Geographical Skills and Fieldwork

Geographical Enquiry

"Geographical enquiry is clearly outlined as an active, questioning approach to teaching and learning which includes values enquiry, and is integrated with the development of geographical skills. It is also explained that enquiry and skills are developed and used when studying the required content and not separately. All work in geography should include an element of geographical enquiry." (Rawling 2000)

What makes some locations better than others when completing fieldwork?

'Local' in scale Range of locations Safety Access

Bar charts

- simple, comparative, compound and divergent A bar graph has vertical columns rising from a horizontal base. The height of each column is proportional to the value that it represents. The vertical scale can represent absolute data or figures as percentages of the whole. Bar graphs are easy to understand. Values are obtained by reading off the height of the bar on the vertical axis. They show relative magnitudes very effectively. Using an appropriate scale, it is also possible to show positive and negative values on the same graph.

What are the key stages of geographical enquiry?

1) Identifying a suitable research question or hypothesis 2) Selecting, measuring and recording data appropriate to the chosen enquiry 3) Selecting appropriate ways of analysing and presenting fieldwork data 4) Describing, analysing and explaining fieldwork data 5) Reaching conclusions 6) Evaluating the geographical enquiry.

Choropleth maps

A choropleth is a map on which data values are represented by the density of shading within areas. The data are usually in the form that can be expressed in terms of area, such as population density per square kilometre. To produce such a map certain stages have to be followed. The material has to be grouped into classes. Before you can do this you have to decide on the number and range of classes required to display your data clearly. A range of shadings has to be devised to cover the range of data. Darkest shades should represent the highest figures and vice versa. Choropleth maps are fairly easy to construct and are visually effective as they give the reader a chance to see general patterns in a real distribution. There are, however, a few limitations to the method: - It assumes that the whole area under one form of shading has the same density, with no variations - The method implies abrupt changes at the drawn boundaries which will not be present in reality

Aim

A clear statement of the purpose of your investigation

Dot maps

A dot map is a map in which the spatial distribution of a geographical variable is represented by a number of dots of equal size. Each dot has the same value and is plotted on a map roughly where that variable occurs.

GIS

A geographical information system (GIS) has the ability to store, retrieve, manipulate and analyse a range of spatially related data. Most associated with electronic or digital maps. GIS allows layers of spatially referenced information to be layered. Information is said to be spatially referenced when it has a location associated with it, for example census data or satellite images. GIS handles data quickly and efficiently, enabling users to produce cartographic work that may have taken many hours to complete using traditional, manual, techniques.

Sample

A limited number of things, such as a group of 100 people or 50 pebbles on a beach.

Graphs with logarithmic scales

A logarithmic scale is drawn in the same way as an arithmetic line graph except that the scales are divided into a number of cycles, each representing a tenfold increase in the range of values. If the first cycle ranges from 1-10, the second will extend from 10-100, the third from 100-1000 and so one. You can start the scale at any exponent of 10, the starting point depends on the range of data to be plotted. Logarithmic graphs are good for showing rates of change - the steeper the line, the faster the rate. They also allow a wider range of data to be displayed.

Discourse analysis

A qualitative analysis method. Discourse analysis is a set of questions for analysing media, such as creative wiring, photographs, television and film. It is a great way of finding out more about informal representations of place. E.g. Tourist brochures and other promotional materials (including websites)

Hypothesis

A specific, focused, directional statement of what you expect to find. Instead of defining a set of individual research questions, you could also choose to identify a series of hypotheses from your overarching aim or research question. As is the case with research questions, your hypotheses will help you to decide on appropriate data collection methods. If you have more than three hypotheses though, your investigation is likely to become unmanageable, so stick to two or three hypotheses that are clear and focused.

Null hypothesis

A statement that there is no relationship between the two variables being considered, or no significant difference between two groups being compared. If you are using statistical tests, such as Spearman's Rank, you will also need to have null hypotheses. Although you will usually start an investigation with an expectation of what you will find, you actually try to find evidence to reject your null hypothesis (which then allows you to accept your alternative hypothesis) rather than trying to find evidence to support your alternative hypothesis.

Databases

A structured set of data held in a computer, especially one that is accessible in various ways.

Weather maps

A synoptic weather chart (or map) is a summary of the current situation. In weather terms this means the pressure pattern, fronts, wind direction and speed and how they will change and evolve over the coming few days. Weather maps and synoptic charts are only useful when you are planning fieldwork.

What is a risk assessment and why are they an important component of fieldwork?

A systematic process of evaluating the potential risks that may be involved in a projected activity or undertaking.

Line (transect) sampling

A transect is simply a line that spans the area of that you are studying, and then you locate your sample points along this line. The length of your transect would be determined by the area that you are sampling. Locating sample plots along the transect either in a systematically (i.e. every 10 meters), "randomly" or in a stratified manner

Research question

An answerable inquiry into a specific concern or issue and indicates what you want to find out in your investigation.

Atlas maps

An atlas is a collection of maps based on a single theme or series of themes. Most academic atlases require you to understand and be able to use lines of latitude and longitude to identify and describe location. You need to be able to use the index and understand how colour and symbols are used to convey information. Atlases contain huge amounts of information that can be useful when studying and comparing countries or regions. Note that it is important, in this rapidly changing world, that the atlas is up to date.

