Internal assessment written for IB Geography assignment. Rudimentary concepts of urban planning and environmental quality addressed

1. i
Geography Internal Assessment
Investigation of Environmental Quality with
distance from CRTBD in Stanley, Hong Kong
School: South Island School
Candidate name: Anahita Sharma
Candidate number: 003258-138
Word Count: 2496

2. ii
Contents
A: Fieldwork question, definition, and geographic context… p.1
Research question and value in geographic context
B: Methodology … p.3
C and D: Data Presentation/Treatment and Analysis … p.7
E & F: Conclusion and Evaluation … p. 17
Bibliography … p. 18

3. 1. Introduction
Research Question: Does environmental quality increase with distance from the central-recreational-tourist
business district (hereafter referred to as CRTBD) in Stanley, Hong Kong?
The fieldwork links to Topic G.3 (Urban Environments).
1.1 Geographic Context
1
The Stanley Peninsula has developed into an important commercial, recreational and tourist honeypot.
Environmental quality is
highly valued, as flat
land is scarce, densely
populated, or valued at a
premium in Hong Kong.
Fig 1.2 demonstrates the
spatial tendency to
concentrate commercial
and residential uses on
flatter land (urban
consolidation).
Introduction
IB Geography Internal Assessment 2011
Anahita Sharma
p. 1
1 <http://www.maps.google.com> “Google Maps”
fig 1.1.1: regional map N
Fig 1.1 displays Hong
Kong Island.
Stanley is sheltered from
Pearl River Delta
discharge, on the
southernmost part.
fig 1.1.2 local map of Stanley
N

4. Environmental quality is “the varied characteristics that relate to the natural environment as well as the built
environment, such as air and water purity or pollution, noise and the potential effects which such characteristics may
have on physical and mental health caused by human activities.”2 It depends on the community perception of an urban
health risk3 - these factors correspond with Stanley’s standards of living:
1.2 Hypotheses: with increasing distance from the CRTBD, there is
- H1: a decrease in litter and waste pollution
Although, dustbins and provided and people employed, there is a large density of direct users - whether commercial
or tourist - in the CRTBD. Waste is linked with environmental toxicity and aesthetic hindrance.
- H2: a decrease in level of noise pollution
High levels of noise pollution are physiologically - in terms of circadian rhythms - and psychologically, damaging.4
- H3: a decrease in the provision of open space
Open spaces offer recreational, ecological and aesthetic advantages, and lower mean air pollution levels [8].
Designated spaces (certified under the Leisure Services & Culture Department) decrease away from the CRTBD, as
they are constructed to serve the function of the district. Hong Kong is associated with “an acute shortage of public
parks and recreational open space relative to the huge population”5.
1.3 Justification
Given the scope, not all aspects are answerable. This investigation focuses on factors especially pertinent to the
significant, recreational-tourist aspect of Stanley.
Environmental quality could decrease with distance from the CRTBD, as the function Stanley serves underscores
the importance of maintaining environmental quality to continue attracting visitors. However, here we expect it to
increase with distance from the CRTBD because of inherent urban stresses: congestion, large quantities of waste
produced by concentrated consumption, traffic volumes inhibiting accessibility, decreased social interaction, and
decreased “landscape quality”.5
Word Count: 300
Introduction
IB Geography Internal Assessment 2011
Anahita Sharma
p. 2
2 <http://en.wikipedia.org/wiki/Environmental_quality>
3 Community Perceptions of Urban Health Risks (Baare & Patnaik)
4 Goines & Hagler 2007
5 Lau, Linda 1982

