methods of data presentation in geography

• All Data Presentations
• 3D Chart Example
• 3D Chart Maker
• Bar Chart (Divided)
• Bar Chart Maker
• Bar Chart (Percentage)
• Bar Chart (Range)
• Beach Profile Maker
• Bi-Polar Graphs
• Bihistogram Creator
• Bi-Polar Chart Maker
• Box Plot Creator
• Cailleux Roundness
• Choropleth Maps
• Coding Analysis
• Cross Section Maker
• Cumulative Frequency
• Divided Bar Charts
• Donut Chart Maker
• Histogram Creator
• Line Chart Maker

Line of Best Fit

• Kite Data Entity Example
• Kites Data Entity Maker
• Kites Species Creator
• Kites Species Example
• Map Cross Section Maker
• Percentage Bar Charts
• Pie Chart Maker
• Pie Chart Maker Example
• Polar Area Chart Maker
• Polar Area Example
• Polar Chart Example
• Polar Chart Maker
• Polar Overlays Example
• Polar Overlays Maker
• Polar Scatter Maker
• Proportional Circles
• Proportional Circle Maker

Radar Charts

• Radar Chart Maker

Radar Chart Overlays

• Range Bar Charts

River Cross Sections

• Rose Diagram Example
• Rose Diagram Maker
• Sand Dune Profiles

Scatter Graphs

• Scatter Graph Maker
• Slope Profile Example
• Slope Profile Maker
• Stacked Bar Chart Maker
• Stacked Bar Example

Triangular Graphs

• Triangular Graph Example
• Triangular Graph Maker
• Beach Profiles
• Lichenometry
• Map Reading
• River Discharge
• Gentrification
• Mapping Techniques
• Slope Steepness
• Minimum Sample Size
• Optimum Quadrat Size
• Random Numbers
• Random Sampling
• Sampling Problems
• Sample Size
• Urban Sampling
• Box Plot Maker
• Beach Volume
• Hydraulic Radius
• Mann Whitney
• Manning's n
• Nearest Neighbour
• River Cross Section
• Scatter Graph Creator
• Simpson's Diversity Index
• Slope Steepness Index
• Spearman's Rank
• Spearman's Calculator
• Standard Deviation
• Statistical Methods
• Wetted Perimeter

Geography Data Presentation Techniques and Methods

Many of the most appropriate types of data presentation techniques used to visualise raw geographical data are shown on this page. We provide the tools to create and save the images shown and these are quick and easy to use, free with no account or log-in required.

Besides creating images, the geography data presentation tools have many mathematical functions. These include the calculation of cross sectional area for beach profiles and river cross sections, trend lines for scatter graphs and standard deviation for box plots.

Simply open the calculator for your chosen technique, enter your data, adjust titles and data labels. An image of your data presentation and the calculations used to create it are then instantly ready for download.

Percentage or Divided Bar Charts

Stacked bar charts.

Min-Max Range Floating Bar Charts

Beach profiles (using slope angle data), beach profiles (using height change data).

Beach Profiles (using absolute height data)

BiHistograms

Bi-polar charts, box plots or box and whisker charts (horizontal), box plots (vertical).

Box Plots (Outliers)

Box Plots (Mean and Standard Deviation)

Cumulative Frequency Chart

Donut (Doughnut) Charts

Kite Diagrams (Any Data Entities)

Kite diagrams (species abundance), line charts.

Map Cross Sections

Polar Area Charts

Polar Charts

Polar Chart Overlays

Polar Scatter Charts

Proportional circles chart.

Rose Diagrams

Sand Dune Profiles (using slope angle data)

Slope Profiles (using slope angle data)

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Data Presentation in Geography

Data Presentation Techniques – How and Why?

Divided Bar Chart

Pyramid Pyramid

Compound Line Graph

Scatter Graphs

Dispersion Graphs

Choropleth Maps

Coming Soon – Data Presentation Techniques – How and Why?

