels and videos, photos for traffic counts. on the group work side the data collection
methods were effective and collect enough data for air and noise pollution however for
congestion if would be better if we took more trials especially for transect 2, due to the
short distance of it and much less intersections than transect 1 we were only able to
collect 4 data site for congestion while 13 data site for transect 1. From my point of view,
we need at least 10 sites to be able to measure the average accurately. Other external
challenges we faced during data collection processes, such as people singing, background
music in commercial areas on streets which affected the accuracy. If the study were to be
repeated, suggesting clearer hypothesis and formulating more specific fieldwork questions
would have a better result, air pollution hypothesis for example was absolutely incorrect.
On the personal aspect, I would say I did not arrange the research enough, missing many
photo and annotations, citations and the graphs could be better, maybe using the same
colors for congestion data to make it clearer and more outstanding. Also, during the
process of data analysis, I noticed I am missing a lot of understanding of my own data,
especially when I made the graphs, unclear explanation, mostly unarranged thoughts
according.
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