What is a literature review?

An evaluative report of information found in the literature related to your selected area of study. The review should describe, summarise, evaluate and clarify this literature. It should give a theoretical base for the research and help you (the author) determine the nature of your research. Works which are irrelevant should be discarded and those which are peripheral should be looked at critically. A literature review is more than the search for information, and goes beyond being a descriptive annotated bibliography. All works included in the review must be read, evaluated and analysed. Relationships between the literature must also be identified and articulated, in relation to your field of research.

Bias

An inclination or prejudice towards or against a specific finding or outcome

Sketch maps

Any annotated map/diagram must feature the following elements: - A title or location of the place in the map - A key to all the symbols - Annotations or labels explaining, elaborating or emphasising particular features. Annotations are usually more than 10 words whereas labels are less - An indication of scale - A north pointer

What are the general rules for writing questionnaires?

Avoid technical terms and jargon - make it accessible and easy to understand Avoid vague or imprecise terms - variety of responses and avoids vague responses Define things very specifically - gives the answers required Avoid complex sentences - question may get lost, confusing Provide reference forms Make sure scales are ordinal Avoid double-barrelled questions Anticipate all possibilities If you want a single answer, make sure your answer choices are unique and include all possible responses Avoid questions using leading, emotional or evocative language - avoids bias

How do you decide what data you need to collect to be able to research a particular topic?

Awareness of the key concepts that underpin your investigation. Awareness of the different data collection techniques you can use to collect data (both primary and secondary). Understand the area you are researching and what information you will need to answer your research question or hypothesis. Aware of the limitations of the researcher, equipment, field site, availability and access to secondary data.

What is Big Data?

Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.

Cons of isolines

Can be difficult to construct There is an element of guess work involved in the positioning of the isolines between values. This makes them rather subjective, especially if there is a lack of known values.

Advantages of systematic sampling

Can be used with large sample population Avoids bias

Advantages of random sampling

Can be used with large sample populations Avoids bias

Disadvantages of systematic sampling

Can lead to poor representation of the overall parent population or area if large areas are not hit by the numbers generated. This is made worse if the study area is very large There may be practical constraints in terms of time available and access to certain parts of the study area

Disadvantages of random sampling

Can lead to poor representation of the overall parent population or area if large areas are not hit by the random numbers generated. This is made worse if the study area is very large There may be practical constraints in terms of time available and access to certain parts of the study area

Cartographic skills

Cartographic skills fall into two main categories. The first group of skills involves the reading and interpretation of a variety of different types of maps. These include atlas maps and weather maps.

Strengths of secondary data

Census data - more accurate Can compare between data sets and own results - Allows validity testing of data Accessible - (Freedom of Information Act) - faster, cheaper to access Temporal variation

How do you design a good enquiry question?

Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible pronouncements], Demonstrate a researchable topic or issue [i.e., feasibility of conducting the study is based upon access to information that can be effectively acquired, gathered, interpreted, synthesized, and understood], Identification of what would be studied, while avoiding the use of value-laden words and terms. Articulation of the study's boundaries or parameters or limitations. Does not have unnecessary jargon or overly complex sentence constructions. Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Coding

Coding is carried out when analysing answers to interviews. The coder (person who analyses the data) looks through all the answers to a question, develops a broad classification system based on the responses, and then uses a code to categorise responses.

Strengths of primary data collection

Collected for your investigation Fresh data Experience gained Realistic view to researcher Original

Inferential and relational statistical techniques

Comparisons are made between two sets of data to see whether there is a relationship between them. Note that even if there is a relationship between two variables, this does not prove a casual link. In other words, the relationship does not prove that a change in one variable is responsible for a change in the other. There are two main ways in which relationships can be shown: - Using scattergraphs - Measuring correlation using the Spearman rank correlation coefficient

Pros of isolines

Data can be represented without artificial area boundaries. Therefore changes in value occur smoothly and not abruptly. This maps maps useful for interpreting general trends in distribution

Sampling considerations

Deciding where or not to use a sampling survey, you need to take sampling considerations into mind: Larger sample sizes are more accurate representations of the whole Most approaches assume that the parent population has a normal distribution where most items or individuals are clustered close to the mean, with few extremes The sample size choice is a balance between obtaining a statistically valid representation, and the time, energy, money, labour, equipment and access available A sampling strategy made with the minimum of bias is the most statistically valid A 95% probability or confidence level is usually assumed, for example, within plus or minus two standard deviations from the mean Working at a 95% confidence level means that up to 5% of the sample may lie outside of this - sample, no matter how good, can only ever be claimed to be a very close estimate.

What is a pilot study and why are they important?

