I think that this hypothesis will be an interesting one to test in Llandudno and that is why I have decided to choose it. I am really not sure whether it will be true or not, because traffic may be mostly concentrated on the road along the seafront, where I would have estimated pedestrian density will be the greatest. However, I will hopefully solve this hypothesis on the trip.
Hypothesis 3 – Sphere of Influence
“Llandudno’s tourist sphere of influence varies according to length of stay of visitors, i.e. holiday makers staying longer than 5 days will come from further away.”
Again, this is an interesting hypothesis to investigate. I would say that it would be true; people who travel further will stay longer in Llandudno. But, there are also foreign visitors to take into account, who may be on a tour around the whole of the UK and therefore may only be day tripping in Llandudno.
Hypothesis 4 – Building and Environmental quality
“Building and environmental quality is greatest in areas most used by tourists.”
I have chosen this hypothesis because building and environmental quality is a sub-topic of the topic ‘settlement’ that we haven’t particularly studied in class. However, I would think that the quality of buildings and the general environment in Llandudno should be the highest in areas most used by tourists, but this view could be contradicted, when testing the hypothesis whilst on the fieldtrip.
Methodology
The term methodology is the process of methods by which you investigate a topic or theory.
In Llandudno, I will investigate four different hypotheses, which are as follows:
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Land Use – “Land use in Llandudno varies with distance from the seafront.”
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Pedestrian Density – “Pedestrian Density will be inversely related to traffic i.e. as traffic decreases, pedestrian density will increase and vice versa.”
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Sphere of Influence – “Llandudno’s tourist sphere of influence varies according to length of stay of visitors i.e. holiday makers staying longer than 5 days will come from further away.”
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Building and Environmental Quality – “Building and environmental quality is greatest in areas most used by tourists.”
To complete my investigation of Llandudno, by penultimately proving or disproving my four hypotheses and ultimately either proving or disproving the theories studied in class, I have:
- Collected my own primary data such as tally charts and a questionnaire at different sites in Llandudno.
- Used mainly techniques decided by the group in class in order to prove/disprove the above four hypotheses. These include: Land use surveys tally charts of pedestrian and traffic density counts, a questionnaire (asked to 7 people at each given site), Environmental surveys.
- Revisited the site.
To make the investigation more structured, I used a sequence of steps at each site:
- Land use survey
- Traffic Density count
- Pedestrian count
- Questionnaire
- Environmental survey
Details of Hypotheses
I will now explain details of each of the four hypotheses investigated in Llandudno.
Hypothesis 1 – Land use
“Land use varies with distance from the seafront.”
Using the base map of Llandudno, I have filled in land uses at given sites spread across the area. I used the method of colour-coding identified properties shown on the map. This method was chosen because it gives a good, visual representation of the data which should be easy to conclude from. I have collected this data, along with the other three members of my group, because it will help me to prove either the Burgess or Hoyt land use models and therefore achieve my first aim of the fieldtrip. The data is primary material as I collected it on the day of the trip. It was an individual exercise but when we are back in class, results will be compared and evaluated as a group.
Hypothesis 2 – Pedestrian Density
“Pedestrian density will be inversely related to traffic i.e. as traffic decreases, pedestrian density increases and vice versa.”
I have used the technique of tally charting to count up data of pedestrians and vehicle types passing through a site in 5 minutes. This process was then repeated at the further 5 sites and results were compared to assume a conclusion. This technique was chosen because it is a fairly simple method to record data. We collected this data to predominately either prove or disprove the second hypothesis of the trip and lastly to achieve the second aim. The data is primary material and we will work in our individual groups. This is so, with more pairs of eyes, we could register every vehicle and each pedestrian entering or leaving the given site.
Hypothesis 3 – Sphere of Influence
“Llandudno’s tourist Sphere of Influence varies according to length of stay of visitors, i.e. holiday makers staying longer than 5 days will come from further away.”
To obtain a Sphere of Influence for Llandudno, as a tourist town, I used the technique of a questionnaire. At each site I, along with the contribution of other group members, asked 7 people the given questions and filled in the tables accordingly. This method, of using a questionnaire, was chosen because it is probably the most accurate process of forming a Sphere of Influence for Llandudno’s tourists. Hopefully, we will be able to prove or disprove the hypothesis as well as drawing a Sphere of Influence diagram of tourists visiting Llandudno and to also achieve the second aim. The questionnaire is primary data and an exercise which each individual group should carry out.
Hypothesis 4 – Building and Environmental Quality
“Building and environmental quality is greatest in areas most used by tourists.”
