Nepal Floods July 2016

Population Mobility, Displacement and Impacted Areas ­ Based on Analyses of Anonymized Mobile Network Data

Flowminder Foundation in collaboration with Ncell (Pvt) Ltd

9 August 2016

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Our mission is to improve public health and welfare We provide global public goods, working with partners to collect, aggregate, integrate and analyze anonymous mobile operator data, satellite and household survey data. We characterize and map vulnerable populations at risk in low­ and middle­income countries.

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Disclaimer: Statements made in this report are the expression of individual views and opinions and do not necessarily reflect the facts or agency policy or guidance, and cannot be construed as official representations of (as examples) statutes or regulations.

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Table of Contents

Overview and Key Findings Introduction Methodology National and District level population flow estimation Short­range movements Calling frequency anomalies Cell tower inactivity Key Findings and Analysis Movements from and to affected districts Short­range movements Anomalies in communication behaviour Cell tower inactivity Conclusion Way Forward Appendix A.1 Flood magnitude estimates A.2 Rainfall estimates B.1 National level population flow estimates (26­30 July 2016) B.2 District level population flow estimates (26­30 July 2016) C.1 Calling frequency (4 April ­ 5 August 2016)

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Situational Overview and Key Findings

The 2016 monsoon in Nepal started on 15th June, entering the Eastern region on 15th June (MFD) and the country’s Far­Western region on 19th June (MFD). Heavy rain from the start ​ ​ ​ ​ of July caused flooding and triggered landslides across the country, affecting 51 districts (Fig. i). The Terai of the Mid­West, West and East Development Regions have been declared as being most affected (UN, 1 August 2016). ​ ​

In this report we explore changes in communication patterns, functionality of the network and mobility of anonymous Ncell mobile phone users. We document changes in the network data at the times and locations of known flood and other disaster events during the recent weeks. We then identify areas with similar signals and population behaviour, some of which have received less attention in available reports.

The analyses are intended to support the Government of Nepal and relief agencies with complementary data on the impact of recent rains as well as providing an overview of population displacement during the same time period. Additionally, as floods have long­term impacts on the population, findings in this report can be used, together with other information sources, to support identification of areas which have been particularly affected, thus indicating areas where further evaluation of needs are most warranted as well as suggesting avenues of development for longer­term work on the use of operator data in response to floods.

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Figure i. Southern lowland areas have reported as most affected by the torrential rain in the region. The map shows areas with increased water levels and potential local floods across Nepal (GDACS* satellite­based estimates of flood magnitude during 21 ­ 27 July 2016 ​ ​ (retrieved 1 August 2016).

Across Nepal, the number of people who spent time outside their home area began to ​ increase at the end of June (national average in Fig. ii). The estimated proportion being away from their home on any given day changed from 27% during April through June to 39% at the end of July and beginning of August.

Particularly sharp increases were seen at the end of July in Sunsari District (23rd July) and ​ in Kapilbastu district (26th July), which may indicate that these districts were especially ​ heavily affected (Fig. ii)

Analyses of call activity (frequency of calls) shows anomalies at the time and location of known major events during the period, such as on the 26 July when the Banganga River ​ flooded areas of Kapibastu district (Fig. iii). ​ ​

*NB Please note that the information provided by GDACS has no official status and does not replace local flood warnings. Please refer to the competent local hydrographic authorities for official information on the flood status.

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Figure ii. Proportion of users seen away from home on any given day (district average).

Figure iii. Cell tower activity in Kapilbastu and surrounding districts between 16:00 and 17:00 on 26 July 2016, corresponding to the flooding of the Banganga River. Blue indicates high call activity, and red indicate low activity.

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Other areas with deviating mobility and communication patterns, include Saptari, Morang, ​ ​ ​ ​ and Jhapa in the Eastern region, and Kapilbastu, Rupandehi and Nawalparasi in the ​ ​ ​ ​ ​ Western region. These results may indicate that these districts were more heavily affected than others. Kapilbastu and Saptari have a much higher than normal proportion of people ​ ​ leaving the district during the last week of July. In Kapilbastu, Rautahat and Jhapa districts, ​ ​ ​ ​ network service interruptions were above average.

Figure iv. Mobility network for 26th July 2016.

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Detailed findings, analyses of the situation in specific districts as well as methodological notes are included below. This report is downloadable on www.worldpop.org.uk/nepal ​ ​

For questions, requests and further information contact [email protected] ​ Introduction

In this report, we present preliminary analysis of anomalies in communication and mobility patterns during the as yet on­going period of of heavy rainfall and floods in Nepal. It’s purpose is to support the Government of Nepal and other relief agencies with complementary data on the impact of the recent rains as well as providing an overview of the population displacement during recent weeks. Floods have long­term effects and the findings in this report can be used, together with other information sources, to support identification of areas which have been particularly affected, to indicate areas where further evaluation of needs are most warranted, as well as to indicate avenues of development for longer­term work on the use of operator data in response to floods.