5. 2. Methodology
2.1 Time and location
Our studies occur between 10.00 am and 12.00 pm on February 23rd (Tuesday), 2011. This affects environmental
quality in terms of noise, traffic, and waste, given a volume of tourists and residents below peak carrying capacity. We
assume the aforementioned relationships will still hold.
2.2 Sampling
The line transect encompasses representative zones.At each site, we consider a general 100 m2 (see fig 2.2.1) vicinity.
!! ! ! ! fig 2.2.1. transect line1
Significant elevation changes are marked with this symbol:
The transect is strategic and pragmatic. Though this introduces a bias, the choice of site is essential to the research
question, as the transect must overlap a balance of land uses. Residential and commercial functions are often separated
(though serve each other in close proximity).
Methodology
IB Geography Internal Assessment 2011
Anahita Sharma
p. 3
1 <http://www.maps.google.com> Google Maps
N
first/starting site site
1 2
3
4
5 6
7
8
9
10
CRTBD:
Library and
market
intersection
public housing! ! ! ! ! ! private housing
! ! ! ! commercial/residential

6. 2.3 Quantification of Hypotheses: Methodology (figs 2.3.1-3)
H1: Noise pollution decreases with distance from CRTBD.: Noise pollution decreases with distance from CRTBD.
Method A decibel meter (± 0.1 dB) gauges ambient noise over sixty seconds, at ten-second intervals.
This is repeated thrice.
Pedestrian and traffic counts over three two-minute intervals are taken to substantiate the
noise levels with potential sources.
Justification The repetition ensures that a representative measure is recorded.
Limitations The sound recorded at the time may not represent general noise.
H3: Visible waste and anthropogenic pollution decreases with distance from CRTBD.H3: Visible waste and anthropogenic pollution decreases with distance from CRTBD.
Method Litter is numbered. The frequency of bins and/or cleaners is observed. As a standard, waste
counted ranges from 5.0-20.0 cm in length (± 0.5 cm).
Justification This reflects pollution by direct users.
Limitations The time period may not be representative.
There may be ‘cryptic’ (hidden) waste, or uncounted large debris.
H4: Availability of open space decreases with distance from CRTBD.
Method The open space is recognized by the Leisure Services & Cultural Department as a “Public
Pleasure Ground”*; this includes playgrounds, pedestrian zones, promenades, sport
terrains, and natural areas.
The space is qualitatively ranked and, using a secondary map, a percentage cover
approximated.
Limitations Quality is subjective, and is ideally judged by frequent users. Depending on its situation,
there may also be more or less space per person - another factor to be considered with size.
Methodology
IB Geography Internal Assessment 2011
Anahita Sharma
p. 4
Decibel meter
Measuring the distance
between dustbins

7. 2.4 Sites
Fig 2.4.1 Table showing distance of each site from CRTBD (displacement)
Site Displacement from CRTBD ± 5m*
1 750
2 600
3 300
4 350
5 250
6 100
7 0
8 150
9 400
10 550
*uncertainty from greatest precision.
The CRTBD at Site 7 is defined by its “location in the heart” where “commercial uses dominate” [2]. Services found
such as the public transport hub, the post office and the public library reinstate this.
Site 1 (fig 2.4.2): Ma Hang Public Housing Estate
Site 2: On the fringe of the same estate
Site 3 (fig 2.4.3): Stanley Plaza Upper Entrance
Methodology
IB Geography Internal Assessment 2011
Anahita Sharma
p. 5
2 Lau, Linda 1982
Stanley plaza
construction work
Stanley plaza
main bus terminus
Ma Hang Estate
inter-building
space with naturally
simulated environments
e.g. ponds

8. Sites 4 and 5 (fig 2.4.4): Near Stanley Plaza Lower Entrance; Promenade
Site 6 (fig 2.4.5): Stanley Promenade / Stanley Main Street roundabout
Site 7 (fig 2.4.6): The CRTBD (in front of Public Library) Site 8 (fig 2.4.7): Public services on Stanley Main Street
Site 9 (fig 2.4.8): Residential area on Tung Tau Wan Road Site 10 (fig 2.4.9): Near Correctional Institute museum
3
Word Count: 302
Methodology
IB Geography Internal Assessment 2011
Anahita Sharma
p. 6
Source of photographs: primary evidence
Promenade with
tree belt
Entrance to Ma Hang
Park
Waterfront restaurant

