Impact of natural hazard on consumer behavior: The case of the 2017 El Niño phenomenon (ENSO) in Peru

1. Impact of natural hazard on consumer behavior:
The case of the 2017 El Niño phenomenon (ENSO) in Peru
Vincent Gauthier • Telecom SudParis - Institut Polytechnique de Paris
Network Science for the Sustainable Development Goals
NetSci 2020 Satellite Symposium • Sept 20th, 2020
Slides.https://bit.ly/355pp4H
! @vincentgauthier
Joint work with
Hugo Alatrista-Salas, Miguel Nuñez del Prado Cortez (Univ. Pacifico, Peru)
Monique Becker (Telecom SudParis - Institut Polytechnique de Paris, France)

2. Background information (I)
Peru.
Peru is a representative democratic republic divided into 25
regions.
1/3 of the peruvians live in the capital city: Lima.
Peru is a developing country, ranking 82nd on the Human
Development Index, with a high level of human development.
Since 2000, Peru is one of the region's most prosperous
economies with an average growth rate of 5.9% and it has
one of the world's fastest industrial growth rates at an
average of 9.6%.
Source: Wikipedia
The 2017 ENSO Events.
The capital city of Lima has been impacted by the 2017 El Niño
costero (coastal) events.
Two mains events: heavy rains and flooding in regions near the
Pacific coast of Peru around mid February and at the end of March
2017.
The aftermath of the two main events on the population have
been mainly due to flooding.
Source: EU Emergency Response Coordination Centre (ERCC)
2

3. Background information (II)
Understanding the El Niño Costero of 2017: The Definition Problem and Challenges of Climate Forecasting and Disaster Responses.
Ramírez, I. J. & Briones, F.
Int. J. Disaster Risk Sci. (2017).
El Niño–Southern Oscillation (ENSO).
The Humboldt current is a cold stream from antarctica that
flows along the coast of Peru. The Humboldt current has a
cooling influence on the climate of Chile, Peru, etc.
The ENSO is the cycle of warm and cold sea surface
temperature (SST) of the tropical central and eastern Pacific
Ocean.
Impacts.
The ENSO events appear on average every 4 years in Peru at
about Christmas time. This warming of the sea temperature
causes a shift in the atmospheric circulation and it brings
heavy rainfall and tropical cyclone formation increases over
the tropical Pacific Ocean.
In 2017, took place a phenomena called El Niño costero (coastal)
which is a different type of El Niño: This phenomena was
composed of two events that took place between January and
April 2017 that had a large impact on the population of Peru.
Some key figures (OCHA):
§ 1 million affected individuals
§ 639 affected bridges
§ 8,481 km of affected roads
§ 230,317 damaged houses
§ 5,244 ha of deteriorated crops
Source:NOAAhttps://bit.ly/3l6jx0E
Source: Wikipedia
Source:https://bit.ly/2EkWZZ9
3

4. Motivations
In this study we aim to develop a set of tools to measure socio-economic characteristic response of a
population in the aftermath of natural hazard.
1. We think that consumer behavior is a key variable to understand people needs during a period of climatic
stress.
2. The study of consumer behavior enables fine-grained analysis of the population behavior.
3. There is a growing literature that tries to understand what is the level resilience of the supply chain and
the retail business during disaster events.
4. We focused our attention on the greater area of the capital city of Peru, Lima.
Can we characterize the societal response of a population during and after a natural hazard with a
consumption dataset ?
Carleton TA, Hsiang SM. Social and economic impacts of climate. Science, aad9837, (2016). doi:10.1126/science.aad9837.
Strengthening Post-Hurricane Supply Chain Resilience. National Academies Press, (2020).
“For centuries,thinkers have considered whether and how climatic conditions—such as temperature,rainfall,and violent
storms—influence the nature of societies and the performance of economies.“
--Carton et al
4

5. Bank transaction dataset
Dataset description.
From June 2016 to May 2017, with approximately 1.5 M clients, 55,000 distinct
merchants, and 116.8 M transactions from both credit and debit cards from BBVA.
Features describing the clients such as anonymous ID, age, gender.
Features describing the transaction, like the timestamp, amount spent in monetary
unit.
Features associated with the bank agency, namely the region, province, and
district, in which the agency of the client was located.
Features characterizing the merchants, such as merchant ID, merchant name,
merchant address, the MCC and the coordinates of the merchants.
MCC: Merchant Category Classification
Transactions distribution.
5
1 Grocery Stores and Supermarkets 5 Betting
2 department stores 6 Universities and professional schools
3 Eating places and restaurants 7 Drug stores and Pharmacies
4 Service stations 8 Tax payments
Top 8 categories in our dataset.