The onset of the 2016 monsoon in Nepal occurred on 15th June, entering the Eastern region 17 June (MFD) and the country’s Far­Western region on 19th June (MFD, 19 June 2016). ​ ​ ​ ​ Heavy rain from the start of July has caused flooding and triggered landslides across the country, affecting 51 districts. According to OCHA reports, as of 1st August, 122 people have died, 19 have gone missing, 67 have been injured, over 6 290 families have been evacuated and over 4 700 homes have been partially or fully damaged (UN, 1 August 2016). ​ ​

Flowminder Foundation in partnership with mobile operator Ncell (Pvt) Ltd, demonstrated during the 2015 nepal earthquake response, that mobile operator data can provide a rich source of information to provide near real­time ongoing measurements of population displacements that can be used during earthquake relief operations immediately after the earthquake event (FF, 2015; Wilson et. al, 2016). ​ ​ ​ ​

The nature of the recent landslides and floods in Nepal are very different from those of the 2015 earthquake, which was a large­scale sudden impact disaster with a clearly defined epicentre and relatively well defined zone of impact. The present flood and landslide events are very different. Incessant rain, high water levels, floods and landslide events of varying severity has occurred across the whole of Nepal at various time periods. Some events have been anticipated by the local populations (slowly developing floods), whilst other occur suddenly and without warning (flash floods, rapid landslides). To support these analyses, it is important to combine operator data with high­resolution satellite imagery. As only low­resolution imagery is currently publically available for the duration of the period of interest, considerable uncertainty remain for some of the findings, which will be improved upon in future work.

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We explore for spatial and temporal patterns in the de­identified mobile operator data of over 12 million mobile phones, and provide estimates of national level population movements. We explore four areas of analysis. First, the estimation of population mobility and displacement patterns at national and district level. Second, the estimation of displacement patterns at the cell tower level. Third, anomalous patterns of calling frequency in the most affected areas. Finally, the correlation of cell tower downtime with flood events.

This work was made possible by the dedicated support of Ncell (Pvt) Ltd.

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Background and Methodology

Flowminder Foundation is a non­profit entity supporting government, local and international agencies with analyses on population distributions, vulnerability, displacement, mobility and other key features. One of the information sources we often use is de­identified mobile phone call detail records (CDRs). CDRs contain the time and associated cell tower of text messages and calls, and can thus be used to study human behaviour and mobility patterns. The CDR analysis described was undertaken in compliance with the GSMA privacy guidelines developed in the context of the Ebola outbreak (GSMA, 2014). ​ ​

Nepal has 26 million mobile phone subscriptions and a population of 27 million people. Ncell is the largest operator, having approximately 13 million subscribers (50% market share). Mobile phone penetration is increasing rapidly: in 2011, 75% percent of households (92% in urban areas, 72% in rural areas) reported having at least one mobile phone (Wilson et. al, ​ ​ 2016).

National and District level population flow estimation

For these rapid analyses we estimate population flows above normal at both national level between districts, and within districts between VDCs.

We determine the daily location of users on any given day. We use a simple metric that the daily location of a user is the location of the last phone call that the user made or received on the given day. If a user made several phone calls we will only use the last one of the day. Of course, a user may not make any phone calls on a given day, in which case they have no daily location on that day.

Flows are simply the number of people who moved from one location to another. We subtract normal flows from the flows that we observe after the disaster events and call these “flows above normal”.

To do this we need the modal daily locations of anonymous users over three separate time periods. The first one is typically a long benchmark period, that does not include any unusual events that would cause large scale movement, the second is a shorter period, also a ‘normal’ period, the flow between these two is counted as the normal flow. Thirdly we use the modal daily location calculated over a short period of time that we are actually interested in (typically the most recent week of our data). We subtract the normal flow from the flow of interest and count this as the “Flow above normal” for the week in question. Flows are scaled by census data population estimates. We define the home location as the modal daily ​ location over the period in question.

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We usually look specifically at inflows above normal into a particular region and present a bubble map for certain key regions of interest. See past Flowminder Reports as examples. ​ We might do this at both the district and VDC level.