9. 3 Data Presentation and Analysis
This section presents and analyses the data in terms of the hypotheses to answer the research question. The independent
variable is the distance from the CRTBD; this has been calculated in fig 2.4.1.
3.1 H1: increase in visible litter/waste pollution with distance from CRTBD.
Fig 3.1.1: raw data table for visible litter and waste pollution on 23rd February, 2011
Site Litter
count
Qualitative observations Time
1 13 Mostly cigarette butts, plastic bags, and tissues found.
There were cleaners cleaning at the time.
10:15 am
2 20 Litter primarily found in bushes - cigarette butts were found despite a no smoking sign. 10:30 am
3 19 Litter found on the road and in the bushes. 10:40 am
4 8 Bins were found at every 10m. 10:55 am
5 1 Bins found at every 10-15m. 11:10 am
6 7 A lot of cigarette butts - certainly a principal source of litter. 11:20 am
7 17 Litter mostly found within the drains. 11:40 am
8 10 Lots of bins in the area. 11:50 am
9 8 Less busy. There are still dustbins despite distance from urban centre. 12:10 pm
10 4 Primarily wrappers 12:30 pm
Fig 3.1.2: graph showing count for visible litter and waste pollution on 23rd February, 2011
The horizontal error bar on fig 3.1.2 represents the uncertainty on the distance from CRTBD (± 5 m).
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 7
Litter count at CRTBD
(0 m distance from)

10. Statistical analysis
A linear regression and coefficient of determination will be used instead of a Rank correlation, as we are uncertain of a
monotonic relationship.
Fig 3.1.3: graph showing distance from CRTBD (± 5 m) against count for visible litter and waste pollution on 23rd
February, 2011 with trend line
The regression line on fig 3.1.3 (the line that produces the smallest sum of the squared deviates from the mean) clearly
shows a weak gradient of 0.002359.
The coefficient of determination (R2) is calculated to show the strength in the relationship between the x and y
variables.
Firstly, the mean is calculated via
= 11 (2 s.f.)
Then the difference between every value at each site from the mean is calculated, squared and added together to give
the ‘sum of squared deviates’.
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 8
Litter count at CRTBD
(0 m distance from)

11. Fig 3.1.4: processed data table calculating squared deviates for every site
(±5 m)
Litter
count
deviate from
mean
squared deviate
0 17 -7 49
100 7 4 16
150 10 1 1
250 1 10 100
300 19 -8 64
350 8 3 9
400 8 3 9
550 4 7 49
600 20 -9 81
750 13 -2 4
sum of squared deviates:sum of squared deviates:sum of squared deviates: 382
Given that the equation of the linear regression is
We can see how the predicted y-values derived through this equation (the regression line) deviates from the actual y-
values.
Fig 3.1.5: processed data table calculating regression sum of squares
Predicted y-value
from linear
equation
Deviate from
predicted
Squared deviate
0 17 9.886 -7.114 50.608996
100 7 10.1229 3.1229 9.75250441
150 10 10.24135 0.24135 0.0582498225
250 1 10.47825 9.47825 89.8372230625
300 19 10.5967 -8.4033 70.61545089
350 8 10.71515 2.71515 7.3720395225
400 8 10.8336 2.8336 8.02928896
550 4 11.18895 7.18895 51.6810021025
600 20 11.3074 -8.6926 75.56129476
750 13 11.66275 -1.33725 1.7882375625
regression sum of squaresregression sum of squaresregression sum of squaresregression sum of squares 365.3042870925
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 9

