What Does the Dashboard Visualize?

Fertilizer Usage – Sub-Saharan Africa (SSA) has the lowest fertilizer usage in the world – insufficient to replace soil nutrients lost every year to crop production. In 2006, the Abuja Declaration on Fertilizer for an African Green Revolution set 50 kg/ha as the primary target to reach. In Ghana, the current level of fertilizer use is 35.75 kg/ha. For nine years, AFO has been working with the Fertilizer Technical Working Group in Ghana to gather and validate the necessary data to calculate apparent fertilizer consumption.  The dashboard then uses the apparent consumption data and FAO national cropland data to calculate national average apparent fertilizer consumption at the nutrient level, compared to the 50kg/ha Abuja target. This data is key to understanding where Ghana stands in its efforts to reach its fertilizer targets.

Level of Subsidies – The Ministry of Food and Agriculture (MoFA) sees increasing fertilizer use as a priority for the country and uses subsidies to help achieve these goals. The dashboard uses Crop Services Directorate of the Ministry of Food and Agriculture historical data for the past eleven years to show the annual government contribution to subsidy price by product. This data was requested by the farmers and makes it clear which products are being subsidized by the government and to what degree. 

Fertilizer Imports – As Ghana is reliant on imports of raw materials for fertilizer and pre-blended fertilizers. Government subsidies 80% of fertilizers, which impacts imports as well. The dashboard shows how import quantity and price changes over time and can be used to help decision-makers understand and take action when needed to ensure sufficient volumes are in-country for the key planting seasons

Fertilizer Price – “High fertilizer prices is a key factor to reduced fertilizer use and it’s good to know what costs contribute to high fertilizer prices. This makes the cost more transparent.” The Dashboard shows the evolution of retail price over time, Evolution of Commercial Price Vs Subsidized Price Over Time, the price by region, and the international price.

Plant Directory – As Ghana leads the way in new fertilizer blends, knowing the location of fertilizer plants, the types of fertilizer produced, and plants coming online is crucial. The plant directory is used by the government, fertilizer distributors, and farmers to know what types of fertilizer blends are produced and available for specific crops. The directory is also used to determine distance and calculate the open market price, and to help determine the type of blends to produce.

What Stands Out?

Having a dashboard does not automatically resolve issues of Ghana’s fertilizer sector, but it can help in planning and in understanding trends in the country and in the sector. From the data on the dashboard, several key components in the sector are coming into focus:

• Ghana’s Ministry of Food and Agriculture (MoFA) is leading the way in subsidizing new fertilizer blends. MoFA has been tendering for crop-specific blends and has expressed a need for information showing the change in product use over time. However, they lack the capacity to collate, synthesize, and maintain the information, specifically across agencies.

• Subsidies have a wide-ranging impact on the market. With more than 80% of fertilizer supply provided through government targeted subsidies, the VIFAA Ghana dashboard will enable MOFA to easily access the data needed to make decisions on subsidizing blends, provide information to farmers and extension agents, and show the historical impacts of subsidies.

• Market variations between markets in the North and the South. One of the biggest surprises in the data was that the price of fertilizer is lower in the Southern part of Ghana than in the North. This was unexpected as fertilizer imports come through the southern region and transport costs should be expected to increase retail price. While there are a few explanations for this, what is more important is the visualizations that show cost chain build-up and using this information in planning.

In upcoming blogs, we will dig into the data in the Ghanaian context to build a better understanding of the sector and decisions made within it. Stay tuned!

A Changing Fertilizer Sector

In the last decade, there has been over $7 billion worth of private-sector investments into Nigeria’s agriculture sector, 95% coming from the fertilizer sector. The fertilizer market in Nigeria is rapidly changing. In the past few years, Nigeria has gone from 2-3 fertilizer producers and blending plants to over fifty.

Mr. Gideon Negedu is the Executive Secretary of The Fertilizer Producers & Suppliers Association of Nigeria (FEPSAN.) He describes feeling called to work in agriculture, as a place where one can be both passionate and profitable. Specifically that, “there is a way to make an impact directly to the people. Given that 90% of [farmers in Nigeria] are smallholder farmers, with a little innovation we could lift people out of poverty.”