Developing and testing adequacy of research instruments Assessing the feasibility of a (full-scale) study/survey - Assessing whether the research protocol is realistic and workable Designing a research protocol Establishing whether the sampling frame and technique are effective - Assessing the likely success of proposed data collection techniques - Identifying logistical problems which might occur using proposed methods Estimating variability in outcomes to help determining sample size Collecting preliminary data Determining what resources (finance, staff) are needed for a planned study Assessing the proposed data analysis techniques to uncover potential problems Developing a research question and research plan

Dispersion diagrams

Dispersion graphs are used to display the main patterns in the distribution of data. The graph shows each value plotted as an individual point against a vertical scale. It shows the range of the data and the distribution of each piece of data within that range. It therefore enables comparison of the degree of bunching of two sets of data.

Principles of ethical behaviour (Lichtman, 2013)

Do no harm: Safeguard against anything that could harm participants or the environment in your study. Privacy and Anonymity: No identifying information about an individual should be revealed. Seek permission from participants. Confidentiality: Information collected should not be given to anyone else. Informed consent: Participants are informed of the nature and extent of the study. Truthfulness and accuracy in reporting data: Will not create data or falsify data regardless of format. Intrusiveness: Remain a neutral researcher. Inappropriate behaviour: Remain a neutral researcher. Data interpretation: Researchers should use their data to fairly represent what you see and hear. Don't over interpret or misinterpret the data collected to present a picture that is not supported by data and evidence. Rapport and friendship: An appropriate level of professionalism should be maintained.

Point sampling

Each point represents the factor that you are studying e.g. houses, trees etc. These are marked on a map/diagram. You may have numbered each point or devised a coordinates /grid reference system. You then use a sampling technique to select the points to study. This approach selects a specific point or a place as feasibly close to the point as possible, for you to study.

Pros of dot maps

Effective in showing spatial density Shows variation and pattern Easy to interpret Purpose is easily understood Easy to generate on a computer

What factors are important to consider when designing a suitable research question/hypothesis?

Feasible Interesting Novel Ethical Relevant Top Tips: Manageable in terms of research and in terms of your own academic abilities. Substantial and with original dimensions. Consistent with the requirements of the assessment. Clear and simple.

Maps showing movement - flow lines, desire lines and trip lines

Flow lines and desire lines are similar in that they both represent the volume of movement from place to place. They are useful to show such features as: - Traffic movements along particular routes - Migration of populations - Movement of goods or commodities between different regions - Movements of shoppers In both methods the width of the line is proportional to the quantity of movement. A flow line represents the quantity of movement along an actual route, such as a train or bus route. A desire line is drawn directly from the point of origin to the destination and takes no account of a specific route. Trip lines can be drawn to show regular trips, for example where people shop.

Non-probability sampling

For some qualitative methods like interviews, it may be impractical to select a representative sample. In non-probability sampling, the sample is selected through the subjective judgment of the researcher. There are three techniques: Convenience sampling - Select people who are easy to reach, e.g. giving out a questionnaire to the first 100 people you see in the High Street on a particular morning. Snowball sampling - Select at least two people. Ask each person to help you find more interviewees. Continue finding new people until you have achieved your desired sample size. Quota sampling - Deliberately select a proportionate number of people from each part of the population.

What can GIS do for you?

Geographic Information Systems has been designed to answer important questions about location, patterns, trends and conditions such as: Where are features found? Points, lines and polygons. If you need to find the closest gas station, GIS can hold your hand there. Searching for an optimum location requires information on traffic volumes, zoning information and demographics over multiple sites. What geographical patterns exist? Ecologists who want to know suitable habitat for elk can gain a better understanding by using GPS collars and forest inventory. What changes have occurred over a given period of time? Never have we've been able to understand climate change before thanks to GIS and remote sensing technology. Safety concerns can be better evaluated using GIS such as understanding terrain slope and the probability an avalanche can occur. What are the spatial implications? If an electricity company wants to build a transmission line, how will this affect nearby homes, the environment and safety. Most environmental assessments use GIS to understand the landscape.

Pros of flow/trip/desire lines

Good to show direction (all) and size of movement (flow) They are able to give a good visual impression of movement (flow) Can show movements such as traffic/migration Desire lines show trends in migration of population Gives clear sense of direction Clear location components

Representative

How closely the relevant characteristics of the sample match the characteristics of the population.

Using field sketches and photographs

In your personal investigation, field sketches and photographs are excellent ways to record exactly what you have seen. Field sketches enable you to pick out from the landscape the features that you wish to identify and perhaps comment upon.

Primary data

Information collected for the specific purpose at hand

Secondary data

Information that already exists somewhere, having been collected for another purpose

Image analysis

It can be used for photographs, paintings, television/film. There are 4 stages: 1. Denotation - make a list of the main contents of the image 2. Connotation - explain what you think the image is trying to convey 3. Mise en scene - look more closely for clues from the background and arrangement of the image. Include people's clothing, hairstyles, pose and facial expressions. 4. Organisation - there are two parts to this: a) Composition - how are the people and objects arranged in the picture? b) Framing - this is where the image begins and ends.

Advantages of stratified sampling

It can be used with random or systematic sampling, and with point, line or area techniques If the proportions of the sub-sets are known, it can generate results which are more representative of the whole population It is very flexible and applicable to many geographical enquiries Correlations and comparisons can be made between sub-sets

What are the key features of 'big data'?

