Geography Investigation: Residential Areas

Page Number

Geography Coursework

Residential Areas
How do Basingstoke’s residential areas change, improve and reflect different urban models?

Applied Understanding

For my geography coursework I am going to study the different types of residential areas throughout Basingstoke; working from the Central Business District and following the main route through Basingstoke in a south westerly direction. When my coursework is complete I am hoping to come up with a conclusion about whether Basingstoke follows the concentric model or if it fits more to the criteria of the sector model.

The central place theory is a geographical theory that tries to explain the size and spacing of humans, however, this theory only works when certain criteria are met, which in reality, aren’t met, for instance: an isotropic, limitless amount of space, an evenly distributed population, evenly distributed resources, consumers all have same purchasing power and no provider of goods can earn excess profit.

The concentric ring model (also known as the Burgess Model) is slightly more realistic than the central place theory and is actually based on a city, Chicago, Illinois. The theory puts forward that a settlement grows evenly (see Figure 1 below) , with the CBD in the middle, then light manufacturing, working class housing, middle class housing and then on the outskirts of the settlement, the high-class housing. At this stage I can see that Basingstoke does, to a certain extent relate to this model.

The sector model also known as the Hoyt model (see Figure 2) explains that the strict growth patterns of the concentric model is wrong and settlements do not grow to an exact plan. It explains that there are reasons for growth in a certain way. It shows that working class housing is generally situated around various transport routes into the CBD, i.e. railroads and heavy volume roads. The model at this stage does not replicate the town of Basingstoke, however, when I have finished my investigation I will be able to see if it does link in anyway.

Figure 2
Source: Google Images

Both these models are based on stereotypical towns and cities, thus they are not going to fit exactly for Basingstoke. However, when my investigation is over I will be able to see if the transition goes in the same order and come up with my own model for Basingstoke.

Before I can investigate anything I must come up with what I want to find out, i.e. hypothesis that will be the basis of my investigation throughout. These predictions will help me come up with the final conclusion about Basingstoke and how it relates to the theories above. I will investigate each of the questions in order coming up with conclusions to each, and then I will come up with an overall conclusion about Basingstoke’s urban distribution.

Hypotheses

The intangible factors will have a higher rating on the outskirts of Basingstoke compared to the inner city areas.
From the interpretation of the models we expect to find nineteenth century housing (terraced) which is cramped, unappealing and polluted due to its close proximity to the CBD. This housing is also older, thus, it would have deteriorated. Within this hypothesis I will also investigate the time the average time the occupant has lived in the area and whether they want to continue to live in the area. I would expect people to will want to leave the area due to pollution, lack of green space, high crime rates and the poor quality of housing we expect to find in the inner city. I will also find out what type of housing is found in this area – I have predicted the majority will be terraced in the inner city.
Index of decay will be higher in the inner city areas.
Working out from the CBD, the housing gets newer because the city is expanding. I am predicting that index of decay will be higher in the inner city because one, the housing is older and there are factors which cause different types of erosion on the structure etc, and secondly people living in the inner city are usually working class (Figure 1 & 2). Within this hypothesis I will also investigate whether people own their houses or privately rent because I am suspecting that there will be a higher amount of private rented properties because people don’t want to spend the rest of their lives living in the inner city.
Externalities will have a higher penalty point than the outskirts of the town.
When the inner city was first being built it was not planned, houses and factories were just built on available land and pushed together so that more and more could be fit into a small piece of land, thus, there will be a lack of green space, more industry and more pollution in the area.
The average area rating will be lower in the inner city and there should be positive correlation moving away from the centre.
The area rating will depend on a number of factors that the occupant rates – due to their being a lack of planning in the inner city, I am assuming that the factors will have a lower rating and as we move out of the inner city, where planning is high priority to make residents lives easy, factors will be rated higher.
There will be more private housing on the outskirts of the town.
As a town develops and grows new housing estates must be built to accommodate the growing population, these are built on the edges of the town (Figure 1 & 2) away from the hustle and bustle of the CBD. People who live here generally have better jobs, better salary thus more disposable income to buy their own property which is bigger and better housing compared to what is available in the inner city and inner suburbs. By the time residents can afford to live in these areas they would of moved up the property ladder, therefore, they will be older, more likely to have a family. I will also investigate these two factors in this hypothesis.

Cyprus Road should have a higher average area rating than anywhere I have surveyed in Basingstoke.
I will collect data for ten roads throughout Basingstoke but as a test to see if the town does relate to the concentric model I am going to extrapolate from my data I have already collected some other data that will allow me to calculate the average area rating of this street. When I have made my own prediction of what the average area rating should be I will then survey the area with a questionnaire. If my prediction is correct, I would then be able to find the average area rating of any street after that on the same route.

Location

The United Kingdom is situated in the far west of Europe but is not connected to the continent by land. Basingstoke is located in the southeast of England (green arrow) and is 48 miles southwest of the capital, London.

