Swanage - human

Justification of inquiry question

Measurable without specialised equipment necessary
Achievable within time available in a small town within a few hours
Simple - a single command word, 'how' looks at a narrow topic

Inquiry question

How does tourism affect the town of Swanage?

3 hypotheses

There will be most people in the central business district
The best environmental quality will be found in the commuter zone
Locals in Swanage think that tourism will have a positive impact on
the town

Method for hypothesis 1

Pedestrian count

Method for hypothesis 2

EQS

Method for hypothesis 3

Interviewing locals

Describe and justify method of data collection for hypothesis 1

Systematic sampling to reduce bias. Groups were assigned a group to regularly stop and count pedestrians for 5 minutes. Provided quantitative data, useful for analysing patterns. Easy to carry out accurately without equipment. >1 people counting and comparing so results are as accurate as possible.

Describe and justify method of data collection for hypothesis 2

All groups were assigned a path. At regular intervals along the path, they stopped and one person (the same each time) rated the area on its open space, traffic, buildings and waste. Can generate a quantitative value for a site which can be compared. EQS can be tailored for specific goals or needs. Same person taking data reduces bias differences.

Describe and justify method of data collection for hypothesis 3

Opportunistic sampling - wait for someone who looks willing. We walked around the town of Swanage and asked locals to answer our questions, ensuring not to hint towards a positive/negative answer. Range of open & closed questions, providing qualitative and quantitative data. Short so lots of people can take part. 25+ people asked so anomalies are less influential.

Describe limitations and improvements for hypothesis 1

Short data times risk being influenced by anomalies and temporary factors e.g. schools, weather. One day only - a snapshot. Busy sited are difficult to accurately account for. Lengthen data collection times. Collect data with directions. Make time of data more varied - time of day/week/year.

Describe limitations and improvements for hypothesis 2

Can be influenced by time and weather etc. Can oversimplify area. Entirely subjective so can be biased. Remove subjectivity by using equipment that makes it more objective (e.g. for sound).

Describe limitations and improvements for hypothesis 3

Objective responses. Opportunistic sampling so may not be representative. Ask more people. Clearer sampling for better representation

Data representation technique for hypothesis 1 data

Scatter graph

Data representation technique for hypothesis 2 data

Proportional circles map

Data representation technique for hypothesis 3 data

Question bank, word grid

Justify the data representation technique for hypothesis 1

Can easily see whether the relationship between two variables in positive or negative
Can easily see if a relationship is strong or weak
Can spot anomalies
Visualise large volumes of data quickly
Easily understood by a non-specialise lay audience
Can read off raw data from data points

Justify the data representation technique for hypothesis 2

You can see spatial patterns really easily
You can plot large volumes of data on one piece of data presentation
You can identify anomalies very easily

Justify the data representation technique for hypothesis 3

You can see a detailed response from the questionnaire
Clearly see whether respondents were happy or not
Clearly see whether the impacts were environmental, economic or social
Simple and east to construct
Good for qualitative data

Describe limitations of the data representation technique for hypothesis 1

Cannot always tell whether one variable has caused the other
Data has to be continuous
Cannot show relationship between more than 2 variables
Small sample sized can give very skewed visual results

Describe limitations of the data representation technique for hypothesis 2

They take a long time to create
Some of the circles overlap one another and data can be hidden/hard to interpret
You cannot tell which of the indicators for environmental quality is most to blame for the bad/good score

Describe limitations of the data representation technique for hypothesis 3

Can't get lots of respondent's responses in
It is difficult to tell the tone of respondents which can alter their initial meaning

Analyse the data collected from the representation technique for hypothesis 1

Overall, the graph has a negative correlation then stays within the range of 0-6 people per 5 minutes. This means that as the distance from the central business district increased there were less pedestrians every 5 minutes.

Analyse the data collected from the representation technique for hypothesis 2

Overall, the relationship was a weak correlation, with no clear trends in data. Despite this, the high street areas were generally lower and the minimum recorded value was found in this area. Areas with fewer houses and more parks were given higher scores, as well as high results along the shore.

Analyse the data collected from the representation technique for hypothesis 3

Overall, the word bank supports the hypothesis of the positive attitude towards tourism. In open question responses, there were 5 positives described and only 2 negatives, both of which were related to the amount of people. As well as this, the bar graph showing how strongly people agreed shows that 17 people strongly agreed and 7 agreed, with nobody answering neutrally or negatively. The word grid shows the most used words were 'traffic', then 'shops' and 'jobs'.

Explain the data collected from the representation technique for hypothesis 1

The graph followed the hypothesis, with the amount of people recorded in the town being much higher than the final recording (1800m away). However, our predictions were that the final point would be the lowest amount, while the graph shows the lowest number of people were recorded at 800m.

Explain the data collected from the representation technique for hypothesis 2

Areas on the high street would be busier and have more traffic and litter, so in turn were rated lower. Areas with fewer houses would have lower populations, making them more peaceful with less litter and traffic. The shore received high scores as the locations are directed towards tourists so therefore are better monitored and upkept.

Explain the data collected from the representation technique for hypothesis 3

In the word grid, the most used word was 'traffic' however all areas rated were said to have free flowing traffic. This is heavily dependent on weather/time of day, however the fact that this was deemed the highest impact and still have low ratings could be considered positive and therefore support the hypothesis. The rest of the data supports this.

Was hypothesis 1 accepted?

Yes

Was hypothesis 2 accepted?

No

Was hypothesis 3 accepted?

Yes

Conclude what is shown from our acceptations and rejections of our hypothesis

We accepted 2 out of 3 of the hypotheses. Our initial predictions were somewhat realistic - there were more people in the CBD and locals thought that tourism has a positive impact on the town however the environmental quality showed no clear evidence of being best in the commuter zone.

Evaluate the question

It was tightly lined in to geographical models. We could detect data in a few hours. Study location and data collection both close together which minimised travel time and carbon footprint. Title followed most principles of the SMART title. Our title was quite broad - we couldn't investigate every single impact of tourism.

Evaluate the data collection

Sampling strategy allowed us to cover a large proportion of Swanage. The pilot study allowed us to calibrate data collection techniques so we could pool class data. We collected both qualitative and quantitative data. Some roads and areas were missed out. Some subjectivity in data collection for environmental quality survey despite calibration at site 1.

Evaluate the conclusions

We interpreted each pieces of data directly under the presentation and included data to support it. We lined the interpretation back to the models. We could draw on 3 hypothesis to make and investigate our title, making stronger conclusions. By pooling data class instead of 1 transect, we could have more confidence in our conclusions. In some instances we did not have enough data to make concrete interpretations. The interpretation of the quotation banks were very subjective. Our links to geographical theory were assumed rather than proved. To truly answer out inquiry question we could have looked at many more hypotheses.