Using Big Data to investigate the influence of climate and demography on wine consumer habits Alastair Reed 1 , Michael Shannon 1 , Daniel Mathews 2 1 Viticulture and Winemaking, Melbourne Polytechnic Contact: [email protected]2School of Mathematic Sciences, Monash University
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Using Big Data to investigate the influence of climate and demography on wine consumer habits Alastair Reed 1, Michael Shannon 1, Daniel Mathews 2 1 Viticulture.
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Using Big Data to investigate the influence of climate and demography on wine consumer habits Alastair Reed1, Michael Shannon1, Daniel Mathews2
1 Viticulture and Winemaking, Melbourne Polytechnic Contact: [email protected]
f(x) = 353.252146300215 x − 52.2410878210395R² = 0.692078572970318
0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.135
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f(x) = − 180.762346031977 x + 33.7165680206313R² = 0.405556053630358
All analysed varieties were correlated to temperature on a temporal scale
5 10 15 20 25 30 35 40 450.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
f(x) = − 0.00359695794237473 x + 0.267774023270689R² = 0.197351352441506
Association between relative Shiraz sales and temperature
All analysed varieties were correlated to temperature on a temporal scale
Association between relative Sauvignon Blanc sales and temperature
5 10 15 20 25 30 35 40 450.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
f(x) = 0.00169245126087001 x + 0.119450352512055R² = 0.10228796113334
Google search associates Shiraz to temperature
10 15 20 25 30 3515
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f(x) = − 0.779314046730358 x + 56.7949665501602R² = 0.370583390432392
Temperature (°C)
Goog
le se
arch
(rel
ative
)
Association between relative fortnightly Google searches and average temperature (excluding Christmas period)
Google search associates Sauvignon Blanc to temperature
Association between relative fortnightly Google searches and average temperature (excluding Christmas period)
20 25 30 35 40 45 50 55 60 650.1
0.12
0.14
0.16
0.18
0.2
0.22
f(x) = 0.000610093115845559 x + 0.144491466125468R² = 0.133136983710597
Link between red wine sales and temperature is consistently stronger than white, except Sauvignon Blanc…
Proportion of stores with significant correlation (r)
Average income** when significant correlation
Average income when insignificant correlation
Cabernet Sauvignon 0.96 (0.29) $1632 $1110
Merlot 0.86 (0.26) $1639 $1436
Pinot Noir 0.57 (0.22) $1793 $1371
Shiraz 0.98 (0.44) $1623 $995
Chardonnay 0.45 (0.17) $1703 $1535
Pinot Gris 0.67 (0.23) $1765 $1303
Riesling 0.61 (0.25) $1778 $1352
Sauvignon Blanc 0.96 (0.29) $1626 $1244
Average 0.76 (0.27) $1695a $1294b
*>0.027 **fortnightly
Geography
Decision Gene approach
Relative purchase figures can be treated the same as allele frequencies (the frequency of gene variants), where an individual has two alleles for each gene
Genotypes:
aa = purchase
Aa or AA = no purchase
We can then use the frequencies to describe the characteristics of a population
Comparing the relative frequency of alleles allows populations to be compared using distance-matrices, visualized with traditional phylograms.
Clustering between distinct geographic areas
Phylogram generated using the Neighbour-Joining (NJ) method on sales frequencies of 7 varieties across 28 retail outlets (derived using POPTREE2 [Takezaki 2010)
Chardonnay sales contradict the cliché
N
High Riesling sales follow SE-NW corridor
N
High Riesling sales follow SE-NW corridor
N
Demographics roughly align with Chardonnay/Riesling distinction
Sauvignon blanc is most popular in an outer suburban ring
N
Summary
Significant associations can be made between developmental and environmental factors and consumer preference
Temporal and spatial trends can be identified but need further analysis for confirmation
We are looking for collaborators to consolidate this research, all welcome!