Difference between revisions of "Group02 Report"
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− | Static visualization and reports do not allow for direct interaction with data, enhanced assimilation of information, quick access to relevant insights and drill-down analysis | + | Data visualization is often an after-thought for many of the practitioners who are collecting and analyzing data. And yet, without clear and compelling communication, analysis will never drive insights and action. Visualization can itself be used effectively in the process of insights discovery. Static visualization and reports do not allow for direct interaction with data, enhanced assimilation of information, quick access to relevant insights and drill-down analysis. |
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<br><br> | <br><br> | ||
− | + | By using interactive, visual data analytics techniques we will be bringing the above-mentioned capabilities to the interface. Following are the objectives of our work:<br><br> | |
+ | (a) A user-friendly and interactive visualization application interface for data exploration that supports both aggregated and drilled down views and analysis which can be used by personnel in energy departments, policy makers and general public alike. <br><br> | ||
+ | (b) Interactive visualization to understand which parts of the world today’s energy requirements are sourced from.<br><br> | ||
+ | (c) Regional energy consumption portfolio and trends for each energy type. <br><br> | ||
+ | (d) Understanding and identifying country clusters as they are set to adopt sustainable energy usage.<br><br> | ||
</div> | </div> | ||
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− | <!--About Data Source--> | + | <!--About Data Source--> |
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==About The Data Source== | ==About The Data Source== | ||
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− | + | Visualizations available on the energy outlook reports consists of basic graph types such as bar, line and pie charts which are static in nature and do not facilitate any discovery apart from what they are made to deliver. A sample visualization is presented below: | |
− | [[File:Group2_critique_viz2.JPG| | + | <br><br> |
+ | [[File:Group2_critique_viz2.JPG|800px|centre]] | ||
− | <br><br>As shown above, the visualization does not allow user to dig deeper into the dataset. Hence, to enhance usability of the the data, we have come up with interactive plots in order to gain deeper insights.<br><br> | + | <br><br>As shown above, the visualization does not allow user to dig deeper into the dataset. There is a lot of scope for improvement in the visualization methodology used and with the open source community contributing aggressively to the plethora of R packages, the possibilities are endless. Hence, to enhance usability of the the data, we have come up with interactive plots in order to gain deeper insights.<br><br> |
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<!--Critique of Existing Visualization--> | <!--Critique of Existing Visualization--> | ||
− | <!--Dashboard Design--> | + | <!--Dashboard Design--> |
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==Dashboard Design== | ==Dashboard Design== | ||
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[[File:Group2_stackedareachart.JPG|700px|centre]] | [[File:Group2_stackedareachart.JPG|700px|centre]] | ||
<br><br> | <br><br> | ||
− | <b>Country-wise percentage | + | <b>Country-wise percentage production across various energy types: Sunburst Plot </b> |
<br><br> | <br><br> | ||
<i>Usage</i>: World energy profile consists of Non renewable energy sources such as coal, oil and gas as well as renewable sources like hydro energy, nuclear energy and other renewables like solar, wind, biogas etc. Sunburst Chart allows the user to interactively drill down and understand the percentage contribution of a region to World energy Production detailed for each energy type. | <i>Usage</i>: World energy profile consists of Non renewable energy sources such as coal, oil and gas as well as renewable sources like hydro energy, nuclear energy and other renewables like solar, wind, biogas etc. Sunburst Chart allows the user to interactively drill down and understand the percentage contribution of a region to World energy Production detailed for each energy type. | ||
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[[File:Group2_sunburst.jpg|700px|centre]] | [[File:Group2_sunburst.jpg|700px|centre]] | ||
<br><br> | <br><br> | ||
− | Figure above shows the Sunburst visualization of the World Energy | + | Figure above shows the Sunburst visualization of the World Energy production. <i>Figure 1</i> in the visualization represents the country-wise percentage production across various energy types. The sunburst plot is interactive and can be used to dig deeper to understand the percentage production of the energy type to the country level. For example,as revealed in <i>Figure 2</i>, China produces 13.4% of the total energy of the world through Coal. Similarly, we can explore different energy type productions. |
<br><br> | <br><br> | ||
<b>Primary Energy consumption for 2017 across the World: Interactive World Map </b> | <b>Primary Energy consumption for 2017 across the World: Interactive World Map </b> | ||
