Redesigning The Hindu Data Point Stories (NID, 2022)

[venkatrajam]

For this assignment, we'll use data stories from The Hindu Data Point.

Select a story that you like, study it carefully and redesign it. Specifically I want you to focus on understanding the data that powers the story, and how it is visually encoded to tell the intended story. Document your design process, capturing the following:

• What is the story the author is trying to tell?
• What is the data he/she is using to tell the story? Describe its details -- type of data, extent of the data, dimensions of the data, gaps in the data, what data is essential and what is irrelevant.
• How is it encoded, problems with it and how you attempted to improve it.
• You may choose to expand or curtail the scope of the data used in the story, or add new data to tell the story better. But do not deviate from the main intent of the original story. In other words, it is a redesign exercise, and hence I do not want you to tell a different, unrelated story.

Provide a link to the original story, capture and display inline, the appropriate parts of the original visualization with annotations, and your own design iterations to produce a coherent documentation, leading up to the final outcome.

Post your submission as a comment in this issue. Use one comment per student and edit it to update progress.

For reference, take a look at what four batches of IDC students (2019, 2020, 2021, 2022) did with this assignment.

[amandeep2496]

Tracing the link between dengue outbreak and monsoon season

Amandeep Singh INxD 2021

- What is the story the author is trying to tell?

1. The article talks about the 5-year pattern across the Indian states and monsoons.
2. Similar spikes in cases in 2012 and 2017
3. Dengue infections mimic the progression of southwest and northeast monsoons.
4. Tamil Nadu, Andhra Pradesh, and Karnataka form about 60% of India's cases
5. Maharashtra and Kerala have a disproportionately higher number of deaths.
6. Through the graphs, the author is trying to show the shift in cases with the shift in monsoons.

- Associated Dataset

I wasn't able to find out the actual corresponding data used in the article. But National Center for Vector-Borne Diseases Control has data from 2015 to 2022 Aug.

Raw Data:

- How the current graphs are done

1. Graph 1: The table graph shows a State's share of India's total dengue cases.

• The heatmap uses color (opacity of red) in order to show percentages of dengue cases throughout the years, across states. The color is not a great indicator of representing the numbers as the human eye won't be able to distinguish between an opacity assigned to 1% to that of 5%.
• The claim of representing the 5-year pattern in cases is also not visible through the heat map.

2. Graph 2: The maps show dengue outbreaks throughout different time periods. Each dot represents an outbreak and the size of the dot represents the number of cases.

• The maps don't show the legend for the size of the dots. This makes it difficult to read what was the number of cases in that particular outbreak.
• The overlap in the dots makes it difficult to read the entire data properly.
• Yearly data is not readable in this visualization.

- Tabulating the data

- Graph Ideation

1. Matrix Plot showing the number of dengue cases per state from 2012 to 2022

2.1 Line chart plotting the number of cases reported by each state over the years

2.2 Line chart showing the number of cases and deaths across states

Redesign

1. Graph 1

• The first graph here shows the number of cases that have been reported by the following Indian states between the years 2012-2022.
• The range of cases through the years for different states was very scattered and vast. To provide the graph with consistent color mapping, I started off with ranges 0-1000, 1000-10,000, and 10,000-1,00,000 and started to calculate the number of entries in the ranges. Further simplifying and breaking down the ranges resulted in the final ranges of (0-100, 49)(100-1000, 54)(1000-5000, 20)(5000-10,000, 4)(10,000-30,000, 1)(30,000-80,000, 1). Subsequent colors were assigned to these ranges.

Through this graph, the following points were observed

• Instead of a 5-year cyclic pattern, a bi-yearly pattern of the high number of dengue cases was observed in the southern states of India.
• The southern states bear almost 40-70% of dengue cases every 2 years, compared to the rest of the country.

2. Graph 2

It was speculated that the labeling of southern states for having many dengue cases was due to the high number of cases reported by the state government. This could be due to the better health facilities and documentation of the cases.

This hypothesis was supported by this graph where 5 southern states' percentages of reported cases were compared to the percentages of cases reported by the rest of the states.

• It is clearly visible from the chart that the number of reported cases in the southern states is higher compared to the other states when looking that the years that are corresponding to the heat map above.

2. Graph 3

The final graph here represents the disproportionate amount of deaths Maharashtra and Kerala face, compared to the number of cases they have/report.

It was also observed that behind Maharashtra and Kerala, Punjab falls in the queue for the deaths-to-cases ratio.

[ghost]

Indians can travel visa-free to only 58 countries

Ankita Singh MDes, Information Design 2021 S2124103

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About the article:

The author attempts to bring attention to the fact that the Indian passport has weakened in the last decade, as per the Henley Passport Index for 2021, which ranks passports according to the number of destinations its holders can visit without a visa or can avail themselves of a visa/ a visitor's permit/ or an electronic travel authority on arrival.

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Chart A:

Purpose: To show the rank of India’s passport among other countries in 2022

Critique:

• Takes a while to tell the magnitude of difference in the number of countries with visa-free access
• Takes a while to mentally locate/ identify/ form an image of the countries

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Chart B:

Purpose: To show the trend in the rank of the Indian passport through the years 2011-2021

—No criticism—

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Chart C:

Purpose: To show the change in ranks of passports in 2021 as compared to 2011

Critique:

(a) Lost Information

• Information lost about the countries that are not annotated
• Information lost about the ranks in the years 2012 to 2020

(b) Processing Time

• Hard to read due to the mental maths required to calculate the rank in 2011
• There might be a better way to represent the delta that can visually depict the rank falling or rising from the original rank

(c) Redundancy

• This chart can be combined with the chart prior to this, since both show the same thing - one for India and one for all countries including India

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Chart D:

Purpose: To show the change in number of visa-free destinations of countries in 2021 as compared to 2011

Critique:

(a) Lost Information

• Information lost about the countries not annotated
• Information lost about the number of visa-free destinations in the years 2012 to 2020

(b) Processing Time

• One must mentally calculate the number of visa-free accessible countries in 2011

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Redesign

Attempts

Ranking of the Indian passport along with the top 3 ranking countries:

• The gradient colours of the chart denote the power of the passports of the countries, where the brightest are the most powerful and the dullest are the least powerful.
• A geospatial visualization helps locate the countries and establish better context.

• The ranking of the visa through the years along with the number of visa-free destinations on the same chart allows us to compare the rise and fall of ranks with respect to the addition or subtraction of visa-free destinations.
• A line graph instead of a scatter graph seems more efficient in showing the magnitude of rise and fall.