Figure 4
Source: Google Maps

Basingstoke is in the county of Hampshire and was originally a market town. It soon became an overflow for London.

Basingstoke is an expanding town and is situated in northeast Hampshire (Figure 5). The town lies in a valley at the source of the River Lodden.

Basingstoke is linked to the capital by the M3 that runs north past Basingstoke on the southeast side, there are two junctions that serve the town, junction 7 in the south of the town and junction 6 in the north of the town. There is no particular reason why Basingstoke stands out to investigate residential areas; in fact, because of its growth it is probably harder to study the town as it has no particular boundaries.

On the next page is a map of Basingstoke showing the places where my study took place, making each with a red dot.

To examine my overall question I need to collect primary and secondary data in a number of different ways. I will conduct a questionnaire that will be put through peoples doors and ask them to answer the questions about the area they live. I will conduct a Bi Polar analysis of each area I study within Basingstoke; I will also carry out an attractiveness survey on each of the areas. To back up what I see I will take photographs of eye-sores and the different types of residential areas I experience throughout my investigation.

Methodology

How do Basingstoke’s residential areas change, improve and reflect different urban models?

I have chosen this key question as the basis of my coursework because as Basingstoke is expanding at an extremely fast rate I wanted to see if the town is growing to one of the theories I have explained further up (Figure 1 & 2) because as technology forwards and demands made by the individual for high quality places to live is it possible for a theorem to help us understand the way our towns and cities we live in are growing? Well personally I think no, and so I have set myself the challenge to investigate Basingstoke’s residential areas and conclude with, if Basingstoke doesn’t fit into any of the other models an actual summary of how Basingstoke changes from CBD to the outskirts, or in theory, the high class housing area.

As well as the above geographical reason for doing my investigation, I also would like to investigate and find out how the areas in Basingstoke do change, people’s opinions, property value, crime rate and the different advantages the areas have to offer. My investigation will go along a set route within Basingstoke, working from the town centre and stopping off at random streets – when I conduct my survey I will use a sample size of 10 – this is large enough to provide accurate results. However, if I had more time to do this investigation it would be a lot higher.

To collect the appropriate data for my investigation I need to use a questionnaire that I will put through people’s doors, ask them to complete it and go back a few days later to collect it. This is primary data. The questions that I include must be relevant to what I want to find out to answer my hypotheses and I don’t want to infiltrate people’s own confidentiality. I have chosen this method for gathering my primary data from the actual residents because instead of knocking on their doors and maybe intimidating them, I am giving them the choice of whether they would like to fill it out. When I have this information about each of the residential areas I can analyse it and use it to fulfil my hypotheses and either deem my hypothesis correct or change it to a correct statement about Basingstoke and conclude my work on it. This is known as my ‘Internal Questionnaire’. (See Figure 7)

A few of my hypotheses require data on the appearance, externalities and intangible factors of the different residential areas. I need to collect data on intangible factors which basically mean what the area feels like, the atmosphere and whether it’s comfortable etc. To gather this data I will use a Bi Polar analysis which will rate the area on a scale from -5 to 5. I have chosen this method for collecting data about the intangibles of the areas because it is changing feeling into numbers so it can be used to create graphs and charts – this will help me to evaluate the area in a visual way. This is a personal view of the area, thus it is primary data. This can be found on my ‘External Questionnaire’ which I complete myself. (See Figure 8)

I also have a hypothesis that requires me to have data about the appearance of the area – I have called this, Index of Decay. I am able to give a rating from 0 – 15 in various things such as paint peeling, broken gutters etc this will help me when concluding about what area is more appealing, better looked after and whether the worse cases of decay are in the inner city. The data is in numbers which enables me to create graphs, charts, tables and use statistics to rate the area. This is on my ‘External Questionnaire’. (See ...

This is a preview of the whole essay

Also on my ‘External Questionnaire’ is a penalty point system that rates the externalities of an area. The higher the number, the worse, in my opinion the area is. This is again primary data, yet biased because it is my opinion. I have chosen to use this method of collecting data about externalities because it changes different qualities of the area into numbers. For instance, if the area has a bad smell then it will receive a higher number out of 15 compared to a place that has no offensive smells. I will then be able to use the total penalty point number and plot it on graphs etc to find if there is a correlation. (See Figure 8)

To collect evidence of my investigation and what the residential area looks like I will take pictures to show what the different areas look like and if there is a problem with the area like bad traffic I have evidence to prove this. I need to use a photographic form of collecting evidence otherwise I have no proof of what the area looks like and whether it actually has a problem or not. With the pictures I will annotate them to show problems or the good things about the residential areas.

For a brief rundown of my methods etc see Table 1.

I will survey ten streets, starting from Sarum Hill in the centre, working south west along Winchester Road, then along the A30 until Brighton Hill roundabout where I’ll go onto Brighton Way. I follow this road until the roundabout and then go along a road called Cliddesden Lane. I stopped at a street called, The Beaches. The Beaches is the beginning of an area called Hatch Warren. I have purposely not collected data from this area. My last hypothesis explains in more detail why I have done this.

