Systematic - System of collecting data
Random - Is a technique of random sampling
Subjective - Own opinion
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Sites:
A38 A37
Site 1 – Florence Park, Almondsbury Site 1 – Sleep Lane, Whitchchurch village
Site 2 – Monks Park, Horfield Site 2 – Rookery road, Knowle
Site 3 – City road, St Paul’s Site 3 – Coronation road, Bedminster
Site 4 – Stokes Croft, Broadmead Site 4 – Baldwin street, Broadmead
Hypothesis 1:
Building height decreases with distance from the CBD.
Building height survey
The building height survey was done by recording the height of the 10 nearest building at each of the sites. Using mode I fond the most common number of storeys for each urban zone listed above. Where I have used ( / ) there was a equal number of buildings of the same height.
Hypothesis 2:
Traffic density decreases with distance from the CBD.
Traffic count
The traffic count was done by counting the number of vehicles passing by for a time of 5mins minutes using a stop watch, I done this to gain the most reliable data possible. For example if I was to time the vehicles that went past for 1 minute and they were stuck at traffic lights I would gain false research. This was including all road vehicles not just cars for example. Motorbikes, push bikes ect…
(Going towards CBD / Going away from CBD )
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Hypothesis 3:
House value increases with distance from the CBD.
Table
House value
The housing value was done by collecting data on the 4 different subjects: cost, living space, quality decay index, housing land space. I believe that the house value is the complete package and not just the cost. Using the internet I manage to find my research using websites like and . To find a price for a urban zone I randomly select 10 homes for that area. I added the costs of the homes and divide the sum by 10 by using the mean I gather the overall sum for that zone. Bedrooms are stated as above using mode I fond the most common number of rooms. Where I have used ( / ) there was a equal number. I used Quality Decay Index (QDI) to indicate the condition of the buildings on a scale of 1-10 table below. I created Housing Land Space (HLS) to indicate the amount of land and land plot the property has, for example off road parking or garden space. Table listed below.
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Both the pictures above are from the RUF the one on the left is from the A38 the one on the right A37 both show big houses 4/5 bedrooms, detached, big gardens with drive ways. Loads of green space, tidy and well kept probably self owned. Spaced out houses and a peaceful community. No shops, 1/ 2 stories tall.
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Semi-detached homes 3 bedrooms with good size gardens with off road parking probably council houses. Local shops and post offices. Houses close together. Picture on the left from A38 in Horfield. Home on the right located in Knowle. 2 storey houses.
Terraced houses 2 bedrooms with no front gardens, more flats and council homes. A busy community with a lot of pedestrians. Few boarded up and abandon houses run down area with 2/3 stories. A traffic increase from being near to the CBD. No off road parking. Picture on left A38, picture on right A37.
No houses just flats and tall buildings 4 stories plus. Expensive land none-residential business society. Lots of services and department stores. Tourism and a sphere of influence.
High traffic count with high amount of pedestrians.
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Data Presentation
Sites:
A38 A37
Site 1 – Florence Park, Almondsbury Site 1 – Sleep Lane, Whitchchurch village
Site 2 – Monks Park, Horfield Site 2 – Rookery road, Knowle
Site 3 – City road, St Paul’s Site 3 – Coronation road, Bedminster
Site 4 – Stokes Croft, Broadmead Site 4 – Baldwin street, Broadmead
Hypothesis 1:
Building height decreases with distance from the CBD.
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Hypothesis 2:
Traffic density decreases with distance from the CBD.
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Hypothesis 3:
House value increases with distance from the CBD.
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Interpretation
Hypothesis 1: Building height decreases with distance from the CBD.
I used a bar chart to show building heights so the bars could represent the buildings. In the graph you can clearly see the building height decreasing from the CBD which I predicted in my hypothesis. On average there is a decrease of one storey for every urban zone from the previous urban zone starting from the CBD outwards. I expected this to happen because the land is so expensive in the Central Business District. In turn companies buying a small plot of land and building up. The land becomes cheaper the further away from the CBD, therefore buying a big plot of land for the same price and not building so high. In the graph you can see site 3 of the A37 had an average height of 2/3 storeys which is dissimilar to site 3 of the A38 which had 3 storeys. I think site 3 of the A37 building height was lower, possible because of different types of buildings in that area. This is because of an equal amount of standard two storey houses to three storey flats. Not like the A38 which had more flats then houses giving a higher height average.
Hypothesis 2: Traffic density decreases with distance from the CBD.
I used a pictorial representation to show traffic moving in and out of town so you can visually compare the quantity of vehicles travelling towards and away from the CBD. In the graph you can clearly see the traffic density decrease from the CBD which I predicted in my hypothesis. As the graph shows there are more vehicles heading towards the CBD then outwards because the centre has a sphere of influence and the majority of work, giving a high over all sum of vehicles. Also the majority lower class residents have no reason to travel out to the higher class suburbs or middle class suburbs, which causes the traffic to decrease the further away you travel from the CBD. The traffic at site 4 of the A37 was a lot higher then site 4 of the A38, possibly because of road works building up traffic in Bristol city centre as regeneration is taking place at the moment in the town centre. 37% of vehicles was located in the CBD between 1pm -3pm. If we were to collect data between the hours 7am -10am I think it would have been around 60% or 70%. With numbers this high you would expect the traffic to decrease with distance from the CBD…but if we where to collect data at 3pm -6pm I think the inner city would have the highest number of vehicles as everyone would had finished work and be travelling home making my hypothesis false.