Indian passport in comparison with neighbouring South Asian countries' passports:

• Ranking of the Indian passport amongst other neighbouring South Asian countries through the years may be of interest

• Ranking of the Indian passport amongst other neighbouring South Asian countries over a decade

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Final Redesign

The 'strength' of the Indian passport has weakened in the last decade, according to the Henley Passport Index for 2021. Between 2011 and 2021, only five destinations were added to India's visa-free access list, which being the parameter for the Henley Passport Ranking, has resulted in India's rank falling from 78 in 2010 to 90 in 2021.

Ranking over time

India's latest rank is its lowest since 2011. Between 2011 and 2021, India's best ranking was in 2013 when in stood 74th among 199 countries. The chart depicts India's ranking over the past 10 years.

Between 2011 and 2021, only five countries were added to India's visa-free access list, the lowest among BRICS nations.

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[kowsie]

T.Kowsalya MDes, Information Design 2021

Title: How many Indians own a fridge, AC, or a washing machine: A state-wise split.

Original Article: https://www.thehindu.com/data/data-how-many-indians-own-a-fridge-ac-or-a-washing-machine-a-state-wise-split/article65526597.ece Data Source: http://rchiips.org/nfhs/factsheet_NFHS-5.shtml

The Author's intent:

1. The author present the percentage of Indians wealth Index based on categories of home appliances and the type of vehicle they own.
2. According to the category of home appliances there are four products that are taken for analyzing data namely: Television, Refrigerator, Washing Machine and AC.
3. Type of vehicle owned by Indians: Bike, Bicycle, Car.
4. The article highlights that only 16% of households in India own all three appliances- Television, Refrigerator and Washing Machine showing that the trio are still luxury.
5. A close to 25% of households don't own a car, a bike or a bicycle.
6. Only 15% of household in rural India owned anyone of these home appliances, whereas in the urban areas the ownership was significantly better at around 40%.

Data encoded:

1. The data is visualized on 9 charts following the same visual style.
2. The data is quantitative and presented as percentage of state-wise split of Indian households wealth index based on the categories of home appliance and the type of vehicle they own.
3. Organizing principle: The data is visualized on Indian map with states marked with individual percentage share of household who own any of the products from the two categories mentioned above.
4. The visual encoding makes use of color gradient to show the highest and lowest percentage value and the numbers are plotted as values on states.

Problems with encoding:

1. The color coding doesn't have a legend, a bit from the article explains that the darker the color, the higher the ownership.
2. The states that are marked on the map are in the form of abbreviation and the percentage marked are not roundoff which adds cognitive load.
3. Its not easy to interpret the highest and lowest percentage of households with the color gradient as the lower ones marked all seems the same.
4. There is no way to compare states.
5. The view only gives the output, not a clear view of the reason for the numbers.
6. The article highlights the comparative percentage of ownership of households in terms of home appliances in urban and rural for which there is no graph or chart to support.
7. The story doesn't take into account of the economic status of states.

Possible organizing variables:

1. Sorting the percentage share of households with a appliance in increasing to decreasing order would provide immediate insight.
2. Comparing economic status of states would help in understanding the effect on share of households.

Possible visualizations:

1. Column chart for states for each product
2. Bar chart for a category and individual product
3. Bar chart of economic status of the states

Redesign Attempt 1 As mentioned in the critique, the visualization on a map gives an overview but fails to provide comparison between states. To provide better insight, the data is encoded on a bar chart with the information of the share of households in each state that owns a type of vehicle. This helps in providing individual insight for each state.

The following are the inference from the above chart:

1. Almost 50% of Indian households own motorized two-wheelers. Punjab tops the list with 75% of households owning a bike while the northeastern States are among the least.
2. More than half of the Indian households own a bicycle. Bicycle ownership was relatively very high among the eastern, northern and north eastern parts of the country
3. Only less than 10% of households in India own a car. The ownership was relatively higher in Punjab, Kerala, Goa, Himachal Pradesh, the U.T.s of Ladakh and Jammu and Kashmir and select northeastern States.

Visualizing the data put together on a stacked bar chart helps in generating comparison between states, where each stack is a type of vehicle. This provides an overview of the share of households in each state that owns a type of vehicle. The following are the inference from the above chart:

1. Majority of the households own a bicycle followed by bike and only about 7.5% of the household own a car.
2. Punjab and Goa tops the list with highest percentage of households owning a bike and car.
3. More than half of the Indian households own a bicycle. Bicycle ownership was relatively very high among the eastern, northern and north eastern parts of the country.

Attempt 2 As mentioned in the critique, its not easy to interpret the highest and lowest percentage of households with the colour gradient. And the states are marked in form of abbreviation and percentage that are not roundoff adds cognitive load. By clubbing or grouping the percentage ratio in 3 buckets, the data could be encoded in a better way by providing an immediate insight.

The map shows the share of households in each state that doesn't own a car, a bike or a bicycle. The following are the inference from the above chart.

1. Close to 25% of households in India do not own a car, a bike or a bicycle.
2. Mizoram, Nagaland, Sikkim and Himachal Pradesh has the highest percentage of families who don’t own a bike, a bicycle or a car.

The map shows the share of households in each state who own all three appliances- television, refrigerator and washing machine. The following are the inference from the above chart:

1. Only 16% of households in India own all three appliances- television, refrigerator and washing machine.
2. Among household across states Mizoram, Goa, Haryana, Punjab and Delhi have the highest percentage of families who own all three appliances.

The chart shows rural-urban gap in car ownership. Hill & North-Eastern states /UTS have highest rural-urban gap in car ownership.

Final Redesign For the final redesign the map showing individual data of particular categories were clubbed together to show an overview of share of households with a household good and the type of vehicle they own. Bar graph below the clubbed map shows the overall share of each commodity. And the stack graph shows individual states data for each commodity that helps in getting individual insight as well as an overview.

[geetanjalikhanna98]

No sexual violence survivor contacted a lawyer, only 4.7% took police help in 2019-21

Author: Vignesh Radhakrishnan, Rebecca Rose Varghese

Redesigned By: Geetanjali Khanna MDes, Information Design 2021 S2124113

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The Narrative & its Intent

My interpretation:

Through the article, the author tries to highlight the mistrust in the system by sexual violence survivors, especially marital rape victims, witnessed through the lack of reliance on seeking legal and police aid. The article highlights this behavior in light of the Delhi High Court’s split verdict on Exception 2 of Section 875 of the Indian Penal Code which states that sexual acts by a man with his adult (not minor) wife are not rape, thereby granting protection to husbands from being prosecuted for non-consensual sexual intercourse with their wives.

Key Points in Article:

The article highlights that for the set of married women in India who have endured sexual violence -

1. Prevalence of Marital Rape: 95% of women are victims of sexual violence by their husbands or former husbands i.e. Marital Rape
2. Underreported Cases with no help sought: 90% of women did not seek help and even out of those who did, none approached a lawyer for legal recourse, and only 4.7% approached the police.
3. Need for Empowerment: The article further explores the mentality of husbands & wives towards the concept of marital rape & how the lack of empowerment through employment & education amongst the set of married women forces women to continue to endure violence.