6. Methods and results
I. Macroscopic approach
a) Events detection with the Kullback-Leibler divergence
b) Causal Impacts
II. Microscopic approach
a) Mobility Markov Chain (MMC).
b) Business ranking evolution in time (PageRank)
c) Core networks evolution of the transaction graph
III. Conclusion

7. Macroscopic approach
Events detection with the Kullback-Leibler
divergence.
We regroup the different purchases into a set of
relevant categories.
We compute de distribution of each category at a
given time and compare it with the distribution at the
next time instant or the mean distribution.
If the two distributions are similar, the KLD of the two
distributions will be close to zero.
Kullback-Leibler divergence.
7

8. Macroscopic approach
Causal Impact.
Given a specific time, the Causal
impact captures causality by
measuring the difference between
two different time series: one
series under treatment, and
another series not under
treatment.
Timeseries: the transaction volume
of the different districts of the
greater area of Lima
Brodersen KH, Gallusser F, Koehler J, Remy N, Scott SL, et al. Inferring causal impact using
Bayesian structural time-series models. The Annals of Applied Statistics. (2015). 8

9. Microscopic approach
Mobility Markov Chain (MMC).
We measure the changes in people
behavior using the MMC to quantify
changes in both individual purchase
category and frequent whereabouts.
MMC Method.
Average NDCG in each of the regions of the
greater area of Lima.
9
We compute mean behavior (for all clients)
in each of the regions of the greater area of Lima

10. Microscopic approach
PageRank of the transaction graph.
1. Based on the analysis of the
purchases made by clients we
build the transaction graph.
2. We slice the transaction
graph daily.
3. We quantify how businesses
reacted during the event by
studying the evolution of the
PageRank of each merchant
in the transaction graph.
Fig 2. Examples.
Merchant B
merchant A
merchant C
Merchant D
client i
client j
client j
client i
client j
client
client i
client j
Sequence of pruchase
between [t1
, t8
[
Sequence of pruchase
between [t8
, t16
[
(t1
, A), (t3
, B), (t7
, C)
(t2
, C), (t4
, D), (t5
, A), (t7
, B)
...
...
a
b
w=2
w=1 w=1
w=1
A
B
C
D
c
Fig 1. Transaction Graph.
10

11. Microscopic approach
Time series clustering.
We cluster the merchants’ ranking
evolution in order to observe what was
the merchants’ behavior during the main
ENSO events.
We found six main behaviors:
1. C#2 and C#5: transient period during
the main event in February.
2. C#1 and C#0: long term changes in the
ranking.
3. C#3: no noticeable ranking evolution
during the events.
4. C#4: ranking evolution during the
second event.
−1
0
1
cluster #0
−1
0
1
cluster #3
−1
0
1
cluster #1
−1
0
1
cluster #4
01-08
01-28
02-17
03-09
03-29
04-18
−1
0
1
cluster #2
01-08
01-28
02-17
03-09
03-29
04-18
−1
0
1
cluster #5
Clusters.
ML Framework.
11