To collate data, in order to prove or disprove the hypothesis, I used a Building and Environmental Quality survey at each of the 5/6 sites. In this technique, I rated different aspects of building and environmental quality from very poor (0-2) to very good (9-10). Some of these aspects include: general appearance of street, house density and building standard. The scores were then added up to give a total out of 50 and scores were compared with other group members. This technique was chosen because it gives a good, all-round summarisation of building and environmental quality, especially when compared with others’ thoughts. This data was collected to either prove or disprove the above hypothesis and therefore achieve my third aim. The data collected is primary data and it will be an individual exercise to be compared later in out groups.
To conclude, in order to be able to plot graphs and interpretation diagrams, the appropriate data needs to be collected and arranged. This will finally allow me to either prove or disprove my four chosen hypotheses and therefore prove or disprove the theories studied in class, such as the Burgess and Hoyt models and Sphere of Influence theories. If I can achieve the above my investigation of Llandudno will be successful.
Hypothesis 1“Land use varies with distance from the sea front.”
I have already explained, in my aims and hypotheses why I chose this hypotheses and how we conducted this investigation.
Results
My group of four students was given site numbers 28, 24, 19, 18, 12 and 7 to colour in the land use of on the land use map. After the fieldtrip to Llandudno, in the follow up lesson, we compared and completed the land use maps in class. I can identify the following results (as examples) of land uses across the site, as shown on the land use map in the Data Presentation section (below)...
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Most of Trinity Avenue was residential, although we did find a few public areas (a playing field, schools) and the odd bank/building society/estate agent/office. Just across the road from site 19 and towards the north shore of Llandudno was a hotel.
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Along the front (overlooking the North Shore) was almost completely occupied by hotels and bed and breakfasts. This road was called The Parade.
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Just behind the majority of hotels and bed and breakfasts stood a long row of retailing (shops) which were sandwiched between Mostyn Street and Somerset Street, Bodafon Street and Adelphi Street.
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Industry was located in south-east Llandudno and was dispersed around Builder Street and Builder Street West as well as Council Street West, Norman Road and a Depot was situated on Cwm Road/Maesdu Road.
Data Presentation
(see next page for Land use map and annotated photos of sites).
Data Interpretation
The results from the land use map (previous page) show that Land use does vary with distance from the seafront. Therefore, I have proved my hypothesis 1 and can now decide whether Llandudno fits the land use pattern of the Burgess or Hoyt model.
However, firstly I shall explain reasons for land use patterns found across Llandudno. To begin with, stretching across the seafront of the North Shore of Llandudno is a row of hotels and bed and breakfast’s. This is because most people who visit Llandudno and stay for a night or longer like to have a sea view. Residential is situated away from the sea front, but does cover the majority of Llandudno. Residential is not situated along the seafront as people who actually live in Llandudno would probably not want lots of tourists viewing their houses everyday.
Next, I have found that almost all retailing (shops) are located just behind the hotels and bed and breakfasts. I predict this is because most tourists congregate in this area (close to the seafront). Also situated here are banks, building societies, estate agents, offices – convenient for the local people.
Also, I have discovered that all residential areas are situated back from the North Shore seafront, but do reach the West Shore seafront. Amongst these, are public buildings and areas such as the cricket ground, playing field, three schools, a coach park, a football ground and fire/Ambulance stations. The West Shore must be less popular with tourists, although there are a few shops. The reason for this could be because there are less shops, hotels and bed and breakfasts. Therefore, more residential areas are situated here. The public buildings are located near to the residential areas because there is obviously a greater demand for them (more residents).
In addition to the above findings and reasons for patterns, bars, restaurants, cafés and pubs are also located near to the seafront, but not generally on the seafront. This is probably because it would possibly be too windy on the seafront during autumn, winter and maybe spring seasons. So, people would not want to sit out in these conditions.
I have also identified an area of land with vacant buildings. This is could be for many reasons. Possibly it could be in a state of renovation for more hotels, restaurants, shops, etc. On the other hand, it could be that there isn’t a demand for any further services and redevelopment of that area is still being planned.
Lastly, industrial areas were located in East Llandudno, away from the seafront but not so far from residential and public areas. This could be because people have jobs in the industry. There appears to be a railway line which runs through part of Llandudno, alongside the main industrial areas. The railway is needed for a means of transport for the industry in Llandudno, and so is located very close to the industrial areas.
Does Llandudno fit the Burgess or Hoyt Land use model?
I can relate my discoveries of land use in Llandudno back to the theories of the Burgess and Hoyt models, discussed earlier in the fieldwork. After analysing all the data I can conclude that Llandudno fits the Hoyt land use model. I have many reasons for this conclusion, which are as follows...
- Medium-class residential is spread across the most part of Llandudno, with high-class closer to the CBD, away from industry and low-class residential (repulsion of land uses).
- The CBD is located on the seafront of the North Shore, and all other services radiate out from it (from most in demand to least popular).
- Industry is situated in a definite sector from the CDB.
