Y11 Geography Human Fieldwork Test

wimbledon village fieldwork aim

to investigate how the quality of the urban environment changes along wimbledon high street

wimbledon village fieldwork enquiry question

to what extent does the quality of the urban environment change along wimbledon high street?

justification of wimbledon village location

wimbledon village in south west london

• walking distance of school - only takes a few mins to get there. so could collect all data in a short amount of time & didn’t need the whole day

• small area = not too much time needed to carry out data collection

wimbledon village transect sites

12 sites along the transect selected by systematic sampling sampling so we could get an overall view of the whole area

wimbledon village risk assessment

getting lost from the group - stay in groups, stay in contact, don’t wander off

traffic (injury/collision) - remember safe procedures for crossing the road, only cross at crossings, wait for traffic lights

environmental hazards (litter/poor air quality) - don’t touch/pick up litter, avoid areas with lots of litter/visible pollution, wear appropriate footwear

wimbledon village methods: sound mapping

recording the main sound you hear at each site, tick box to record the dominant sound

another way to collect qualitative data about the environment without relying on sight. used to investigate the quality of the urban landscape

less bias (live noise, indicate what you hear), not been edited so is reliable, set options make it more objective, can compare between sites

depends on area & time of day, loud noises can drown out others, hard to distinguish individual sounds, no quantitative data, no change between sites

wimbledon village methods: soundscape

teacher gathers sound data at each site using a decibel app to record sound levels in dB. 3 recordings at each site & take average

quantitative data (can compare & do statistical tests), digital tools are more accurate + precise

heavily dependent on time of day, better tech could be used (less accuracy/reliability), precision can be lost through anomalous measurements eg big truck passing by, sound levels vary a lot

wimbledon village methods: observations

make observations describing the environment around us using words, looking at surfaces & spaces

live data, can be used to support EQS, gives more info & context

subjective, not quantitative, influenced by time of day, can choose what to include, can’t compare/test that much, can be not detailed enough

wimbledon village methods: environmental quality survey

uses an observer’s judgements to assess environmental quality against a range of indicators (cleanliness, traffic, pollution, green spaces, street furniture etc). uses a bi-polar scale (-2 to +2) to indicate a negative/positive assessment. 0 = neither good or bad

tick score for each indicator at each site. have 1 person do it so data is consistent & less subjective & varied

quantitative data that can be analysed, gives a picture of the whole transect, can compare between sites

very subjective, biased, opinionated, dependent on time of day, took a long time, lots of repeats, data didn’t change much between sites

wimbledon village methods: photos

take a photo at each site & annotate them back in the classroom. colour code annotations to reflect positive & negative aspects of the urban environment (shocks, cleanliness, traffic & congestion, people & cars, positive & negative effects)

live data, gives context, can annotate later, very quick & easy, can get an overview of area studied

dependent on time of day, biased (can choose direction/area), can be edited, don’t show the whole picture, qualitative (can’t analyse/test/compare), only one view

wimbledon village methods: land use survey

record the land use function of buildings along the whole transect. use RICEPOTS system while walking from site 1-12 to show land use on both sides of the room

later use digimaps to create a digital map for the area

includes every building - no bias, can compare across transect, specific, objective, reliable

some overlap in categories, qualitative data, some buildings are 2-storey, can take a while, can be hard to figure out what code to use (time consuming), some buildings don’t fit into code

data presentation: EQS

bar graphs

visual (can clearly see patterns & trends between sites), can see change across the transect, quantitative data

lots of info/data, (1) can’t see overall environmental quality, not geolocated (can’t identify where areas are), (2) doesn’t give lots of data

data presentation: observations

word cloud

easy to see overall descriptions of areas quickly

no qualitative data, no data about how often words came up, not a lot of words in total

data presentation: soundscape

goe-located decibel readings using Arc-GIS software. put data into a spreadsheet then uploaded it to website

geo-located so you can clearly see change, can extract quantities from key

less precise - no exact measurements

data presentation: photo annotations

annotated photos using colour coding of positive & negative observations

how to do data analysis

describe what results show, use actual numbers from results, use TEA

explain results, think about urban environments (environmental quality) & factors that influence the quality of these spaces (pollution, green space, noise, traffic, building quality, service provision)