FEPSAN is a national trade association representing the needs and interests of fertilizer manufacturers, blending plants, major distributors, dealers, and farmers in the country. At FEPSAN, Mr. Negedu works directly with the association members, many of whom are newly emerging actors, to expand the fertilizer sector – making it more investment and market-driven.

Using Rumors for Planning

Mr. Negedu describes the paucity of data in the fertilizer sector and making do with what data was available, however limited. In the past, both he and his members relied on rumors and hearsay for information about markets, opportunities, and even crop varieties. One goal to improve agricultural outputs is increased region and crop-specific fertilizers; but as Mr. Negedu explains, “you can’t really [target a region or crop] if you don’t have information… It is all rumors. ‘Everybody says in place X they grow a lot of corn,’ so you set up a blending plant only to learn that the market is not there, it is actually in town Y… But you have to work with what you have.” 

Additionally, the fertilizer sector has shifted from a heavily subsidized, public procurement and tender heavy space to a more market-driven approach. For the members of FEPSAN looking to invest in the market by building production or manufacturing plants, having the correct information is crucial.

Developing the Dashboard

The VIFAA Nigeria Dashboard was co-designed by Development Gateway and AfricaFertilizer.org (a project of IFDC),  in conjunction with stakeholders like Mr. Negedu and FEPSAN. Built on trusted data validated by Nigeria’s Fertilizer Working Group, the dashboard displays fourteen indicators including apparent consumption, price, availability, and a searchable plant directory. Additionally, this information has been overlaid on the cropland under production map, which is the first cropland mapping in Nigeria since 1973. 

Mr. Negedu first heard about the dashboard two years ago, and while he was interested, it seemed lofty and not realistic. Over time, working with the VIFAA team, getting a sense of the methodology and process, he started to understand that it was something serious. Looking at the final product, Mr. Negedu says that the dashboard is a “goldmine” of planning data for FEPSAN’s members.

Building toward Data-Driven Decisions and Evidence-Based Advocacy

For FEPSAN, price data is the most important feature of the dashboard. Nigeria is not completely self-sufficient and continues to import some of the key raw materials needed to make a complete fertilizer blend. The cost information, specifically granular information about what goes into the cost chain buildup (transportation, logistics, etc.) and the interplay with the final price is very useful in helping blenders to plan. 

He also expressed that there is not enough evidence-based advocacy in Nigeria. Through the data validation process, the private and public sectors have both bought into a baseline of information, which is now presented on the Dashboard. Building toward a more market-driven approach came about by advocating to the government and showing that some of the interventions in place disincentivized the private sector. This dashboard and it’s trusted data strengthen FEPSAN’s evidence-based advocacy. Mr. Negedu explained, “[The benefit is] not just to our members, there is value for us in our advocacy… on the issues for the fertilizer industry.”

Crop Yields and Food Security

There are 17 nutrients required for plant growth with (N) nitrogen, (P) phosphorus, and (K) potassium serving as the essential trio. In Nigeria, there is a drive to produce and use more region and crop-specific fertilizers in an effort to increase crop yields and food security. Professor Victor Okechukwu Chude is the Registrar/CEO of Nigeria Institute of Soil Science (NISS), where his work centers on these issues. His passion for agriculture is derived from working with his grandfather on the family’s farm; and has been driven by the desire to produce food for his family and for the rest of Nigeria. In his role, Professor Chude focuses on soil fertility, plant nutrition, and promoting the sustainable management of Nigerian soils. NISS sees soil science as a way to ensure environmental sustainability, high agricultural productivity, and food security in the country.

Unreliable Data

Professor Chude explained that sourcing data in Nigeria can be a herculean task. He often relies on data derived from student works and research, FAO Stats, and internet research. “It’s tough… students know that their degree is dependant on reliable data, but some of the data that is out there is not reliable.”

For Professor Chude, another consideration is producing data and analysis that can be understood by the end-users, often agricultural extension agents or farmers. Using raw data can be a challenge, specifically when working with constituents with lower data literacy. “Data sometimes looks so complicated, you wonder where to start and how [to] figure it out.” 

Integrating Visuals and Analysis

The VIFAA Nigeria Dashboard was co-designed by Development Gateway and AfricaFertilizer.org (a project of IFDC),  in conjunction with stakeholders like Professor Chude and NISS. Built on trusted data validated by Nigeria’s Fertilizer Working Group, the dashboard displays fourteen indicators including apparent consumption, price, availability, and a searchable plant directory. Additionally, this information has been overlaid on the cropland under production map, which is the first cropland mapping in Nigeria since 1973. 