- Low-class residential is located close to industry and railway line/roads. This was probably the old CBD.
Hypothesis 2
“Pedestrian density will be inversely related to traffic i.e. as traffic decreases, pedestrian density increases and vice versa.”
As already explained, I collected my data in the process of tally charts. This is shown in the previous methodology section. I used tally charts because they are probably the easiest method I know. I tested both pedestrians and vehicles in and out of the different sites at different times. My results are shown in the table below.
At first sight, the results appear to be very scattered and there does not seem to be an instant correlation. However, if I look at the sites where most pedestrians and vehicles came in and went out, the above table indicates that where there are more pedestrians, there are fewer vehicles. This initial interpretation will either be proved true or false when I present and interpret the data further.
Data Presentation
For this hypothesis (hypothesis 2) I have presented the data in two ways. This will enable me to interpret the data more effectively. My two methods were both graphs:
- A located bar graph (shown on the larger scale street map of Llandudno).
- A scatter graph of pedestrian density against vehicle density
The located bar graph is on the next page, along with the scatter graph, which is followed by a data interpretation section. I will then explain whether the hypothesis is proved or disproved and why.
I used excel to create totals, from my previous data table, for pedestrians and vehicles entering and exiting each site. This was so that I could create the two graphs, located bar graph (on map of Llandudno) and scatter graph (created on excel). Here is the totals table from excel... and the formulas I used to create them:
Data Presentation – Hypothesis 2 continued
Data Interpretation
The results from the located bar graph show that, on the whole, pedestrian density was inversely related to traffic density in Llandudno i.e. as traffic density increases, pedestrian density decreases. However, in my opinion, the scatter graph was not as clear as to whether the hypothesis being tested is true or false. The data seems to be randomly scattered and doesn’t have a definite correlation (either positive or negative). Despite this, I have drawn in a ‘line of best fit’ (using Excel) but I do not think I can conclude from it because the points are so scattered. Therefore, I have chosen to interpret mostly from the located bar graph.
So, my located bar graph shows that the majority of the sites fit my hypothesis, as where there was a higher number of vehicles, there was a lower number of pedestrians (both in and out of the site).
Nevertheless, there appears to be a few sites where pedestrian and traffic density was equal or very close to being equal. For example, at site 23 both pedestrians and vehicles are equal (5). At site 5 the two columns are practically the same length. Referring back to the totals table I can supplement this as my data shows there were 18 pedestrians and 13 vehicles in and out of the site. There were more pedestrians than vehicles because that area has quite a few shops, so tourists and inhabitants would be located here rather than vehicles.
The two above mentioned sites (5 and 23) are not a reliable source to prove the hypothesis, but most of the other sets of data strongly match the hypothesis. For example, sites 6, 10, 13, 16 and 21 are particularly reliable for proving the hypothesis. These sites all had a lot more vehicles pass through them than pedestrians. A reason for this could be because the areas are more accessible to vehicles (which could be delivering goods to the shops or restaurants). On the other hand, in the case of site 21, where there were 79 vehicles and just 6 pedestrians, the land surrounding this site was mostly residential so fewer tourists would visit. I would assume the pedestrians found here were home-owners or renters.
Site 1, which is situated on the seafront (North Shore) had 41 pedestrians and 33 vehicles pass through it. This area is probably been designated for pedestrians as a suitable, attractive and safe place for tourists to gather. Therefore, most vehicles would tend to enter Llandudno by a different route, e.g. lorries or vans with deliveries for factories or other industry could enter via Maesdu Road (accessible by a roundabout) and tourists visiting Llandudno may park away from the seafront due to busy periods or expensive prices for parking.
Another pointer I have noticed is that the majority of the sites we investigated had more vehicles than pedestrians travelling through them. Possible reasons for this could be:
- Vehicles can travel faster than pedestrians. In the 10 minutes at each site that we used for counts more vehicles would have passed by than pedestrians for this reason.
- Some areas may have been more accessible by vehicles than pedestrians.
- Some visitors may just be passing through to bypass somewhere else or just pass through Llandudno as a more scenic route to wherever they may be going.
The only anomalies of this finding were sites 1, 5 and 7. For sites 1 and 5 I have already stated why more pedestrians would be located there. I would predict that site 7 has more pedestrians than vehicles passing through it (71 pedestrians, 57 vehicles) because that is where the main shops are situated and so this area and beyond would be busy with people shopping for various goods meaning fewer cars, etc would pass through.
Is the Hypothesis proved or disproved?
Overall, I believe I have proved the hypothesis but the results were not as definite as they were for the land use survey.