Located Bar Chart

Desire Line

Proportional Symbols

Coming soon! – I want to…

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Essential Graphical Techniques in Geography pp 1–46 Cite as

Concept, Types, Collection, Classification and Representation of Geographical Data

• Swapan Kumar Maity 3  
• First Online: 30 November 2021

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Part of the book series: Advances in Geographical and Environmental Sciences ((AGES))

Geography is a scientific discipline which emphasizes on the collection, processing, suitable representation and logical and scientific interpretation of various types of primary and secondary data for better understanding and explanation of the spatial distributions and variations of different geographical features and phenomena on or near the surface of the earth. This chapter focuses on the concept and types of data used in geographical analysis, sources of each type of data, methods of their collection as well as the advantages and disadvantages of their use. Major differences between various types of data are discussed clearly with suitable examples. It includes the detailed discussion of the concept of attribute and variable, types of variables and differences between them. Different types of measurement scales used in geographical analysis, their characteristics and application in geographical study have been explained with numerous examples. Techniques of classification, tabulation and processing of the collected data on different basis (i.e. based on location, time etc.) are discussed properly with special emphasis on the preparation of frequency distribution table and related terminologies. Methods of representation of all types of geographical data, their appropriateness and advantages and disadvantages have been explained with suitable examples.

• Geographical data
• Primary data
• Secondary data
• Data collection
• Measurement scale
• Data processing
• Data representation

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Maity, S.K. (2021). Concept, Types, Collection, Classification and Representation of Geographical Data. In: Essential Graphical Techniques in Geography. Advances in Geographical and Environmental Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-6585-1_1

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10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2024

Leah Nguyen • 27 Oct 2023 • 10 min read

Finding ways to present information effectively? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

• What are Methods of Data Presentations?
• #1 – Tabular

#2 – Text

#3 – pie chart, #4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

• 5 Mistakes to Avoid
• Best Method of Data Presentation

Frequently Asked Questions

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day. Let’s dive into 10 ways to present data efficiently.

#1 – Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of presentation of data. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

Recordings to ways of displaying data, we shouldn’t overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds and Q&A sections to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum.

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors’ behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

Got a question? We've got answers.

What is chart presentation?

When can i use charts for presentation, why should use charts for presentation, what are the 4 graphical methods of presenting data.

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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NEA update – Sampling and Data presentation.

No doubt you have by now spent considerable time considering the different methods you will use to collect data. This is valuable and important as methods are the salient factor in determining the accuracy of the data and subsequent conclusion therefore weaknesses in the methods reduce the certainty of the conclusions.

However, in a similar vein, it is important to spend an equal amount of time and effort considering the sampling strategy of the project. Sampling is another aspect of data collection that refer to the where, when, and how much data you will collect. 

The sampling heavily influences the reliability of your data collection. If you only ask one or to people what they think and report this- it is accurate but not reliable or applicable to the whole population.

 You may have a different sampling strategy for each different method and this also includes secondary data. In a perfect world you would sample everyone and everything all the time to mean that your data is 100% representative of all opinions / ideas/ data / groups etc.. however this isn’t possible in any study and the NEA does not expect this! What is  expected is that you have considered sampling methods and have made attempts to improve reliability whilst also reflected on the impact of your sampling in the evaluation of your conclusions.

Three main approaches to sampling….

Random sampling: This is where each member of the population is equally likely to be included. For taking random samples of an area, use a random number table to select numbers. Use pairs of numbers as x and y co-ordinates. You could use a metre rule interval markings (e.g. to take pebble samples on a beach) or grid references (e.g. to find random samples in a city).

Stratified sampling: This is where a proportionate number of observations is taken from each part of the population. For example, an urban ward may contain 8 deprived super output areas and 2 undeprived super output areas. A random sample may by chance miss all the undeprived areas. By contrast, with a stratified sample, you can make sure that 80% of your samples are taken in the deprived areas and 20% in the undeprived areas.

Systematic sampling: This is where observations are taken at regular intervals. For example, every 10 metres along a line running from seashore inland across a beach, or recording the age of every fifth person in a shopping centre.

Once you have decided on your sampling strategy you might also want to consider how you will sample the space / place you are going to visit. This also gives you three options.

• Point sampling. Take observations at individual points, like separate houses on a street
• Line sampling. Take observations along a line, like a beach from sea shore to base of the cliffs or along a highstreet.
• Area sampling. Make a square on the ground or on a map. Take observations within that square. For example, place a quadrat on the beach and measure pebble shape and size within that area. If you have a small area or village you could apply the area to this.

The image below shows how these two different approaches can be combined to produce an overall sampling strategy. Whilst there are obvious costs and benefits of each different approach for the NEA it is more important to show you have considered sampling and have a strategy then fixating for ages on which approach is best. 

Data Representation (presentation and analysis)

This part section of the report is worth 24/70. This can seem a daunting but… by now you have your data collected so the hard bit is behind you! 