It is constantly being updated or created in near or real time. There is a huge variety of sorts of information. The information is exhaustive in scope. It attempts to capture the data of whole populations. It is very detailed. One part of big data can easily be related or linked to other parks. It has flexibility. This means you can add new fields of data or more up-to-date data easily It is huge in volume, consisting of terabytes or perabytes of data.

Crowdsourcing

It is difficult to analyse individual people's behaviour and attitudes using traditional large-scale data sources such as population censuses and social surveys because they tend to deal with large anonymous groups rather than individuals. They focus easily on measured attributes and characteristics of a population, rather than attitudes and behaviours. They also offer a snapshot rather than a continuously changing view. However, new data sources contain a wealth of information about people's geographical behaviour at an individual level; they have the potential to revolutionise our understanding of social phenomena. These sources are commonly referred to as geo-spatial crowd-sourced data (GSD) or volunteered geographical information. GSD is increasingly being used to study individual people's daily behaviours and explore how there dynamics can potentially lead to civil unrest. It is also being used to look at the way people respond to natural disasters.

Distribution of the data set

It is possible that each of these measure of central tendency could give the same result, but they are more likely to give different results. For them each to give the same result the distribution of a data set would have to be perfectly 'normal', and this is extremely unlikely when using real data. It is more likely that the distribution of the data set will be skewed towards the lower end of the distribution (positive skew) or towards the upper end of the distribution (negative skew). The more it is skewed, the greater the variation in the three measures of central tendency. None of these measures give an reliable picture of the distribution of the data set. Measures of the dispersion or variability of the data should therefore also be provided.

Isoline maps

It is possible to draw a map on which all points of the same value are joined by a line. This allows patterns in a distribution to be seen. The best known example of such isolines (also called isopleths) is on Ordnance Survey maps where contour lines join places of the same height. This technique can be applied to a number of other physical factors, such as rainfall (isohyets), temperature (isotherms) and pressure (isobars), as well as human factors, such as travel times (isochrones) for commuters and shoppers. Some rules regarding isoline construction: - They connect points of equal value - They represent continuous surfaces (like a ground surface). It doesn't suddenly disappear. - Isolines do not cross or touch - Values on one side of the isoline are higher or lower than values on the other side - The interval between isolines is the same over the entire map (unless specified otherwise) Isoline maps of the environment almost always represent an overhead view, looking straight down.

Cons of dot maps

Lack precise location and value of each individual item. Too many dots will fail to be useful as they will all merge into one, or too few and it will create the impression of emptiness. Regular spacing of dots is extremely difficult to achieve, as is accurate counting Areas can be missed out if they don't conform to the scale Will only indicate general variations between areas.

Simple and compound line graphs

Line graphs are appropriate when you want to show absolute changes in data. When several lines are plotted on the same graph, it is important to recognise whether it is a simple or a compound line graph: - On a simple line graph, the line represents the actual values of whatever is being measured on the vertical axis - On a compound line graph, the differences between the points on adjacent lines give the actual values. To show this, the areas between the lines are usually shaded or coloured and there is an accompanying key It is possible to show two sets of data on the same graph. The left hand vertical axis can be used for one scale and the right hand vertical axis for a different scale. This can often give a useful visual impression of the connection between two sets of data.

Cons of flow/trip/desire lines

Maps lack precise interpretation unless statistical data is added (flow) Desire and trip lines are only interested in source and destination areas, there will be convergence from a central location. Could be improved by altering width according to data. Hard to draw Flows can be in the same direction / overlap May be difficult to show meeting points of the wide bands without overwhelming the map.

Weaknesses of secondary data

May be out-of-date Virus / inappropriate sites Unreliable sources Authenticity and copyright issues

Descriptive statistics

Numerical measures are used to tell about features of a set of data. There are a number of items that belong in this portion of statistics such as: 1) The average, consisting of the mean, median, mode or mid-range. 2) The spread of a data set, which can be measured with the range or standard deviation 3) Measurements such as skewness 4) The exploration of relationships and correlation between paired data 5) The presentation of statistical results in graphical form These measures are important and useful because they allow scientists to see patterns among data, and thus to make sense of that data. Descriptive statistics can only be used to describe the population or data set under study. The results cannot be generalised to any other group or population.

Interviews

One of the main ways of collecting qualitative data is through interview surveys. These can be detailed and flexible with open-ended questions and the opportunity for respondents to give their opinions without being limited to responses in option boxes. The interviewer has to be well prepared, with a specific aim for each interview, which matches the topic under consideration as well as the role of the interviewer. They can also be loosely structured so particularly interesting points can be pursued and you can adapt to follow the flow of the conversation. Examples where interviews would be appropriate include: - Studying the attitudes of residents to the development of a nearby housing estate - Finding out, from an entrepreneur, the reason for their choice of location of a new commercial development.

Why is critical thinking so important?

Perception is powerful and creates truth for you. Perception is the path to belief, and you act upon what you believe. Our perceptions are what define our behaviour. When we refuse to allow any flexibility in our perceptions, we close our mind to possibility and sometimes even the truth. Your perspective affects your experience. Our perception of the world affects our decisions, how we vote, what we buy etc.