Having proved the hypothesis I have therefore also partly achieved my second aim of the fieldtrip to Llandudno. Although my second hypothesis was not specifically related to finding out Llandudno’s Sphere of Influence, I have discovered the Sphere of Influence within Llandudno itself as being the area served by the CBD (Central Business District). This is where most shops, hotels, B & B’s and offices etc are located and so this is where most people visiting Llandudno travelled for their required goods, services, leisure activities, etc.
Hypothesis 3
“Llandudno’s Sphere of Influence varies according to length of stay of visitors i.e. holiday makers staying longer than 5 days will come from further away.”
As I have already explained (in the Methodology section) I collected data to either prove or disprove this hypothesis using a questionnaire. An example of this questionnaire can be found in the Details of Hypotheses pages of my methodology. The two main questions, which were needed to enable me to either prove or disprove the hypothesis, were: ‘Where do you live?’ and ‘How long is your stay?”
Results
The results I found on the fieldtrip to Llandudno are listed on the next page. After these are collected group results which were collated in class following the fieldtrip.
Initially looking at the results I found on the fieldtrip to Llandudno, I can identify that most people travelling a further distance to Llandudno stay for longer. If this first identification is found to be true when I interpret the data further, I have proved my hypothesis.
However, firstly here are some examples as evidence for my initial assumption:
- One person who came from Anglesey in North Wales (journey length of 30 miles) was only visiting Llandudno for the day. Whereas, two different people interviewed, who came from Glasgow (journey length of 5 hours) were staying in Llandudno for a week.
- Another person interviewed from Wolverhampton, West Midlands (journey length of 3½ hours) was staying for 2-3 days, but someone else who travelled for 9 hours from Maidstone in Kent to visit Llandudno was staying for 3 weeks.
The rest of my 30 interviewees are listed on the following page, however in order to prove my hypothesis I will need more results as evidence. Otherwise, the investigation would not be an accurate one or a fair test. So, hence my table of collected group results on the page following my 30 interviewees. This was collated in class from our results this year and previous year’s results in order to allow us to interpret from a more reliable source of data.
Collected Group Results
Briefly, from the above collected group results, I can initially observe that holiday makers staying for 5 days or longer travel from further away. Some holiday makers travelling from foreign countries such as Sweden, Canada, the Czech Republic and France.
To balance this statement, I can also initially observe that day-trippers or people staying for a few days do not travel so far and all of these people come from the UK. For example, the 11 people from Shropshire, (data collected over more than 1 trip to Llandudno) were day-trippers to Llandudno.
These two examples of both sides of the spectrum indicate to me that my hypothesis is true; however, I will now present the data in a more visually good technique in order to then go on to interpret the data in more detail and conclude this hypothesis by proving, disproving or finding the hypothesis uncertain and linking this decision to the theories discussed earlier in the study and the aims.
Data Presentation
Included in this section of hypothesis 3 – Sphere of Influence is a density map which shows the number of people who have visited Llandudno from counties in England and the UK as well as foreign countries and also the average length of stay. In addition to the density map, there is also a flow line map which shows where the different numbers of people have travelled from to get to Llandudno.
*Both of these data presentation techniques can be found in this wallet.
Data Interpretation
From the density map, I can observe that, on the whole, visitors travelling from further away stay in Llandudno for a lengthier period of time than those who live nearer to Llandudno. My key on the density map, which is transferred for each of the counties in England we collected and collated data for, onto the map of England itself, indicates people from those counties nearer to Llandudno were day-trippers. These were people from North Wales or Llandudno itself, Lancaster, Merseyside (Liverpool), Chester and some of Cheshire and Shropshire. I think that people from these places only stayed in Llandudno for the day because they live close enough to endure a short journey home and so there is no need for them to stay over night.
Visitors to Llandudno from Stoke/Staffordshire, Derbyshire, Nottinghamshire, Leicester, Warwickshire and Herefordshire were found to have stayed in Llandudno for 2-3 days. I believe their stays to be longer than 1 day because they have a slightly longer distance to travel to Llandudno and so decide to lengthen their stay in order to avoid more hours of travelling on the same day. Another reason for a longer stay could be because they are enjoying Llandudno for a short break. However, this does not seem as likely for the counties mentioned because they are generally more rural areas of England, not urban large cities.
People visiting Llandudno for 5 days or more came from Yorkshire, Manchester, Lincolnshire, the West Midlands, London, Hampshire and Scotland (Glasgow and Edinburgh). For the majority of these counties, I would think that people visited Llandudno for 5 days or more because their journey times were even longer, perhaps 4-5 hours and evidently more for the counties located in Scotland. However, there are two anomalies of this finding. These are Manchester and the West Midlands; Manchester being the strongest anomaly. I have identified these as being anomalies to the finding because they are counties situated closer to Llandudno, Manchester especially so, and were found to have people from them who stayed in Llandudno for 5 days or more. Although, they are anomalies to my thoughts about why people from London, etc stayed for more than 5 days in Llandudno, I can suggest another reason for why people from both Manchester and the West Midlands stayed in Llandudno for 5 days or more. My reason for these anomalies is that the two counties themselves are quite largely populated, busy cities. Therefore, people living and working in these areas would need a break from the stressful, highly densed traffic, noise and pollution cities of Manchester and Birmingham (for example) in the West Midlands.