We use the following periods: ● Benchmark: 2016­04­01 until 2016­06­20 ​ ​ ● Comparison: 2016­06­21 until 2016­06­25 ​ ​ ● Focal: 2016­07­26 until 2016­07­30 ​ ​ Short­range movements

We estimate population displacement at the cell tower level at a national level in order to detect patterns that correlate with flood events.

As before, we determine the daily location of users on any given day, and assign stable home locations based on the benchmark period. We determine the subset of users who make or receive at least one call every day within the focal period. We refer to this group as persistent users. For each day in the data set, we determine the number of persistent users ​ with the subset seen away from their home location and assign those counts to each home location. We refer to this value as the leavers from home. In addition, we determine the ​ number of persistent users seen at a location other than their home location, and assign those counts to the new location. We refer to this value as the arrivers from home. ​ ​

We use the following periods: ● Benchmark: 2016­04­01 until 2016­07­20 ​ ​ ● Focal: 2016­07­21 until 2016­08­02 ​ ​

For the focal period, there are 2.6 million persistent users. There are missing days of data for 11, 12, 13 July 2016.

Calling frequency anomalies

We estimate the temporal and spatial distribution of anomalies in calling frequency at the national level in order to detect patterns that correlate with flood events.

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We determine the call frequency per hour for each cell tower. We estimate “normal” call frequency behaviour by calculating the average calls per hour for any given hour across the data set. Under normal conditions, call behaviour will exhibit regular daily and weekly cycles of calls. Anomalies are determined by a threshold value of two and three standard deviations from the mean. Using this method we can detect an unusual drop or rise in call frequency at each cell tower for any given hour across the data set.

Cell tower inactivity

We determine periods of cell tower inactivity per hour for each cell tower. Cell towers may show inactivity for a number of different reasons, such as power failure, overload, network problems, overlapping coverage, service interruptions etc. If a cell tower actually fails, this could result in calls be routed via another nearby tower, which may affect the calculation of cell tower level population estimates. We assess the spatial and temporal distribution of inactive towers, and assess the impact on the analyses.

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Key Findings

In the absence of of good quality high­resolution data on the extent of the flooding, we have used a) daily flood magnitude estimates provided by GDACS* (see Figures 1 and A.1), b) ​ ​ rainfall estimates provided by GIOVANNI (see Figure A.2), c) assessments by humanitarian ​ agencies, and d) media reports to determine the focus areas for our analyses. We specifically focus on two groups of districts: ● Eastern region: Saptari, Sunsari, Morang, Jhapa ● Mid­Western and Western regions: Kailali, Banke, Dang, Kapilbastu, Rupandehi, Nawalparasi

Figure 1. WorldPop population density estimates (WorldPop, retrieved 1 August 2016), and ​ ​ GDACS satellite­based estimates of flood magnitude during 21 ­ 27 July 2016 (GDACS, ​ ​ retrieved 1 August 2016)

*NB Please note that the information provided by GDACS has no official status and does not replace local flood warnings. Please refer to the competent local hydrographic authorities for official information on the flood status.

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Movements from and to affected districts

Flows of people between districts increased by 570,000 during 26 to 30 July 2016, compared to during the benchmark period 21 to 25 June 2016. Figure 2 shows the estimated increase in number of people moving out of a district (left figure; outflow shown for each district) and into each district (right figure; inflow shown for each district).

Saptari district in the Eastern region, and Kapilbastu district in the Western region have been highlighted as heavily affected (UN, 1 August 2016). For these districts we show the ​ ​ destination and origin of people moving out of and into these districts (Fig. 3 & 4: estimated above normal flows for the focal period 2016­07­26 until 2016­07­30). ​ ​ ​ ​ ​

Bhojpur district showed a lower number of people leaving compared to before the monsoon, which may be related to the reported obstruction of the Diktel­Helasi road in neighbouring Khotang district (KP, 27 July 2016), and the reported obstruction of flights at Bhojpur Airport ​ ​ (THT, 25 July 2016). ​ ​

Further figures can be found in the Appendices B1 and B2. ​ ​ ​ ​

Figure 2. Estimated increase in population movement out of each district to any other district (left), and into each district (right) from any other district.

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Figure 3. Estimated above­normal outflows (left) and inflows (right) Saptari district. Above­normal flows are indicated between the neighbouring districts of Siraha, Sunsari and Udayapur, as well as Kathmandu Valley.

Figure 4. Estimated above­normal outflows (left) and inflows (right) for Kapilbastu district. Above­normal outflow is indicated to Rupandehi district. Above­normal inflows are indicated from the neighbouring districts of Rupandehi, Dang, and Arghakhanchi.