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Deep Dive into the Data

NHFS-5 Data Set Link

The dataset is acquired from the 5th series of National Family Health Survey conducted for the year 2019-2020, providing information on population, health, and nutrition for India and each state and union territory.

The survey covers a detailed chapter on different forms of marital domestic violence including physical, sexual, and emotional abuse. Results of the survey have been summarized with relative population percentages by different criteria & background characteristics to look at the problem with multiple lenses.

Critique: Story-telling Data & its Visualization

I. Story-telling:

1. The main aim of the narrative is to reflect the mistrust in the legal system in light of the split verdict lacking legal protection for victims of marital sexual violence, which has not been mentioned explicitly in the narrative. Neither does the present title highlight the focus on Marital Rape throughout the story.
2. The segments in the narrative feel unrelated as how the data supports this narrative has not been mentioned explicitly. Neither are the key insights highlighted. Many key data points are only mentioned in the text and not visualized.
3. From the data, it can also be seen that the maximum %age of women aged 18-49 who have ever experienced sexual violence have been married and thus, highlighting the reality of the society where married women are not safe from sexual violence (Clearing a common misconception). Out of this set, then focusing on the most common sources of crime (perpetrators) will highlight the extent of marital rape cases in the country.

II. Visualizations:

Chart 1: Sources of Crime - Answering who are the perpetrators

• The following data shows the %age of different perpetrators reported by ever-married women within the age group of 18-49 who are victims of sexual violence. The graph is a Tree Map that shows the relative %age of different perpetrators (Part-To-Whole) by the area covered.

• Essential to highlight the issue of Marital Rape as the majority of ever-married women reported they were abused by people they had intimate relationships with and not by strangers, with Husbands or Former Husbands forming 95% of the perpetrators.

• Gaps: The subset of women (ever-married victims of sexual violence between the ages of 18-49) considered for this chart is not highlighted visually. The visuals can be manipulated by using higher-ranking channels of encoding i.e. using length over the area to reflect differences in magnitude.

Chart 2: Sources of Crime - Answering who the victims approach to seek help

• The following data shows the %age of different sources of help sought by ever-married women within the age group of 18-49 who are victims of sexual violence & reported the crime. The graph is a Tree Map that shows the relative %age of different sources of help (Part-To-Whole) by the area covered.

• Essential to highlight the mistrust in the system due to lack of legal protection from the system as none of the ever-married victims of sexual violence, who reported the crime, sought legal recourse.

• Gaps: The subset of women (ever-married victims of sexual violence between the ages of 18-49 & sought help) considered for this chart is not highlighted visually. It misses shining light on the extent of mistrust in the system observed in the fact that over 90% of victims of marital rape do not approach anyone for help. Being a Tree Map Chart (Part-To-Whole), it fails to highlight the central theme of the article that lawyers are not approached. Must highlight absence.

Tables 3 & 4: Financial Dependence & Sexual Coercion

• The following data aims to show how empowerment results in a decline in marital sexual violence cases through factors of education of the husband and wife, and financial independence of the wife (wealth quintile as per data).

• Gaps: However, the data used is not as relevant as it only highlights the current scenario where men have more employment opportunities and higher pay than their female counterparts. The problems are well known and do not highlight surprising insights. Rather showing the impact of empowerment in terms of education and better pay in reducing domestic abuse cases is more relevant.

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Finding Better Supportive Data

I. Addition of data points to support the narrative

From the above table of women having experienced sexual violence between the ages of 18-49, %ages of victims of sexual violence by Marital Status were calculated. This helped shine a light on how ever-married women were more likely to have been victims of sexual violence, highlighting the prevalence of marital rape in society.

II. Conversion of existing data points from text to visualizations

Certain key data points that convey the main theme of the narrative have been hidden away in the text of the article. Visualizing this data can help paint a clearer picture of the narrative.

For Ex: To highlight the mistrust in the system, it is equally important to show the number of victims who did not seek help as it is to show the lack of victims seeking legal recourse.

III. Broadening the extent of data

Inclusion of data from the following tables to better highlight the need for empowerment to tackle marital sexual violence:

• Percentage of women aged 18-49 who have ever experienced sexual violence, by background characteristics including Schooling & Wealth Quintile, India, 2019-21

• Percentage of ever-married women aged 18-49 who have ever experienced emotional, physical, or sexual violence committed by their husband, according to background characteristics including Schooling & Wealth Quintile, India 2019-21

• Spousal violence by husband's characteristics and empowerment indicators including Husband's Schooling

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Redesign

I. Concept:

II. Visualization Attempts:

Chart 1: Sources of Crime - Answering who are the perpetrators

• Evolution: Tree-Map --> Pie Chart --> Bar Chart

• Reason: The Tree Map & Pie Chart use area as a channel whereas the Bar Chart uses length with double-encoding of size to better highlight the difference in magnitude.

• Explorations:

Chart 2: Sources of Crime - Answering who the victims approach to seek help

• Evolution: Tree-Map --> Pie Chart --> Bar Chart

• Reason: The Tree Map & Pie Chart use area as a channel whereas the Bar Chart uses length with double-encoding of size to better highlight the difference in magnitude. It is not possible to show the absence of victims seeking legal recourse in any Part-To-Whole representation but can be seen clearly in the selected representations.

• Explorations:

Tables 3 & 4: Financial Dependence & Sexual Coercion

For the newer data, to highlight the impact of empowerment factors like education and financial independence, the following charts were explored:

• Evolution: Multi-set Bar Chart (Small Multiples) --> Line Chart

• Reason: The Line Charts better reflect the trend of decline in marital sexual violence with increase in the number of years of schooling completed & wealth quintile.

• Explorations:

Schooling

Wealth

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Design Attempt 1:

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Final Long-Form Data Narrative/ Story

[dmrunal08]

The highs and lows in States’ infant mortality rate rankings

• Mrunal Deshmukh, NID | M.Des 2021

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What is the author trying to say?

1. Objective of the article:

• To show the growth of Indian states with its previous rankings.
• To show growth against its country counterparts in global rankings.

2. Author wants to point out that well doing states did not show much improvements in the ranks.
3. They also want to show that improving from a lower base is tougher and vice-versa.

The section needs to be given careful attention in order to understand the comparison and pool of the countries the study is looking at.

Multiple parameters to be understood before jumping into the tables. Can we use pre-attentive processing here?

What is the data used?