In encouraging farmers to use fertilizer, Professor Chude described the need for information on the prices of various products, fertilizer use, fertilizer availability, and crop-specific fertilizers. This information feeds into decision-making at NISS and at the regional or individual levels.

Transforming Decision-Making

Professor Chude describes the dashboard in terms of improvement to efficiency. First, because much of the analysis has already been done and is being visualized on the dashboard, which saves significant staff time and reduces calculation errors. It also improves the ease of doing business through increased knowledge. The dashboard also reduces barriers to collaboration for partners by increasing the compatibility of the data. 

He also sees the benefit to farmers questioning which products to use. The dashboard is highly visual. It shows the value of fertilizer and how it can impact a farmer’s income. Extension agents, who are already trusted by the farmers, can explain the benefits and leave easy-to-understand data visualizations with the farmers, which can “become very impactful.”

Overall, Professor Chude sees the value in the way data has been transformed on the dashboard. It increases his ability to make decisions and scale specific projects. “This [dashboard] is highly commendable. It is easy to use and contains a lot of useful and helpful data. it will enhance our work as soil scientists and extension agents in making recommendations.”

Over the last two years, Development Gateway (DG) has collaborated with Africafertilizer.org (AFO), the International Fertilizer Development Center (IFDC), the Federal Ministry of Agriculture and Rural Development, Quantitative Engineering Design (QED), the private sector, research institutions, and development partners to map Nigeria’s demand, supply, and use of fertilizer data. These findings have come together through the Visualizing Insights on Fertilizer for African Agriculture (VIFAA) program, which is generously supported by the Bill & Melinda Gates Foundation and implemented in Kenya, Nigeria, and Ghana.

The Nigeria Dashboard

Together, partners co-designed a publicly available dashboard that aims to address key decision-making needs. The Dashboard contains 14 core indicators, such as fertilizer retail price, cropland under production, and monthly product imports, to highlight and visualize key aspects of Nigeria’s fertilizer supply chain. Partners worked extensively through group and individual sessions to gather feedback on data priorities, receiving input from stakeholders across institutions. Data for the dashboard comes from a broad range of private and public fertilizer stakeholders, with Africafertilizer.org gathering, validating, and updating the data on a regular basis. A full list of data sources and methodology for each indicator is available on the dashboard site.

Cropland Under Production

In a first for the VIFAA Program, partners worked to generate comprehensive cropland under production maps, some of the first in Nigeria’s recent history. Collaborating with machine learning and satellite imagery experts, QED, resulted in the generation of an updated map that provides information on the total land under production (across all crops) in Nigeria; what percentage is under production versus uncultivated; and how production breaks down by state and geopolitical zone. The partners hope that the dashboard and use of innovative tools to fill data gaps are the beginning of boosting data quality and use across Nigeria’s fertilizer sector.

Background

The Des Chiffres et Des Jeunes (DCDJ) Data Fellowship is a flagship DCDJ program that places technically-trained Ivorian youth into organizations where they encourage and increase the degree to which data is used for decision making. It is a unique opportunity for young people to sharpen data skills while contributing to a strengthened data ecosystem and to data-related resource availability in Côte d’Ivoire. DCDJ is led by Development Gateway and funded by the Data Collaboratives for Local Impact (DCLI) program. The DCDJ Data Fellowship builds young peoples’ skills to strategically catalyze sustainable change, DCDJ leverages local capacity to improve data access, sharing, and availability.

Before joining the DCDJ Fellowship, Armel had been a masters student interested in honing his data science skills and gaining hands-on experience. After the Fellows training, Armel was placed with CMS Private Center Plus.

Problem

While conducting an assessment with the data management team and also the lab, Armel learned that when new patients were admitted, a healthcare worker must calculate the schedule of that patient’s care program. This calculation includes accounting for the patients’ total years of infection and the types of support needed to manage their condition. It is then used to set up a specific appointment, lab test, and medication schedule. Each additional year in treatment adds a layer of complexity to the calculations. For patients with an extended infection history, like those who had been receiving care for 20 years, making the calculation could take over five minutes of busy administrators’ time. Despite being critical to patient care, the information and calculations were also being entered into the system manually, which introduced room for error. It was clear that this calculation was time consuming, and that it could be efficiently automated.

