Visual Analysis on Call Data Records for Improving Disaster Resilience

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D4D 2014: Submission from the Rochester Institute of Technology
Project: Visual Analysis on Call Data Records for Improving Disaster Resilience
Authors:
Chitturi S, Davis J, Farooqui S, Gohel J, Gonsalves S, Gunda R, Raina A, Sawant A, Sawant T,
Vijayasekharan A, Wasani J, and Tomaszewski, B.
Project Submission Contact: Brian Tomaszewski. Ph.D. – bmtski@rit.edu
Abstract
Natural disasters like floods have compounded effects on agriculture, which in turn, result in
food insecurity and malnutrition. The Senegalese population is particularly vulnerable to floods
due to existing poverty. We analyzed Data for Development (D4D) Call Detail Records (CDR)
datasets to (1) find statistically significant spatial clusters of areas vulnerable to floods and (2)
identify spatial interactions between call origins and destinations to understand calling behaviors
during flooding events. In depth contextual inquiry was done to identify flood occurrences within
the peak flood month of August and with a particular focus on Dakar and Saint Louis. Visual
analysis was conducted on call records from August 27 and August 29. Our analysis revealed
several interesting spatial interaction patterns. Specifically, we found that that there were higher
number of calls and sms during the peak flood dates. Visual analysis of call origins and
destinations also potentially revealed existing social networks that are potentially utilized for
economic and social relief during floods. Areas contacted during floods are located not only
near large population centers like Dakar and Saint Louis but also in farther away regions like
Kaolack and Ziguinchor. We argue that identifying spatial interactions during floods using CDRs
can help build resilience to natural disasters and related events such as food insecurity and other
flood-related health issues. We conclude the paper with specific recommendations for future
work based on our findings.
1. Introduction
Senegal is one of the 16 countries that make up Western Africa. Its capital, Dakar, is located on
the Cape Verde Peninsula and is the westernmost city in Africa. Senegal has a population of 13.7
million with 46.7% of the population living in poverty [2, 6]. Its average annual rainfall is
600mm which primarily occurs during the rainy season between June and October [1]. During
the rainy season, Senegal often experiences extreme flooding events [5, 11]. Of the almost 14
million people, 11.4 million live in an area known as the Sahel region or the Sahel Belt. The
Sahel region covers a major portion of Senegal, including Dakar. The combination of high
poverty rates and seasonal flooding make many Senegalese living in the Sahel vulnerable to

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natural disasters and the after effects of natural disasters [8]. People living in the Sahel often
require immediate food assistance as a result of existing poverty conditions coupled with the
effects floods that in impact in agricultural production causing food insecurity [3, 9, 10, 13]. In
September 2013, the WFP (World Food Program) launched a mobile cash transfer program to
the food insecure people in Senegal through mobile phones [3, 4].
The Orange Mobile Company provided a mobile Call Detail Records (CDR) dataset for Senegal
from the year 2013. The data contains outgoing calls, incoming calls and text messages from
1666 antennas around Senegal. In this study, we examined calling patterns related to flooding
events to understand how people and places in Senegal interact during flood events. Using the
data provided by Orange, we found interesting patterns in the data that correspond to the flood
dates in Senegal. We believe that these floods and the region’s food insecurity and malnutrition
cases are correlated which could account for the 50% rise in malnutrition cases from 2012 to
2013 [5, 8]. In the following section, we first describe our methodology for processing, storing
and retrieving the CDR datasets that were the basis for our analysis into calling behaviors, spatial
interactions and flooding events.
2. Methodology
2.1 Database Creation
A database schema design was implemented that mirrored the structure of each file provided by
Orange for the Data for Development (D4D) challenge [12]. Each .csv file in the dataset had a
corresponding table created in the database. These files were then loaded into their appropriate
table using SQL Server Business Intelligence Development Studio. After the tables were loaded,
primary keys and foreign keys were created on the tables. Creating the keys after loading the
data reduced the data import time. The primary keys also created indexes on the table which
increased the speed of the querying the data. We then created views by joining voice, sms and
site tables in order to find patterns for a given region which corresponds to multiple site_ids on
the day of major events such as floods. We also used views to restrict number of rows for
prototyping purposes. We leveraged the views to abstract table complexities and provide a
cleaner virtual table containing all the information needed for further in-depth analysis. Views
can perform as fast as the direct query on the tables, so we created indexes on tables to bolster
view’s data retrieval performance [14].
2.2 Spatial Analysis
A geo-database was created with different reference datasets such health centers, cellular tower
locations, settlement areas and nutrition. Using Geographic Information Systems (GIS), these
reference datasets were then overlaid with CDRs extracted from our central database (discussed

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previously in section 2.1) for specific flood dates identified for Dakar and Saint Louis (discussed
next in section 3) to identify spatial patterns during the floods. We used the XY to Line
Management tool1
of ArcGIS [7] to identify call tower origin and destination pairs for the flood
dates using tower latitude and longitude coordinates combined with relevant call data (duration,
call date, etc.). As seen in Figures 2, 3, and 4, the XY to Line tool provides a visual summary of
tower orgin to tower destination calls. The Getis-Ord-Gi* spatial statistic2
was then used to
identify the statistically significant clusters or “hot spots” where the most calls were being made
during flood dates using outputs from the XY to Line tool. The Getis-Ord-Gi* spatial statistic
helped in identifying statistically significant spatial clusters of high values and low values by
making use of aggregated data and dependence between the attributes – in this case, tower
locations sending and receiving calls during a flood. In the following section, we describe out
contextual inquiry into flooding in Senegal and results of our inquiry using the aforementioned
methodology.
3. Contextual Inquiry: August 2013 Flooding in Senegal and Call Activity
Contextual inquiry on Senegal clearly illustrated that the major flood regions were Dakar and
Saint Louis for specific dates in August 2013 (Figures 1.1 and 1.2).
Figure 1.1: Dakar – August 2013 call patterns.
Note spike in calls on 27 August and then the
gradual drop off after 27 August.
Figure 1.2: St. Louis – August 2013 call
patterns. Note spike on 29 August and then the
gradual drop off after 27 August.
We first conducted analysis on Dataset 1 (antenna to antenna traffic on an hourly basis) to
identify specific calling patterns related to August 2013 flooding events. Visual exploration of
1
http://resources.arcgis.com/en/help/main/10.2/index.html#//0017000000tv000000
2
http://resources.arcgis.com/en/help/main/10.2/index.html#//005p00000011000000