Ethical

Potential harm to subjects or sites? Potential breach of subject confidentiality?

Why is ethical practice important?

Protects rights of individuals and communities that research takes place in, as well as the environment in which the research is taking place in. Maintains a favourable environment for scientific enquiry. Growing public demand for accountability.

Qualitative methods

Qualitative methods are ways of collecting data which are concerned with describing meaning, rather than drawing statistical inferences. What qualitative methods lose on reliability they gain in terms of validity. They provide a more in-depth and rich description of the subject that is most often used in cultural geography.

Quantitative data

Quantitative data are numerical data such as metric-level measurements that are associated with the scientific and experimental approach and are criticised for not providing an in-depth description.

Quantitative methods

Quantitative methods (or geostatistics) are those which focus on numbers and frequencies rather than on meaning and experience. Quantitative methods provide information which is easy to analyse statistically and whose reliability is known. Quantitative methods are associated with the scientific and experimental approach and are sometimes criticised for not providing an in-depth description.

How do you know you can trust information you find when researching?

Questions to ask: Accuracy - Is the information reliable? - Is the information error-free? - Can the information be verified against other reliable sources? Authority - Who is the author? - Do they have the qualifications to speak/write on the topic? - Is the author affiliated with a reputable university or organisation on this topic Objectivity - What is the intended purpose of this information? - Is the information facts or opinions? - Is the information biased? Currency - When was the information published? - Is the information current or out-of-dated - Does currency matter in this topic? Coverage - Does the information covered meet your information needs - Does it provide basic or in-depth coverage

Radial graps

Radial plots always appear as a circle. The points on the outside of the circle usually show direction (compass points), but they can show other things e.g. months of the year. The bars coming out from the centre show another variable - the distance they reach from the centre shows the size of the variable. The most common radial diagram is a wind rose. The bars point in different directions to show which way the wind is blowing from. How far the bar reaches from the centre shows how often wind blows from that direction. The diagram also shows a third variable - each bar is split into different coloured sections to show the frequency of different wind speeds from each direction.

Random Sampling

Random sampling is the simplest form of sampling methods where all members of a given population have an equal chance of being chosen for the sample group. One technique for ensuring a random sample is to assign numbers to the population and choose the sample via a random number selection technique, such as the random number button on a calculator. This removes any bias. However, to achieve the perfect random sample, the sample must be finite and all members of the population must be known and listed to avoid bias.

How was crowd-sourced data used in the relief of the Haitian earthquake (2010)?

Relief efforts had to get supplies and resources to the parts of the country most desperately in need, but it was difficult to know where to deploy resources because there was no systematic plan or data in place to help make such decisions. Even some of the most basic informational needs, like detailed roadmaps and locations of critical assets, were not available. People and organisations around the world realised that they didn't have to be physically present in Haiti to prove meaningful assistance to those who were. Information about opportunities to help spread quickly through a variety of online outlets, including blogs, emails and tweets. OpenStreetMap volunteers from around the world downloaded satellite images in order to trace and record the outlines of streets, buildings and other places of interest. These traces were uploaded into the OSM database and worked together with material from on-the-ground volunteers in Haiti who, using portable GPS devices, were able to upload additional information.

What to include within your conclusion.

Restate the main idea of your essay, or your thesis statement Summarise the main points of your essay (it can be useful to do this by using research questions / hypotheses as subheadings. Synthesise by linking all these key findings together to come to an overall conclusion which clearly links to overall aim. Show how your findings are significant and which gaps in knowledge/literature they help to fill. Consider the overall context; how could your study be extended/ Ethical implications? Leave the reader with an interesting final impression.

Weaknesses of primary data collection

Restrictions: level of experience, access, cost, time, accuracy due to access to more specific equipment In questionnaires, people may give fake, socially acceptable answers

What is sampling?

Sampling is the process of collecting data from some sites or people in order to obtain a perspective on the population. It is applicable to both qualitative and quantitative methods.

Remote sensing

Scanning the earth by satellite or high-flying aircraft in order to obtain information about it.

Scatter graphs

Scatter graphs are used to investigate the relationship between two sets of data. They can be used to simply present data, but they are particularly useful in identifying patterns and trends in the relationship that might lead to further inquiry. A general trend line (best-fit) can be added to the graph so that the relationship can be easily observed. If it has a positive gradient is has a positive relationship and vice versa. Other features of scatter graphs include: They can be placed on arithmetic, logarithmic or semi-logarithmic graph paper The independent variable goes on the horizontal axis and the dependent variable on the vertical axis It is possible for a correlation to emerge even when a relationship is only coincidental Points lying some distance from the best-fit line are known as residuals (anomalies). These can be either positive or negative. Identification of residuals may enable you to make further investigations into other factors that could have influenced the two variables

Advantages of dispersion diagrams

Shows the spread from the mean. Very visual Gives an indication of the reliability of the data Can work out mean, range, median, lower quartile, upper quartile and interquartile range Can compare graphs easily for analysis Anomalies can be shown Can work out standard deviation

How will you evaluate each part?

