Motives to flee: modeling Syrian refugee crisis

Ahmed Alhanaee*, Denes Csala*

* Masdar Institute of Science and Technology

Abstract: In this paper we analyze the Syrian refugee flow with the aim of uncovering the refugees’ moral

priority system when choosing asylum. Due to space and time constraints we limit our analysis to the ~ 1

million Syrians who sought asylum in Lebanon. Using regression analysis on the destination cities’ amenities,

we create a priority list of basic needs for refugees, broken down by gender and age groups. Furthermore,

using the gravity migration model, we create a detailed flow matrix for the source locations in Syria and the

destination locations in Lebanon. Using Facebook’s Graph-search algorithm and publicly shared information

about hometown and current location, we try to recreate the results of the gravity model – with limited success.

I. INTRODUCTION

The long-term goal of this project is to uncover motives for the people for moving one place another. Due to the ongoing crisis in Syria since 2011, people started fleeing Syria to neighboring countries such as Iraq, Jordan, Egypt, Lebanon and Turkey within few years Syrian refugees are now scattered all over the world. 2015’s European mainstream media voices were loud of Syrian (and other) refugee flows. However, this phenomenon caused perhaps the starkest division of European right and left ideological sides so far in the history of the Union, with the axial question posed by Europeans being the motivation of people fueling what the mainstream media calls the largest migration postbellum. This strongly divides the European nations referring to the flow of people as refugees and migrants, terms sometimes used interchangeably but at other times as a clear distinguishment between actual war refugees and economic migrants.

Although it seems obvious why people flee, people didn’t leave just for one event. Some run for their life, and some flee to for better apportion ties somewhere else - and different factors. Safety of the family and better life are at the center of this action. We see some people taking extreme course of action in their trip that caused many deaths of their loved ones.

Moreover, people’s motives start to change over time and maybe distance from home location. for example many people left home mainly due fear of death, while some leave due to shortage of resources (water, electricity, sewerage, ) and disruption of services (education, health, ...) or due to lifestyle. Once people reach a safe place they ideal place changes. Now they are looking for with better education for their kids, better job opportunities, or better health or shelter. Some motive might even get more complex and may see returnees. It is also important to note that Syrians are in fact barely even the single largest group of people knocking on the doors of Europe now – being matched in numbers by Afghanis, also fleeing from conflict and havoc. However, unquestionably under the umbrella of the Syrian refugee crisis, many other, previously smaller flows have been reactivated and intensified

from East and West Africa, Central Asia, as well as non-EU member Balkan states.

Throughout this project we try to illustrate the demographical dynamics of the Syrian refugee flows. Due to space and time limitations we try to create a global model, but we concentrate and be collecting data about people in camps. Also finding different paths people take to reach their destination. Eventually our aim is to build a mathematical model for the people’s migration patterns and course of their trip, possibly dynamically highlighting and categorizing the motivation triggers behind their decision to undertake such an endeavor.

Our final goal is to understand the motives of the people for the migration in hope of better utilization of the available resources to provide the best chance of integration and conflict management. We hope that our work can reduce fatality and injuries along their journey. And maybe we can help provide solution that eliminate or shorten the trip.

II. LIMITATION AND ASSUMPTIONS

There are many technical issues related to tackling this problem: from getting the data to understanding real motives and knowing what constraints limit the refugees’ movement.

Geography

Because of time and space limitations of this project, we constrained this analysis to the ~ 1 million refugees who fled from Syria and resettled in Lebanon. This choice, however, depending on data availability, does not set a boundary for our analysis and it easily is scalable to other locations as well.

Data

There are two types of data that the main data provider for this work, the United Nations Higher Commissioner for Refugees (UNHCR):

• Aggregate yearly datasets on number of people divided by major refugee status types

• High granularity, potentially weekly data, reported by the UNHCR camp management sites in the adjacent countries to the Syrian conflict (Jordan, Lebanon, Turkey, Iraq, Syria, Egypt)

Data available in UNHCR camp directorates are not in the same format. First, getting data from UNHCR is not easy since their data is saved in PDF formats and we’ve been trying to contact them directly to get better data. Also, each country collect data in their own way. Some collect granular data up to the individuals. While others just collect data for camps or for cities.