Types of data presentation

So.. what counts as data presentation- basically anything which you have collected and made and will help you answer you aim / subquestion. The type of data you have collected will heavily influence the data presentation. This could / should include the following;

• Annotated maps / photographs
• Graphs (line graphs/ bar charts / scatter graphs/ radar graphs)
• Word clouds
• Interview quotes (might be worth putting the whole interview transcript in the appendix)
• Data tables
• Heat maps (use arcgis)
• Statistical analysis – averages (mean/ mode / median/IQ range / Spearman’s rank / Mann Whitney U / Chi  Squared etc..)

As with the rest of the report it is much easier and more manageable to use heading and subheading throughout the report. There are many different ways to structure this however my top choice is to use your subheadings / hypothesis to break it up. I also am now of the opinion that it is best to combine the data presentation (graphs / photos / annotated maps ect… and text as one rather than having all the graphs and then pages and pages of text where you have to keep referring back to the evidence- for example if you are investigating deprivation in South  East London you might have decided to focus on crime, health and environmental deprivation. Therefore the layout might be

Crime- How does the amount of crime differ across South East London?

• Choropleth Crime Map – Annotated map
• Average of Crime by borough – Data Table
• Analysis   – Explain how and why the crime levels might be different (explanation)

Environment – How does the environment quality differ within  South East London?

• Map of Air pollution in London
• Table of air pollution in London
• Analysis – how and why does the air pollution vary (explanation)
• Scatter graph comparing crime and environment
• Analysis – how and why does the crime and environment quality data match

It is easy to fall into the trap of repeating yourself in the analysis so tricks like “as previously explained” or “see above’ can avoid this. Remember this is not an essay or exam – you should be reworking what you have written so if you think of something or it clicks as you work through the presentation and analysis section you should go back and edit the report. Don’t be precious about what you have already written… remember the finished product is what will be judged and if you waffle this will affect your overall mark. If in doubt think.. Does this answer my aim / subquestion and if the answer is no- it shouldn’t be there!

How to analyse your data / graphs.

Once you have produced the graphs they can be copied into your report. All graphs in your report should be given a figure and caption – see below.

Figure 1 – Picture showing my face when the teacher asks how I am getting on with my NEA

After this I find using the T.E.A.M acronym useful to ensure you cover everything. The order is not that important and write this in a more formal paragraph. 

T.E.A.M stands for:

TREND: Talk about the general pattern in the graph / picture / map. i.e. generally crime increases as you get closer to the city centre.

EVIDENCE: This is where you should quote evidence (numbers / facts / words) that support the pattern. I.e. the highest crime rate is found in Borough with 128crimes recorded in August whilst Dulwich Village had the lowest number of crimes recorded in August with 23. 

ANOMALIES: Data that doesn’t fit the general pattern or goes against the trend i.e. however Lower Sydenham had much higher crime rates than other more suburban areas of S.E. London with 115 crimes recorded in August.

MEANING: This is the most involved part of the analysis and is where you explain the trend / pattern. It requires you to suggest the meaning of the pattern and answer the subquestion. This is the area the examiners will pay the most attention to. Don’t forget to state the obvious and fully explain your points simply.

Some complex graphs / charts / statistical analysis.

Google sheets is your friend in this case. As soon as you have your data one of the first jobs should be to type this into google sheets or microsoft excel / numbers. This will mean you can then begin to produce the graphs.

Spatial Density / Distribution 

ARCGIS – Heat Maps (you need to latitude and longitude of the variable – which can be taken from your phone of google maps).

These can be simply produced using just ArcGIS web browser (free)

https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/heat-mapping-in-two-simple-steps/

GEGraphs -These google earth graphs require you to download google earth and GE graph application.

Spearman’s Ranks

Guide: https://geographyfieldwork.com/SpearmansRank.htm  

Template to populate with your data: http://udel.edu/~mcdonald/statspearman.xls  

Mann Whitney U

Guide: https://www.geography-fieldwork.org/a-level/before-starting/analysis/

Template to populate with your data:  http://www.holah.karoo.net/Mann-Whitney%20U-test.xls  

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2 thoughts on “ NEA update – Sampling and Data presentation. ”

How can you present data from a likert sclae

You have a couple of options. Once you have the number of people that have each opinion (qualitative data) a pie chart or radar graph work well. I have also seen it done with proportional symbols but a bit more complicated and less specific to analyse.

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