Strengths Weaknesses Impacts on results Impacts of conclusions Improvements How improvements would improve accuracy and validity of results and conclusions.

Cons of proportional symbols

Symbol congestion / overlap - especially if there are large variations in the size of symbols or if numerous data locations are close together. Difficult to produce Not accurate / can't extract exact data Overlap can occur making it difficult and confusing to read / interpret

Systematic sampling

Systematic sampling is the process of selecting a predetermined 'member' from a sampling list. For example, every fifth data point would be selected from list of population members. For this reason, systematic sampling is also called Nth name selection. For example we will work along the sand dunes recording the angle at every 5 metres.

Probability sampling

The aim of probability sampling is to select a sample which is representative of the population. There are three techniques: Random sampling - This is where each member of the population is equally likely to be included. Stratified sampling - This is where a proportionate number of observations is taken from each part of the population. Systematic sampling - This is where observations are taken at regular intervals, such as every 10 metres or every 5th person.

Area (Quadrat) Sampling

The area that you intend to study is divided up into a series of squares (quadrats). An OS map has these marked on already! They can vary in size depending on what you are studying, e.g. farms or drainage patterns. Simply number the squares and use a sampling technique to select the ones that you are going to study On a smaller scale quadrat sampling is a classic biogeography tool. They are placed in a habitat of interest and the species within the quadrat are identified and recorded. This is suitable for sampling plants and some slow-moving creatures such as millipedes and insects.

What is a conclusion?

The conclusion is intended to help the reader understand why your research should matter to them after they have finished reading the paper. A conclusion is not merely a summary of the main topics covered or a re-statement of your research problem, but a synthesis of key points and, if applicable, where you recommend new areas for future research.

What is an evaluation?

The evaluation is where you judge the importance and significance of your investigation. You should say what went well and what didn't go so well for both methods and the overall investigation. You should also discuss where your investigation could be taken in the future (either by yourself or others).

How do I write a literature review?

The goal of the researcher is to determine the current state of knowledge about a particular topic by asking, "What do we know or not know about this issue?" In conducting this type of research, it is imperative to examine several different sources to determine where the knowledge overlaps and where it falls short. A literature review requires a synthesis of different subtopics to come to a greater understanding of the state of knowledge on a larger issue. It works very much like a jigsaw puzzle. The individual pieces (arguments) must be put together in order to reveal the whole (state of knowledge).

Interquartile range

The inter-quartile range in calculated by ranking the data in order of size and dividing them into four equal quartiles. The boundary between the first and second quartiles is known as the upper quartile and the boundary between the third and fourth quartile is known as the lower quartile. The upper quartile (UQ) is the value that occurs as (n+1)/4 th position in the data set when arranged in rank order (from highest to lowest) The lower quartile (IQ) is the value that occurs at 3(n+1)/4 th position in the data set. The difference between the upper and lower values is the interquartile range. 1QR = UQ - LQ The IQR indicates the spread of the middle 50% of the data set about the median value, and thus gives a better indication of the degree to which the data has spread, or dispersed, on either side of the middle value.

The internet

The internet is a vast network of unfiltered information sources, (i.e., anyone can put anything on it, bypassing editorial or peer review). It is of utmost importance that we evaluate information on the Web before it is used and cited. Here are some quick hints that can help you decide whether the information given in a particular web page is reliable or not: Look for information about the author, e.g., links that say "Who we are", "About this site", etc. See if the author/web master provides e-mail address or other contact information so that he or she can be contacted for enquiries or further information. Look for hints on authority in the URL (Internet address): Top-level domain tells you what type of institution the information comes from .com -- a commercial site (may be trying to sell a product) .edu -- an educational institution (usually reliable but may not if it is a personal web page of a member of the institution) .gov -- a government department or agent .net -- network access provider .org -- a non-profit organization (may or may not be biased) a "~" in the URL usually indicates it is a personal webpagee.g., http://personal.univ.edu/~smith/abc.htm - The quality of information can vary greatly among personal web pages.

Arithmetic mean

The mean is calculated by adding up all the values in a data set and dividing the total sum by the numbers in the data set. The arithmetic mean is of little value on its own and should be supported by reference to the standard deviation of the data set.

Stratified sampling

The most accurate method involves an initial analysis of the population being studied and its division into categories. The sample is allocated to the size of these categories, so in the example below 70% of the sample points will be in the area of granite. The specific sites within this area are then selected by other sampling technique. This method ensures that the results are a true representation of the whole population.

Why do we sample?

The most accurate way of investigating a geographical topic is to measure everything, such as recording every type of land use in Durham. Why do researchers not do this and sample instead? This is often impractical! Too costly and not enough time. To avoid this problem, it is expected that you will limit the amount of information that you collect, as long as you are careful that your sample is representative of the whole population.

Pie charts and proportionally divided circles

The pie chart is divided into segments according to the share of the total value represented by each segment. This is visually effective - the reader is able to see the relative contribution of each segment to the whole. On the other hand, it is difficult to assess percentages or make comparisons between different pie charts if there are lots of small segments. When a number of pie charts are drawn proportional to the value each represents in total, they are called proportional divided circles. The construction of a proportional circle is as follows: - Use the formula: r = √(V/π) where V is the value that you want the total pie chart to represent and r is the radius of the pie chart - Draw a circle of radius r on graph paper.