Solution

Armel, with the support of the DCDJ SuperFellows and several other Fellows, worked to create an easy-to-use application that would automatically and accurately calculate the patient program schedules. Built as a simple Excel document, health care providers can enter the date the patient started treatment and the current date, and the application will then calculate all upcoming appointments, lab dates, and needed lab types.

Process

After identifying the calculation problem as a constraint to healthcare administrator productivity, Armel worked with the data management team to support the new tool development process. The team learned the underlying calculations of the program schedule and created Excel functions that would automate them. Then they tested the application with several healthcare providers to confirm its effectiveness. After development, Armel provided staff training to healthcare administrators to ensure the new application was being used effectively. He describes the application as very intuitive and easy to understand and use, so training can be done by existing staff in the future, without having to rely on Armel or other Data Fellows.

Outcomes & Impacts

The application has reduced the number of errors introduced into the patient scheduling process, while also saving the time required for repetitive, manual calculations. Prior to the application, it would take five minutes per program calculation. Now it takes less than 30 seconds. It was clear to Armel that this system was of value to the clinic staff and was being used. When one staff member accidentally deleted the program from the computers at the facility, they quickly called Armel to reinstall it for them. 

More effective scheduling also benefits patients. Prior to the new tool, healthcare providers would often only see the patient when they were in need of a medication refill, and would not know to remind patients of upcoming lab appointments. If patients miss their lab appointments, it is difficult for doctors to make informed decisions about patient care – they may be missing data on viral load, viral suppression, and medication. With the automatic calculations, patients and providers both have a much clearer understanding of expectations prior to appointments, and patients benefit from a more comprehensive continuum of care.

Since the Government of Makueni County launched its Open Contracting Portal, more counties across Kenya have become interested in establishing their own Open Contracting Portals. Elgeyo Marakwet County recently launched their own portal at the end of April. DG has worked closely with the county to understand the customizations needed in order to meet their needs and has added additional features to the system.

One of these features is a prequalified supplier module, which allows Elgeyo Marakwet county to assign suppliers to specific items they are prequalified for. This functionality makes it easier for the county to monitor their prequalified suppliers and ensure that tenders requiring prequalification are awarded properly. We have also created a set of exports that generate necessary information for reporting to the Kenya Public Procurement Regulation Authority (PPRA). This will reduce the time and effort needed to create these reports, which are required on a regular basis from PPRA. 

Since we have seen an interest in the system from counties across the country, as well as interest from other subnational and national entities outside of Kenya, we have made it easier to customize what data fields, forms, and charts are shown in the system by creating a feature manager. The feature manager allows us to set up a customized instance of the Open Contracting Portal more easily. We have taken an approach of specifying certain features that should be in the shared “core” of the OC Portal code. This core includes new features and forms that could be useful for other users, while keeping certain specifications in the forked code, such as county logos and other hyper-specific changes. This allowed us to maintain a shared codebase, while not over-developing the feature manager in order to change the small bits and pieces.

We are excited about how far the development of the Open Contracting Portal has come, and are already seeing more interest from other counties in Kenya as well as interest from other countries. Our plans are to continue to support the increase of open contracting through the use of the Open Contracting Portal inside and outside of Kenya.

For more information about Open Contracting in Kenya, register for Using Contracting Data to Improve Service Delivery on May 20th. 

For as long as Development Gateway has specialized in data, we have also specialized in data visualizations. In that time, we have discovered the pitfalls and learned ways that data visualizations can increase data use. In this post, we look specifically at selecting the right type of visualization for the story you want to tell.

Types of Stories

Your data can tell many different stories. For each visualization consider which story is most important. This will help you understand how best to visualize the information. Are you:

• Comparing quantities – Showing at least two variables at once
• Comparing trends – looking at changes over time
• Showing Relationships – display a connection or correlation between at least two variables
• Exploring composition – reviewing parts of a whole
• Showing distribution – looking at how your data are spread out

Story 1: Comparing Quantities

Using an Informational Table is great for when absolute values of your data are important, although you may have to do some manual calculations in order to understand the story the data are telling. Adding color scales (e.g., green for increases, red for decreases) or symbols (up/down arrows) to your table can make your story more intuitive.

