Rural Fieldwork

berinsfield key facts

nucleated village, 32km from reading and 13.1km from oxford. accessible via A4074

what does secondary data tell you about berinsfield?

IMD says west berinsfield is more deprived than east berinsfield.

rural fieldwork aim

to what extent does primaery data support the level of deprivation shown by secondary data sources for berinsfield,

do our EQ surveys match the secondary data

do our perceptions of berinsfield’s level of deprivation based on our word clouds (epitome words) and photgraphs match the secondary data?

why are these rural fieldwork aims appropriate

covers geographical theory that is part of the spec, eg deprivation and differences in rural villages.

we have secondary data which is up to date.

Names of specific sites,

west - kennet close

bullingdon avenue

shops

east - colne road

leach road

shops

types of sampling we used

stratified sampling:

used 3 sites from each side to reflect the fact that the IMD suggests they are different

census data suggests there are variations within each side.

data collection methods

epitome words

we will walk around each half of the village and after we have been to all threee sites in each side of the village we will note down own three epitome words, words which come to mind which we think describe the characteristics of each area.

environmental quality survey

each aspect, rate it -2 to +2 with -2 beign a negative opinion. turning these opinions into values makes it a quantitative technique.

risk assessment

getting cold/wet - wear suitable clothing for the forecasted weather and make use of layers that canbe added or taken off if the weather changes.

slips trips and falls - wear sensible shoes and watch where you are going

traffic - where possible stay on the pavement. if no pavement then walk in single file and face oncominf traffic. take respnsibility for looling and corrsing the road.

data presentation

divergent bar graph

shows positive and negative values from a set of data. bars go above and below the x axis.

EQ summary scores

West = 10.1

East = 3.1

great variation

Problems and advantage with divergent bar graph

advantages -

can summarise large amount of data in a visual and easy to interpret from

shows positive and negative values on the same axis.

disadvantages -

too many bars make the graph look cluttered

can be difficult to show a wide range of values on the y-axis

Epitome words overview

97 recorded words

east - highest frequency = 12 people, green 10ppl = dirty 9ppl = boring 8 ppl = rundown.

greater variety of words noted for the east, opinons more varied and more negative for the east overall.

west- 86 recorded words,

highest frequency word with 23 people stating it as one of their epitome words was ‘community’ 19ppl notes ‘quiet’ 13 ppl = green and communal = 9

size of words shows frequency. and more positive adjectives used here.

advantages + disadvantages of Epitome words

adv- easy to interpret as the larger the word the more times the word was recorded. most common viewpoint stands out.

different shapes and colours makes data more engaging.

dis- might not accurately reflect the contecnt of words if slightly different words ares used like ‘large, huge, giant’ display emphaises the frequency of the words, not importance - can be misinterpreted.

evaluation of photograph presentation technique

adv - quick way to capture info about landscape or feature,

photographs could be correlated with the other data sets to aid data analysis like photos of housing linked to EQ surveys.

dis- only shows one view, at one point in time. may be biased in taking of the photos. focused spec on points to prove.

analysis of data

EQ scores - west - 10.1

east - 3.1

does not match IMD as suggests the west would be more deprived

EQ score for site 1 doesn’t match with census as being the least deprived area in the west.

eq score for site 5 matches with census as being the least deprived area in the east

eq scores for site 2 (11.9) and Site 3 (6.9) in the West and Site 4 (-2.4) and Site 6 (-1.4) in the East

vary but according to the Census should all have the same level of deprivation. So, our primary data

does not match the secondary data.

epitome words for the west were more positive than the east which does not match IMD decile levels for deprivation

conclusion of data

The primary data does not support the level of deprivation shown by the secondary data

source of the Index of Multiple Deprivation map which showed that the West of the village was more

deprived than the East. Our overall EQ scores are higher for the West than the East and the epitome

words more positive for the West than the East.

Why are there differences?

The IMD area South Oxfordshire 006A (East) includes a large open space of farmland and bridleways

which would increase the score for the Living environment domain making the East of Berinsfield seem

less deprived than it is.

More in-depth Census data shows that people on the West are older and more likely to be living on a

pension than those in the East who are likely to be managers, directors and professionals so earn

more. This could explain why the IMD scores are higher in the East than the West.

The Census data shows that in the East more people have level 4+ qualifications so are more likely to

earn more. This would increase the IMD score.