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Hypothesis 3: House value increases with distance from the CBD.
I used 4 different graphs to show house value as I based my research on 4 different topics. As I’m not basing house value just on the price tag.
The first graph I used was a line graph to show housing prices of the urban zones. I thought this was the best and easiest way to show these results. As you can see in my graph the house cost don’t actually increase from the CBD, in fact it decreases before increasing. But there isn’t too much difference between the CBD price and site 1 of the RUF on both the A37 and A38. However there is a huge difference from what you get for your money, a small posh 2 bedroom flat located in the CBD or for around the same amount a 4/5 bedroom home located in the RUF with your 2 garages and swimming pool! From the information I have collect on house value I would expect the cost of houses to decrease in the inner city then increases till the RUF. But in my research it was different for the A38. There was a constant decrease until the outskirts of Bristol. I thought this might be because of regeneration again Bristol is at the moment building a new shopping centre and places around it is being updated. I think the reason for my graph giving these results is because of site 3
St. Pauls which has been known for being a run down area, and being very close to the centre it may have had some regeneration and I know there is gentrification by local residents who try to re-develop areas in there community. This may have caused an increase in housing prices.
For Housing Land Space (HLS) I have used a pie chart which I thought would best fit land plot. The basic data I have received in my graph is that the further away from the Central Business District the more land your home has to offer, which is what I expected to happen because of over crowded and populated city. Now if I was to compare HLS to Housing prices from the two graphs you can undoubtedly see the huge difference of value for money, a small piece of land in the polluted CBD or large space of green land for exactly the same price. In the graph site 3 of the A37 has an extremely high HLS for a low class suburbs I think that this judgement was is too high and possible a mistake. Site 1 of the A37 is higher then site 1 of the A38 maybe because the village hasn’t had much development as the other village.
I have used an area graph for Quality Decay Index (QDI) to represent the state of the house. From this you can noticeably see the QDI improving the further away from the CBD I also expected this to happen. The graph has 2 peaks one of them being the CBD is which understandable and the other being site 3 of the A38 St. Pauls which was due to singularly homes letting down the area where we collected are information. I felt this was a key part of my hypothesis, that actually condition of the house is the most important.
The last graph was a scatter graph for average number of bedrooms also again the number of bedrooms increase with distance from the CBD which once more is proving my hypothesis. Three times ion my graph I had a joint average. This was maybe due to home owners extending their homes increasing the number of bedrooms. The CBD can’t have a high number of bedrooms because of the lack of space unlike homes in the RUF. I expected this to happen because of the lack of green space and the increasing need for housing.
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Conclusion
The aim of this piece of work was to compare and study urban zones of a city, in reference to the 1920’s Burgesses model of urban development. I have done this by testing 3 hypotheses. I have come up with the conclusion that Burgess has the basics structure of a city without the detail and is very limited. The zones are very similar to what Burgess suggested.
Hypotheses:
- Building height decreases with distance from the CBD.
- House value increases with distance from the CBD.
- Traffic density decreases with distance from the CBD.
I support my 1st hypothesis that building height decreases with distance from the CBD since I have fond this statement to be true for two main reasons. The land is extremely expensive in the centre forcing to build taller building then in the suburbs. Also a high population in and around the CBD and inner city increasing the need for more housing, causing flats to be built. I think my data collection was a success. I don’t believe I could have made it that much more accurate then what I have done. But if I was to do this project again I would increase the number of building in each urban zone from 10 to 30 and record the building types, flats, council estates, houses, and business. I also fond that the odd high buildings in the suburbs where newly build flats which is a sign that the CBD and city is expanding.
I also support my 2nd hypothesis for the reason that my research and data confirmed that traffic density decreases with distance from the CBD. I also fond that people are drawn to the Central Business District because of what it has to offer such as services, work, entertainment etc. increasing the traffic level in the CBD, so the further distance away from the CBD the more of a decrease in traffic there will be also urban zones are less populated there further way from the centre. I think that my data collection was a success but could have been improved if I was to do this project again. I will increase the amount of time taken to record the amount of vehicles and I would do this at 3 different times of the day. If I was to record traffic at 5pm rush hour I don’t think it would be as reliable then if I had 3 samples.
I also again support my 3rd hypothesis since in general the house value does increase with distance from the CBD. I fond that the overall package is a better value for money, the prices from the RUF match the prices of a 2 bedroom flat located in the CBD.I think my research was very accurate but if I was to do this again I would gather information on more houses expanding my results and rising the chances of it being even more reliable. Everything I based on house value had some improvement from the CBD.
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Geography
Urban
Morphology
Coursework
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