Pros: 

• Allows you to view all the details of your data
• Allows you to look at qualitative variables in addition to quantitative

Cons:

• The data story is not as immediately intuitive. It is not as easy to see patterns, relationships, and trends. For example, how quickly can you decipher whether the total cases of diseases of children under five have declined from 2015 to 2016?
• If there are too many data points, the core of the story is hard to get to

Bar Chart: Categories (Vertical & Horizontal)
Bars charts are one of the most intuitive ways to visualize quantities across multiple categories. Rectangles or “bars” can be plotted vertically or horizontally, with the heights/lengths of each bar proportional to the values that they represent. Because proportionality is so important for understanding bar graphs, the y-axis for bar charts should always start at zero. Plotting your bar chart horizontally can keep your axes clean and tidy if your data labels are very long.

Pros: 

• One of the most intuitive and easily understood data visualizations to display differences in relative quantities. Ask yourself, “why not a bar chart?”
• Can be easily adapted to include multiple variables or groupings

Cons:

• When you have multiple variables or categories (i.e., grouped bar charts), meaning can get lost
• When you have a wide range of data values or a large scale, differences in quantities are harder to see 
• Bar charts are usually interpreted as a “snapshot” of a singular moment in time. Be careful not to display change over time with bar graphs – use a line instead to show trends!

Waterfall Chart: Cumulative Net Value vs. Compounding Values

The waterfall chart is used to portray how an initial value is affected by a series of intermediate positive or negative values. A Cumulative Net Value Waterfall Chart is great when you know that your data has both positive and negative values, and you are interested in displaying the cumulative effect. Waterfall Charts can also display values that compound over time. Meaning when consecutive values start at previous values instead of at zero. Values in waterfall charts can either be time-based (see the first image below),¹ or category-based (see the second image below).²

Examples of data that are good for waterfall charts:

• Budget tracking
• Inventories
• Profits
• Cash flows
• Consumption

Finally, you can also use waterfall charts to show deviation from a set value over time by displaying the bars as above or below that set value. This is sometimes also referred to as a diverging bar chart. With this chart, the emphasis is on the relative change rather than absolute values.

Pros:

• Allows you to demonstrate the cumulative effect and  or positive and negative values at the same time
• You can accurately visualize when your starting values are not zero, which allows you to demonstrate progress against a certain starting point.

Cons:

• Sometimes not as intuitive, which makes them less usable for wide audiences.
• Not very precise. This type of visualization works best when relative increases and decreases are enough to tell your story. 

Bubble Charts: Three Variables
Bubble charts are useful when relative size or quantity is an important factor in one of your variables. For example, the larger the country’s population, the more people are employed in agriculture. Thus, the relative size of the bubbles is an important piece of this story. In this example, on an interactive map, hovering over a circle displays the three variables for South Africa: employment, agri-land, and population. The visualization shows the relationship or testing correlation among three variables.³ 

Story 2: Comparing Trends

Line Chart: Time Series, Multiple Variables, Descriptive Statistics
Using Time Series Line Charts you can connect discrete but continuous data points with line segments to show general change over time. This works well because lines are visually intuitive. In line charts that display change over time, units of time should always be represented on the x-axis.

Line charts are also great because you can plot values for multiple variables at once, but be careful, your story will be lost if:

• You have too many lines
• The user needs to refer to the key too often
• The scale is too big or small
• There is no real trend over time

Include descriptive statistics to communicate your story in more detail. For example, the second chart above displays actual values in orange, forecasted or predicted values in blue, and the standard deviation in the gray shaded area.⁴ If your line chart isn’t conveying a story clearly, consider adding the following details to your visualization:

• Averages 
• Regression lines
• Estimated values
• Min/max values
• Upper/ lower quartiles
• Baselines/ targets
• Totals

Circular Area Chart: Cycles

If your story involves cycles, circular area charts are a great way to visualize processes over time and/or as part of a cycle, for example, when you anticipate seasonal changes. These charts are also commonly called radar charts or spider charts for the spiderweb or radar shapes that they produce.

With this type of visualization, you can clearly see how often the data values are zero. In the example, you can clearly see that imports of specific types of fertilizer (DAP) vary drastically at different times in the year. However, this type of chart is only useful when only relative differences in values are important because it is harder for the reader to interpret absolute values.