12. The coefficient of determination (R2) is expressed by the difference between the ‘regression sum of squares’ and the
‘sum of squared deviates’ as a fraction of the total sum of squares:
This shows a weak association between the linear relationship and the data, as the x-variable only accounts for
approximately 4.3% of variation in the y-variable. This means we reject H1 in favour of the null hypothesis; waste
pollution does not decrease with distance from the CRTBD, where ‘waste pollution’ is the incorrect disposal of
small items.
This does not mean that there is no correlation between land function and litter count. The litter count was lower at
tourist sites 4-6 where there were more dustbins, as well as being lower at Sites 9-10 which were far from the CRTBD.
Although visible waste is not correlated with distance from the CRTBD, it is linked to the employment of people to
keep tourist areas clean regardless of pedestrian behaviour. Whilst waste is more voluminous in developed areas
(CRTBD), it is often more visible in less-developed areas, the latter linked to EQ.
What is gleaned from the data is that the litter count is a result of an interaction between both distance from the CRTBD
and the sphere of influence of a site. A greater distance from the CRTBD can certainly account for a low litter count.
Conversely, as the costs of maintenance and upkeep are presumably high, there are few trade-offs to not regularly
cleaning up a site off the CRTBD. The cost of hiring workers and setting up
waste disposal facilities within the CRTBD, however, is balanced by gains
made through means such as tourist revenue. It is
more valuable to invest in dustbins (fig 3.1.8) in a
profiled area than in one little accessed.
This also depends on land use; several cleaners
seen at Site 1-2 were working on a public housing
estate which was further away from the CRTBD.
The study confirmed that waste
disposal is strongly linked to EQ: clean public
areas were aesthetically attractive and tended to
be in areas with high pedestrian density. This is perhaps
because better EQ covaries with the number of visitors.
The anomalously high litter counts at Sites 2, 3 and 7
attribute themselves to an abundance of small items, and the
very low count of 1 at Site 5 points to the efficacy of dustbins
and cleaners - the promenade was clean given its size (fig 3.1.6). There is no apparent pattern.
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 10
fig 3.1.6: clean promenade (Site 4)
fig 3.1.7: small items made the bulk of
waste
fig 3.1.8: availability of dustbins
(above: Site 5; below: Site 10)

13. 3.2 H2: decrease in level of noise pollution with distance from CRTBD.
Fig 3.2.1: data table showing noise pollution levels measured at each site using a decibel meter
Time
(s)
Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Site (± 0.1 dB)Time
(s)
111 222 333 444 555 666 777 888 999 101010
10 71.068.460.960.468.469.486.181.083.280.076.778.368.171.275.279.283.780.080.777.384.680.777.384.671.473.281.062.462.262.6
20 70.866.661.265.468.464.782.185.099.181.079.674.570.281.067.380.582.782.690.792.587.590.792.587.573.566.076.065.561.159.8
30 64.564.660.064.170.762.580.278.187.285.078.476.281.273.675.480.780.481.476.686.577.476.686.577.468.269.271.368.165.457.5
40 69.461.062.874.266.664.279.178.886.483.782.883.274.668.570.187.777.177.778.387.378.878.387.378.871.270.072.154.263.464.1
50 61.662.560.175.565.467.686.380.780.885.482.483.480.569.166.282.275.679.570.778.086.370.778.086.367.073.469.658.160.154.9
60 63.463.560.870.766.680.188.480.676.876.975.676.169.171.567.382.781.080.591.283.079.591.283.079.580.072.679.255.366.958.8
Mean1
66.864.461.068.467.768.183.780.786.282.079.378.674.072.570.382.280.180.381.484.182.481.484.182.471.970.774.960.663.259.6
Mean2 64.164.164.1 68.168.168.1 83.583.583.5 80.080.080.0 72.372.372.3 80.980.980.9 82.682.682.6 75.975.975.9 72.572.572.5 61.161.161.1
Notes
10:15 am
Rest area
Birds/
frogs
Running
water
10:15 am
Rest area
Birds/
frogs
Running
water
10:15 am
Rest area
Birds/
frogs
Running
water
10:30 am
Not noisy;
many cars
10:30 am
Not noisy;
many cars
10:30 am
Not noisy;
many cars
10:40 am
Buses,
cars,
incessant
drilling
10:40 am
Buses,
cars,
incessant
drilling
10:40 am
Buses,
cars,
incessant
drilling
10:55 am
Lots of al
fresco
diners;
drilling
10:55 am
Lots of al
fresco
diners;
drilling
10:55 am
Lots of al
fresco
diners;
drilling
11:10 am
No cars;
no traffic
11:10 am
No cars;
no traffic
11:10 am
No cars;
no traffic
11:20 am
Cars;
tourists;
crowded
11:20 am
Cars;
tourists;
crowded
11:20 am
Cars;
tourists;
crowded
11:40 am
Next to
road; lots
of buses/
services
11:40 am
Next to
road; lots
of buses/
services
11:40 am
Next to
road; lots
of buses/
services
11:50 am
Lots of
traffic;
one-way
traffic
control
11:50 am
Lots of
traffic;
one-way
traffic
control
11:50 am
Lots of
traffic;
one-way
traffic
control
12:10 pm
Clear
area ;
wider road
12:10 pm
Clear
area ;
wider road
12:10 pm
Clear
area ;
wider road
12:30 pm
Empty
area
12:30 pm
Empty
area
12:30 pm
Empty
area
The standard deviation for the noise levels at each site provides a representative variation of noise levels. A mean is not
considered by itself. One standard deviation encompasses 68% of the noise data.
Calculations:
Where is standard deviation, is the datum value, the mean, and the number of values.
Worked example: Site 1 (all values collected are used)
= 3.7 (1 d.p.)
Fig 3.2.2: table showing standard deviations for noise level at every site
Site
Standard deviation
(± dB)
1 2 3 4 5 6 7 8 9 10
3.7 4.9 5.2 3.5 4.8 2.7 5.6 6.0 4.3 4.1
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 11