link back to enquiry question, say what your results suggest about it

conclusion

where you get to answer the main question. draw on your evidence to back up your answer. mention anything that supports is + anything that goes against it

evaluation

at the end of the whole process look back over each stage & evaluate for WWW + EBI (problems, improvements to make results more accurate/reliable)

evaluate each stage of enquiry (data collection, planning, analysis, conclusions)

aims vs hypotheses

aim: explains what the enquiry is attempting to achieve (investigate how the quality of the urban environment changes along wimbledon high street)

hypothesis: a statement that can be tested & is clear, directional & measurable

primary data

data collected by the student eg questionnaire data, river / sea measurements, videos, photos, interviews

data is reliable & valid, specific to the enquiry, as much as needed can be collected, collection method is known, up to date

time consuming, may need specialist equipment / resources, sample size needs to be large to be accurate

secondary data

data collected by someone else but used by the student eg census results, weather data, old photos, maps, newspaper articles, websites

easy + quick to access, low cost / free, large amount of sources available

not specific to enquiry, no control over quality, may be biased or out of date

quantitative data

data that records quantities eg numerical data from questionnaires, traffic counts, river data (velocity, discharge), weather data

can have larger sample size, can be collected quickly, data collection can be duplicated, more objective, more reliable

meaning behind results isn’t clear, human error / equipment error can lead to mistakes

qualitative data

data that records descriptive information eg field sketches, photos, non numeric questionnaire data, interview answers

more in-depth, more valid

often a small sample size, enquiries aren’t easy to duplicate, hard to compare, low reliability, time consuming

other methods: questionnaires/interviews

questionnaires can be used to gather a large sample of data. interviews are more in-depth and tend to gather a smaller data sample

different question types:

• closed questions: answers are limited to single words, numbers or a list of options

• statements: use a scale to judge people’s views eg strongly agree / disagree

• open questions: respondent can give any answer

sampling methods

sampling gives an overview of the whole feature/population as there isn’t enough time/equipment/access to measure the whole area. provides a representative & statistically valid sample of the whole

1. random: each member of the population is equally likely to be included in the sample. least biased, can be used with a large population. representation may be poor, some sites selected might not be accessible / safe

2. systematic: samples are selected at regular intervals eg every 500m or 10th person. quick & easy, more straightforward, covers the whole study area equally. possible bias increase, possible over or under representation

3. stratified: population is split into groups & a proportionate number of measurements is taken from each group to ensure everyone is represented. can be used alongside other types, comparisons can be made between groups. proportions of groups need to be known & accurate

continuous data

can be any value in a range et temp, noise level

discrete data

can only be certain values eg number of cars

bar graph

one of the simplest methods to display discrete data. useful for comparing groups of data & changes over time

summarises a large set of data, easy to interpret & construct, shows trends clearly

requires additional info, doesn’t show causes / effects / patterns, only possible with discrete data

compound/divided bar chart

bars are subdivided to show information with all bars totalling 100%. main use is to compare numeric values between levels of a variable like time

population pyramid

type of histogram used to show the age-sex of a population. can be used to show the structure of an area / country. patterns are easy to identify

line graphs

one of the simplest way to display continous data. both axes are numerical & continuous. used to show changes over time / space

shows trends & patterns clearly, quicker & easier to construct than a bar graph, easy to interpret, needs little written explanation

doesn’t show causes / effects, can be misleading if scales on axis were altered, can be confusing with multiple lines

pie chart

used to show proportions, area of circle segment representing proportion. can also be drawn as a proportional circle. can be located on maps to show variations at different sample sites

clearly shows proportion of the whole, easy to compare different components, easy to label, info can be highlighted by separating segments

doesn’t show changes over time, hard to understand without clear labelling, hard to compare 2 sets of data, can only be used for a small number of categories (lots of segments becomes confusing)

rose diagram

uses multidirectional axes to plot data with bars. compass points used for axis direction. can be used for data like wind direction, noise or light levels

triangular graphs

axes on all 3 sides going from 0-100. used to display data that can be divided into 3 eg soil content, employment. must be in percentages

scatter graph

used to show relationship between 2 variables eg river characteristics. points not connected. best-fit line can be added to show relations.