Story 3: Showing Relationships

Scatter Plot: Two Variables

A simple plotting of individual values along the X and Y axes can be useful when you want to view the relationship between two variables, but you are not sure what that relationship looks like. In this example, we are looking at the relationship between the percentage of Nitrogen in the grain and the yield.⁵ The line represents a regression – a kind of “linear average” that is calculated using the minimum shortest distance between the and the line real data (ordinary least squares method). This can be calculated using statistical software.

Scatter plots can also show you when there is no relationship or correlation between your variables at all. In this example, there is no clear relationship between variable x and variable y. An example of this is the second chart above.⁶

If precision or absolute values are less important, and you would rather generalize or summarize your data, you can calculate some transformations to review trends more generally. An example of this is shown in the third chart above.⁷ This is also a great strategy for removing unnecessary clutter!

Types of transformative calculations:

• Normalization 
• Regression lines
• Combining multiple variables at once (ex: averaging consumption across multiple districts)

Story 4: Exploring Composition

Pie Chart: Composition

A Pie Chart is a type of graph in which a circle is divided into sectors that each represent a part of a whole. It is great for when your story is focused more on relative proportions than precise quantities, or when approximate values are acceptable. If you need precise quantities, a bar or line chart might be a better option. This type of visualization is most useful when you’re just looking for one or two major players (for example, a pie chart that shows browsers: Chrome, Internet Explorer, and Firefox) in relation to one another. Be careful that there are not too many components and that there is some variance in proportions. Otherwise, comparisons and labels are harder to make and interpret. For example, without looking at the numbers or percentages, can you immediately tell whether there was more NP or MOP imported?

Pros:

• Allows you to quickly visualize components with respect to a given total
• Shows relative proportions of those components

Cons:

• Not as precise for comparing absolute values
• Humans have a hard time interpreting differences in sizes of circles or arcs (see: Jastrow illusion)
• It is a snapshot in time
• Circles can take up a lot of space!

Semi-Circles: Progress

Half circle charts are a good way to visualize progress towards a target or goal, like key performance indicators. One note, the graph below could be improved by adding quantity labels. For example – if mango production is at 90%, roughly how many more mangos need to be produced in order to meet the target?⁸

Story 5: Showing Distribution

Showing distribution is about explaining how variables are spread out. For example in maps we see distribution across locations; histograms show the distribution of frequencies or counts, continuous data, and ranges; box plots show distribution and variance together.

Choropleth Maps: Distribution

Choropleth Maps provide an easy way to visualize data variances across geographic units for example by country, state, region, divisions, or counties.⁹ In choropleth maps, the colors displayed are proportional with values. These types of visualizations are good when geography is central to your story, such as  informing decisions on:

• Resource allocation
• Targeting 
• Needs-based programming

Pros: 

• Allows visualization on information trends by location, usually by geographic unit
• Color scales allow you to categorize, organize, and visualize your data into ranges 
• Great for when your data is not precisely geolocated – i.e., you only have states, regions, counties, etc.

Cons:

• Location disaggregation limited by the geographic admin unit selected (i.e., you cannot visualize variances within the administrative unit)
• You can only show one variable at a time, unless you add symbols
• Requires a geolocation (admin unit) for each unit of data 
• If your audience is not as familiar with the geography of this particular place, the meaning can get lost
• Consider pitfalls like whether your map is emphasizing raw values when per capita would be more appropriate; be aware when your data is really just showing population density and not concentrations of trends

Maps can be created with software like ArcGIS, Google Maps. Analytical software can also help calculate things like minimum distance between two points of interest.

Heat Maps: Concentrations

A heat map, also known as an isopleth map,  is a graphical representation of data where data are mapped to their precise location and represented as a range of colors. Heat maps allow you to visualize frequencies and concentrations across administrative units. It is useful when you anticipate differences across geographic areas (e.g., urban vs. rural settings) usually related to population concentrations.¹⁰

Pros:

• Allows you to view frequencies/concentrations and distributions of data where it actually occurs
• Not limited to administrative unit

Cons:

• Can only show one variable at a time
• Requires precise geolocation (coordinates) 
• Some other geographic details may get lost – e.g., bodies of water, cities, borders
• If your audience is not as familiar with the geography of this particular place, the meaning can get lost
• Consider pitfalls like whether your map is emphasizing raw values when per capita would be more appropriate; be aware when your data is really just showing population density and not concentrations of trends

Key difference between heat maps and choropleth maps is that the visualization of your data is not limited to the administrative unit. But choropleth maps are only useful if distribution or concentration is important – you need some significant variances in your data (such as population concentration) across locations in order for the story to emerge (otherwise it is all strange colors).