State Name IMR 2009 IMR 2014 IMR 2019 Close by country (by rank) Income group for the country

Red here is used to imply something positive. Although due to mental models, the user can misread it as a degrade in the rank. The “minus” sign adds to the misread idea.

Key parameters:

1. 3 time series

2. Improvement here is a decrease in the number.

3. Comparative study with various countries.

4. States become a different entity.

How is the data encoded?

Tabular formats. No visuals are used to support the tabular data.

How can this be improved?

1. Geo-spatial mapping could be used to deliver the info easily.

2. The comparative analysis can be done through a line graph/scatter plot.

3. The introductory image can be impactful to the seriousness of the topic.

Attempt 1: redesign

Attempt 2: Graphs

Final Redesign:

This is the final layout and structure of the story. It is made in the form of a long-form article to retain the seriousness of the topic and make it simple for the user to compare the huge data in chunks.

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[Rucha-ux]

How many electric vehicles and charging stations are there in India?

Rucha Dave MDes, Information Design 2021 S2124112

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About :

Publication Details: The article was published in The Hindu (Data section) on 25th July 2022 by Vignesh Radhakrishnan and Rebecca Rose Varghese. It gives an overview of how the market for electric vehicles and charging stations are increasing across all the states of India compared to non-electric vehicles.

Story : The article compares the growth of the electric vehicles market in India keeping three major points / criterias in mind. First, growth in number of electric vehicles being bought (across years and across states). Second, growth in the number of EV charging stations in comparison to the retail petrol pumps across states. Third, trends in electric vehicles meeting the safety standards across brands.

Data : The data has been collected from a report published by Ministry of Heavy Industries, Lok Sabha. It reflects the following:

Graph 1 ( Across Time) Number of new EV registrations for 4 years. Proportion of new EV registrations for every 100 non- EV registrations for 4 years.

Graph 2 (Across States) Total number of EVs for each state Proprtion of EVs in each state wrt every 1 Lakh non-EVs

Graph 3 (Across States) Total number of EV charging stations for each state Proprtion of EV charging stations in each state wrt every 100 petrol outlets

Graph 4 (Across Brands) Number of EVs calledback in April 2022.

The data is normalised wrt non EV vehicles across states to avoid the confusion of population and sizing. It is focused on providing a comparitive judgement of how EV market is growing against the non-EV market.

However, a few other key factors like costs of EVs and battery life might bring richer insights. Also when the main focus is the proportionate study, how relevant are the actual numbers?

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Encoding :

Graph 1

• markings here on the right side confusing.

• The line graph talks more about the trend more than the proportion. Other type of representation might justify the data better.

• The article suggests a slow increase but the way of presentation does not visually justify the insight.

Graph 2&3

• Bubbles might not be the best choice of mark because the overlap is causing confusion. Opacity is not helping much. For Eg: the marked area shows the potential confusion where a few points might be missed.

• The total number of electric vehicles is not helping us form a conclusion here and requires unnecessary added calculations.

• There is no demarcation of which states are performing better.

• Since this article, refers to steps that Government is taking to promote EV sales, maybe we can add a mark of the target set by the government for 2022 versus where each state has reached.

Graph 4

• The graph only shows number of vehicles. Maybe proportion division based on “cause” might be more insightful.

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Scope :

• Retitle the article

• Remove a few existing data points

• Add data related to price / battery life

• Redesign the graphs

• Add a mark to compare : TARGET BY 2030

• By Times of India

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Graph Ideations :

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Graph Iterations :

• Trying graphs out of existing data

Adding data for countries with top 5 EV trends

• This highlighted the insight that India's growth is slow.

• For both graphs, I dropped the total / actual state-wise number as it did not add to the main point of the article. It served as extra information but added to the confusion in reading the graph / deriving insights.
• The map graph highlights states in the order of their proportion of No. of EVs. This is successful in highlighting a few outshining trends but otherwise, it seems flat in providing readers with an opportunity to infer other insights from the graph.
• The length helps in making quicker comparisons but the radial aspect serves no purpose, adding to the difficulty in readability. Again, does not offer scope for other insight explorations.

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Feedback:

1. The first graph is fine.
2. The 2nd and 3rd graph combined into the main graph might help the narrative better. Also, it needs to have a scope of exploration and also highlight the obvious peaks.
3. Think about how you can make the graph legible and add details for richness.

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Final Graph Development:

Process :

• Started with a scatter plot between No. of EVs for every 1Lakh Non-Evs (X axis) and No. of charging stations for every 100 petrol outlets (Y axis).
• The mark was kept circle outlined to avoid missing overlapping points
• The values on both axis were binned into 2 groups and the annotation was done using colour. Orange for No. of EVs and Blue for No. of charging stations.
• Further, Zones were marked on the basis of the mean of the 2 scales. So, one can read which states have performed better than the mean and which have fallen short
• The zones were marked using colour: Lowest of both scales (Red), Highest of both scales (Green).
• Points were labelled and key insights were highlighted giving statistics

Final Graphs

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Final Piece:

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Feedback :

1. The Quick Glance section needs more coherence.
2. The main graph's double-coloured glyph is confusing.
3. The elaborated text is in a confusing arrangement too.

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Corrected Poster

Thank you!

[karthikeya-gs07]

India reduced arms imports from Russia, while China’s dependency increased

Authors: Vignesh Radhakrishnan & Jasmin Nihalani

Karthikeya GS MDes, Information Design 2021

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The Narrative/intent

In this article, the authors want to emphasize the arms imports of India, arms exports from Russia, and the dependency of countries on Russian arms exports. The narrative tries to educate the readers about the Indian arms import trend and sheds light on individual arms exporters from 1952. Although the aim is to show India's reduction in dependency on Russia and China's increase in dependency, the article also dabbles a bit with other countries' relations with Russia.

The key aspects: Dependency, change in trend, India - Russia, China - Russia, World - Russia, 2017 -2021.

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Key Points in the article

1. India’s arms imports have reduced significantly in the last five years (2017-2021). This is the first such drop after the country recorded a consistent increase for all the five-year periods beginning 1991.
2. Russia has been the most preferred source for India’s defense purchases since at least the 2000s.
3. France took over Russia at the first position in 2021.
4. Despite the deviation, Russia contributed 46% of arms in the last 5 years.
5. India, China, Egypt, Iraq, Belarus, Kazakhstan, Syria, Algeria, and Vietnam were the leading arms importers from Russia in the 2017-2021 period.
6. The reduction in dependency is only seen in India & Vietnam compared to 2012-16.
7. SIPRI (Stockholms International Peace Research Institute) holds the database on all the import/export of armed weapons between countries.
8. The unit of measurement is TIV (Trend Indicator Value). The TIV is based on the unit production costs of a core set of weapons and is intended to represent the transfer of military resources rather than the financial value of the transfer. It is a standard unit that measures trends in the flow of arms.
9. The article groups 5 years to compare the TIV. India’s dependency reduced from $19,432 million TIV to $15,356 million TIV from 2012-16 to 2017-21.
10. India's top exports are from Russia, the UK, France, US & Israel.
11. India imported 7,068 TIV 2017-21 from Russia, which formed 46% of its total arms imports. At least 14 countries are sourcing more than 50% of their arms needs from Russia.
12. India’s dependency on Russia decreased by 22% between the two periods, whereas China’s dependency increased by 28%.