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Short­range movements

We analyse the population displacement at the cell tower level for the subset of 2.6 million anonymous users that make or receive at least one call every day during the focal period of 2016­07­21 until 2016­08­02. Figure 5 shows the averaged proportion per district of these ​ ​ users seen away from home. Since the onset of the monsoon on 15 June there has been an increase in users leaving, with a marked increase from 20 July, over the period of most intense flooding.

We break down the analysis to investigate the leavers and arrivers in the focus districts. Figure 6 shows the proportion of leavers and arrivers in the Eastern Districts. The peak seen between 20­25 July corresponds to flood events in those districts. Figure 7 shows the proportion of leavers and arrivers in the Mid­Western and Western districts. There is an increase in all districts, with the strongest signal seen in Kapilbastu district over 25­30 July.

In Figures 8 and 9, we plot the spatial distribution of cell towers with the highest number of leavers and arrivers, respectively, over each day in the focal period. Clusters can be seen across the Southern districts.

Figure 5. Average proportion per district of persistent users seen away from home (leavers).

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Figure 6. Proportion of users seen away from home (top) and arriving from home (bottom) for the East districts.

Figure 7. Proportion of users seen away from home (top) and arriving from home (bottom) for the Mid­West and West districts.

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Figure 8. Top 50 cell towers with highest number of users away from home on each day over the period 21 July ­ 1 Aug.

Figure 9. Top 50 cell towers with highest number of users arrived from their home tower on each day over the period 21 July ­ 1 Aug.

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Anomalies in communication behaviour

We analyse the temporal and spatial distribution of anomalies in calling frequency, which is known to exhibit rapid changes in response to shocks, such as floods and landslide events. Figure 10 shows the call frequency relative to the data set average for the same hour in the same weekday (see Figure C.1). The large peak in calls on 13 April 2016 corresponds to ​ ​ Nepali New Year. After the onset of monsoon on 15 June, call volumes are typically below the data set average.

Figure 10. Call frequency relative to weekday hourly mean (missing data 11,12,13 July).

Anomalies based on a 2­sigma threshold are shown in Figure 11. Several festival days are indicated by large peaks in call frequency e.g. Nepali New Year, Mata Tirtha Aunsi, etc. During the period of intense flooding from 20 July onwards there is a marked increase in anomalies related to unusual decreases in call frequency (green line).

We now concentrate on anomalies within the focus period, as shown in Figure 12, and decompose the signal into the components related to the focus districts. The Eastern districts exhibit an increase in anomalies over 23 ­ 26 July. The Mid­Western and Western districts exhibit an increase in anomalies over 22 ­ 29 July.

In Figures 13 and 14, we show the spatial variation of anomalies with flood signal magnitude estimates on 23 and 26 July respectively. Locations with high levels of anomalies correlate well with flooded areas.

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Figure 11. Hourly 2­sigma call frequency anomalies.

Figure 12. Hourly 2­sigma call frequency anomalies for Eastern (top) and Western (bottom) districts (focus period). Unusual increases (blue) and decreases (green) in call frequency.

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Figure 13. Call frequency anomalies per tower for each district on 23 July 2016, , overlaid with pixels of GDACS flood signal magnitude (darker grey pixels indicate higher flood signal magnitude).

Figure 14. Call frequency anomalies per tower for each district on 26 July 2016, overlaid with pixels of GDACS flood signal magnitude (darker grey pixels indicate higher flood signal magnitude).

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Cell tower inactivity

We determine periods of cell tower inactivity per hour for each cell tower, and plot the hourly and daily aggregates in Figure 15. There is an increase in hourly inactivity during the focus period around 26 July. The spatial distribution of inactive towers on 26 July during four 1­hour periods is shown in Figure 16. A cluster of inactive towers appears in Kapilbastu district which corresponds with the peak in those users seen away from their home tower in Figure 6, indicating Kapilbastu as a potential affected area on the 26th July.

Figure 15. Summed inactive tower hours per hour (top) and per day (bottom) relative to the data set mean.

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Figure 16. Spatial distribution of inactive towers at four time slots (0800, 1200, 1600, 2000) on 2016­07­26. Number of inactive towers present in each VDC. The cluster of inactive towers that appears in Kapilbastu district indicates potential affected area.

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Conclusion

In this report, we derive preliminary insights from mobile network data into behavioural and network response to the monsoon flooding in July 2016. We show that the onset of the monsoon triggers significant movement of people away from their home area. By analysing short­range movements we show an increase in the movement of people away from their home areas in the most affected districts. Call frequency across the network is shown to decrease during the monsoon. Anomalies in calling behaviour, in particular unusual decreases in call frequency, are shown to occur in the affected districts. We show that cell tower inactivity increases during the worst period of flooding events, and that it is possible to detect clusters of inactive towers within the affected areas over time.