Moreover, the foreign countries from which visitors to Llandudno were found and interviewed, said that they were staying in Llandudno for 5 days or more. This would be the most obvious assumption because to it would take a few days to travel to a foreign country and find the sea side resort, off the coast of England you were staying in. So, you would need to spend more than a few days in order to enjoy the area. People were interviewed from many foreign countries, such as France, Ireland, Canada, Sweden and the Czech Republic. This also indicates that Llandudno is a popular sea side resort for tourists, English and foreign alike to visit.
The flow line map showed the number of tourists visiting Llandudno from the different counties they were interviewed as being from. This presentation of data does not aid my conclusion as to whether my hypothesis is proved or disproved. But, if I were to explore another hypothesis on Sphere of Influence, such as “Llandudno’s Sphere of Influence varies according to actual distance from Llandudno, i.e. as distance from Llandudno increases, so does the number of visitors.” I could interpret the data shown on the flow line diagram, which I believe does, overall, prove this hypothesis. I can see this from the diagram because the width of line decreases to counties further away, in distance, from Llandudno. I can suggest that this is because less people are likely to want to visit Llandudno if they would have to travel for longer to get there. The majority of the people in Llandudno, on the day of the fieldtrip, came from North Wales or Llandudno itself and the fewest from the foreign countries and the furthest away counties such as Lincolnshire (East) and Hampshire (South).
Is the tested hypothesis proved or disproved?
I believe that this hypothesis (the main one investigated in Llandudno) is proved, for the reasons explained previously in data interpretation. I have also discovered Llandudno’s Sphere of Influence as being on a very large scale, which stretches as far as foreign countries such as Canada, Sweden and the Czech Republic. Therefore, I have achieved part of my second aim of discovering Llandudno’s Sphere of Influence. I would also say that I have proved the Central Place Theory, explained earlier in this study. As Llandudno is a relatively large settlement in Wales, its closest rival for tourists being Rhyl, also in North Wales it has quite a lot of high order services to provide visitors with entertainment, shopping, etc. However, Llandudno’s actual population is not as large as a town or city. Hence, it fits the exception to one of the rules for Central Places, explained earlier in this study:
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The larger the settlement, the higher the order of services --- exception: tourist resorts that have a small population but large number of functions.
Hypothesis 4
“Building and environmental quality is greatest in areas most used by tourists.”
As previously stated, in the Methodology section, I collected the data in order to either prove or disprove the above hypothesis. My method consisted of recording value judgments of each site investigated (from 1-10 for each judgement). I used a grid with different factors of building and environmental quality to consider, e.g. house appeal. The 5 values were then totalled to make up a score out of 50 for each site, tested in Llandudno.
Results
Whilst on the fieldtrip, my group of 4 students recorded value judgements using the method explained above. When we were back in class, everyone’s results were discussed and we collated a set of group results, which would then allow us to both present and interpret the data, as we now had a full set of results.
Here is a table, which I created using Microsoft Excel, of the group results:
Data Presentation
I have decided to use two different formats, as I have already mentioned, to display the data in the previous table for Building and Environmental Quality in different sites located across Llandudno. Firstly, I will create a pie chart and secondly a scatter graph. The scatter graph is draw by hand (using graph paper) and follows the pie chart, which I used Excel to create. This is shown below:
Data Interpretation
There are many approaches that I could take to interpret the data for this hypothesis. Nevertheless, I believe the most obvious approach is to look back at my pedestrian counts (hypothesis 2) as this hypothesis needs to compare the concentration of tourists with building and environmental quality.
From the previous 2nd hypothesis, I can say that, on the particular day that our class visited Llandudno (14th June 2005), most pedestrians were located at sites 1, 6, 7, 8, 13 and 14. Some of these sites are the same sites that building and environmental quality was the greatest, as I initially discovered when looking at the table of results – reiterated by pie chart. The common sites for both highest building and environmental quality and largest number of pedestrians are: 1, 6, 7 and 8. These sites are situated near to the sea front and so it is likely they will be well maintained for tourists to enjoy the area. If most or, more hopefully, all of my results were highest where pedestrians were greatest in and out of the site, my hypothesis would have been proved. Unfortunately, this is not the case, so I will have to investigate further.