Different namings for refugees exist in different jurisdictions. In neighboring countries of Syria, since it is administrated by UNHCR, refugees are similar in status, but upon going to Europe they are called asylum seeker which seems to differ from country to country.

For this project we are relying mainly on United Nations Higher Commission for Refugees which operate in most countries around the world, and we specifically used the weekly reports showing the status of refugees in Lebanon. This status includes the new registered refugees per district in Lebanon. Also it includes number of households and number of refuges awaiting registration. On the national level we got information about origin of Refugees (city in Syria), and also we got gender and age groups of the Syrian refugees.We decided to take Lebanon for our experiment, because it has complete recent data. Granularity level is good enough to produce good result. However we had to do data entry for over 100 PDF documents into excel sheet. Our data set contains over 70 weeks or Syrian refugees report from mid-2013 to mid-2015 in 25 Lebanese districts originating from 14 Syrian districts. Another data we used is information about the organizations working in Lebanon to support the refugees. These organization were categorized into Education, Food, Health, Livelihood, Protection, Housing, Social cohesion, Water and Basic needs. We use this as an indication for the situation of in each district. In addition, we are using data collection from Google for location data. For district information such as population, area, unemployment rate, we used Lebanese official sources. Data analytics mainly done in R language.

Model

The other challenging part of our project is choosing and applying and perhaps modifying the right mathematical migration model. There are many different ways to model human migration, and each has its own pros and cons. We will be sampling the most important of these in the literature review section of this proposal.

III. BACKGROUND

In this part we will cover topics related to migration as well previous refugee crises. Since most migration models include refugees as a particular form of human migration under some external pressuring factors, we will focus on the migration literature.

Modern migration theory intensified in the 1970s as a result of the involvement in far-eastern conflicts of the United States,

but it really took off with the fall of communism in Europe in the end of the 80s and early 90s. The conflicts in Africa and as a result the intensifying refugee flow to Europe in the 2000s also prompted researchers to look more deeply into the root causes of not only migration but also social integration upon arrival.

Perhaps the most prominent mathematical model that is widely used for many social phenomena, including migration is the gravity law (Zipf, 1946). Yet there are other models and theories that developed to model the pattern of migration.

Revenstein “law of Migration”: in 1880, Ernst Georg Ravenstein published “The law of migration in the journal of the Statistical Society, which established the theory of human migration that sill forms the basis for modern migration theories. It considered the implication of distance and type of migrant.

Lee (Push-Pull) model: is a model that explains the decision of migration to based on four factor, characteristics of Origin, characteristics of Destination, Characteristics, Nature of intervening Obstacles, and Nature of the people.

Gravity migration model (Karemera, Oguledo, & Davis, 2000) is derived from Newton’s law of gravity. It measures the degree of interaction between two places. And it is based upon the idea that as the importance of one or both of the location increases there will also be increase in the movement between them. This model have been widely used in the empirical analysis of migration due to their relatively good forecasting performance. But since it is based observations, it is considered that it cannot be confirmed scientifically.

The spatial interaction model is a common deterministic model used in migration analysis based on Gravity model. Where the migration between to origin i and destination d is proportional to the product of populations at origin and destination and inversely proportional to the distance between them.

(Kunz, 1973) created the first universal topology of characterizing migrations and a seminal paper by (Simini, Gonzalez, Maritan, & Barabasi, 2012) combined the lessons from scale-´ free networks with previous migration models to yield a unified model of migration, suggesting that the gravity model combined with the spatial model can give an accurate model of migration and indicate different ‘threshold levels’ at which humans are triggered to move. Studying the interaction of two population groups competing for one patch of land, (Auger & Poggiale, 1996) found that migration is much faster social phenomenon that population growth and people would have and a particular choice of density dependent migrations leads to an aggregated competition model. In an attempt to view migration a lifecycle process, (Ryan, Dooley, & Benson, 2008) propose a resource-based model for migration, but they specify that the resources in this case must be understood as those fulfilling the needs of individuals – and they might change over time so they must be monitored before, during and after migration. Already in (Borjas, 1989) postulated that migration is an economically driven phenomenon and can be explained purely by the laws of markets, attributing a net economic loss to the worsening conditions that would trigger the migration and he also analyzed the impact of migrants on target countries’ economies – applied to Vietnamese refugees in the US by

(Montero, 1979) – while (Boyd, 1989) characterized migration mainly as a social phenomenon, driven by family ties.

