Investigation of environmental quality with distance from CRTBD in Stanley, Hong Kong

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

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

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 2011Anahita 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

http://www.maps.google.com


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


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

http://en.wikipedia.org/wiki/Environmental_quality


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


p. 3

1 <http://www.maps.google.com> Google Maps

N

first/starting site site

1 2

3

4

5 67

8

9

10

CRTBD:Library and market intersection

public housing! ! ! ! ! ! private housing! ! ! ! commercial/residential



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


p. 4

Decibel meter

Measuring the distance between dustbins

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


p. 5

2 Lau, Linda 1982

Stanley plaza construction work

Stanley plaza main bus terminus

Ma Hang Estate inter-buildingspace with naturallysimulated environments e.g. ponds

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


p. 6

Source of photographs: primary evidence

Promenade withtree belt

Entrance to Ma Hang Park

Waterfront restaurant

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


p. 7

Litter count at CRTBD(0 m distance from)

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’.



p. 8

Litter count at CRTBD(0 m distance from)

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



p. 9

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.



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)

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 10101010 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 amRest area

Birds/frogs

Running water

10:15 amRest area

Birds/frogs

Running water

10:15 amRest area

Birds/frogs

Running water

10:30 am

Not noisy; many cars

10:30 am


10:30 am


10:40 amBuses, cars,

incessantdrilling


incessantdrilling


incessantdrilling

10:55 amLots of al

fresco diners; drilling

10:55 amLots of al


10:55 amLots of al


11:10 amNo cars; no traffic



11:20 amCars;

tourists; crowded

11:20 amCars;

tourists; crowded

11:20 amCars;

tourists; crowded

11:40 amNext to

road; lots of buses/services

11:40 amNext to


11:40 amNext to


11:50 amLots of traffic;

one-way traffic control





12:10 pmClear area ;

wider road


wider road


wider road

12:30 pmEmpty area

12:30 pmEmpty area

12:30 pmEmpty 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



p. 11

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.



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

Fig 3.2.6: table showing traffic count at every site

Trial Site Site Site Site Site Site Site Site Site Site Trial

1 2 3 4 5 6 7 8 9 101 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 NotesPark area

Mostly taxis; some mini buses

Mostly double decker buses;mini buses

None; pedestrianised

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 Site Site Site Site Site Site Site Site Site Site Trial

1 2 3 4 5 6 7 8 9 101 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.



p. 13

CRTBD

Site 10Site 1

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



p. 14

Mean noise/ sound (dB) 80

70

60

!! !

10! !

20Traffic (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).

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



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)

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



p. 16

5 <http://www.lcsd.gov.hk/en/ls_others.php> Leisure Culture & Services Department 2008

6 Jan Jacob Trip (2007)

http://www.lcsd.gov.hk/en/ls_others.php


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


p. 17

1 <http://geography.geography-dictionary.org/Geography-Dictionary/Carrying_Capacity>.

http://geography.geography-dictionary.org/Geography-Dictionary/Carrying_Capacity


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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2. "Conservation in Hong Kong." Wikipedia, the Free Encyclopedia. Web. 5 June 2011. <http://en.wikipedia.org/wiki/Conservation_in_Hong_Kong>.

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Conclusion and Evaluation


p. 18



http://en.wikipedia.org/wiki/Conservation_in_Hong_Kong








http://www.medscape.com/viewarticle/554566_3

http://www.medscape.com/viewarticle/554566_3





http://udel.edu/~mcdonald/statbigchart.html

http://udel.edu/~mcdonald/statbigchart.html

http://en.wikipedia.org/wiki/Stanley,_Hong_Kong




http://www.tradingeconomics.com/hong-kong/urban-population-percent-of-total-wb-data.html

http://www.tradingeconomics.com/hong-kong/urban-population-percent-of-total-wb-data.html

http://geographyfieldwork.com/urban_sampling.htm