Way Forward

Floods have long­term effects and the findings in this report can be used, together with other information sources, to support identification of areas which have been particularly affected, to indicate areas where further evaluation of needs are most warranted, as well as to indicate avenues of development for longer­term work on the use of operator data in response to floods.

In the short term, it is of interest to determine the rate of return of people to the affected communities once the flood waters recede and areas begin to recover. In the longer term, an assessment should be made of the potential for targeted analysis of at risk populations in areas designated as being at flood risk (GSC, 2 August 2016). ​ ​

There have been numerous reported landslides over the past month, including several where roads providing the main transport links between communities have been affected. The use of a similar targeted analysis of the landslide­affected communities could be used to assess the impact on mobility between those communities and further afield (SCN, 6 August ​ ​ 2016).

In order to better understand some of the patterns seen in the analysis, and to validate assumptions, an important next step will involve comparison with high­resolution satellite imagery of the flood area extent, in companion with rainfall data from the monsoon period.

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Appendix

A.1 Flood magnitude estimates

Figure A.1 GDACS satellite­based estimates of flood magnitude during 21 ­ 27 July 2016 (GDACS, retrieved 1 August 2016). ​ ​

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A.2 Rainfall estimates

Figure A.2 Time averaged maps of TRMM rainfall estimates during 20 ­ 25 July 2016 (top) and 25 ­ 30 July 2016 (bottom) (GIOVANNI, retrieved 5 August 2016). ​ ​

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B.1 National level population flow estimates (26­30 July 2016)

Figure B1.1. Estimated Jhapa district outflows.

Figure B1.2. Estimated Jhapa district inflows.

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Figure B1.3. Estimated Morang district outflows.

Figure B1.4. Estimated Morang district inflows.

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Figure B1.5. Estimated Sunsari district outflows.

Figure B1.6. Estimated Sunsari district inflows.

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Figure B1.7. Estimated Saptari district outflows.

Figure B1.8. Estimated Saptari district inflows.

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Figure B1.9. Estimated Nawalparasi district outflows.

Figure B1.10. Estimated Nawalparasi district inflows.

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Figure B1.11. Estimated Rupandehi district outflows.

Figure B1.12. Estimated Rupandehi district inflows.

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Figure B1.13. Estimated Kapilbastu district outflows.

Figure B1.13. Estimated Kapilbastu district inflows.

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Figure B1.14. Estimated Dang district outflows.

Figure B1.15. Estimated Dang district inflows.

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Figure B1.16. Estimated Banke district outflows.

Figure B1.17. Estimated Banke district inflows.

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Figure B1.18. Estimated Kailali district outflows.

Figure B1.19. Estimated Kailali district inflows.

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B.2 District level population flow estimates (26­30 July 2016)

Figure B2.1. Estimated Jhapa district VDC outflows.

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Figure B2.2. Estimated Jhapa district VDC inflows.

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Figure B2.3. Estimated Morang district VDC outflows.

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Figure B2.4. Estimated Morang district VDC inflows.

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Figure B2.5. Estimated Sunsari district VDC outflows.

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Figure B2.6. Estimated Sunsari district VDC inflows.

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Figure B2.7. Estimated Saptari district VDC outflows.

Figure B2.8. Estimated Saptari district VDC inflows.

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Figure B2.9. Estimated Nawalparasi district VDC outflows.

Figure B2.10. Estimated Nawalparasi district VDC inflows.

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Figure B2.11. Estimated Rupandehi district VDC outflows.

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Figure B2.13. Estimated Rupandehi district VDC inflows.

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Figure B2.14. Estimated Kapilbastu district VDC outflows.

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Figure B2.15. Estimated Kapilbastu district VDC inflows.

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Figure B2.16. Estimated Dang district VDC outflows.

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Figure B2.17. Estimated Dang district VDC inflows.

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Figure B2.18. Estimated Banke district VDC outflows.

Figure B2.19. Estimated Banke district VDC inflows.

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Figure B2.20. Estimated Kailali district VDC outflows.

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Figure B2.21. Estimated Kailali district VDC inflows.

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C.1 Calling frequency (4 April ­ 5 August 2016)

Figure C.1. Normal weekday hourly call frequency distribution.

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Figure C.2. Call frequency (top) and call frequency relative to weekday hourly mean (bottom).

Figure C.3. Daily call frequency.

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Figure C.4. Daily 2­sigma call frequency anomalies.

Figure C.5. Hourly 2­sigma call frequency anomalies (focus period).

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