Another interpretation is that two further sites where building and environmental quality was high, pedestrian density was low. These sites were 18 (43/50 and a total of 6 pedestrians) and 19 (40/50 and a total of just 2 pedestrians). From this data, read off from the scatter graph, I can suggest that a reason for building and environmental quality being so high in a non touristy area could be that people living in the area want to keep their houses and gardens neat and tidy, because it is their ‘pride and joy’, so tourists have the insight not to largely invade their land.
Moreover, even though some of the sites situated near to the main sea front in Llandudno (North Shore) had good building and environmental quality, others did not. For example, at sites 2 and 3 27/50 was awarded and at site 4 28/50, which are both less than half, indicating not very clean or well-maintained areas. However, this information does match the hypothesis because there were a lower total of pedestrians through these three areas: site 2 - 22 pedestrians, site 3 – 13 pedestrians and site 4 – 8 pedestrians. I would think a less clean, unattractive area would discourage tourists from walking through it
Finally, having created the scatter graph and pie chart, I found that the pie chart was slightly helpful with making interpretations but the points plotted on the scatter graph were again (as I found on the previous scatter graph) quite random, so to interpret from this graph was quite hard as there didn’t seem to be any correlation. If I had to pick out a correlation it seemed to be neither positive nor negative as all the points were mostly scattered along a straight line.
Also, my group (class) results were not very reliable because the valued judgements were made by other members of the class and everyone had their own opinion on the general appearance of the street/site in terms of building and environmental quality. Therefore, my data is not completely accurate. If I had the chance to test this hypothesis again I would probably change the method of achieving data. However, how I would do this I do not know as the method used seems to be the best and most obvious to me!
Is the Hypothesis proved, disproved or uncertain?
Overall, I think that the hypothesis is uncertain. I believe this because of the indefinite correlation of the scatter graph and data found in general. In my Hypotheses section I did say that I thought this would be an interesting hypothesis to test because it was something we hadn’t really studied in class all that much and possibly more common knowledge. I did expect to find that building and environmental quality would be highest in areas most used by tourists but it seems that there are quite a few sets of data that contradict this hypothesis, so I do not think that it can be completely proved. On the other hand, I do believe that the data tends towards the hypothesis being true because they is some evidence of it.
Even though I have not fully proved or disproved the hypothesis, I have achieved my third aim of the fieldtrip to Llandudno. This was to collate notes on building and environmental quality at specified sites in Llandudno, which I have successfully completed in this Hypothesis 4 section.
Evaluation
Overall Conclusion
In each hypothesis section of this study, I reached a conclusion about whether the hypothesis had been proved or disproved at the end of the section. Previous to this, I presented and interpreted the data for each hypothesis. This allowed me explain what I had discovered for land use, pedestrian and traffic density, sphere of influence and building and environmental quality in Llandudno, on 14th June 2005.
For land use in Llandudno, I presented the data on a coloured-in land use map complete with a key – this can be found on the page following 18 and before 19 (Data Interpretation). From this I interpreted the land use in Llandudno, giving reasons for what I had found – this can be found on page 19. A final conclusion was met, where I decided the hypothesis had been proved and land use did vary with distance from the sea front and also that Llandudno matched the Hoyt land use model. I explained why and how this linked back to the theory discussed earlier in the study – page 9.
For pedestrian and traffic density, I presented the data on a located bar graph and a scatter graph – these can be found after page 22 and page 23. Even though the scatter graph was not very helpful with interpretations, I was able to interpret mostly from the located bar graph. My interpretations can be found on pages 24-25 as well as my conclusion. I decided that the hypothesis was proved, but not as strongly as land use, so pedestrian density was inversely related to traffic density. Also, by discovering where pedestrians and traffic was in the whole I also discovered the sphere of influence within Llandudno itself, as being the area served by the CBD.
For Sphere of Influence, I presented the data on a density map and a flow line map – these can be found in the wallet for page 28. I interpreted the data and strongly proved my hypothesis correct (hence Llandudno’s Sphere of Influence does vary according to length of stay of visitors) as well as creating another hypothesis, which appeared to be relevant for the data collected, this was also proved. My interpretations and conclusions can be found on pages 29-30. I was also able to link back what I had discovered about Llandudno’s Sphere of Influence to one of the theories about Sphere of Influence – the Central Place Theory. This is also explained in the conclusion for this hypothesis.
For building and environmental quality, I presented the data in a pie chart and on a scatter graph – these two methods of data presentation can be found on pages 32-33. My approach to interpret the data for this hypothesis was to look back at the data for hypothesis 2 and compare the concentration of tourists with building and environmental quality. My interpretations can be found on pages 33-34. I found it to be uncertain whether building and environmental quality was highest in the areas most used by tourists. I concluded why and what I had expected to find on the fieldtrip to Llandudno in terms of building and environmental quality.