− | <br><br>To understand who | + | <br><br>To understand who the major primary energy consumers of the world and their respective CO2 emissions are, we have implemented an interactive world map and a bar graph both in plotly, which are then integrated using shiny as shown in the figure below. World map is being made using leaflet and icon markers are used to indicate OECD and OPEC countries. |
<br><br> | <br><br> | ||
Critique: This method was chosen to make the exploration interesting. Choropleth Map was not considered as countries are custom aggregated in the report and for some countries values are not listed. | Critique: This method was chosen to make the exploration interesting. Choropleth Map was not considered as countries are custom aggregated in the report and for some countries values are not listed. | ||
<br><br> | <br><br> | ||
<i>Tools Used</i>: Plotly | <i>Tools Used</i>: Plotly | ||
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<br><br> | <br><br> | ||
[[File:Group2_map1.jpg|1000px|centre]] | [[File:Group2_map1.jpg|1000px|centre]] | ||
<br><br> | <br><br> | ||
− | + | In the map, bars are sorted in descending order and when a user clicks on a bar, corresponding country is highlighted in the map and a tool-tip displays the Energy consumption in Mtoe, along with the CO2 emissions and whether the country belongs to OECD/OPAC. Alternatively, the user can click on any country of interest on the map to get the information on the primary energy consumption and CO2 emissions for the particular country. This is designed to make the exploration interesting and intuitive. In this case, choropleth map will not be an efficient visualization as countries are custom aggregated in the report and for some countries values are not listed. | |
<br><br> | <br><br> | ||
[[File:Group2_map2.jpg|1000px|centre]] | [[File:Group2_map2.jpg|1000px|centre]] | ||
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For each of the energy categories namely oil, gas, coal, other renewables(Hydro and Nuclear data is not considered as a pane as production data is not available for these energy types.) countries are plotted on a scatter plot of percentage production vs percentage consumption. The size of the bubble on the scatter plot indicates the absolute amount that the country produces and the color of the bubble indicates the absoule amount that the country consumes of that energy type, as shown in the visualization below. | For each of the energy categories namely oil, gas, coal, other renewables(Hydro and Nuclear data is not considered as a pane as production data is not available for these energy types.) countries are plotted on a scatter plot of percentage production vs percentage consumption. The size of the bubble on the scatter plot indicates the absolute amount that the country produces and the color of the bubble indicates the absoule amount that the country consumes of that energy type, as shown in the visualization below. | ||
<br><br> | <br><br> | ||
− | [[File:Group2_scatter1. | + | [[File:Group2_scatter1.jpeg|1000px|centre]] |
<br><br> | <br><br> | ||
The plot is interactive and the selection of a country/bubble on one pane, highlights the respective positions of the country in other panes aiding insights discovery, as shown in the visualization below: | The plot is interactive and the selection of a country/bubble on one pane, highlights the respective positions of the country in other panes aiding insights discovery, as shown in the visualization below: | ||
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<i>Tools used</i>: Plotly with Crosstalk for interactivity | <i>Tools used</i>: Plotly with Crosstalk for interactivity | ||
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− | [[File:Group2_scatter2. | + | [[File:Group2_scatter2.jpeg|1000px|centre]] |
<br><br> | <br><br> | ||
<b>Relative positioning of countries in terms of usage per energy type: Ternary Plot </b> | <b>Relative positioning of countries in terms of usage per energy type: Ternary Plot </b> | ||
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A Terenary plot is used for understanding the relative positioning of countries in terms of usage of Renewable, Hydro & Nuclear energy and non renewable energy sources. | A Terenary plot is used for understanding the relative positioning of countries in terms of usage of Renewable, Hydro & Nuclear energy and non renewable energy sources. | ||
<br><br> | <br><br> | ||
− | <i>Usage</i>: This plot at a glance helps in identifying countries that source their energy needs from renewables compared to non renewables etc. Also the bar chart of the absolute measure of the usage updates with the country selected in ternary chart to aid exploration and discovery. | + | <i>Usage</i>: This plot at a glance helps in identifying countries that source their energy needs from renewables compared to non renewables etc. Also the bar chart of the absolute measure of the usage updates with the country selected in ternary chart to aid exploration and discovery. Year slider lets user to visualize the positioning of the country each year. And a slide through years 2006-2017 helps in identifying if a country has taken a serious stand on adopting sustainable energy options. |
<br><br> | <br><br> | ||
