Project Gallery
This project proposes to visualize our planet’s hurricanes evolution through time and space from 1842 to 2016.
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The main visualization is a world map on which we can see hurricanes trajectories after a selected year. A slider below allows you to select the starting year.
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The second visualization is a line chart where multiple hurricanes selected on the map can be compared.
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The last visualization shows the distribution of the number of hurricanes over the years correlated with average temperature variations on Earth.
Vizualisation of the climate in Australia
- Interactive map with the big cities
- Correlation of climate with seasons and density of population
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Analysis of climate with the phenomenon El Nino
This project has been done in the course Data Visualization of the Master Artificial Intelligence at University Claude Bernard Lyon 1.
The objective is to do an overview of the energy consumption of the digital industry. Once the issues and participants are identified, we propose ways to achieve more socially responsible digital consumption.
Rain, wind, snow: how has climate changed in mainland France from 2010 to 2018?
We designed an application composed by three connected visualisations :
- A map of France, showing one indicator by weather station of our dataset. Those indicators contain 4 variables, each in a different color scale and a different size : temperature, wind, rain and snow. Their intensity increaases with the value of the variable. The min and max scale values can be seen in the bottom right hand corner. The values correspond to time point given by the gobal slider of the application.
- A projection in a plane formed by two variables. It aims to reveal correlations between two features, at a given time point. Those variables can be changed, and the axis will be rescaled as needed. The temporal evolution of the stations can be shown by launching a movie from the current time point.
- A line chart that can be accessed from the two previous visualisations. It shows one of the 4 variables in time. The variable can be chosen by hovering over the corresponding part of an indicator of the map, or in the projection graph. A vertical line shows the position of the current slider.
The different visualisations are linked to each other, such as when a station is hovered over in a graph, it is also enlarged in the other one.
Electric Vehicles versus Thermic Vehicles (EVsTV)
This project aims to compare the environmental cost of the production of both electric vehicles and ICEV (internal combustion engine vehicles, “voitures thermiques” in french, hence the name of the project). The visualization has been designed to be a decision support tool for future buyers, so we hope that by using our work, people will have a better understanding of what lies behind the production of each and make their choice accordingly.
This website makes it possible to visualize the records of the 3 main atmospheric pollutants (NO2, PM10, O3) at the different stations in the Auvergne-Rhône-Alpes region.
These records are the monthly averages over one year (December 2017 to December 2018).
140 years of temperature disturbance
The topic of this project is the evolution of the global temperature between 1880 and 2017. The principle is to show the global warming thanks to data visualisations and to warn people about this problem.
In order to achieve this we made several things :
A Bar Plot that shows the climate disturbance every decade A Line Chart that shows the evolution of the anomalies A map of the world that shows where and how the temperature is changing in the world
This project is part of the Data Visualisation course taught at the Claude Bernard University of Lyon (France). The main goal was to design visualizations using D3.js on the topic of climate. Because people have a growing interest for Mars, we chose to work on the climate of this planet, by comparing it to the climate of Earth. This would give viewers an idea about what life of this planet would be. We aimed to give a representation of the variations of temperature of each planet with respect to the planet’s position relative to the sun. These visualisations are intended for a wide audience, in a context of general education. They are supposed to help answer questions such as how different is the climate on Mars compared to Earth, what is the mean temperature or is there a seasonal cycle on Mars.
LifeHabitsChanger: CO2 emission calculator
This visualization allows us to observe the carbon footprint of our choices and lifestyle, at an individual level. We assume that global warming is a fact, and is (in part) caused by too much greenhouse gas emissions, including CO2. We therefore want to raise awareness and inform citizens, by allowing both to calculate its personal CO2 emission (according to several criteria, such as food, transport, heating, …), but also to simulate different scenarios and to observe the impact of a modification of a habit (if I ate less beef, I would avoid the emission of X kg of CO2). A secondary objective is to simulate habits changes in order to achieve the COP21 objective.
Daily Weather Data for Austin (TEXAS):
We have data on the climate of the city of Austin, the data are: temperature, sea level, wind speed, as well as fog visibility). We have different statistics (average, values: Max, Min). We need an account on how to react is very much, climate change is correlated, make a regression to see how the measures evolve over time, see seasonality, cyclicity as well as the auto- correlation of observations for each measure.