14. Fig 3.2.3: graph showing distance from CRTBD (± 5 m) against noise levels (dB) on 23rd February, 2011 with trend line
Horizontal error bars are derived from uncertainty on distance from CRTBD (± 5 m).
The vertical error bar is ± 1 standard deviation (fig 3.7 calculations).
A strong negative correlation is evidenced by the linear regression, apart from where seeming anomalies are identified
(circled on fig 3.2.3 - see figs 3.2.4-5).
There is a relationship between the
distance from the CRTBD and noise
pollution. The two relatively high,
adjacent points diverging from the
trend are due to construction work in
those areas (left).
The WHO1 has documented the
physiological and psychological effects of noise pollution. Some of these
include “hearing impairment... interference with spoken communication... sleep disturbances... cardiovascular
disturbances...impaired task performance... annoyance reactions”. It is important to make that distinction between
ambient human and natural ‘sound’ (Site 1) and ‘noisy’ construction and vehicles (Sites 3-4). We also took the traffic
and pedestrian counts at each location to see to what extent they varied with noise level.
However, although there is large variation at each site as indicated by the standard deviation error bars, a generalization
over definite distance decay can be tentatively made.
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 12
1 Goines & Hagler (2007)
Mean sound level (dB)
at 0m from CRTBD
fig 3.2.4: Overview of construction - Site 3: note attempt to screen around perimeter
fig 3.2.5: Site 4: screening construction
with trees, benches to maintain EQ

15. Fig 3.2.6: table showing traffic count at every site
Trial SiteSiteSiteSiteSiteSiteSiteSiteSiteSiteTrial
1 2 3 4 5 6 7 8 9 10
1 0 8 15 0 3 7 20 10 6 0
2 0 6 8 0 2 6 23 6 11 0
3 0 9 13 0 3 3 21 21 3 0
Mean 0 7 12 0 3 5 21 12 7 0
Traffic Notes
Park
area
Mostly
taxis;
some
mini
buses
Mostly
double
decker
buses;
mini
buses
None;
pedestr
ianised
Police
helping
traffic
Police
here as
well
Vans
and
lorries
- Taxis;
lorries;
buses
-
Fig 3.2.7: table showing pedestrian count at every site
Trial SiteSiteSiteSiteSiteSiteSiteSiteSiteSiteTrial
1 2 3 4 5 6 7 8 9 10
1 9 10 23 20 20 32 25 2 5 0
2 6 4 12 22 22 33 30 1 7 0
3 12 7 11 23 23 44 30 4 8 0
Average 10 7 18 20 20 36 28 2 7 0
Notes - - - Tourist
group
Stanley
Main
Street;
tourists
Bus
stop
- - - -
Fig 3.2.8: bar graph showing traffic count, pedestrian count, and mean noise level by site (Site 6 is CRTBD)
From fig 3.2.8 we can see that there is some relationship between noise levels and pedestrian counts. The correlation is
not as apparent with traffic, although where noise pollution is relatively low, the traffic count is also low. Noise
pollution levels provide us with an indicator of the activity within an area and leads to other inferences; where noise
levels are higher, there is more likely to be greater congestion, a higher concentration of solid particulate matter, and
denser streets. These should be considered in conjunction with the breadth of a road.
There are also variations in noise level over the period of a day (peak congestion hours), so this does not reflect ‘true’
noise pollution levels.
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 13
CRTBD
Site 10
Site 1

