Skip to content

visualizedata/yann-kerblat-thesis

 
 

Repository files navigation

Thesis | Major Studio 2 (under construction)

Initial Mindmap

Wireframe as of March 1

Current XD wireframe link as of March 8

Abstract (300 words)

Winemakers are some of the most seasoned climate scientists in the world. Agricultural techniques required for harvesting grapes accumulated over decades (sometimes centuries), coupled with precise knowledge of environmental patterns surrounding their vineyards, provide winemakers with unrivaled expertise and effective methods to optimize wine production. Because climate is the most essential ingredient required for wine production, it affects the level of suitability of grape varieties to a particular terrain, and determines the type and quality of the wine that can be produced. However, recent extreme weather events also confirm that grapes are extremely sensitive to sudden changes in temperature and other unforeseen fluctuations. These shifts raise difficult questions for the wine industry and could potentially disrupt centuries of cultural practices and agricultural norms in winemaking while also unlocking new opportunities in adapting to climate change, something that will require innovation and flexibility.

While the topic of climate change in the wine industry attracts much media and academic attention and generates important discussions within the wine industry, most of the existing empirical evidence is either geographically localized, confined to a specific grape variety, and/or difficult to grasp for wine enthusiasts. In addition, it is also difficult for a wine aficionado to access updated and/or user-friendly applications of cutting-edge climate change information suited for the wine industry. The Ripe for Disruption project provides a glimpse into the future of the wine industry, by using geovisualization tools to help the reader better grasp which wine regions (and wineries) are more likely to be on the frontlines of global warming in the future. A closer look at extreme weather events that recently impacted the “Old World '' and the “New World '' is also provided to make sense of these macro-level shifts, before finally, exploring the different opportunities and adaptive changes the wine industry might go through as a result.

Problem statement

I plan to examine the relationship between wine production patterns and climate change effects (current + expected) at a global level.One of the objectives would be to help the audience better understand which wine varieties (or wine-producing areas) are more likely to be resilient to climate shocks in the future.

Some questions I'm interested in exploring:

  1. Which are the wines that are the most exposed to climate change?
  2. What's the profile of a typical wine that is at risk from global warming? (Are there any visible geographical/socio-economic/environment features of these at-risk wines)
  3. Is there a clear correlation between grape type and level of climate risk?
  4. What are the most promising techniques and emerging techniques that exist that can help wine producers adapt to this new reality?

Current "pain points"

  • Is it better to articulate a forward-looking lens (i.e. "in what direction are we going in?") or put forward a data-driven narrative based on historical records and/or current trends?
  • Which geographical lens to pick? Which scale to use to produce this evidence? Need to decide how to handle all the various scales of impact (viticulture is both large and small scale..!).
  • Level of geographical coverage of a given dataset - depending on data constraints, need to decide if I limit the spatial analysis to a specific region/set of wine-producing regions (the "Old World" for example)?

Outline (as of Feb 15)

Introduction

  • Provide short context for reading and explain what's specific and interesting to the relationship between wine production & climate change/global warming.
  • Provide an overview of the analytical challenges with this topic and explain why I want to start explore this topic at a more macro level.
  • Provide an overview of the data sources and what the reader can expect to learn in terms of insights in this project.

Section 1 - Spatial overview at global level

Which wines/wine-producing regions are the most exposed to climate change? Are there any strong characteristics of these wines/wine-producing regions?

Section 2 - Temporal dimension at regional level

How does climate change shift harvest calendars? What are the implications for regions/local areas that rely heavily on wine production?

Section 3 - Emerging practices and innovations at local level

What are the most promising techniques/innovations that highly-exposed regions piloting/scaling up? It would be interesting to look at pros & cons.

Conclusion

Summary of research findings/analysis and shed some light on these growing practices (what's are the trends and patterns that we can observe?

Datasets ideas to probe/explore (pending)

  • Identify dataset on climate change trends & forecast?
  • Identify dataset on wine production zones?
  • Check out GL-DAS reanalysis data (land use cover)?
  • Locate wine regions dataset (global - to show where wine is grown)?
  • Analyze dedicated overtime grape stock in hectares and yield?
  • For wine regions points, I could try to extract the average value for certain growth metrics from 1980 - 2020 (for example)?

Target audience

  • Industry-level practitioners involved in:
  1. Climate Change Adaptation/Climate-Smart Agriculture Policy (especially wine industry)
  2. Parametric Insurance & Weather Forecasting
  3. Land Administration & Cadaster (in rural zones)
  4. Trade, Logistics & Supply Chain
  • Wine Geeks as well as Food + Travel Enthusiasts

References (qualitative)

UI design features (ideas)

  1. Timeline slider to show long term patterns & geographical spread
  2. Mapping forecast/trends to blend climate risks with vulnerable wine regions
  3. Filter to contrast old world vs. new world (or expensive vs. affordable wines)

Noteworthy spatial/temporal visuals (ideas)

image Source

image
Source

image Source

image Source

image Source

Releases

No releases published

Packages

No packages published

Languages

  • HTML 97.6%
  • JavaScript 2.4%