On the next few pages are blank copies of my different surveys, The ‘Internal’ and ‘External’ respectively.

I have annotated them outlining why I have asked this question and to what hypothesis it relates to – I have done this to show I have not asked for any unnecessary information from the person filling out my survey. It is also highlighting the relevance that each question has to my investigation and that I cannot form acceptable results without it.

Figure 7

Figure 8

Table 1

For each of the hypothesis I have just mentioned the main method I will use throughout the investigation as a breakdown of the last few pages. Most of my hypotheses will use more than one of my methods and nearly all of hypotheses will be backed up with photographic evidence.

Data Presentation and Interpretation

The intangible factors will have a higher rating on the outskirts of Basingstoke compared to the inner city areas.

As explained in section one I predicted that the intangible factors will be higher on the outskirts of town. Within this hypothesis I am also going to investigate whether there is a relationship between the intangible factors and the time the occupants have lived in the area, whether they want to continue living there and what type of housing they live in. It is useless if I compare every single street I have surveyed on this hypothesis, so I will just compare the three streets on the outskirts, The Beaches, Vivaldi Close and Cumberland Avenue respectively; then the three streets working away from the CBD, Sarum Hill, Bounty Road and Penrith Road respectively.

Figure 9
Source: Google Maps
The black stars show the roads being used in this hypothesis
the orange star shows the CBD

The data for intangible factors was gathered from the ‘External Questionnaire’ (Figure 8), below is a table showing the total for each street included in this hypothesis investigation.

Table 2
Intangible factors and time in months

My results for the intangible factors are showing a positive correlation; the further you move away from the CBD the more welcoming the street becomes. I can relate these results back to the concentric theory because it states that a town grows in circles and the estates situated further away from the CBD will be of better quality in various ways. However, I cannot relate this data to the Hoyt model because I do not have sufficient data.

The average time the occupant has lived in the area does not seem to show much correlation whatsoever. I would have expected to see a lower average time lived in the area, a lower intangible score.

Although I personally cannot see a correlation in the two sets of data above I will use the statistical calculation called Spearman’s rank which will give me a number between -1 and 1 which indicated whether or not there is a correlation in the data, positive or negative.

Null Hypothesis: There is no correlation between intangible score and average time lived in the
property in months

To calculate Spearman’s Rank I will use the formula:

d = difference n = number of ranks

Rs =1-(6∑ d2 / n3-n)

Table 3
Spearman’s Rank

Rs =1-(6∑ d2 / n3-n)
∑d2= 24 therefore 6∑d2= 144
n = 6 therefore n3-n = 210
Rs = 1-(144/210)

Rs = 0.32 (correct to 2 significant figures)

Null Hypothesis: Rejected.

I hypothesized that there was no correlation between the two variable statistics of intangible score and average time lived in the area. After calculating spearman’s rank for the two sets of data I have found that there is in fact a weak positive correlation between the two. This means that the lower the intangible score the less time occupants have lived in the house. So to relate this back to the theories explained in part one; in the inner city we expect to find older housing that’s unplanned, dirty and slummy and people just don’t want to live in that kind of environment thus (according to my data) the further into the city, the lower the intangible score, the less time occupants have lived there. On the outskirts of the town the higher the intangible score, the longer people have lived there.

Another part of this hypothesis is to investigate whether or not people want to continue living in the area; I predict that people living in the inner city will not want to continue living in their area because of the environment, crime rate, pollution, lack of open space etc.

Table 4
Continue living in the area

Above you can see that most of the residents are happy with where they are living except for Sarum Hill, which is the worse place to live so far from my investigation. To display this information I will do a basic technique, a divided bar chat – although simple it will visualize what’s above and then I can see if there is a pattern. (Figure 12 – next page)

Figure 12
Stacked Bar Chart

On the route out of Basingstoke’s CBD along Winchester Road, you can see above that from Sarum Hill to Cumberland Avenue the amount of residents not wanting to move increases. This is probably because housing quality is getting better thus attracting more people that don’t want to leave. Also, referring back to when I calculated spearman’s rank it said there was a weak positive correlation for intangible factors (the main part of the hypotheses) and therefore this bar charts results are just because if the intangibles are better people don’t want to leave the area.

Conversely, the chart shows that Vivaldi Close takes a considerable drop and a total of 4 people don’t want to continue living there, on the surveys that the occupants filled out they have said that it’s not because of the area they want to leave, its due to the sizes of the houses. One resident told me that the close is made of just one and two bedroom houses. In today’s society, that just isn’t big enough for a couple who want to start a family etc, so they want to leave.

So far my hypothesis had been proved correct with the two studies I have done so far, the final section of this hypothesis I am going to investigate is the type of housing that is in the area i.e. terraced, semi-detached etc. When I have done this I can conclude my hypothesis.