<i>Critique</i>: Given that energy data logically can be divided in to three categories i.e non-renewable, hydor and nuclear and other renewables, ternary plot aids in plotting large number of countries. Dominant characters are identified and clusters emerge enabling classifications/identification of trends | <i>Critique</i>: Given that energy data logically can be divided in to three categories i.e non-renewable, hydor and nuclear and other renewables, ternary plot aids in plotting large number of countries. Dominant characters are identified and clusters emerge enabling classifications/identification of trends | ||
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<!--Dashboard Design--> | <!--Dashboard Design--> | ||
− | <!--R Packages Used--> | + | == Some Useful Insights == |
+ | {| class="wikitable" | ||
+ | |- | ||
+ | | <div style="font-family:Century Gothic; border-radius: 1px "> | ||
+ | The Geofacet plot reveals that Japan is reducing their nuclear power production drastically after 2011 nuclear power plant leak, Fukushima Daiichi due to tsunami and earth quake<br><br> | ||
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+ | Japan’s core focus has shifted from nuclear to other renewables. Last 5 years growth is remarkable, it has replaced about 33% of its nuclear consumption through other renewables<br><br> | ||
+ | |||
+ | Taiwan which is at higher risk for tsunami and earthquakes is following similar the same trend and has made significant reduction in their nuclear consumption. In 2016, winning government has stated phasing out nuclear power generation in their agenda<br><br> | ||
+ | |||
+ | With the reduction in the prices of Oil, the consumption from 2013 to 2017 has gone up for Oil across all countries. This can be visualized using the time-series pattern in the Geofacet graph for Oil<br><br> | ||
+ | |||
+ | For Finland, Iceland & Sweden, it is observed that a major proportion of energy consumption is from other renewable sources of energy<br><br> | ||
+ | |||
+ | From the Sunburst plot we can see that China produces 13.4% of the total energy of the world using Coal; Saudi Arabia produces 4.31% of the total energy of the World using Oil<br><br> | ||
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+ | China, US & India are the top 3 countries in the Primary Energy Consumption in 2017<br><br> | ||
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+ | From the Geofacet plots we can see that China has been trying to reduce it's Coal consumption after 2012. If we look at Africa, Algeria has been able to reduce it's Coal consumption significantly over the years.<br><br> | ||
+ | |||
+ | The ternary plot shows us that the major energy source for Norway in terms of consumption is Hydro energy, which is a good indication. However, if we look at India, the major energy consumption is for Coal and Oil which are non-renewable sources of energy. US primarily consumes Oil, followed by Gas.<br><br> | ||
+ | |||
+ | China, over the years has tried to increase the percent contribution from renewable, hydro and nuclear energy sources as opposed to non-renewable energy sources.<br><br> | ||
+ | </div> | ||
+ | |} | ||
+ | <!--Some Useful Insights--> | ||
+ | <!--Conclusion/Future Work--> | ||
+ | |||
+ | ==Conclusion/Future Work== | ||
+ | {| class="wikitable" | ||
+ | |- | ||
+ | | <div style="font-family:Century Gothic; border-radius: 1px "> | ||
+ | The original dataset has several other metrics like prices of each energy source, reserves in each country, electricity generation, trade movements etc. These variables also must be included for analysing energy dynamics and the world energy outlook. | ||
+ | <br><br> | ||
+ | Data driven visualization and analysis on energy dataset reveals patterns in production/ consumption of energy across countries with time. The relative contribution of different energy resources in a country’s consumptions helps in quantifying the effort that the country puts in moving towards the sustainable energy initiative. | ||
+ | <br><br> | ||
+ | Having said that, the domain knowledge and experience of personnel in energy industry is essential to support or rule out the insights that visual analysis has revealed . | ||
+ | <br><br> | ||
+ | This application is a starting point for interactive analysis of energy data using R visualization packages and shiny application platform. Further, detailed breakdown of renewable sources, energy prices data and trade movements data can also be used to enrich current data and derive additional insights. Adding electricity generation data and power distance would reveal the dynamics of today’s world energy landscape. | ||
+ | <br><br> | ||
+ | </div> | ||
+ | |} | ||
+ | <!--Conclusion/Future Work--> | ||
+ | <!--R Packages Used--> | ||
+ | |||
==R Packages Used== | ==R Packages Used== | ||
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<b>Crosstalk</b>:Provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e. static .html files) <br><br> | <b>Crosstalk</b>:Provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e. static .html files) <br><br> | ||
<b>Geofacet</b>:Provides geofaceting functionality for 'ggplot2'. Geofaceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation<br><br> | <b>Geofacet</b>:Provides geofaceting functionality for 'ggplot2'. Geofaceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation<br><br> | ||
+ | <b>rgdal</b>:Bindings for the 'Geospatial' Data Abstraction Library<br><br> | ||