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Dataset

The dataset is acquired from the SIPRI database of all Arms Transfers. It is a data generative interface that allows one to filter & obtain specific data. Also see, cleaned dataset.

1. The article looks at arms transfer between counties from 1952 to 2021.
2. It groups 5 years and adds the total million SIPRI TIV.
3. The data the article looks closely at is: Arms import of India between 1952-2021, Arms export from Russia between 1952-2021, and Total Arms import of all the countries between 2012-2021.
4. The dataset comprises two types of columns: Name of the county & Years.
5. The database is updated every year (data until 2021 available)

Note: The above screenshot is data that is cleaned for this specific article's narrative.

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Critique

Graph 1: Total million SIPRI TIV imports of India from 1952 to 2021 in a 5-year interval

The first graph shows the total million SIPRI TIV of Indian imports from 1952-2021. The period is grouped by 5 years. The information conveyed by this graph is ‘flat’ i.e only total import.

A stacked bar graph, categorized by countries contribution (exports of countries).

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Graph 2: Trend of imports, from top 5 imports of India from 2000 to 2021

With this graph, the author wants to visually show the trend change in the import of SIPRI TIV in 2021. The green line indicates Russia takes downfall whereas the blue line indicates France's pickups.

Graph 1 and Graph 2 are two broken views that could have been combined using a stacked bar chart to project the same insight.

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Graph 3: Total arms imported from Russia (vs) Total share of arms imported from Russia - of all countries between 2017 & 2021

In this graph, the author shows the total imports of each county on the X-axis and the total share of arms imported by the country on the Y-axis. This is done to show two dimensions of imports by country. The share is the percentage of Russian imports between 2017-21 and the total amount in million SIPRI TIV.

The amount of time taken to decode this is relatively high because of the following reasons:

1. The overall import value is not mentioned and hence there is no visual or parametric data to compare the information.
2. The scatter plot hides the county label, which forces 'reveal on hover'. The pre-attentive processing is not feasible with this form of representation.
3. Overlap / higher density near (0,0).

   ---

Graph 4: Change in dependencies of countries on Russia between 2017 & 2021.

For the above graph, the author has tried to differentiate between the countries that increased or decreased their dependency on Russian arms imports. Synonymous to the previous chart, the graph is again plotted between the share of Russian arms imports and the total million SIPRI TIV imports from Russia.

The author has inverted the axis representation. This causes a break in the narrative flow as the reader is acquainted with the inverted representation in the previous chart. This chart also has the same annotative issues which the previous chart has, like no comparative data or labels for reference.

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Initial insights from the dataset

1. Russia only started importing to India in 1992.
2. 33 countries have reduced dependency on Russia from 2012-16 & 2017-21.
3. 31 countries have increased dependency on Russia from 2012-16 & to 2017-21.
4. 20 countries stopped importing arms from Russia after 2016.
5. 14 countries started importing arms from Russia after 2016.
6. Mongolia, Syria, Iran, and Equatorial Guinea became completely dependent on Russia between 2017-21.
7. Rafale fighter jet deal, Anti-tank guided missiles, ammunition for IAF - France deal, could have spiked the imports.

---

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Redesign

Ideation

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Attempt 1

the following redesign is a different take on Graph 1 & Graph 2. As mentioned in the critique, the two graphs are showing flat or one-dimensional information. By combining both in a certain way (using a stacked bar chart) a better insight could be generated from a single graph.

This stacked graph gives a wholesome picture. Each category of the stack is the top 5 importers of imports of India. The following are the inferences from the above chart:

1. The UK, the US, and France have been long-time exporters of arms to India.
2. The UK was our top exporter from 1952 to 1991.
3. Russia Took over the UK in 1992 as the top exporter and stayed so until 2016.
4. Israel became our exporter between 1997-2001.
5. India's imports have always been on a rise except for the first decline after 2017.
6. The dependency on Russia drastically dropped between 2017-2021. The dependency on France increased in the same period.
7. India's imports high an all-time high between 2012-2016 with a total of about 19,000 million SIPRI TIV.

   ---

Attempt 2

With this attempt, a quick glance at how the arms import from Russia has changed with time from 2012 to 2021 is observed.

The inferences from this chart are mentioned below:

1. India has been the highest importer of arms from Russia through the time period mentioned.
2. The arms export to India from Russia declined drastically in 2014 and since. The dark blue changed to teal as time passes.
3. A few countries have been on and off with importing from Russia like Cameroon, Brazil, Armenia, etc.
4. A reduction in the number of countries importing arms from Russia is observed.

The following map shows the change in the dependency of countries on Russian arms imports between 2017 and 2021. Also see, the interactive map.

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Attempt 3

The following graph shows the dependency change of countries over Russian arms imports between 2017-2021

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Attempt 4

Redesigning the previous attempt by anchoring the beginning of 2017 and observing the change. This helps in the relative comparison of dependency better.

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Final Redesign

For the final redesign, the approach of data visualization was adopted. The topic has been changed to 'Change in Russian Arms Dependency between 2017 & 2021' and in the underlying narrative, a comparison between China and India is being done. A key aspect of perceiving information is comparison. An observation was made that the redesign attempts made weren't providing a parameter to compare the change in arms import from Russia. On revising the key point of the article, the aspect of 'share of Russian imports' was dominant. Hence, to stay in line with the existing narrative a decision was made to visualize shares as a stacked bar graph of total imports by a country, and total imports from Russia by a country. This gives a better insight into dependencies and country strength for reference.

Furthermore, a drill down into the granularity of information was performed where the information of what category of weapons were imported was also integrated into the representation to provide a deeper understanding of country arms import behaviors.