I am now going to investigate the different types of housing on the same three streets so I can come up with a conclusion to my hypothesis which when it comes to wrapping up my coursework as a whole I know exactly what I found from this hypothesis.

Table 5
Type of housing

For the above data I have created a percentage stacked bar chart, it is the easiest way to show this data. There is a lot there and so I need to use a technique that is going to break it down as much as possible so I can see if there is any kind of correlation that shows terraced houses are being faded out and the production of post 1900 semi-detached Victorian houses are being developed.

Figure 13
A stacked percentage bar chart

This percentage bar chart shows clearly how, from the centre of town where the cramped together terraced housing that dates back to the industrial revolution begins to fade out as you move away from the CBD. In Bounty Road we see the introduction of detached housing and then in Penrith Road semi-detached hold 40% of the housing there. From Sarum Hill to Cumberland Avenue terrace housing is significantly faded out then when we reach the newer part of the town that has been built in the last twenty to thirty years we see the re-introduction of terrace housing. Both Vivaldi Close and The Beaches are situated in the part of Basingstoke according to both the concentric and Hoyt models where the middle-class housing is found, thus, it is private but small and convenient for first time buyers – hence the reason for the mass terraced housing. However, although terraced like Sarum Hill the quality of housing here is on a much better scale and is clean and tidy and had been planned by developers and accepted by the council.

The intangible factors will have a higher rating on the outskirts of Basingstoke compared to the inner city areas.

Above is the hypothesis that I have just finished investigating. Basically this hypothesis is true and I have backed it up with evidence. I compared the intangible factors with the the time the resident have lived in the area and found the higher the intangible factor the longer the average time a resident had lived in the area. Also with this hypothesis I investigated whether residents wanted to continue living in the area and the type of housing they live in.

So far, with evidence from this hypothesis I have to say that Basingstoke minimally relates to the concentric model. I have evidence for this as Figure 12 shows that residents want to get away from the CBD which is known for its poor class housing and cramped conditions. Figure 13 shows that Basingstoke has developed according to the concentric model in a southwesterly direction.

In conclusion, when leaving the CBD you find that the intangible factors get better and they do end with the highest rating being nearer the outskirts of the town. Secondly, the time people have lived in an area relates to the intangible score – higher intangible score, longer the average time a resident has lived in the area. Thirdly, residents wanting to stay in their area of residents increases as you make your journey from CDB to outskirts. Lastly, the types of housing relates to the concentric theory and terraced housing does fade out when moving from CBD to the outskirts then there is a sudden growth in the amount of terraced properties on the estates built in the last twenty to thirty years.

Index of decay will be higher in the inner city areas.

In this hypothesis I will investigate whether there is a relationship between where residents reside and if the index of decay is higher in the CBD and gets better as you work your way out from the centre. In theory, the newer, planned houses are on the outskirts of the town, thus they will have a lower index of decay. However, the nineteenth century, older, unplanned houses in the more central part of town should have a higher index of decay.

In this hypothesis I will use every street that I have surveyed. For the first part of the hypothesis I will only be using the ‘external’ questionnaire – this is the survey I did myself of the area.

Table 6
Index Of Decay

At first look, it is easy to tell that as we move away from the CBD the index of decay is dropping – this is what I’d expect to find when referring to both the urban models. Penrith Road’s index of decay increases by 3 points, removing it from the decreasing pattern that starts from the centre of town. This could be due to a number of factors; 1) a higher crime rate, 2) older houses than Bounty Road (this would mean that Basingstoke does not relate to the concentric theory) or 3) the residents to not have enough disposable income to spend on their home to keep it tidy etc.

We also see a significant increase in decay in Cumberland Avenue. When I surveyed this street I noticed that these houses were a lot older than the actual area they are situated in. The houses are older so this explains why the street has a higher index of decay. The rest of the streets that I have surveyed you can see that the index of decay decreases as the distance from the town centre increases.

To evaluate this data I am going to use a cumulative frequency graph – this will enable me to find the lower quartile, upper quartile and median. With these values I can plot a box plot and then I will be able to appraise the spread of the data.

On the next page is a table which is the beginning of the stages of creating a cumulative frequency graph and then a box plot.

Table 7
Cumulative Frequency

I will now plot the ‘Cumulative Frequency’ data from this table onto a graph and then using that graph I will come up with the upper quartile, median and lower quartile.

Using the cumulative frequency graph, I have found the following figures:

Upper Quartile: 32

Median: 20

Lower Quartile: 12

With these figures I can draw up a box plot which enables me to evaluate the spread of the data.

I am now going to calculate the mean of the values and indicate it on the box plot with a red cross. To calculate the mean I use all the data from Table 6 and divide it by the number of streets.