One of the most striking characteristics if the current Syrian refugee crisis is the seemingly dormant state for 4 years (due to being largely underreported by the mainstream media and people fleeing at comparably constant rates to today’s values into neighboring countries) and then a sudden intensification of flows towards Europe in the past year. (Zolberg, 1989) suggested that due to globalization and economic integration, labor outsourcing, migration will intensify in the next few decades – a prediction that what spot on even without foreseeing the unpredictable social catastrophes in the Middle East or Africa (Neumayer, 2005).

With the advent of many international organizations getting well-funded statistics departments, there was a surge in the need of characterizing data streams. Therefore, many different definitions of a what constitute of a refugee and what constitute a migrant appeared, oftentimes with the sole purpose of presenting the refugee numbers in a ‘better light’ (Zetter, 2007).

IV. DATA

As the refugee crisis is a complex social phenomenon, we can categorize the data sources into three groups:

• Social media data

◦ Existing social media aggregator streams

◦ liveuamap (http://syria.liveuamap.com/), mining Twitter, YouTube and social media accounts of large news portals in real-time for conflict and refugees crisis related posts

◦ Location information embedded in Facebook profiles, Tweets and Instagram photos

From the social media sources, we use our own Facebook-harvester for demographic data profiling.

• Semi-official and highly embedded data

◦ NGOs: There are many NGOs are collecting about refugees trying to get into Europe. For example, number of boat crossing, people rescued, and fatalities. The problem with this is that they do not have a unified reporting system and often the data is reported in sources that hard to mine, such as blog posts or infographics.

◦ News aggregator streams, such as GDELT present

a very rich but hard to mine data as well.

• Official data

◦ UNHCR: They have great amount of information related to the Syrian refugee since they control and administer most refugee camps in Egypt, Jordan, Iraq, Lebanon and Turkey. They have got weekly reports per camp on:

Number of households

Population,

Demographics

Rates of change of the above.

1 Image source: http://www.rajivvij.com/2008/09/maslows-hierarchy-revisitedthe-

eastern.html

They also have data about the health situation, educational situation, and financial situation of these camps. The problem of these data is that it is not organized and stored in PDF format. Therefore we need to do some kind of scraping to yield a good data set. We have been trying to contact UNHCR here in Abu Dhabi and Jordan and Lebanon. With no luck on getting cleaner data.

◦ UN and World Bank statistics to understand the pre-crisis Syrian society

◦ EUROSTAT: Euro statistics got information about Asylum seeker in Europe, on a monthly resolution with some demographic data included

From the official sources, we use our scraped data from the weekly PDF reports by UNHCR for the Syrian refugees in Lebanon.

V. DATA ANALYSIS

Exploratory analysis

In this project we are trying to understand what motivates people to move from one place to another. This is obvious for moving from or fleeing Syria to other countries, which is to flee war areas and to a safer place. But once people get out of the country, we would like to know what they looking for or what is a suitable place for them to resettle. It could be better work opportunities, or people go to bigger cities no matter what. Or it could be some other criteria such as protection, education, basic needs… once we get better understanding of this we can propose better solution for refugees crisis. A tempting assumption is to use Maslov’s (1943) pyramid of needs (Figure. 1):

Fig. 1. Maslov’s model on the hierarchy of needs1

Our initial findings were that the increase and decline of refugees depend on age group, gender or other events. For example in October 2014, refugees were refused admission in Lebanon and some other countries, and you can see it in Figure 2. The results were not flat but decreasing. This indicates that

refugees are exiting Lebanon either to other countries such as Turkey or Europe or – going back to Syria. This information is important to understand why people. And how long they stay in one country.