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CDR data for August 2013 revealed several patterns which showed a drastic rise in number of
calls and sms on particular days. Specifically, we identified 27 August 2013 for Dakar and 29
August 2013 for Saint Louis as significant flooding dates.
4. Results & Discussion
A significant pattern was found in analyzing Call Data Records (CDR) dataset 1 (Antenna to
Antenna) with respect to specific flood dates in Dakar and Saint Louis. The results for the pre-
and post- flood dates for Dakar and Saint Louis yielded a visual pattern which showed a drastic
outreach to regions such as Kaolack, Thies and Louga identified through hot-spot analysis.
Dakar also reached out to localities outside the aforementioned hot spots, irrespective of the
proximity to the flood affected area such as Ziguinchor, Kolda, Tambacounda and Matam. We
were not able to determine why these specifically areas were contacted. Existing research on
flooding events and behaviors reflected in CDRs is still a very new field [16, 18, 19]. A recent
study conducted by United Nations Global Pulse (2014) examining CDRs during floods in
Mexico found that increases in mobile activity provide signals of flooding impact and call
volumes increase in impacted areas [15]. However, the Global Pulse study did not discuss where
people impacted by floods were calling to or why they called one region or another. From our
study, we can infer that people being affected by floods (as reflected in high frequency of call
volumes from certain cell tower locations) were calling other locations in Senegal more than
normal. We wish to suggest that the reason for this behavior is that people affected by floods
were likely contacting relatives, friends or other people more frequently than normal in order to
provide social or financial support as this is a very common behavior during disasters [20]. This
inference is illustrated in Figures 2.1 and 2.2. It is visually evident that normal calling
interactions between Dakar and other areas increase dramatically during flood events.
Figure 2.1:
Dakar – 25 August (Pre-Flood Date).
Figure 2.2:
Dakar – 27 August (Flood Date)
Note increases in call activity as visually
reflected in increased lines.

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When mapping call flows from Saint Louis for the 29 August flood date, a similar pattern
emerged like seen in Dakar. Increased call numbers were seen during the flood event, this time
targeted towards the Kaolack, Thies and Dakar region (Figures 3.1 and 3.2). Calls originating
from St. Louis also indicate that people living in St. Louis could have strong social and economic
connections with Dshra, Linguere and Keberner.
Figure 3.1:
Saint Louis – 25 August (Pre-Flood Date).
Figure 3.2:
Saint Louis – 29 August (Flood Date).
A cumulative spatial analysis was done for both Dakar and Saint Louis based on the outgoing
calls on an hourly basis from Dataset 1 which revealed an interesting pattern seen in Figures 4.1
and 4.2. This pattern shows a huge concentration of calls being made to Kaolack, Matam and
Thies.

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Figure 4.1: Outgoing Call Pattern for Dakar and Saint Louis on the 27 and 29 August flood
dates.
Figure 4.2: Incoming and Outgoing calls on the 27 and 29 August flood dates overlaid on heat
map

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Finally, based on our analysis, we identified the top ten towers making calls during flood events
in Dakar and St. Louis as a starting point for making recommendations based on our study
(Figure 5.1 and 5.2).
Figure 5.1: Top ten call volumes by tower during August 2013 flooding events – Dakar.
Figure 5.2: Top ten call volumes by tower during August 2013 flooding events – St. Louis.

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In both cases, call origins and destinations had the same tower ID. Thus, as per previous research
on CDR during flooding events discussed previously in this section, it is likely that locations
around these tower locations are particularly vulnerable to floods.
5. Conclusions and Recommendations
Analyzing the outgoing and incoming call data patterns for the most affected flood areas (i.e.
Dakar and Saint Louis), we conclude that these places, during flood events, are likely contacting
other locations throughout Senegal for social and economic support. We also observed call
patterns outliers with calls being made to regions far away irrespective distance to flooded areas
(as seen in Figures 2, 3, and 4).
Based on our analysis, we recommend the following:
1. Further analysis of outgoing calls in and around Koalack, Matam and Theis during the flood
events. This could help determine (1) who is being called and why they are being called during
floods for understanding food security and other health issues that arise during the floods and (2)
gaining insight into existing social networks that can potentially be utilized before, during, and
after natural disaster events for building societal resilience [17].
2. Conduct neighborhood-level disaster resilience studies on area around the tower locations
shown in Figures 5.1 and 5.2. As previous research indicates, calling patterns can serve as
surrogates for understanding location most vulnerable to floods [15]. Ideally, further
understanding of the geographical context around these locations can also help to reduce risk and
build disasters resilience.
3. Compare and investigate if correlations can be found between floods events, food security
situations and mobile cash transfers like those done by Orange in 2013 [3]. Ideally,
understanding this broader sequence of interrelated events can lead to better predictability and
use of CDRs to identify and anticipate food security and other shock situations before they occur
or mitigate their effects. Furthermore, understanding such event sequences can lead to improved
resilience to disasters and better decision making for targeted relief efforts like those conducted
by Orange [3].
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