By either proving, disproving or finding my hypotheses uncertain and linking my conclusions back to the theories discussed earlier in the study, I have ultimately achieved my four aims of the trip, which can be found on page 7 of the study. I also believe that I have achieved my overall aim of the study. This is shown in the above conclusion and previous conclusions of findings whilst on the fieldtrip to Llandudno. I also think I now have a greater understanding of processing and assessing information fro doing this fieldwork on Llandudno. This acquired understanding and knowledge will be very useful to me in the future.
Main Evaluation
In this section, I will assess the methods I used, the data I found and the conclusions I made in this study of Llandudno. To make this more understandable, I will comment on each hypothesis in turn. The factors I will comment on are: Reliability of Methods, Accuracy of Results and Validity of Conclusions.
Hypothesis 1
Reliability of Methods
The quantity of data for hypothesis 1 was comprehensive enough to make a conclusion, as we gathered information on land use in class from the separate areas each group had coloured-in whilst on the fieldtrip to Llandudno, 14th June 2005. In other words, everyone in the class had a complete land use map to interpret and conclude from. However, there was one clear problem with the collection of data. This was that because the land use maps were filled in by numerous individuals, the actual type of land use decided upon was inaccurate. This is because, for example, some people may have coloured-in an area to be residential when they were unsure and the area was actually vacant buildings. So, overall the data is potentially very inaccurate.
The main disadvantage of the land use map technique was the fact that it was completed by so many different people, hence making very unreliable results.
The results could be improved by using various different methods. One method could be to spend more time in Llandudno itself and one person take a day to fill in land use in order to conclude from a more accurate set of results. Another method could be communicating with a local council and to ask for an official land use map of Llandudno, which would provide me with a completely accurate set of results. These two methods could be carried out on another, lengthier trip to Llandudno and the study could be repeated.
Accuracy of Results
As I have already explained (above) the results were not as accurate as they could be, if I repeated this investigation for a second time. The inaccuracy which I acquired is undoubtedly linked with the technique I used, discussed in the methodology section.
Validity of Conclusions
The conclusions I made on land use for hypothesis 1 (decision was the that Llandudno fits the Hoyt model) may not relate to other student’s conclusions because different people will interpret the data in different ways.
My conclusion may be invalid or wrong because, as I have already mentioned, the accuracy of my results was not that great. This was due to the method I used for collecting data, also as already explained.
Supplementary to the two methods stated under the heading ‘Reliability of Methods’ in order to improve the enquiry process and increase the validity of my conclusions I could repeat the study a further three times, using three different person’s opinions of land use. From these opinions and my own, I could create my own land use map. This would hopefully allow me to obtain a greater degree of accuracy of results and therefore a more valid conclusion.
Hypothesis 2
Reliability of Methods
I believe I did collate both on the fieldtrip to Llandudno and back in class, a large enough range of results in order to interpret and conclude from for this hypothesis. On the other hand, if I was able to obtain another, similar set of results (either from a previous year’s visit or from revisiting the site myself this current year-2005) then my conclusion would be more valid. I will go into more detail of this idea in ‘Validity of Conclusions’.
There didn’t appear to be any major problems arising in terms of the method used to collect data for hypothesis 2, apart from it being quite hard to record the number of pedestrians and vehicles in and out of the busier sites in Llandudno. Hence, allowing the accuracy of data to drop slightly. This was probably the main disadvantage of using this particular method. However, I cannot see a completely different, better method for recording data for this hypothesis. I think that there will always be a factor affecting the accuracy and reliability of results in any given investigation. Another minor disadvantage of the method was the keeping to the timescale. This was set to be 5 minutes, but keeping to exactly 5 minutes was pretty impossible. So, this again probably affected the accuracy of our results.
The results could be improved a little by the group being more accurate with time keeping as well as possibly having each member of the group record 1 or 2 vehicles types and 1 person keeping the time. This could work for the busiest sites at least.
For example: 1 person – pedestrians in
1 person – pedestrians out
1 person – cars in
1 person – cars out
etc...
For this sort of organisation, you would undoubtedly need more than 4 students per group. The results from each student could then be compared and copied, after the count, to the tally chart.
If I revisited Llandudno for a second time, this is probably how I would record data for the pedestrian and traffic survey.
Accuracy of Results
As I have already discusses, the results are not as accurate as they could be. If I were to carry out the investigation again, I would follow the above method for recording data, along with other members of my team. The inaccuracy of results obtained in 14th June 2005 is obviously linked with the technique used for collecting data for pedestrian and traffic surveys (hypothesis 2). The actual method I used is fully explained in the methodology section of this study.
Validity of Conclusions
The conclusions I decided upon for hypothesis 2 – pedestrian and traffic density being inversely related should relate to other student’s conclusions. These students would have to have investigated the same hypothesis as I did, which I did prove.
However, as with land use, people can interpret the data in different ways and so it is not conclusive that my conclusions will directly relate to other student’s.