Disadvantages of stratified sampling

The proportions of the sub-sets must be known and accurate if it is to work properly It can be hard to stratify questionnaire data collection, accurate up to date population data may not be available. It may be hard to identify some factors e.g. people's age or social background effectively

Which parts of the enquiry need to be evaluated and why?

The research aim and sub-questions - too many questions, questions which are too vague, have too much scope, are too limited Site selection and sampling methods - not enough sample sites (underrepresentation), inappropriate sampling method, time/date of data collections, number of times data is sampled. Data collection methods - accuracy of equipment, availability of equipment, availability of field assistants, time/date of collection, external factors e.g. weather, large public event, human error Data presentation methods - enough data points to present information, range of techniques, human error, access to ICT/GIS Data analysis methods - enough data points to carry out statistical tests, time/date Conclusions - having enough evidence/data, skill of investigator, background knowledge of topic.

Why is it vital to formulate an effective research question/hypothesis?

The research question is the starting point of the study. Everything flows from the research question. It will determine the population to be studied, the setting for the study, the data to be collected, and the time period for the study. A clear and concisely stated research question is the most important requirement for a successful study.

Sample size

The size of sample usually depends upon the complexity of the survey being used. When using a questionnaire it is necessary to sample sufficient people to take into account the considerable variety introduced by the range of questions. Sample size can be restricted by practical difficulties and this may affect the reliability of results. Your aim should be to keep the sampling error as small as possible.

Standard deviation

The standard deviation of a range of data is a measurement of the degree of dispersion about the mean value of a data set. It is calculated as follows: - The difference between each value in the data set and the mean value is worked out - Each difference is squared, to eliminate negative values - These squared differences are totalled - The total is divided by the number of values in the data set, to provide the variance of the data - The square root of the variance is calculated. The standard deviation is statistically important as it links the data set to the normal distribution. A low SD means that the data are clustered around the mean value and that dispersion is narrow. A high SD means that the data are more widely spread and that dispersion is large. The standard deviation also allows comparison of the distribution of the values in a data set with a theoretical norm and is therefore of greater use than just the measures of central tendency.

Population

The total number of things, such as all residents of a city or all pebbles on a beach.

Statistical population

The whole from which a sample will be chosen for a research exercise.

Cons of choropleth maps

The whole of an area with one shading pattern appears to have the same density with no variation in it, but in reality this is not usually the case and there will be variations within each area. They give the impression that density changes abruptly at the boundary line of each area but this will not happen in reality and changes are more likely to be gradual and bear little relation to the boundary lines. With line shading it's difficult to draw lines accurately and it's time consuming. With the colouring methods, the shades of colouring are difficult to achieve, especially with a large number of classes unless more than one colour is used.

Thematic Coding

Thematic coding is a form of qualitative analysis which involves recording or identifying passages of text or images that are linked by a common theme or idea allowing you to index the test into categories and therefore establish a 'framework or thematic idea' about it.

Measures of dispersion

There are three measures of dispersion or variability: Range Inter-quartile range Standard deviation

Maps with located proportional symbols

These are maps that include symbols which are proportional in area or volume to the value they represent. Symbols of representative sizes, such as squares or circles, or even small graphs, such as bar graphs or pie charts, can be placed on a map to show spatial differences. It is important that you take great care in placing symbols on a map. It is essential to avoid too much overlap, but it must be clear which area or place the symbol represents.

Qualitative data

These are non-numerical data that are used in a relatively unstructured and open-ended way. It is descriptive information, which often comes from interviews, focus groups or artistic depictions such as photographs.

Range

This is the difference between the highest value and the lowest value in a data set. It gives a simple indication of the spread of the data.

Median

This is the middle value in a data set when the figures are arranged in rank order. There should be an equal number of values both above and below the median value. If the number of values in a data set is odd, then the median will be the n+1/2 item in the data set. If the number of values in the data set is even, the median value is the mean of the middle two values.

Mode

This is the value which occurs most frequently in a data set. It can only be identified if all the individual values are known.

The Spearman rank correlation coefficient

This is used to measure the degree to which there is correlation between two sets of data (or variable). It provides a numerical value which summarises the degree of correlation, so it is an example of an objective indicator. Once it has been calculated, the numerical value has to be tested statistically to see how significant the result is. The test can be used with any data set consisting of raw figures, percentages or indices which can be ranked. The method of calculation: - Rank one set of data from highest to lowest (e.g. highest value ranked 1) - Rank the other set of data in the same way - Beware of tied ranks. In order to allocate a rank order for such values, calculate the average rank they occupy. E.g.. if there are three values for rank 5, add together the ranks 5,6,7 and divide by three, giving an 'average' rank of 6 for each one. The next value in the sequence will be allocated rank 8 - Calculate the difference in rank (d) for each set of paired data - Square each difference - Add the differences together and multiply by 6 (A) - Calculate the value of n³-n (B) - Divide A by B and take the result away from 1 The answer should be a value between +1.0 (perfect positive correlation) and -1.0 (perfect negative correlation) Words of warning: - Have at least 10 sets of paired data, otherwise test is unreliable - No more than 30 sets of paired data, calculations become too complex - Avoid too many tied ranks