236 / 10 = 23.6

The box plot shows that Basingstoke’s index of decay has a negative skew which means there is less streets with lower index of decay. This means that Basingstoke has an overall high index of decay – you can also see this in the box plot above as right of the median is bigger than left of the median indicating the fact I just indicated. The street with index of decay that has a result of plus twenty is more towards the CBD in Basingstoke – 20 being the median we can see that there is a split between working class and middle class housing and this split seems to come apparent at Portacre Rise.

This data proves my hypothesis correct; the inner city does in fact have a higher index of decay compared to the outer parts of Basingstoke. Also as we move out of Basingstoke along the southwesterly route we expect the index of decay to get better – my data analysis has proved this.

Secondly within this hypothesis I want to know whether or not people own their properties or rent the properties. I will then use a scatter graph to see if there is correlation between index of decay and the amount of people who actually own their homes. I expect to find the higher the index of decay – the less people own their homes.

Table 8
Rent or Own

Figure 16
Line Graph

As you can see from the line graph above there is no real patter between where the resident is situated and whether they own their home. Portacre Rise and Cumberland Avenue both have the most owned properties, and the other streets vary.

I will now combine the data that shows whether people own the homes with the index of decay data to see if there is any correlation between the two. The two sets of data I want to investigate are very different and the only suitable way to find if there is a correlation is to use the statistics calculation of Spearman’s Rank. Using a scatter graph or something similar with this data would not give me accurate or clear results – so although I am repeating something I have done already in hypothesis one, I am doing it for a good reason.

Null Hypothesis: There is a weak correlation between residents owning their homes and having
a low index of decay rating.

Table 9
Spearman’s Rank

Rs =1-(6∑ d2 / n3-n)

Rs =1-(6∑ d2 / n3-n)
∑d2 = 24 therefore 6∑d2 = 744
n = 10 therefore n3-n = 990
Rs = 1-(744/990)

Rs = 0.25 (correct to 2 significant figures)

Null Hypothesis: Accepted.

My null hypothesis said that there is a correlation between the index of decay and whether or not the occupants own their own home. This is true; there is in fact a weak correlation between them. This means that the higher the index of decay the more likely the occupants are to own their own homes.

Over the course of this hypothesis I have found out two main things about Basingstoke. The Index of Decay is higher in the inner city and there is a weak positive correlation between Index of Decay and whether the occupant owns their home.

2) Index of decay will be higher in the inner city areas.

In conclusion to this hypothesis I have found that Basingstoke does again (as in hypothesis one) minimally relate to the concentric theory because I have found Index of Decay actually gets better as you leave the CBD and make your way out of Basingstoke on the southwesterly route. This means the residential areas actually get in a sense better because the way people look after their properties changes and gets better, hence receiving a low Index of Decay rating. Also to conclude, I have found that residents own more homes away from the CBD – this is probably due to them not wishing to buy a family home in the centre of town and property prices in Basingstoke are also higher in the CBD due to supply and demand; there is a lack of nineteenth century properties and people can’t afford to live in them, thus having more rented properties in the area.

I have now finished and completely investigated this hypothesis. I will now go onto investigate hypothesis 3.

Externalities will have a higher penalty point in the centre of town compared to the outskirts.

For my third hypothesis I am going to study on the environment in which the residential areas are situated in. I have hypothesized that the externalities will have a higher penalty point in the centre of town compared to that of the outskirts of town because back in the day when settlements first began to expand during the industrial revolution the need for compact housing was high and therefore the amount of green space etc became extremely limited.

So I can examine the correlation of externality features I created the table in Figure 8 which is based on my view of the area. Using this primary evidence and all of the streets I surveyed in my investigation I will examine this hypothesis.

Table 10
Externalities

There doesn’t appear to be any exact pattern in the data above and the values fluctuate at random intervals. However, we can see one thing and that is that the highest penalty point is for Sarum Hill which is located in the centre of town and with the lowest score of The Beaches, located in the district of Hatch Warren in Basingstoke. Hatch Warren is one of the newest estates in Basingstoke.

During the course of collecting my data I took some photos to use for doing my coursework. ON the next page are some pictures that I took myself of some of the residential; their bad points and some good points. Using these pictures I hope to come up with a reason for why I have such unbalanced results for this hypothesis. It is fair to say that Basingstoke follows no pattern for externalities.

Figure 17
Bounty Road

Figure 18
Portacre Rose

Figure 19
Cobbett Green

Figure 20
Cobbett Green Satellite Image
Source: Google Maps

Key

Yellow Arrow: A30

Red Rectangle: Cobbett Green

Blue Triangle: Retail/Business Park

I have used the above satellite image of the area showing where Cobbett Green is etc because this is the easiest way to understand why, although out of town Cobbett Green has received such a high penalty point. It is situated on a main road with industry directly opposite that attracts heavy load vehicles down the dual-carriageway.