16. The hypothesis is generally supported: noise levels are a source of urban stress, and is greater in the CRTBD. At the
time, noise levels were not a disturbance in Sites 1-2 and Sites 8-10 (residential areas). In Sites 3-4 the construction
was a source of noise pollution but this was mitigated by the addition of noise and visual screens. In the CRTBD (Sites
5-6), where there was a relatively higher concentration of transport vehicles and commercial activity, noise levels were
particularly high.
fig 3.2.9:! triangular graph showing relationship between noise level, traffic and pedestrian count
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 14
Mean noise/ sound (dB)
80
70
60
!
! !
10
! !
20
Traffic (vehicle) count
12
24
36
!
! ! Pedestrian count
Site 1 Site 8
Site 2! Site 9
Site 3! Site 10
Site 4
Site 5
Site 6
Site 7
KEY
Fig 3.2.9 above shows the three factors in greater relative
proportion. The relationship between the three variables is clear
as evidenced by the size of the triangles. At some sites, the
vertices are not evenly spread. At Site 4, for example, the traffic
count is zero, as it is based in a pedestrian zone. Noise levels are
still very high, given the construction. When all three variables
are high, at Site 7 for example, it is clear that there is a link
between noise levels and traffic/pedestrian count. Fig 3.2.9
generally shows an increased triangle area near Sites 6-7
(CRTBD).

17. 3.3 H3: decreased provision of open space (LCSD-established recreational facilities) with distance from
CRTBD.
Fig 3.3.1: table showing rankings for open space quality
Rating Description
A1 Excellent / Accessibility to large natural spaces e.g. country park, beach
A2 Excellent / Large-sized park or rest garden; good integration of natural
environment
B Very Good / Medium-sized sitting areas; playground; approx. 20 m length
C Satisfactory / small-sized sitting areas; approx. 5-10 m length
D Poor / Little to no attempt at establishing open space or area of temporary repose
Fig 3.3.1 rates the quality of open space at each site according to the above criteria - an estimated open space percentage cover is also
made.
A map was used because secondary data enables us to spatially analyse open space (see attached). From it, it is clear
that Stanley’s services have evolved around a strong - perhaps colonial, given its status as Hong Kong’s former CBD -
residential base (‘the neighbourhood unit concept’2). The beginning of the transect sees an estate of compact high-rises -
this evolves into a pattern of spacious, low-rise buildings or villas towards the centre.
This ‘up-zoning’3 - i.e. early establishment of residential function - is significant because housing is an
environmentally-sensitive function4. Stanley’s recreational-tourist-district status must have grown out a combination of
this up-zoning and a unique quality as a former fishing village.
From Sites 1-5, open space is predominantly categorized in A2 whereas in Sites 6-10, they predominate under the D
ranking. We must reject the hypothesis as the provision of open space does not correspond with the distance from
the CRTBD. There are A-graded open spaces near the CRTBD (Sites 4-5) as part of its tourist attractions, but the
former sites 1-2 - public housing estates - are as well equipped with open space, as they serve as inter-building buffers.
This relates to Stanley’s physically narrow, flat land: land uses are mixed and, if zoned, not clearly in line with the
distance from the centre.
Public land in Sites 1-2 is residential, and given a higher population density, the natural spaces provide a source of
recreation for residents. Within Sites 4-5, the quality of open space (a large promenade and adjacent park) serves tourist
purposes and ultimately generates income for the area. There is not as much value attached to providing open space on
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 15
2 Shean McConnell (1981)
3 Tackling the Problem of Conflicting Land Uses in Hong Kong: A Planner's View (Pun Chung Chan)
4 (Integrated) Environmental Zoning (J. Pearce)