12. −50
0
50
∆error(%)
Cluster #0 Cluster #1 Cluster #2
10−3
10−2
10−1
100
−50
0
50
|∆error|(%)
Cluster #3 Cluster #4 Cluster #5
Food
Unlabeled
Health
Clothing
Gas
Technology
Trans.
Housing
Insurance
Hotels
NightLife
10−3
10−2
10−1
100
Food
Gas
Health
Unlabeled
Clothing
Hotels
NightLife
Technology
Trans.
Food
Gas
Health
Unlabeled
Clothing
NightLife
Technology
Trans.
Hotels
Housing
a
100
)
Cluster #0 Cluster #1 Cluster #2
b
Microscopic approach
Interpretation.
Summary of the most important
business’s categories found in each
cluster.
For instance, some gas stations have
been out of order (#5) when others
have been over solicited (#2).
Distribution of the business's categories per cluster. The top of each
figure indicates the relative differences between the proportion of a
given category inside a cluster and the proportion of that category in
our dataset.
Fig 8. Time series clustering.
Table 1. Summary of most important merchant categories found in each cluster
cluster ranking over represented categories under represented categories
#0 0
health food
#1 0
gas, clothing unlabeled stores
#2
0
health, gas, technology, trans-
ports
food
#3 0
health food
#4 0
food, gas health, clothing
#5 0
gas, food clothing, night-life
In Fig. 9, two additional phenomena can be observed: first, an increase in insurance
purchases after the first event in February (cluster 3), and second, an increase in
purchases of technology related items during first event (cluster 2). We observe a surge
in the purchased of new insurance policies after the first event. It should be noted that
Peru is an underinsured country (only approximately three in hundred houses are
insured), and in the aftermath of the event it appears that people decided to purchase
new insurance policies. With respect to technology-related items, a query to the
database at our disposal revealed that no transaction were made in that category from
February 15 to 19, 2017. We observed a shift in purchases after February 19. This
behavior appear to be due to the purchases of prepaid cell phone plans.
We note that people’s response to the crisis were very heterogeneous in time, space
12

13. Microscopic approach
Resilience of the transaction graph : tracking the
evolution of the core/periphery structure of the
transaction graph.
We studied the resilience of the
transaction graph by measuring the
evolution of the size of the core networks
structure.
What is the set of businesses that remain
in the core structure during the events.
Result:
1. The size of the core structure shrinks
during the events
2. The ENSO events are clear anomalies
3. The businesses categories that
remain in the core are food,gas and
health.
0 5 10 15 20 25 30
Kshell
102
103
104
105
#transactions
20/02
22/02
24/03
26/03
a b
Fig 2.Evolution of the size of the core networks structure in time.
Ma A,Mondrag ́on RJ.Rich-Cores in Networks.PLOS ONE.2015;10(3):e0119678. doi:10.1371/journal.pone.0119678.
Fig 1. Evolution of the k-shell structure.
13

14. Conclusions
Consumption behavior seems to be a good index to characterize the "societal response"
of a population facing a crisis such as a natural hazard.
Coarse-grained analysis reveals the presence of the El Niño Costero phenomenon
and the recovery time in each territories.
Fine-grained analysis demonstrates a change in individuals' purchasing patterns and
in merchant relevance because of the climatic event. Also different types of
businesses have been impacted differently during the events. There is a wide range
of patterns both in space and time (duration).
Overall, despite a clear slowdown of the economic activities during the natural
hazard, the results indicate that society withstood the stress given that the country
is accustomed to this type of event.
14

15. Perspectives
Measuring the societal response during and after a crisis is becoming more and more
relevant and feasible (the current COVID-19 epidemic is another example).
The societal response is clearly a multimodal phenomena. One modality cannot
encompass all the facets of the problem.
Different crisis may trigger different societal responses ? is there a universal behavior?
On the contrary, one type of crisis may have regional specificity ?
Multiscale approach need to be developed: long-term impact and short-term shift in
behavior need to be studied more deeply.
15

16. Thank !!!!!
Impact of natural hazard on consumer behavior:
The case of the 2017 El Nino phenomenon
(ENSO) in Peru
Impact of natural hazard on consumer behavior: The
case of the 2017 El Nino phenomenon (ENSO) in Peru.
Hugo Alatrista-Salas, Vincent Gauthier, Miguel Nuñez
del Prado Cortez, Monique Becker, 2020.
Dataset. https://doi.org/10.7910/DVN/LYXBGR
arXiv.2008.04887
! @vincentgauthier
" https://complex.luxbulb.org/
# vincent.gauthier@telecom-sudparis.eu

17. Dataset Demography
0 0 0 2 0 4 0 6 0 8 1 0
f
0 00
0 25
0 50
0 75
1 00
C(f)
class 9
class 8
class 7
class 6
class 5
class 4
class 3
class 2
class 1
1 2 3 4 5 6 7 8 9
Class
0
100
200
300
Size(103
)
0
5
10
(103
)
Size
<P>
<P>
35
40
45
averageage
class
0.25
0.50
0.75
Fractionofwomen
b
c d
a
e
02500050000
Men (pop)
0 25000 50000
Women (pop)
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85-89
90-94
age 9: rich
8: middle upper
7: lower upper
6: upper middle
5: middle
4: lower middle
3: lower
2: middle lower
1: poor
17