Figure 21
White House Close

On the last few pages I have used pictures that I have taken during my investigation to try and explain why I have such random results with no pattern. I have targeted the streets that have in my opinion serious issues and in some cases, like that of Bounty Road, I have pin pointed a good value to the area to explain why I have found results like I have.

No referring back to my data I collected on externalities, I will now present it. I cannot display this data in a scatter graph as I do not have another variable. To visualize this data I am going to use a radar chart. I am using a radar chart because I can see how the data cycles from inner city all the way out to the outskirts of town.

Figure 23
Radar Chart

As you can see, there is no pattern that sticks out with this data. The only thing that is bluntly true about my hypothesis is that the first road, Sarum Hill, has the highest externality point and the furthest road, The Beaches, has the lowest. The data I have collected can give me no final conclusion and therefore my hypothesis has been rejected.

The average area rating will be lower in the inner city and there should be positive correlation moving away from the centre.

This data has been collected by using my ‘internal questionnaire’, see Figure 7. This primary data will be extremely reliable because it has come from the occupants own view of the area and not my own opinion. I am expecting things like crime to be higher in the inner city, and this will affect the overall rating of the area. I surveyed ten people in each of the streets I investigated; I will use all of the streets in this hypothesis, as the results will be the basis for my final hypothesis.

Table 11
Area Rating

1: Sarum Hill 6: Cobbett Green

2: Bounty Road 7: White House Close

3: Penrith Road 8: Cumberland Avenue

4: Cordale Road 9: Vivaldi Close

5: Portacre Rise 10: The Beaches

In the above table is all the information I need to calculate the average area rating for each of the residential areas. There is not enough space in the table to write the full name of the area, hence the reason they have been allocated a number.

In the next table is the average area rating for all of the streets. With this I can then begin to calculate values for my scatter graph.

Table 12
Average Area Rating

To draw an accurate scatter graph, I need to calculate the double mean. The double mean is plotted on the graph and then the line of best fit must go directly through it. Doing this means that I can precisely evaluate whether there is correlation for the values.

The double mean will be plotted at, 2.1,3.

On the next page is my completed scatter graph, the double mean is in red.

On the scatter graph, the streets average area rating results have been separated into 4 quadrants via the double mean. Doing this enables me to find if there is correlation. In the upper right quadrant there is two streets and in the lower left there is also two streets, indicating weak positive correlation. The line of best fit also indicated that there is weak positive correlation because the gradient is not to steep.

Relating this evidence back to the concentric theory it mildly proves that Basingstoke does relate to this theory.

I have proved my hypothesis correct because I have found that Sarum Hill does in fact have the lowest area rating and that The Beaches has the highest. There are mixed results in between but resultantly there is weak positive correlation, meaning that Basingstoke’s average area rating increases as you moved away from the centre; CBD.

There will be more private housing on the outskirts of town.

In this hypothesis I am predicting that the outskirts of town will have more houses that are owned by the individual resident and that the property is not rented, may it be privately or rented from a housing association. For the initial enquiry into this hypothesis I will use all the residents I surveyed from every road. After I have successfully found out the answer to the above hypothesis I then want to investigate a few other things within this hypothesis, I will use data from the first three streets of the enquiry, Sarum Hill, Bounty Road and Penrith Road and the last three roads on the outskirts, Cumberland Avenue, Vivaldi Close and The Beaches. In this part of the enquiry I am going to investigate the age of the resident and whether this affects if they live with their family or with their partner.

First of all I am going to begin with the subject of this hypothesis. Below is my table of results showing if people own their properties.

On the next page is my area chart to show how many people own their properties and how this changes on the route out of town.

The chart above shows that in fact there are few houses owned on the outskirts and also the same is reflected in the inner city areas. There are a high proportion of houses privately owned in Portacre Rise and Cumberland Avenue. These houses were bigger, more land and often semi detached. I spoke about this point earlier on in the enquiry.

I believe that Portacre Rise and Cumberland Avenue will have a higher percentage of residents of an older generation. On the next few pages I will see if this is true. If it is true then I have found that the more houses privately owned, the older the occupants and more likely they will have/had a family in the house.

To do this I will use two pie charts to show the percentages in a visual way.

For Portacre Rise, I was correct it is apparent that because there is a higher amount of private properties the age of the occupants is higher. You can see this via the yellow section above, it indicates the amount of residents who are over 60 years of age.

The pie chart below is for Cumberland Avenue. The only other street to have all ten residents surveyed actually own their own homes is Portacre Rise (above pie chart) here we also find a higher amount of people over 60. Here there is a higher percentage of over 60 year olds and in conclusion it shows that the more private houses in a street, the higher the age of the occupants.

Secondly, I wanted to know if these areas were more likely to live with a family/children. I have done this using a doughnut chart. It is basically the same as a pie chart but varies in visualization so it is easier for comparison between the pie chart for age and this for who the residents occupy their home with.

This shows that half live with their partner (husband/wife etc) and half still live with their family.