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Redesign

Download the high-resolution image. Download the cleaned dataset

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[Gagarina1]

Silent survivors: 80% wives in Tamil Nadu say husbands are justified in beating them

Authors - Vignesh Radhakrishnan, Rebecca Rose Varghese

• Gagarina Nath, National Institute of Design INXD 21

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The Story :

The article is talking about the percentage of women suffering from domestic violence silently without revolt, taking Tamil Nadu as the reference state for the discussion. The results of the [National Family Health Survey-5 (2019-20)] have revealed stark truths about the domestic violence inflicted on women and their muted response to it. About 80% of women in Tamil Nadu said that their husbands were “justified” in beating them if they argued with him, refused to have sex and for other similar reasons. As a result, about 45% of married women have experienced spousal violence, the second-highest share in the country behind Karnataka. In stark contrast, only about 50% of men in Tamil Nadu felt it was justified to physically abuse their wives, stemmed from the fact that 48% of them had witnessed their father beating their mother — the highest such share among all states. Due to the normalization of their husband’s behavior, over 80% of women in T.N. neither sought help nor talked about the violence meted out to them. And even among those few who did seek help, a majority informed their relatives about their plight and not the concerned authorities. Husbands’ drinking patterns played a crucial role in the degree of violence. The more he consumed alcohol, the more was the domestic abuse.

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The Dataset :

Data is taken from National Family Health Survey 5 ( 2019-2021 ).

• States of India
• % of women justifying abuse
• % of men justifying abuse
• % of women experiencing abuse over the states
• % of women suffering from abuse according to the type of alcohol consumption
• % of women staying silent over the abuse according to the states
• % of women complaining just to their immediate family according to the states

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The Graphs used

What is working in the graph ?

• Less luminance difference between the background and non-target values which makes it easier to spot the targeted value, in this case Tamil Nadu.
• Amount of information is very specific

What is not working in the graph ?

• Information is lost in case of the unlabeled circles, only few states are labeled.
• The values on the x-axis is spaced non uniformly which adds to confusion. i.e., 15-50 and 50-85 have the same spacing.
• Could have used the x-axis for the states, and the y axis for the percentage to give a better inference.
• Spacing between the marks is less which makes it look jumbled up, wrong use of proximity and values are not deciphered properly.
• The states are not sorted on the basis of percentage.
• Comparison on increase/decrease of % of women justifying abuse can be included from data of the previous years.

• The values are not sorted which fail to give an overall idea of what the graph has to say.
• There is a lot of overlap of values adding to confusion.
• The idea was to show the stark difference of men's and women's view on justification of violence which can be deciphered better with a combined chart, rather than using individual charts.
• Information is lost in the unlabeled circles.

• The values are not uniformly placed in this graph too
• Overlapping of the marks
• Unnecessary clustering is occuring due to proximity
• Most circles are left unlabeled
• The graph shows the percentage of women experiencing physical/sexual violence but since most states are left unlabeled , it fails to give an overall understanding. Emphasis is lost about Tamil Nadu being the second highest state in case of domestic violence since most information in the graph is lost.

• The glyphs here is not in context with the purposeof the graph , which shows how consumption of alcohol results in more domestic violence.
• Values are randomnly plotted on the graph, the space between

The Redesign

• The states are placed on the x-axis, the percentages of women justifying abuse are placed on y-axis which gives a holistic view of all the states.
• The values are sorted which lessens cognitive load, makes it very easy to understand in first glance
• Tamil Nadu is the targetted value, contrasting colour is used to highlight.

The interactive version https://public.flourish.studio/visualisation/11183398/

[8PocketElf]

India’s press freedom ranking slips to 150, its lowest ever

Rashmi B | Information Design 2021 | NID Bengaluru

article

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What is the story the author is trying to tell?

• it’s a story about how journalism & free press was a major tool during independence
• but in recent years (2000s) the state of freedom of press has declined
• various socio-political & economic factors have lead to the situation where media personels safety is in question

  ---

  What is the data used to tell the story?

• india is compared with other countries against various categories of data to show it’s curent standing
• india is also compared to it’s previous rankings to show the state of degradation

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How is it encoded ?

• highlighting of written data overpowers the chart
• at first glace color encoding seemed to be categorizing data by regions instead of rankings
• only highlights data of few given countries

• this does give a fair idea of countrie’s ranking across various categories
• the negative aspect of lower rankings is not obvious due to increment in y axis

• shows the genral trend over years
• change is visually static
• the graph at first glance seems to be positive as it is shown as upward direction which is contrary to the truth

  ---

  dataset

  

data source

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redesign

comparing all countries across various factors like global score, political score, legislative score, socio-economic score, safety score

interactive version

india's ranking in various categories compared against rest of the countries

comparision of changes in ranking compared to previous year of 2021 for all countries

INTERACTIVE

comparision of global rankings for asian countries

INTERACTIVE

rank comparision of various countries over the years

INTERACTIVE

rankings of india over the years

INTERACTIVE

changes in ranking of india over the years

changes in rankings of countries over the years

interactive

[Rajeshm006]

Article: How BSNL bled: The story behind public telecom giant’s fall in 6 charts

What is the author trying to say?

• This article shows the fall of BSNL compared to other players in the market.

Data: The author is trying to tell the story; the visuals are a little inadequate for the story the author is trying to convey.

Chart 1:This chart shows the market share across significant players.

Critique:

• BSNL is the main narrative of the story but is not highlighted correctly.
• “In 2005, BSNL commanded a market share of 21%, the same as Bharti Airtel and slightly higher than Reliance Communications. By 2022, BSNL’s share reduced to 10% while three private players controlled the rest of the market. “ - this point in the story can be highlighted in the graph correctly.

Charts 2,3,4: Tells about the revenue, profit, and loss

Critique:

• Three charts represent similar data, whereas charts 3 and 4 illustrate the same data only, but in a different format; no. Of. Charts can be reduced.
• “The dirt cheap tariffs with which Jio entered the market forced other players to reduce their charges. This impacted the average revenue of both BSNL and other private service providers” - narrative can be embedded in the charts.
• Data about other players in the market is not highlighted; average revenue can be plotted to show the gain and loss of BSNL comparatively.
• The Colour of the graphs can be appropriately used to highlight.

Charts 5: % of employee benefits in total expense to the company

Critique:

• These graphs can be compared across the revenue and loss of the company; more revenue making makes the company spend less on employee benefits, whereas PSU, like BSNL, still trying to give the employee benefits, can be highlighted.

Charts 6: Teledensity

Critique:

• This chart doesn’t show whether this was for only BSNL or for all the vendors across urban and rural area areas are areas; comparing and contrasting other players in the market could have added value to the story. “ SNL was crucial in improving teledensity (connections per 100 people) in rural and far-flung areas. “

Graph Iteration 1:

In this Graph, I'm trying to tell the story of how the BSNL fall happened with respect to major events with technologies that have not been told in the original story.

Data: https://docs.google.com/spreadsheets/d/1dEgz612XL1f1mKCsKmMniEpruP-oiJbnSnQFVRFGBD0/edit?usp=sharing

https://www.trai.gov.in/sites/default/files/PR_No.53of2022_0.pdf

https://www.trai.gov.in/release-publication/reports/telecom-subscriptions-reports

https://www.trai.gov.in/release-publication/reports/financial-reports

https://dot.gov.in/sites/default/files/2022%2002%2028%20Telecom%20Stats%20STT.pdf

https://dot.dashboard.nic.in/DashboardF.aspx

---

Article : The fall of Tring Tring - BSNL !

