UK Markets: Place and Flow Issues - Assignment Example

This paper details the results of a quantitative analysis of a survey on different markets in London, with the data collected from the Portobello Road Market being used for the proceeding analysis and discussion. The focus of the analysis is on the examination of place issues and flow issues and their treatment within the context of this report. To be more specific, the paper undertakes an examination of survey responses relating to market time of travel, amount of spending,. attractions of the market and the preferences of respondents relating to shopping. The analysis differentiates survey responses by different survey respondent groups and analyzes those differences using appropriate statistical analytical methods. residents of the UK on the one hand and non-residents of the UK on the other (Present Flowing Data 2007; Cheshire 2012; Cheshire 2012b).

Out of 797 respondents, 141 are non-residents. 33 non-residents visit at least once a month. 41 visit at least once a week. 27 visit at least once a year. 37 has never visited before. 8 traveled by bike, 20 by bus, 6 by car or motorbike, 51 by tube/train, 1 by coach, 54 by foot.

655 are residents. 191 visit at least once a month.  158 visit at least once a week. 90 visits at least once a year. 47 visit less than once a year. 168 have never visited before. two has no data. 38 traveled by bike, 76 by bus, 13 by bus or by tube/train, 60 by car or motorbike, 229 by tube/train, 1 by coach, 340 by foot, and 3 by taxi.

Tabulating the data on people flows, we get the following tables:

Residents vs non-residents

Count

Residents

655

Non-Residents

141

Visit Frequency

Frequency of Visit

Residents

Non-Residents

At least once a week

158

41

At least once a month

191

33

At least once a year

90

27

Less Than Once a year

47

0

Never visited before

168

37

Mode of transport

Residents

Non-Residents

Bike

38

8

Bus

76

20

Bus or Tube/Train

13

Car or motorbike

60

6

Tube/Train

229

51

Foot

340

54

Taxi

3

0

Coach

1

1

Going to money flows, 103 non-residents spent money from 5 to 120, while 507 residents spent money ranging from 1 to 250. Tabulating this we get the following:

Residents

Non-Residents

Spent Money

507

103

Amount Range

1 to 250

5 to 120

We get the mean, median, and standard deviation of the spending for residents and non-residents to get an idea of the spending flows for each group:

One can see from the data that the respondents are heavily skewed towards those who are residents, and those who walk. Residents moreover visit with a greater frequency than non-residents, as can be seen from the plots below. One can say that this distribution is representative of the mix of non-residents and residents in the normal population of market-goers.

One can see that while there are heavily more resident respondents than non-residents in the sample population, the proportion of those who spend money in each of the groups are the same roughly, skewed heavily towards spending for both groups. On the other hand, as a percentage of the total group, more residents are frequent visitors to the markets, at least once a week, compared to the non-residents (Present Flowing Data 2007; Cheshire 2012; Cheshire 2012b).

The empirical data on people and money flows in this experiment shows that in London, a large proportion of those who go to market are residents, not because non-residents do not go, but that this effect is more a factor of the general mix of residents and non-residents in the general population. On the other hand, median and mean values for spending per person within each group are roughly the same, and so are the standard deviation values for spending. This tells us that in general money flows are not differentiated along with resident and non-resident lines. On the other hand, relevant people flow metrics tell us that a greater proportion of residents go to market relatively frequently, in comparison to non-residents. This set of empirical data is useful when understood in the larger context of understanding people and money flows in cities and in urban areas of Europe in general (Present Flowing Data 2007; Cheshire 2012; Cheshire 2012b).