This chart shows that more people live with their family and children – relating this back to the age of residents in Portacre Rise, there is only 60% of residents over 60, and we get results that show more live with their family and children. However Cumberland Avenue had 80% of its residents over 60 and only 50% still live with their family/children. So when I said that if age is higher there will be more people who own their homes, this is correct and secondly, the older the resident is the less likely they are to live with family/children, they are more likely to live with their partners.

So in this hypothesis I have found that there is in fact no pattern in the amount of people who privately own their homes working away from Basingstoke’s CBD out along the southwesterly route.

However, I have found in this hypothesis that if the residents in the street are older they are more likely to own their own properties and are less likely to be living with their family/children, but are likely to be living with their partners.

Cyprus Road should have a higher average area rating than anywhere I have surveyed in Basingstoke

This hypothesis was not intended to be a long one and does not involve masses of writing and describing results etc. I wanted to put in this hypothesis as a test to the data on how the residents rated their own areas. Back in hypothesis 4 I found the average area rating from all ten of my surveyed occupants and below is the table of results, I am going to use the statistical calculation of Standard Deviation to extrapolate from my data what the average area rating will be for Cyprus Road. Cyprus Road is the street, if I had carried on my investigation throughout Basingstoke along the southwesterly route all the way to Beggarwood, but I only surveyed ten roads. However, after I have calculated the estimated average area rating for Cyprus Road using Standard Deviation I will go out and collect data on the street using my ‘internal questionnaire’ and get from it just the data on the area rating and see if my result from the calculation tallies in with what the occupants actually rate their area. If it does then I would be able to calculate, near enough, the average area rating for any street along the southwesterly route of Basingstoke.

I will now use the Standard Deviation formula which will help me to find the average area rating of Cyprus Road.

Using this formula I got the result of:

5.46/9 = 0.79

With the result 0.79, all I have to do is add it onto the value of The Beaches average area rating and this will give me the estimated average area rating for Cyprus Road.

4.291 + 0.79 = 5.1 (2 s.f.)

It is impossible to get over 5 on the average area rating and this means that I expect Cyprus Road to receive a high 4 as an average for their area rating.

Now I have estimated this result I will go and survey the road manually.

I have surveyed the road and got these results:

Cyprus Road Average = 4.777.

I have found that using Standard Deviation I have been able to find that Cyprus Road would achieve a high four on the average area rating and it did. Now if I wanted to find the next street all I would have to do would be to put the average for Cyprus Road in and calculate Standard Deviation again and this would give me and estimate for the next road after Cyprus Road’s average area rating.

So by doing this I have proved that Basingstoke does have a better area rating as you move away from the CBD and so it does refer to both the urban models and shows that as you move away the city does in fact get better. I have investigated this from all angles and found it to be correct.

Evaluation

My coursework investigation looked at how Basingstoke reflects two different kinds of urban models; the concentric theory and the Hoyt theory. Over the course of my investigation I have found that my results often relate back to one of the theories. Each of my conclusions at the end of studying a hypothesis I have commented on whether my results prove or disprove my initial key question. In my evaluation I am going to assess how the enquiry went, how it can be improved by questioning how reliable my results actually are and whether the data collection methods I have used are dependable and whether they have affected the accuracy of the results and thus the validity of each of my conclusions at the end of a hypothesis.

The first major step for my investigation was actually collecting the data so I could investigate my key question efficiently, precisely and accurately. I needed two sets of data for my enquiry to run smoothly, first I needed the data I’d collect myself, i.e. my ‘external questionnaire’. I knew collecting this would not be an issue because I was doing the questionnaire myself. The second data I needed to collect was the residents own view of the area they reside, I did this using the ‘internal questionnaire’. When I was planning my investigation; even before I had began to write up my investigation I wanted to collect a sample of 25 from each of the roads I surveyed. This was not going to be the case. For each of the roads I surveyed I put out 50 ‘internal questionnaires’ requesting that the resident would fill it out and leave it outside their front door for me to collect at a later date. I only got back on average around 12 completed surveys from each of the streets; therefore I could only use a sample size of 10 from each road. Although, having a sample size of just 10 did have its good points, when it came to interpreting the data it didn’t take me hours to enter the data into the computer. On the other hand, my results will reflect the lack of data as I know personally they are not as accurate as they would have been had I had a sample of 25. Nonetheless, I solved this by reducing my sample size and continuing with the investigation to, in my opinion, a high standard. The third set of data I collected was photographs that I took myself of things I thought was an issue or something good about the area. Collecting this data was not an issue because I only had to rely on myself to get the results I wanted.