Fig. 2. Number of refugees per age group from 2013 to 2015

Another finding is related to the deaths caused by cold weather around the cold months of December and January. The weather is affecting the age group 0-4 years as show in Figure 3.

Fig. 3. Number of refugees by age group unstacked. Notice the children deaths in cold months

The finding itself is not new. However the ability to quantify it and the ability to relate to the driver that forces people to move and how much people are willing to sacrifice in their endeavor to reach safer places is. Unfortunately the age group is available

2 Distance calculated on spatial distance and not based on shortest driving path.

Therefore a city like Zahle could be closer than a city like Baalbek.

on country level and not on district level. Getting more detailed information about the age groups – or perhaps improving the gravity model and the social media-driven model shown in the latter parts of this report – will help us understand the extent of the tragedy and support us in providing a better solution to the refugee’s crisis as well as understanding how the work of the aid organizations is the most effective in reducing the death toll.

Fig. 4. Gender ratio over years

Another finding was knowing the number adult males lost in the war in Syria. In many places the number of females is greater than the number of male. And see this in Figure 4 could be normal. Whoever seeing the decline of adult male population (age group 18-59) over the past few years wouldn’t be abnormal – as it is alarmingly expected. The further the crisis continues, the more difficult will be for people to return their normal pre-war rates.

Regression analysis

Our next task is to find how people migrate from one place to the other and to find what motivates them to go to city a rather than city b. Finding these motives can be difficult, let alone ranking them. To find the key motives, we start with skimming our data availability. Our first intuition was people will prefer a city closer to Syria or big cities with more population – exactly as per the theory outlined by the gravity migration model. However looking at number of new refugees compared to the population and distance 2 , the correlation between distance and refugees increase was negative and close to zero. But taking the population of the destination as an attractor the tests show accept the alternative hypothesis that

population has positive effect on the number of refugees coming to the city.

Fig. 5. Distance and population effect on number of

refugees coming to a certain city. Circle size is the based on the

number of refugees from Syria arriving to a certain city.

Distance is measured from the geocenter of the country of Syria.

Next we look at set of other attractors based on the number of organizations working in each sector – by fitting and ordinary least squares regression model to the number of refugees as the dependent variable, with population, education, protection, distance and refugee increase as explanatory variables. Our finding shows great correlation between protection and number of refugees which means that refugees prefer going to safer cities even if it is far.

Fig. 6. Correlation matrix between the main attractors. On

the X axis of the plots, the explanatory variables are

represented, by city. The values for education and protection

represent the number of organizations reported to be working

in that particular field in the target city. The other available

variables (food, health, shelter) show similar distributions to

education.

The need for protection surpasses the need for many other needs including education and food. Furthermore the results are in good match with the maslovian needs.

Fig. 7. Protection, explained as the number of organizations

working in this field, and population effect on number of

refugees coming to a certain city. Circle size is the based on the

number of refugees from Syria arriving to a certain city.

Distance is measured from the geocenter of the country of Syria.

Gravity migration model

One of the problems of collective refugee data reports is the lack of origin-destination breakdown. Usually data exists per destination, but source country or source city but destination country. The gravity model can yield a source-destination matrix. Here we use the gravity model form as described by (Simini, Gonzalez, Maritan, & Barabasi, 2012).

The gravity law assumes that the average number of

travelers between two locations 𝑇𝑖𝑗𝐺𝑀 , can be expressed as a

function of the two populations and the distance (Fig. 8.) as

𝑇𝑖𝑗𝐺𝑀 = 𝐶

𝑚𝑗𝛼∙𝑛𝑖

𝛽

𝑟𝑖𝑗𝛾 . Taking the logarithm on both sides we obtain

ln(𝑇𝑖𝑗𝐺𝑀) = ln(𝐶) + 𝛼 ln(𝑚𝑗) + 𝛽 ln(𝑛𝑖) − 𝛾ln(𝑟𝑖𝑗)

Then, using ln(𝑇𝑖𝑗𝐷𝐴𝑇𝐴) as the known data, we can estimate

the values of ln(𝐶) , 𝛼, 𝛽, 𝛾 through a least-squares regression analysis.