Chi-squared test

This technique is used to assess the degree to which there are differences between a set of collected (or observed) data and a theoretical (or expected) set of data, and the statistical significance of the differences. The observed data (O) are those that have been collected either in the field or from secondary sources. The expected data (E) are those that would be expected according to the theoretical hypothesis being tested. Normally before the test is applied it is necessary to formulate a null hypothesis. The aim of the chi-squared test is to find out whether the observed pattern agrees with or differs from the expected pattern. This can be measured by comparing the calculated result of the test with its level of significance. To do this the number of degrees of freedom must be determined using the formula (n-1) where n is the number of observations.

Measures of central tendency

Three such measures: Arithmetic mean Mode Median

Relevant

To geographical knowledge/theory? To individual interest? Will your question be of academic and intellectual interest? The question arises from issues raised in the literature? You should be able to establish a clear purpose for your research in relation to the chosen field. For example, are you filling a gap in knowledge, analysing academic assumptions or professional practice, monitoring a development in practice, comparing different approaches or testing theories within a specific population or area?

Interesting

To the investigator? This is essential. The question needs to intrigue you and maintain your interest throughout the project. There are two traps to avoid. Some questions are convenient - the best you can come up with when you are asked to state a question on a form. Some questions are fads - they arise out of a particular set of personal circumstances. Once the circumstances change you can lose enthusiasm for the topic and it becomes very tedious. Make sure that you have a real, grounded interest in your research question, and that you can explore this and back it up by academic and intellectual debate. It is your interest that will motivate you to keep working.

Novel

To the topic of geography you are researching? The question should not simply copy questions asked in pieces of research. It shows your own imagination and your ability to construct and develop research issues. It needs to give sufficient scope to develop

Triangular graphs

Triangular graphs are plotted on special paper in the form of an equilateral triangle. It is only possible to use it for a whole figure that can be broken down into three components expressed as percentages. The triangular graph cannot therefore be used for absolute data or for any figures that can not be broken down into three components. The advantage of using this type of graph is that the varying proportions and their relative importance can be seen. It is also possible to see the dominant variable of the three. After plotting, clusters will sometimes emerge, enabling a classification of the items involved.

What should you consider when evaluating?

Usefulness - did it help you achieve your key aim. Accuracy - is data correct and precise? Validity - are the conclusions and interpretations factually sound? Reliability - would someone else get the same results? Do results reflect the whole population? Are they representative.

Pros of proportional symbols

Very visual Can represent a large range of data Not dependant on size of area One advantage of proportional symbol maps over dot density maps is it is generally easier for map readers to extract numbers from the map since estimating the size of the symbol is less tedious than costing many little dots.

Why do we use GIS?

Viewing and analysing data geographically impacts our understanding of data. "A geographic information system (GIS) lets us visualize, question, analyse, and interpret data to understand relationships, patterns, and trends." - http://gisgeography.com

Pros of choropleth maps

Visual impression of change over a space - gives a general impression General anomalies can be identified Easily done by hand or on the computer Doesn't breach data protection Good for data which involves density reading Easy to interpret

Why do we do geographical enquiry?

We learn through actively engaging with our environment. We construct new knowledge by relating it to what we already know and challenging existing thinking. Encourages higher order thinking, "this occurs when a person takes new information and information stored in memory and interrelates and/or rearranges and extends this information to achieve a purpose or find possible answers in perplexing situations." Lewis and Smith (1993, p.136) To develop our knowledge and understanding of key geographical concepts and processes, develop subject-specific and transferable skills, personal and social development, application of theory to practical scenarios and assessment.

Interpreting the results of spearman rank correlation coefficient test

When interpreting the results of the Spearman rank test, consider the following: What is the direction of the relationship? If the calculation produces a positive value, the relationship is positive or direct. In other words, as one variable increases so does the other. If the calculation produces a negative value, the relationship is negative, or inverse. How statistically significant is the result? When comparing two sets of data, there is always a possibility that the relationship between them has occurred by chance. It is therefore necessary to assess the statistical significance of the result. In the case of Spearman rank test, the critical values must be consulted. These can be obtained from statistical tables.

Disadvantages of dispersion diagrams

Works better with lots of data The standard deviation can easily be manipulated and can be bias.

Ordnance Survey Maps (OS)

You must be able to interpret and use OS maps at a variety of scales. Each grid square of an OS map contains a huge amount of information. This can be on the physical landscape and landforms or the areas and/or the human landscape. OS maps can be used as a source of information or as a base map for display of fieldwork investigation findings.

Feasible

You need to be realistic about the scope and scale of the project. The question you ask must be within your ability to tackle. For example, are you able to access people, sites, statistics, or documents from which to collect the data you need to address the question fully? Are you able to relate the concepts of your research question to the observations, phenomena, indicators or variables you can access? Can this data be accessed within the time and resources you have available to you?