Bharat Sanchar Nigam Limited is a government-owned telecommunications service provider headquartered in New Delhi, India. It is under the ownership of the Department of Telecommunications, Ministry of Communications, Government of India. It was incorporated on 1 October 2000 by the Government of India.

But this government-owned BSNL has been running in loss for lost 13 years.  Currently, the subscriber base of Jio and Airtel is three times that of BSNL.  Not only is the subscriber base falling, but the revenue BSNL makes from each user is also a fraction of what private service providers get. The average revenue earned per user by BSNL is 2.5 times lower than what private players make.

Several factors are involved in this fall of BSNL, like technology (4g allocation and towers all over the country ). Private players like Jio’s introduction to the market and more competitors’ involvement made it difficult for BSNL to play in the telecom sector.

In 2005, BSNL commanded a market share of 21%, the same as Bharti Airtel and slightly higher than Reliance Communications. By 2022, BSNL’s share had reduced to 10%, while three private players controlled the rest of the market.

Though BSNL was the first in the telecom market to launch 3G technology in 2009, The 4G launch by airtel affected the market share of BSNL. Then with private players’ increase in technology, there was a fall in BSNL’s market share. With JIo’s introduction in 2016, cheap dirt tariffs and 4G technology, and other private players, BSNL also faced trouble capturing the market share.

Chart 1: Shows the market share of wireless subscribers with BSNL performance across various factors.

In May 2019, Indian telecom operators, especially Airtel and Vodafone Idea, saw massive drops in their subscriber base. According to the latest Telecom Subscription Data for May 2020 by TRAITRAI’sTRAITRAI latest Telecom Subscription Data for May 2020, Airtel saw a decline of 4,742,840 or 4.7 million subscribers, while Vodafone Idea saw a similar decrease of 4,726,357 subscribers.

However, Reliance Jio continued to add more subscribers to its network during the same period. The telco could add 3,657,794 or around 3.65 million new subscribers in May 2020 alone. BSNL also attracted new customers to its network (201,592 subscribers).

With new attractive plans under Rs.200 and MNP, BSNL made 5 million subscribers to port in and reduced the count of 3 million subscribers to port out to attract new customers. But with, with the adverse effect of covid and the lockdown scenario in India, the very next year, April 2020, made a tough time for the telecom sector, with nearly 8.2 Million telecom subscribers lost during this time; many private telecom sectors faced difficulty along with BSNL. Still, Jio retained the top position in the Indian telecom sector with 33.85 % of the market share.

Chart 2: Shows the BSNL port in and port out subscribers over a decade

Dwindling subscribers and relatively low earnings from existing customers took a toll on BSNL’s revenue. From ₹40,000 crores in FY06, revenue halved to ₹19,052 crores in FY22. While revenue dipped, expenses increased to over ₹30,000 crores and remained above the mark for 15 years.

Until FY20, employee benefits formed 40% of BSNL’s expenses. Following the 2019 rescue package, a bulk of which was used for funding the Voluntary Retirement Scheme, the workforce was cut down from 1.8 lakh in FY18 to 64,500 in FY21. 

The government of India introduced an impactful revival package in the year 2019, which include BSNL and MTNL that, provides merger, the monetization of their assets and VRS to staff to turn the combined entity profitable.

Though BSNL has made less profit, in recent years, BSNL has been trying to cut down its expenses simultaneously.

Chart 3: Shows the BSNL expense and revenue

However, the situation may be that BSNL still bags a market share of around 94% in rural wireline subscriptions. The legacy of the wireline telecom market is still with BSNL.

Chart 4 (yet to come): Rural wireline subscribers' market share

[speckx]

Decline of States' Share of Taxes | Meghana

• Article critique

• Narrative framework

• Redesign attempt 1

[ParvathyRaju98]

From paracetamol to stroke medicine: Check how much medicine prices increased this year

Published by : Jasmin Nihalani

Parvathy Raju Arangath M.Des Information Design NID S2124110

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The Authors Take

The author wants to shed light on how the cost of essential drugs has increased over the past nine years, as declared by National Pharmaceutical Pricing Authority but moreover How exactly it has drastically elevated in the year 2022. It aims to study how in the year 2022, the prices of essential medicines will rise by 10.77%, being the highest surge in the past nine years. These drugs are classified as essential because they are safe and efficacious and collectively address most public health concerns. Comparisons are made with retail inflations over the same period to comprehend better how the trend patterns are for both commodities.

The title has been framed in such a way that it sounds relatable to the reader with the use of common medicinal names like Paracetamol, Stroke Medicine etc. Actual Data Visualisations do not comprise any of these data.

Keywords: National Pharmaceutical Pricing Authority, Wholesale Price Index, Inflation, Ceiling Price

The Author provides the following Visualizations to support their claim.

Visualisation One - LINE GRAPH

• The chart shows the year-wise movement in the Wholesale Price Index used by the NPPA to fix the ceiling prices of essential drugs.
• The X-axis represents the years, and the Y-axis represents the WPI. The blue dots denote the corresponding WPI to that year
• The Graph is interactive, which presents the WPI value when hovered on

Inferences

• In the last years, the average hike was 2.5%
• But, in March 2022, the body approved a hike of 10.77%, the highest in at least nine years
• The biggest drop was in the year 2015, with a 6.56% dip

But, since it's a singular line graph, we have no idea whether the WPI rise and drop are good or bad. There is nothing we can compare it to. Putting it into perspective with other values might help understand the impact of the data

Corresponding Data Set

The Data set is qualitative and comprises the past nine years and their WPIs, which had to be extracted manually. The rise or drop percentage was later calculated.

Visualisation Two-LINE GRAPH

• This line graph goes ahead to compare medicine and retail inflations over the past nine years
• The X-axis represents the monthly breakdown of all nine years, and the Y-axis represents the inflation rate percentages
• The Blue line denotes Medicinal Inflation, and the Yellow line denotes the Retail inflation

Inferences

• While the overall retail inflation has remained above the 6% mark for the past two consecutive months, inflation in medicines surpassed the general index for over a year and touched 8.66% in July 2021.
• In the month of February 2019, there was the biggest gap between Medicinal inflation & Retail inflation being 9.63% and 2.57%, respectively

The graph brings into light the inflation rates of two commodities but does not go ahead explaining more. Maybe if the reason why the drops and rises are so significant and correlated might help infer the narrative better

Corresponding Data Set

The Data set is qualitative and comprises the past nine years and their WPIs, which had to be extracted manually.