My ‘external questionnaire’ I feel didn’t have any disadvantages because it helped me significantly during my investigation and when using the data I had collected from it I found it to be flawless and gave me answers in every direction. The data I collected for the externalities, in the ‘external questionnaire’, I don’t think could have been collected in any other way, I needed to change what the area looked, felt, sounded and smelt like into numbers. I did this by using a Bi-Polar analysis amongst other things, they can all be found on my ‘external questionnaire’. The results from my ‘external questionnaire’ could have not been improved by collecting more data because I can only analyse the street once with the same questionnaire when filled in by myself. However, if I had wanted to take the investigation to the next level I could have asked another person, or a group of people (i.e. friends) to do the survey for me then with the results I could have found the average from each of the different surveys within the ‘external questionnaire’ thus giving me unbiased results. The ‘internal questionnaire’ only flaw that I found was that people simply were not taking the time to fill it out for me. When people had filled it out and I collected it, the data I gained was extremely useful and has been used throughout my enquiry. The issue here was not having enough data, having a sample size of 10 frankly is not enough to bases a massive investigation on at this level but I had to continue and with more data my results would have been a lot more reliable and my conclusions would have been extremely valid because I would have had more data to evident them with. If I were going to do this investigation again and were going to use the same ‘internal questionnaire’ I would most definitely change the way I went about it. First of all, I would not have got 500 copies of my questionnaire and put them through 50 doors at the 10 streets I surveyed I would get 10 copies of the questionnaire along with a tally table and knock on residents doors and ask them if they would answer a few questions for me. This would have meant I could of sampled the 25 residents I wanted to, or even more. Not only would it of saved time and effort but would have saved an immense amount of paper.

As an overall of the coursework, I do feel that my results are accurate and can give a valid conclusion to the main key question. However, I do not feel my results are accurate at some levels, i.e. the amount of people who own their own homes. The residents who filled out the question may have lied, not have known if their home was owned, thus making my results mistaken and frankly wrong. However, because I have said some of my results are inaccurate is it not at the fault of my data collection methods, but to the fault of the individual whom filled out the questionnaire. Where I have used the ‘external questionnaire’ I do personally feel that my results are truthful because I did the analysis myself. However, the flaw with this is that it’s my individual view of the area and someone else might argue I was unfair with the number I gave the street. Using the data collection method I did for the ‘external questionnaire’ made my results slightly biased because it was my own view but this is not to the fault of the questiionaire itself but my own fault because I could of asked 5 different people to fill out the ‘external questionnaire’ and then have averaged the results.

All of my results reflect the key question and prove it correct. However, the only results I have found to be random and not have a sturdy answer is that of the externalities (hypothesis 3) because there was no pattern and I expected to find that as you moved away from the centre of town the externality penalty point would decrease. The reason for this rejection could be down to the fact it was I who filled out the ‘external questionnaire’ thus giving the street an externality penalty point. At the end of the day I have an opinion and my personal opinion could reflect the results and for instance if I personally enjoy green space and can’t see lots, although there is some I may give a higher penalty point than someone who is indifferent on green space. In my key question I wanted to find out how Basingstoke changes and reflect different urban models. My results support this key question because for hypothesis one that asks whether the intangible factors are higher in the centre of town I calculated spearman’s rank and found that there was in fact weak positive correlation between that and the time people have lived in the area so I found that more people want to live in an area with lower intangible score and the lower intangible score is found on the outskirts of town. So this data supported my key question because it showed that Basingstoke changed and reflected the concentric theory which depicts that as you move away from the CBD the area gets newer and with this is means it gets nicer etc.

At the end of each of my hypothesis investigations, I concluded what I had found out. But these conclusions could be incorrect, inaccurate and unfair. The main problem that would have affected my conclusions is not having enough data to base them on in the first place. The more data I collect would have given me a more reliable conclusion because a whole view of something is better than looking just inside the box.

To increase the accuracy of my results would be to increase my sample size as a number one priority. I have explained many times why this would be of help to me. However, I have only examined Basingstoke in a south-westerly direction and can only conclude that Basingstoke does improve and relate to urban models (key question) in this direction – but to enhance my results I could take a different route out of town and make my why north into other districts of the town. In doing so, I would be able to come up with my own urban model especially designed for Basingstoke; if I had a chance to do this investigation again I probably would because I would set myself more time to collect the data etc.

To increase the reliability of my ‘external questionnaire’ I would most definitely ask a few other people to fill one and out and then I could average the results for each of the sections, i.e. intangible factors, externalities etc. This would give me an un biased view of the area and it would not be to my personal values, but to a selection of people’s values thus giving me more of a spread of results.

To increase the validity of my conclusions throughout the investigation I need to follow the steps in the last two paragraphs because there is no way I can improve their validity without the data to do so; I can only base my conclusions on the data I have collected. Doing the above two steps outlined in the above paragraphs, I may find that my conclusions would be very different and therefore making them more valid because of the sample size they have been based on.

I have now come to the end of my geography investigation and have found many things out about my town in which I personally reside and I have also learnt a lot about the environment I live in and how it actually changes as you move away. Although this coursework is long I feel it is to a high standard and has been finished extremely well.

Nathan Edwards

Candidate Number: 3063

Centre Number: 58421

Geography Coursework