The Census data also shows that the West has more flats, maisonettes and apartments than the East as

well as housing without central heating. These are considered to be cheaper and of lower quality

which could explain why the IMD score for the West is lower than for the East. This would not be

obvious from the outside.

Our primary data did not focus on income and employment deprivation. These have a combined

weighting of 45% in the IMD. We looked at the outdoor living environment by recording the housing

quality, air quality and traffic. In the IMD, the outdoor living environment only has a weighting of 9.3%

in the IMD so could explain the differences between our primary data and the IMD data.

The values for IMD can conceal pockets of deprivation such as those identified by the Census map of

Berinsfield.

The primary data (EQ scores, epitome words) support to some extent the Census deprivation data. Our

EQ scores for Site 5 matches with the Census as it is shown to be one of the areas with the least

deprivation in the village. Our EQ scores for other sites generally identify areas with deprivation in line

with the Census but the EQ scores vary considerably although they are classed as the same level of

deprivation by the Census.

evaluation of enquiry

accuracy - So to be accurate investigations should:

Use precise equipment e.g.

up to date maps

environmental quality scale with very clear

descriptors

use a clicker to count people

Use more than one person to complete subjective

data e.g.

Group decisions on environmental scores

Compare to secondary data

Sources or possible error:

Measurement error: mistakes made when

collecting the data (such as someone misreading a

clinometer)

Operator error: differences in the results collected

by different people (such as different people

giving different environmental quality scores).

Sampling error: where a sample is biased. Some

elements of the population are less likely to

reliability -

The extent to which the data you have

collected is consistent; if you came and

collected the data again you should get

similar results.

To be reliable investigations should:

Have a large enough sample size to

ensure a representative range of data

is collected.

Conduct the method in the same

way with the same equipment at

each site e.g.

Same person does

environmental score at each

site

Record people/traffic for the

same amount of time at each

site

Use the same categories/key

at each site

Conduct the method in the same

conditions at each site e.g.

The same time

The same weather

The same day of the week

validity -

e.g. The environmental

quality survey provides

a score that can be

linked to the Living

Environment Domain of

the IMD so therefore it

is a relevant method to

investigate variation in

deprivation.

EQ survey strengths and limitations and how we minimised these issues

Subjective – based

on opinions. People

have different

opinions

Looking at census

data and IMD before

may have affected

our judgements

Before trip, use

photos and agree

scores as a class to

create a benchmark

for judgements

Avoid looking at IMD

and census before

fieldwork

Epitome words strengths and limitations and how we minimised these issues

92*3=276 words for

each side of the

village – good

sample size

Discussions as a class

and between friends

may have influenced

opinions

Different words may

have been used for

the same thing

Could ask residents to

get their ideas and

contrast with ours

Don't discuss ideas

Provide a word bank

to select from

EQ survey impact on results and impact on conclusions.

Collated 92 results

– extreme values

will be moderated

Most people's

data showed that

the west had

higher scores than

the east so overall

picture is the same.

No impact on

overall conclusion

EQ, Epitome words

and photos all show

same conclusions.

Epitome words impact on results and on conclusions

Large sample

provides good set

of results to

contrast each side

of the village

The differences in

the word clouds

were sufficient to

suggest reliable

differences were

noted.

Limited impact on

conclusions

because group data

was collated

The sample size was

large making it

more reliable

Factors that may influence rural deprivation and quality of life

Resource deprivation

Problems of poor

income and low-quality

housing.

Opportunity

deprivation - This

relates to a poor

availability of services,

including education

and healthcare.

Mobility deprivation -

Referring to locations

that are isolated as a

result of low-density

transport networks to

reach jobs or other

services.

explain 2 reasons why particular aims or questions were developed

Reviews of the Index of Multiple Deprivation and the way it is

calculated have suggested that is conceals small pockets of deprivation

in rural areas. The argument is that deprivation in urban areas tends to

be geographically concentrated in certain neighbourhoods e.g. inner

city areas, whilst, in contrast, in rural areas deprivation often exists at

the scale of streets rather than whole neighbourhoods.

Using the IMD showed that Berinsfield was split down the centre into a

more deprived area (West) and a least deprived area (East).

But then looking at the

2021 census data for

deprivation it showed that

within Berinsfield both

sides of the village have

pockets of more and less

deprived areas.

So our fieldwork aimed to

see if the review was

correct and see if the

primary data matched this

secondary data.