Yearly Ice Extent from 1979 to 2016
Within the framework of our Data Visualisation class of Master 2 Intelligence Artificielle and Data Science (https://lyondataviz.github.io/teaching/lyon1-m2/2018/), we propose you a visual display of the annual variation of the North and South polar ice cap extent.
It contains two circular bar charts which symbolize the annual extent for each ice cap. When you select one of the bars (a year), you get two new graphics: the monthly temperature evolution and the monthly evolution of the ice extent during this year for the two poles.
The global subject of this project was the visualization of “Climate Data”.
We as a group decided to concentrate our efforts on the ecological footprint, the deficit / reserve ratio and the overshoot day.
You can find more details in the README.md.
The impact of the environment on Human health
The aim of this project is to make the wider public aware of the consequences of global warming on human health.
Visualization of WHO (World Health Organisation) data about the number of deaths due to the “environment”. The modifiable “environment” includes many factors as:
- air pollution,
- soil and water pollution by chemicals,
- the access to unsafe water, sanitation and hygiene (water pollution),
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the ultraviolet exposure.
The Visualization of the temperature around world major cities
This project consists of visualizations whose primary purpose is to describe the variation of average temperatures from 1750 to 2013 in the major cities of the world. The data provided for each city are: date, average temperature of each month, Latitude and Longitude. Attention was also paid to the different countries and the world in general. However, in order to bring a secondary interest to this work, we put the results obtained in relation with the variations of the rate of CO2 emission and the increase of the level of the sea over the years.
This study gives an overview of the climate change suffered by our planet but also allows to bring a dimension of awareness about the importance of this change on our way of life.
Our visualization is intended to show the evolution of sea level. We have retrieved sea level data from 1880 to 2015 based on the assuseaLevel.pngmption that sea level was 0 in 1880. We also have temperature data from 1880 to 2015.
In addition, based on these data, we made predictions (on R) of sea level and temperature up to 2400. We have made three predictions: an optimist, a pessimist and an intermediary. For this purpose we used an exponential model by varying the parameter with 3 different values.
The data sets were retrieved from this address: https://datahub.io/core/sea-level-rise#resource-sea-level-rise_zip
Our objective is to show the correlation between temperature increase and sea level rise but also to show the possible consequences of these increases.
Visualizations on CO2 emissions
We have therefore chosen to work on CO2 emissions, and thus to respond to several issues. See that CO2 emissions have been constantly increasing since the industrial period and the most polluting countries by year / since a given year.We also wanted to check the correlation between CO2 emissions and temperature increase, to see if there is a link between CO2 emissions and sea level rise..
Climate events in the United States: seasonality and influence on the price of insurance
This project aims to highlight the link between the damage caused by climate events in the US and the price of home insurance. We used a dataset that lists more than 50 000 climate events in the United States during the year 2017, with just over 50 different types of events (storm, tornado, …). For each event we have the states that have been affected and the damage caused (in $). For each type of event, a map of the United States has been created where each state is colored according to the amount of damage caused by all such accumulated events. Next to it is a cloud of words: each state is represented with a size proportional to the price of home insurance in this state. We can observe the correspondence between the damage caused by climate events and the price of insurance in each state. In addition, we have also done a HeatMap which represents the frequency of different climatic events throughout the year. We observe the impact of the seasons on the types of events encountered.
Climate Migration Many papers claimed that the number of climate migrants will reach 143 million in 2050. This number was first enunciate in a report of the World Bank Group named “Groundswell, preparing for internal climate migration” which forecast the numbers of migrants for three parts of the world, South Asia, Central and South America, and Africa.
The report explains that climate change and disaster will have a heavy impact on the migratory flux. It predicts three scenarios more or less optimistic in function of our implication to protect the environment. We decided to draw a visualization to show what this number really means.
Impact of global warming by country and global factors
These visualizations focus on which countries are the most affected by global warming - in terms of temperature anomalies - and put this in perspective with global factors such as the evolution of global temperature and the CO2 concentration in the atmosphere, by month and year since 1850.
