We can see from the transect bar graphs that from the start of my transect 2.4, it consists of a few widely spread houses, this goes on for about 500 paces. Then there is a lone Fire Station, 40 paces in length, next we have a chunk of relatively dense residential property of 450 paces, broken up by garages and streets. Now there are a few lone shops followed by lots of tree land and car parks. Then there is a wide river and an old industrial building; backed onto the last two houses before the centre. We then hit a dense area of shops and Banks and offices (including the Market Square) for 500 paces.
This contains my central point halfway through. From here, we go back into dense residential for 200 paces. This makes sense that shops and offices are in the centre as it is more likely that they will be able to afford the high prices in the centre. These high prices are due to the high demand of the central positions too. Also, the residential areas are more likely to be out of the centre as they can’t afford these high prices in the centre. Another reason for shops being in the centre is accessibility, there are more people in the centre as it is the focal point of the town. This means that there will be a high density of customers in the centre.
In the centre the buildings are higher than those on the outskirts. This is because the people who own plots in the centre want to maximize the overall floor space of their limited ground floor area. They do this by building upwards. Going out from the centre, there are some more shops for 300 paces then we move back to an area of residential. This is further reinforced by the dispersion chart which shows where each land use type prefers to be. It shows that the shops prefer the central areas, along with the offices. Then Industry prefer the area just outside the centre and that residential prefer the cheaper land outside of that.
Environmental Quality Bar Chart:
I depicted this with a set of bars split in two, showing the separate scores for buildings and surroundings. This data shows how the EQ scores decrease as we moved into the centre. The quality was better out of the centre in the residential area, as there were less houses to more space. Also there are areas for children to play safely, and areas of grass for recreation and just looking nice and pleasant. The houses are also better looking and slightly bigger than those in the centre. The quality of life is lower in the centre as there is more rubbish, pollution and general filth in the most well used areas. Also the houses are much more densely packed together and therefore appear smaller than those out of the centre.
Vehicle and Pedestrian densities line Graphs: These can be completed together as they both follow the same pattern. This is that the densities are higher in the centre than the outer zones. This is because the centre is a much more accessible place, so more people congregate there. Although it is indirectly, as it is accessibility which causes shops to position themselves there, which then in turn attracts people. The vehicles followed the same pattern as half the people shopping were already underway and walking, and half were arriving or leaving in their cars.
I have been able to fulfil my aim and complete the investigation properly. I have tested the model on Buckingham and found that it does conform to it. This is especially good, as all segments have conformed. Therefore I can conclude and say that my findings and results do generally agree with the model, with the exception a few anomalies of out of centre shops. Therefore having completed the investigation, I can say that the model does not need changing, although it could be added to, at a later date.
Although my investigation was successful, it did have its limitations and weaknesses. I would have been much better, for instance, to have actually measured the width and height of the buildings if there was enough time available. This could have been done with the use of a trundle wheel or tape measure. Also, the method of data collection for pedestrian and vehicle densities was too inaccurate.
This is because they were timed over too short a period of time allowing anomalies to arise, which would otherwise have been evened out over time. Another problem with my data collection methods were that when scoring on Environmental Quality, it was a very opinionated view. This could be improved by scoring each point along the transect with a set making scheme or system. Also the use of paces for measuring the width of buildings was very inconsistent, as my paces were not really consistent.