Histograms: Frequencies

Histograms allow you to view frequencies, or the distribution of your data, by grouping data into ranges or intervals. Histograms are good for when you’re observing frequencies across a known or continuous distribution of intervals, like age,  test scores, or probability. They are also great for displaying data that usually follow a “bell curve” distribution by showing the precise value within the range indicated by the x-axis. 

Usually with histograms, the values along the x-axis are usually the intervals/ranges across a known distribution, and the y-axis is the frequency (or the number of times data values occur within the given range). For example, it’s clear that the majority of farmers reported a 100-300% increase in yield after the intervention. As with bar graphs, the y-axis should always start at zero for all histograms.

At first glance, this may look like a bar graph. The key difference between histograms and bar graphs is that you cannot reorder the x or y-axis without losing meaning.

For example – how could you rearrange the ranges on the x-axis? (hint: you can’t – it wouldn’t make sense)

Pros:

• Good for visualizing whether your data are skewed in one direction or another
• Understand where values fall along a series of ranges 

Cons:

• Sometimes, vertical axis as frequencies is not as intuitive
• Generalizes your data according to ranges
• Only works when frequencies or distribution are important

In the first example – it’s pretty clear that most farmers reported a positive increase in yield – which is pretty good! However, a significant number of farmers reported a decrease in yield – i.e., the results are not as positively skewed as we would hope.

Double Histograms and/or violin plots can also be useful for displaying multiple distributions at once, such as population age distribution by gender.¹¹

Box Plots: Variance

Great for when you want to display range and variance across categories. The “box” in box plots captures where 75% of your data values fall, while the lines coming out of the box show the range of outliers in the remaining 25% of your values. For example, fertilizer type B recorded the highest instance of growth, but also has a very wide range of effects on growth overall.¹² In fact, sometimes growth was less compared to the control group.

This chart also shows that you can bet on more predictable increases in yield values with fertilizer types A and C than you might with B.

Box plots are created by plotting values of descriptive Statistics (aka “central tendencies”):

• The line starts at the minimum value
• The top line of the box represents the first/upper quartile = halfway between median and minimum
• The line in the middle of the box represents the median = the “middle value” of your data (if you laid out your data across a number line)
• The bottom line of the box represents the third/lower quartile = halfway between median and maximum
• The line ends at the maximum value

Pros:

• Clearly displays descriptive statistics that summarize how your data varies across categories – median, upper quartile, lower quartile, and outliers 
• “Box” clearly shows the range of values where 75% of your data fall 

Cons:

• It is not the most intuitive visualization, especially for audiences that are unfamiliar with descriptive statistics
• Requires calculation of mean, Q1, and Q3

Combining Stories: Trends + Compositions

There are several ways to combine stories, such as displaying trends and compositions together. A prerequisite for this combination is data sets with values associated to different moments in time (it can be daily, monthly, quarterly, annually.)

Stacked Bar Chart: Trends + Composition 

A Stacked Bar Chart uses bars to show comparisons between category totals (monthly imports) and subcategories (fertilizer type) that make up those totals. The overall height of each bar represents the total for that month, reflected in their height on the y-axis. The benefit of this type of visualization is showing multiple part-to-whole relationships. A few caveats: be careful as a Stacked Bar Chart won’t work if you are not displaying parts of a whole. Additionally, consider color and consistency. The color order of the categories as they are stacked on the bar should always the same, in the example blue – orange – grey – yellow. This consistency helps viewers interpret when certain color values are zero, and so they do not have to visually search for the variable of interest. Here, it is clear that there were no diammonium phosphate imports from July to September.