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Gathering Data

The next step to solidify the narrative was to collect data to best support it. I started by gathering data on essential drugs, their prices in 2021 / 2022 and how much the prices have increased in the span of one year. It includes drugs for cardiovascular diseases, cancer therapy, anaemia, diabetes, thyroid, fever and infection. The ceiling price of over 850 drugs is revised on April 1st annually based on the Wholesale Price Index. The Data set is from the National List of Essential Medicines 2015 and This Hindu Data Set

About the Data

The Data set contains 870 medicines considered essential in the market and studies the cost inflation from 2021 to 2022 and the ceiling price change. Here are the following details of what the table entails

• Name of Medicine - The counter name of the drug

• Dosage form and strength - If it’s a capsule/injection/powder form and the prescribed dose for it

• Unit - The unit base used for measurement

• Price in 2022 - Price of the drug in the year 2022 April in Indian Rupees

• The increase from 2021 - The increase in the drug price from 2021 in comparison to 2022 in Indian rupees

• Type - What category of medicine do they fall in, Eg: Immunologicals, Anaesthetic

• Use - What is the use case of the medicine Eg: Neonatal care, Vaccines etc

There are a few drugs for which the type and use cases are not given, but that does not change the inferences. There are not many irrelevant data too in the set. The most essential out of the collection are the ceiling and inflated prices.

Initial Observations

• The price rise has had an uneven impact
• While the rise was relatively low for cheaper medicines, the more expensive medicines have recorded a phenomenal rise. For example, Trastuzumab, a drug used to treat breast and stomach cancer, has risen by 6500 INR while Paracetamol injections by only 2 INR

Another data set I acquired was the Wholesale Price Index Dataset from the The World Bank Database This was to put the medicinal inflation and WPI trends into perspective with the general retail inflation rates.

This dataset contains all the WPI inflation rates from 1960 to 2021 of all the countries in the world. From this, I extracted the data of India for the past nine years and analysed the trend.

Initial Observations

• The lowest drop was in the year 2017, hitting almost 3%, but unlike the medicinal WPI, it has not touched the negatives
• The highest was in 2013 at around 10.01%, and then it has been in consequent decline with very few surges but nothing as phenomenal as 2013's WPI

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Redesign Strategy

This story can basically be split into two-man parts PART A The WPI Trend and the Medicinal Inflation Rates PART B The Top 5 common drugs and their price rise from 2021 to 2022

I brainstormed on several ways it can be represented.

• Line Chart to denote the trends, but that would be too many lines on one graph
• Multiple Bar Charts, but it would be hard to read and infer from
• A combination of Area Chart and Line chart also helps in prioritising which data to read and which one is supportive to the main narrative.
• A stream graph, but it proved to be too clustered.

Redesigned Visualisation

The main inference which was concluded from the graph redesign was the lowest dip and the highest surge.

• The lowest dip is accredited to the sudden change in global financial equations following the devaluation of the yuan by China's central bank has forced the domestic industry onto the back foot. The worsening of the deflationary trend has been attributed largely to easing commodity and food prices
• During the pandemic, the industry was impacted by a rise in prices of raw materials (active pharmaceutical ingredients, or APIs), freight, plastic and packaging material. The industry will be relieved once the new prices are notified. It has been facing huge cost-escalation due to imported raw materials in some drugs as steep as 60-70%

Redesigned Combined Visualisation

Iteration 2

It gives a snapshot view of how the medicinal inflation rates have impacted relation to retail rates too. The price rise of drugs has had an uneven impact.

Part B Visualisation

Iteration 2

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Final Redesign

[mohnishlandge]

Article- Pandemic impact: Marked decline in Maths, Science scores among rural, SC/ST students

Authors : Rebecca Rose Varghese & Jasmin Nihalani

Redesign By : Mohnish Landge (M.Des Information Design NID)

Data source - National Achievement Survey

Tools used: Flourish, Tableau, Rawgraphs & Adobe Illustrator (for making the visualizations).

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Authors Intent of choosing rural and SC/ST students

National Achievement Survey (NAS) data showed that due to the pandemic, the marks scored by Indian school students in the examinations dropped significantly across classes. Further analysis shows that the impact was more pronounced among rural students. The mean score of Class X rural students in the Science exam declined by 47 marks in 2021 compared to 2018, whereas for urban students, there was a smaller decline of 38 marks. Similarly, the impact was greater on Schedule Caste (SC), Scheduled Tribe (ST) and Other Backward Classes (OBC) students compared to the general category.

There are three visuals and two maps to explain the story in following way :

1. No gender divide

2. For class VIII Science exam, score of urban students declined by 8, whereas score of rural students declined by 23

3. Among SC/ST/OBC students

4. & 5. Mapping the drop of Urban/Rural & category wise comparison

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Critique:

• The difference between different categories student is not very obvious due to choice of the data visualization method.
• The culmination of story author wants to represent is not on point.

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Redesign

The entire redesign is summarized through a flourish story where the weak point in the visuals are changed to better support the narrative. Click Here!

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Final WIP:

[raahgeer]

UAPA, PMLA, Section 153A : Rise in cases, low on convictions

Original article - Vignesh Radhakrishnan Redesign - Chinmay P (M.Des, Information design)

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The story covers from the insights of the National Crime Records Bureau (NCRB) reports that the number of people arrested under UAPA, PLMA and Section 153A from the year 2018-2020.

Further breaking down the story and understanding the context before 2018 would help establish a connection of why there is a rise in cases and the overall conviction rate declining

Exploring the nation wide cases and further going into understanding state-wise arrests from 2018-2020

[KadambariKomandur]

Data | 75 years of independence: A comparison of India’s growth with other nations across ten indicators

207c21856.png)

Initial iterations

Final Narrative https://www.figma.com/proto/S7nLH7XpxJLEpX5rk5tBCQ/Untitled?page-id=0%3A1&node-id=56%3A13400&viewport=-733%2C-405%2C0.45&scaling=min-zoom&starting-point-node-id=55%3A5881

[harshkauntey]

### Only 1 in 4 teachers in India trained to teach online classes

Harsh Kauntey MDes, Information Design 2021 S2124111

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About the Article: Here the author is trying to bring the light on teaching module during the pandemic times in India as covid 19 forced schools to stop physical classes and shift the entire system to online mode of teaching, author focuses on percentage of teachers that were trained to teach online in this pandemic scenario.

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Existing Graphs

Graph 1 It shows a heat map for overall % of teacher trained to teach online with in different management schools at various education level.

Graph 2 This graph shows a heat map of % of teacher trained to teach online with in different management schools through out different states in India.

Graph 3 Graph showing pupil to teacher ratio in different states in India

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Graph Ideations

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Final Graphs