18. private complexes as there is on public, accessible land, because it serves a larger population under the “Public Pleasure
Grounds”5 scheme: the LCSD aim to provide low cost facilities to the public.
As evidenced by a comment made by an elderly man strolling the estate (Site 1), open space does not necessarily
increase EQ. It must consider the demographics of its users; this man was dissatisfied with the lack of bathroom
facilities. Because the population density at Site 1 is so much larger than at the other sites, the space available per
person is lesser, and this ought to be accounted for in addition to percentage cover. We may have considered
accessibility to open space in terms of where the nearest open space is as there does not seem a wholesomely greater
advantage in direct accessibility.
From the map it is clear that there is a greater availability of open space in Sites 1-5 than in Sites 6-10.
However, open space may not necessarily indicate EQ it is unused or incongruous with the needs of the people in that
area. In Sites 7-10 where little to no open space was provided, there seemed to be no demand for it; therefore, a lack of
open space did not depreciate EQ. The quality of open spaces is reinforced by their position in “vibrant urban centers...
one of the most important locations for buzz... an insufficiently accessible location is less likely to become a successful
public space.”6
Word Count: 1417
Data Presentation and Analysis
IB Geography Internal Assessment 2011
Anahita Sharma
p. 16
5 <http://www.lcsd.gov.hk/en/ls_others.php> Leisure Culture & Services Department 2008
6 Jan Jacob Trip (2007)

19. Conclusion
In conclusion, there was a link between environmental quality and distance from the CRTBD, but not a strong one
given Stanley’s unusual zoning patterns.
H1 (litter) - unsupported: although was a decrease in the density of land users with distance from the CRTBD,,
distance did not clearly affect the litter count. It also varied with factors such as the number of dustbins, the number of
people employed to maintain the areas, (especially in the CRTBD), and the land use. Sites 1-2, as public estates, served
high population densities and had a higher litter count regardless of its distance from the CRTBD.
In H2 (noise pollution), the data supported a decrease in noise levels with distance from the CRTBD. We found
that vehicles, human activity, and construction generated the most noise. These are considered sources of other types of
pollution, such as solid particulate matter; noise pollution gave us an indicator of the prevalence of other types of
pollution.
H3 (accessibility to open space) marked that open space varied with land use; it was established in public areas where
it was needed regardless of distance from the CRTBD. H3 was unsupported.
Word Count: 190
Evaluation
The method permitted the collection of data appropriate to the research question.
The investigation was conducted when Stanley did not operate to its peak ‘carrying capacity’ (the number of people an
area can support1) as a CRTBD. Therefore the data collected for noise and waste pollution - which certainly vary with
the density of people - are not considered representative. The weather (cold and rainy) affected the pedestrian and traffic
counts.
To improve the investigation, we could draw several transects to collect data over radial rather than linear distances
from the RTBD, given that Stanley is rather nucleated. Collecting more data from more sites of equidistance from the
RTBD would be more representative than one site per distance from the CRTBD.
The accuracy of the methods was acceptable. There were systematic errors in the specific parameters for the type of
waste: other large materials that may have classified as human waste were dismissed by their large size.
The investigation could be repeated over different seasons to increase reliability and see if environmental quality varies
temporally and over different intervals: over a day, a week, a month, a season and a year.
Conclusion and Evaluation
IB Geography Internal Assessment 2011
Anahita Sharma
p. 17
1 <http://geography.geography-dictionary.org/Geography-Dictionary/Carrying_Capacity>.

20. To improve the validity of the study, an investigation into the relationship between land use and environmental quality
would be a more useful variable than the distance from the CRTBD (which is subjectively determined). In addition,
investigating the variables that surround one particular factor that strongly relates to environmental quality (noise
pollution for example) would be more effective than looking at several factors that may not cohesively represent it.
Some factors are more relevant to than others, and are also more easily quantifiable e.g. collecting data on variation of
air particles or pollutants over a period of time.
Word Count: 287
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Conclusion and Evaluation
IB Geography Internal Assessment 2011
Anahita Sharma
p. 18