Pros:

• Stacked charts are great for showing category subtotals (monthly imports) and how subcategories (fertilizer type) contribute to those totals
• Multiple part-to-whole relationships over time or across multiple categories (x-axis)

Cons:

• These visualization types can easily be misinterpreted
• Precise quantities are less clear
• Requires careful labeling and color schemes to be intuitive

Like traditional Bar Charts, Stacked Bar Charts can be flipped horizontally (see example two above). This is a great strategy when space or size is a concern – like if your data requires a long x-axis to clearly display the information. In this example, The x-axis has been scaled to display 100 MT (instead of 1MT). This allows your data to “stretch out” a bit so the comparison is easier to read.

Stacked Bar Chart: Percent + Composition 

You can also stack proportions/compositions to display parts of a whole, like in the third example. This works well when relative proportions are more important than absolute values. For example, can you easily tell from this graph how many MT of NPK were imported in August? Not really, you only know that it was a vast majority of all imports for the month of August were NPK. Can you tell from this graph which month imported the most fertilizer? Not at all. 

Area Charts (stacked line chart): Trends + Composition

Area Charts are similar to stacked bar charts, but using lines. This type of visualization is great for observing trends, and the components of those trends. The same rules apply for interpreting quantities. Be careful how you format your data though, you don’t want lines crossing each other!

Area Charts can be used to tell a story that combines percentage, composition, and trends. This is helpful in visualizing proportions over time. Again, this works well when relative proportions are more important than relative quantities. One caveat is that this really only works if one variable is consistently greater than others (otherwise you get lines crossing themselves) and when values between variables are large enough to review the difference between them (otherwise there will not be any space between the lines.)

Pros:

• Good for showing part-to-whole relationships over time
• Can show percentages as well as quantities
• Shows groups within sub-groups

Cons:

• Be mindful that you’re actually displaying compounding values! For example,  the amount of urea imported in 2013 is roughly 100,000 MT (not 500,000) 

Components of Components: Sankey Diagrams

Sankey diagrams can display either component of components, or flows and the proportional size of those flows from one thing to another This type of visualization is more common in interactive maps, where hovering over a category (clearing) reveals its components and sub-categories. It is most useful when the values contributing to a stacked bar or line chart are too small to visualize in a stacked bar or line graph; or when subcategories are different within each category.

Features: Double

This type of visualization is great when you want to show two variables at the same time, and the two variables have some relationship to each other. For example, if you were to stack order amounts and sales side by side, you would not see how they are related.¹³

Putting it all Together: What is Your Story?

When thinking through your story, and the best way to visualize it, ask yourself a few questions.

• What kind of story am I trying to tell with this indicator? Know what your data is telling you, but also know what your audience needs to hear.
  • Example 1: I need to compare values across different location to identify priority areas.
  • Example 2: I need to analyze how the composition of X evolves in time.  

• What type of graphic would allow me to tell this story? 
  • Example 1: A map
  • Example 2: An area chart (or stacked area chart, if you have subcategories)

• Does my indicator have necessary data & variable(s) to create the selected graphic? 
  • Example 1:  this visualization requires data to be geotagged
  • Example 2 : the data needs to add up to a whole/100%

• Is the story displayed clearly? Is there a way I could rearrange to reduce clutter?

If it does not look right, or your story is not clear, there is probably a better way to visualize your data. Consider these options:

• Switch between bars and lines, and/or horizontal and vertical – which one tells your story better?
• Does your data have outliers? Try labeling them instead of stretching out your graph.

Once completing your visualization review the graphic by asking yourself a few questions:

1. Is this a useful presentation of data?
2. What did it tell you?
3. What action would you take as a result of looking at it?

After you have selected the types of charts needed to visualize data, it is time to think about the actual visualization. In the next post, we will identify the ways that visualizations can be tricky, and provide methods for testing the visuals, and highlight examples of best practices.

Sources
1. SyncFusion
2. Tutorialspoint
3. CanvasJS
4. Wallet Investor
5. Science Direct
6. Mendoza B, Guananga N, Melendez JR and Lowy DA. Differences in total iron content at various altitudes of Amazonian Andes soil in Ecuador [version 1; peer review: 2 approved]. F1000Research 2020, 9:128 (https://doi.org/10.12688/f1000research.22411.1)
7. Cape Breton University
8. Slide Team
9. Stack Exchange
10. ArcGIS Developers
11. CIA Factbook (USA, Kenya)
12. Statistics by Jim
13. Data Pine