+ | <b>leaflet</b>: Library to create Interactive Web Maps with the JavaScript 'Leaflet'<br><br> | ||
+ | <b>shiny</b>: Web Application Framework for R<br><br> | ||
+ | <b>shinythemes</b>: Themes for use with Shiny. Includes several Bootstrap themes<br><br> | ||
+ | <b>shinydashboard</b>: Create dashboards with 'Shiny'. This package provides a theme on top of 'Shiny', making it easy to create attractive dashboards<br><br> | ||
</div> | </div> | ||
|} | |} | ||
<!--R Packages Used--> | <!--R Packages Used--> | ||
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==References== | ==References== | ||
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− | About BP: https://en.wikipedia.org/wiki/BP<br><br> | + | [1] Shneiderman, B. (2005) “The eyes have it: A task by data type taxonomy for information visualization” IEEE Conference on Visual Languages (VL96), pp. 336-343<br><br> |
− | Data | + | [2] About BP: https://en.wikipedia.org/wiki/BP<br><br> |
− | R Packages Description: https://cran.r-project.org<br><br> | + | [3] Data: http://www.bp.com/statisticalreview<br><br> |
+ | [4] http://ryanhafen.com/blog/geofacet <br><br> | ||
+ | [5] https://hafen.github.io/geofacet/ <br><br> | ||
+ | [6] https://github.com/timelyportfolio/sunburstR<br><br> | ||
+ | [7] R Packages Description: https://cran.r-project.org<br><br> | ||
</div> | </div> | ||
|} | |} | ||
<!--References--> | <!--References--> |
Latest revision as of 18:03, 12 August 2018
World Energy Production & Consumption: A Visual Study
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Contents
Motivation
Energy growth drives the well-being and prosperity across the globe. Growing demand for energy has to be met in a safe and environmentally conscious manner. Rapidly changing energy dynamics determine the course of our economic development, geopolitics, technological breakthroughs, massive investments and trade flows.
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Objective
Data visualization is often an after-thought for many of the practitioners who are collecting and analyzing data. And yet, without clear and compelling communication, analysis will never drive insights and action. Visualization can itself be used effectively in the process of insights discovery. Static visualization and reports do not allow for direct interaction with data, enhanced assimilation of information, quick access to relevant insights and drill-down analysis.
|
About The Data Source
Critique of the Existing Visualizations
Visualizations available on the energy outlook reports consists of basic graph types such as bar, line and pie charts which are static in nature and do not facilitate any discovery apart from what they are made to deliver. A sample visualization is presented below:
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Dashboard Design
Global trend in Energy consumption for years 2006-2017 across energy types: Stacked Area Chart
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Some Useful Insights
The Geofacet plot reveals that Japan is reducing their nuclear power production drastically after 2011 nuclear power plant leak, Fukushima Daiichi due to tsunami and earth quake Japan’s core focus has shifted from nuclear to other renewables. Last 5 years growth is remarkable, it has replaced about 33% of its nuclear consumption through other renewables Taiwan which is at higher risk for tsunami and earthquakes is following similar the same trend and has made significant reduction in their nuclear consumption. In 2016, winning government has stated phasing out nuclear power generation in their agenda With the reduction in the prices of Oil, the consumption from 2013 to 2017 has gone up for Oil across all countries. This can be visualized using the time-series pattern in the Geofacet graph for Oil For Finland, Iceland & Sweden, it is observed that a major proportion of energy consumption is from other renewable sources of energy From the Sunburst plot we can see that China produces 13.4% of the total energy of the world using Coal; Saudi Arabia produces 4.31% of the total energy of the World using Oil China, US & India are the top 3 countries in the Primary Energy Consumption in 2017 From the Geofacet plots we can see that China has been trying to reduce it's Coal consumption after 2012. If we look at Africa, Algeria has been able to reduce it's Coal consumption significantly over the years. The ternary plot shows us that the major energy source for Norway in terms of consumption is Hydro energy, which is a good indication. However, if we look at India, the major energy consumption is for Coal and Oil which are non-renewable sources of energy. US primarily consumes Oil, followed by Gas. China, over the years has tried to increase the percent contribution from renewable, hydro and nuclear energy sources as opposed to non-renewable energy sources. |
Conclusion/Future Work
The original dataset has several other metrics like prices of each energy source, reserves in each country, electricity generation, trade movements etc. These variables also must be included for analysing energy dynamics and the world energy outlook.
|
R Packages Used
We have used the following R packages to come up with our visualizations: |
References
[1] Shneiderman, B. (2005) “The eyes have it: A task by data type taxonomy for information visualization” IEEE Conference on Visual Languages (VL96), pp. 336-343 |