My original conclusions may be wrong because of the two factors I identified in the previous sections for the evaluation of hypothesis 2. These could be justified if I obtained another set of results from previous years, as more sets of data will provide me with more reliable results. Or, I could revisit the site and use the new method of recording data I decided upon in the previous ‘Reliability of Methods’ and ‘Accuracy of Results’ sections for this hypothesis.
Furthermore, I would have to follow the more accurate procedure for recording data, as already explained under the ‘Reliability of Methods’ heading, in order to improve the enquiry process and hopefully increase the validity of my conclusions.
Hypothesis 3
Reliability of Methods
For hypothesis 3, I think that the quantity of data collected and collated both on the fieldtrip to Llandudno and back in class, was comprehensive enough to present and then interpret from.
Problems with collecting data could have been that the exact places where people live were not very accurate or were written down incorrectly. For example, I live in Alsager, which is classed as being in Staffordshire because of the ST postcode but the town of Alsager itself is actually situated in Cheshire. Therefore, similar mistakes such as this could have been made, as some people would say the county they lived in was Stoke/Staffordshire when in fact it is actually Cheshire, in this particular situation. Nevertheless, this shouldn’t have made a lot of difference to the accuracy of results for the investigation into my hypothesis because it was on a much larger scale.
Apart from the potential above problem, I cannot think there would be any more disadvantages of the method used for collecting data in order to discover Llandudno’s Sphere of Influence. So, overall I believed the method we used to be a reliable one.
As with most investigations, the results could be improved by collected more data and also more varied data. This would enable the conclusion to whether the hypothesis was proved, disproved or uncertain to be even more accurate. If I were to repeat the study I would try to collect more data from questionnaire, to help me achieve the conclusion explained above.
Accuracy of Results
As I have already discussed, in the above ‘Reliability of Methods’ section for hypothesis 3 – Sphere of Influence, I believed the results we collected both on the fieldtrip to Llandudno, 14th June 2005 and collated results from previous years to be of a medium-high level of accuracy for the reasons also already explained.
Again, the few problems with inaccuracy are linked with the method used to collect data, because with more collected data our overall conclusion for hypothesis 3 would be even stronger.
Validity of Conclusions
As for the other 3 hypotheses, everyone will have interpreted the data in a slightly different way and using different strategies, meaning my conclusions may not directly relate to other student’s conclusions.
Also, my conclusions may be invalid/wrong because I have either interpreted the data in an invalid way and could have gone off on a tangent with one idea, leading me to an invalid conclusion. Or, the data itself not being as accurate as it could be, as the quantity and variety is not great enough.
Improvements to the enquiry process that would increase the validity of my conclusions have been previously explained in both ‘Reliability of Methods’ and ‘Accuracy of Results’. The main improvement that I could make, if I were to conduct the study a further time, is to collect more data for Llandudno’s Sphere of Influence by completing more questionnaires and attempting to make them as accurate as possible.
Hypothesis 4
Reliability of Methods
Overall, I do not think we had a great enough range of data, for hypothesis 4 – building and environmental quality, to be able to interpret the data successfully and make a valid conclusion.
The only real problem with the data collection for this hypothesis was similar to the problem experienced with land use for hypothesis 1. This was that because the valued judgements were made by many different individuals, and everyone had their own opinions on the general appearance of the varying sites, the results obtained from each group and later discussed in class were inaccurate. This was the main disadvantage of the technique used.
The results could be improved by allowing only one group of four students to conduct the same investigation into building and environmental quality in Llandudno. The four individual’s opinions could be averaged out for each factor of building and environmental quality in order to assume a more accurate, valued judgement. Of course, using this method would, without a doubt, take longer to complete the task. But, it would hopefully provide us with a more reliable set of results. The study would have to be repeated and another trip to Llandudno arranged.
Another method which could improve the accuracy of the results a lot more would be, as I have already mentioned for land use, to organise a document, compiled by the local council, to be presented to the group. This method would probably be the only strategy of obtaining a completely accurate set of results. However, it could also be more hassling.
Accuracy of Results
I have already discussed the inaccuracy of the results found for building and environmental quality and I believe that they are related to problems with the methodology, also described previously.
Validity of Conclusions
My individual conclusions may not be related to other student’s work because, as I have repeatedly mentioned throughout this evaluation section, not everybody will have interpreted the data in the same way from their presentation of the data. My conclusions may be invalid because the group did not have a large enough set of results, in my opinion and/or the results were not accurate enough. When repeated, data for hypothesis 4 would hopefully be more reliable to make conclusions from, as we would hopefully have more accurate data to interpret from. This would be because of the improvements explained previously (for hypothesis 4) to the enquiry process, after which the validity of my conclusions should increase.