clearly shows data correlation, shows spread of data, easy to identify anomalies & outliers

points not labelled, too many points can be hard to read, only show 2 sets of data

choropleth map

maps are shaded according to a pre-arranged key. each shade represents a range of values. often one colour in different shades. can be used for a range of data eg annual precipitation, population density, income levels

clear visual impression of changes over space, shows large amount of data, groupings are flexible

makes it seem like an abrupt change at boundary, hard to distinguish between shades, variations withing value set aren’t visible

proportional symbols map

symbols on the map drawn in proportion to the variable represented. usually circle / square used but can be an image. can be used to show a range of data eg population, electricity generation, traffic / pedestrian flows

illustrates the differences between many places, easy to read, data is specific to particular locations

not easy to calculate actual value, time consuming to construct, positioning may be hard with larger symbols

data analysis

once data has been collected & presented it needs to be analysed. analysis is the process that makes sense of the data collected. it identifies patterns, trends, significance, connections, and meaning in the data. involves stages:

• describing data shown in graphs / photos / maps

• identifying highest & lowest results

• identifying patterns & trends

• identifying relationships between data

statistical methods

can be used to help explore & explain results gathered during data collection. mean, median & mode are measures of central tendency:

mean is calculated by adding all values in the set and dividing by the total number of values

median is the middle value of a set of data. numbers are arranged in rank order to find middle value

mode is the value that occurs most frequently in a set of data

range is a measure of dispersion - the spread of data around the average. it’s the distance between the highest & lowest value. interquartile range is the part of the range that covers the middle 50% of the data

anomalies are results that don’t fit the pattern / trend. they need to be described & explained

evaluation - limitations of data collection

• accessibility of sample sites - could all be accessed

• size of sample - was it large enough

• duration of data collection - was it long enough to collect data needed

• methods - were methods appropriate to meet the aim & test the hypothesis

• equipment - issues

• human error - mistakes in recording data / reading equipment

• time of data collection - did weather / time impact results

• unforeseen issues - problems on the day eg road works, river flow that affect results

improvements to data collection

• increasing sample size

• taking more measurements

• looking at a wider range of secondary sources

• using other (more accurate) equipment eg a flow meter

evaluating conclusions

to evaluation the conclusion you should examine whether

• the conclusions reflect the aims & hypothesis set out at the start

• the aim & hypothesis were appropriate - could they be easily assessed

• the location was appropriate

• the accuracy of results could be improved if data collection were to be repeated

fieldwork enquiry process

1. introduction & planning: coming up with a question & fieldwork location, planning ahead, risk assessments, aims & hypothesis

2. methods & data collection: deciding how to collect information needed eg where to collect the data from in the location. collecting data, thinking of limitations

3. data presentation: presenting data so you can analyse it easily & make links - maps, graphs, charts, photos

4. data analysis: explaining what the data shows eg making links between different pieces of data

5. conclusions: trying to find an answer to the original question, assessing how sure we can be of it

6. evaluation: looking back over each stage and evaluating it for WWW & EBI eg problems, things that could have been done differently

evaluation for different fieldwork stages

methods: were sampling methods representative & unbiased, was timing & frequency appropriate, were methods accurate, precise & ethically sound - avoiding harm/offence & gaining consent where needed

data presentation: was data suitable for analysis, were graphs & statistical measures correct, were presentations valid & did they acknowledge all secondary sources used

data analysis: justify choice of statistical tests & minimise subjectivity in qualitative analysis. check for statistical significance, calculation errors & maintain confidentiality throughout

conclusions: were conclusions based on valid methods & reliable data, were all trends & errors considered, were findings supported by evidence & ethically responsible

values: difference in total EQS score of sites

goes from 14 in site 1 to -5 in site 12

values: word clouds

prevalent words in site 1 = green, common, trees

prevalent words in site 12 = busy, noisy, cars, traffic, people

values: decibel readings

40.2 at site 1

89.1 at site 12