In the implementation of the gravity model to the available data of Syrian refugees in Lebanon, we take the following approach:

1. We assume that the distribution of populations in target locations in Lebanon follows a distribution proportional with the rank-size rule3.

2. We estimate α, β, γ and C beta using a separate regression for each available flow of new refugees for every week of data (71 weeks in total)

3. We obtain the final regressions parameters by averaging the parameters over all weeks (average model)

4. As a backup, we estimate α, β, γ and C using the entire time series (total model)

5. We recalculate the refugee flows using the fitted parameters. We are interested in the latest available data in particular, in order to be able to compare to social media values later on. The result is a source-target matrix of Syrian and Lebanese locations.

6. We compare the results to social media data, mined through Facebook’s Graph Search (a similar matrix)

The parameters obtained after steps 3 and 4 are:

α β γ C

Average Value 0.65 0.46 0.70 0.87

σ 0.19 0.25 0.28 0.05

Total Value 0.35 0.66 0.62 0.89

σ 0.35 0.36 0.71 0.13

Table 1. Regression estimates of gravity model parameters

Using a geocoder service 4 , we created the necessary distance pairs between the Syrian and Lebanese locations.

Fig.8. Distance pairs between Syrian and Lebanese locations. Great circle5 distance, measured in kilometers.

3 A scale-free power law, often observed for population distributions

https://en.wikipedia.org/wiki/Rank-size_distribution

The resulting source-destination matrix contains the number of people that is predicted by the gravity model to flow from each Syrian location to each Lebanese location. These are cumulative numbers as of our latest available data week, 19/03/2015. It shows that the largest source locations are the largest cities of Syria, Homs, Damascus, Dar’a and Aleppo. The largest receiving locations are Akkar, Zahle and Baalbek by far – interestingly not the largest cities of Lebanon. The largest people flow is by far the Homs-Akkar (Fig. 9.)

Fig.9.Refugee source-destination matrix based on the gravity migration model. Numbers in the matrix represent thousands

of people fleeing from a location on the X axis to a location on the Y axis. Locations are usually cities, but at the destination

can represent refugee camps as well.

Assessing the accuracy of the gravity model is not an easy task and it requires the use of a multi-tier statistical metric. For this report, we will confine to reporting the absolute and the percentage deviation of the numbers, as we judge that they have enough explanatory power.

We will have to present the deviation for the Syrian (Fig. 10) and Lebanese (Fig. 11) cities separately. The gravity model matches the reported data surprisingly well – only performing poor for the smaller flaws, a known issue, as pointed out in the SM of (Simini, Gonzalez, Maritan, & Barabasi, 2012). In the large flows, the gravity model vastly underestimates the flows of Aleppo – quite probably due to it being affected stronger-than-average. This is exactly the kind of metric that can be used to distinguish between willing migration and forced migration – fleeing. For the case of the target cities in Lebanon, the model predicts the top 10 cities usually within a 30% error margin.

4 Pygeocoder https://pypi.python.org/pypi/pygeocoder/1.1.4 5 https://en.wikipedia.org/wiki/Great_circle

Fig.10. Gravity migration model deviation in absolute and percentage terms from the reported values. This present the

data for Syrian cities, corresponding to the X axis on Figure 9.

Fig.11. Gravity migration model deviation in absolute and percentage terms from the reported values. This presents the data for Lebanese cities, corresponding to the Y axis on Figure 9.

In order to be able to prepare with social media data in the next section, we plot the distribution of the destination locations in Lebanon, for each source location in Syria, as predicted by the gravity model (Fig. 12).

Fig.12. Distribution of the people flow to destination locations in Lebanon, for each source location in Syria, as predicted by the gravity model. Distributions are measured by people flow.

Social media model

We have decided to validate or at least contrast the findings of the gravity model with a qualitatively completely different data – from social media. The advantage of this is that we will be able to use self-reported rather than collected data to test the validity of the gravity migration model.

In Facebook, the user has the option of specifying the hometown and the current city. As per Facebook’s default user privacy policy, this data is public. Then using Facebook’s Graph Search algorithm we can generate a list of people who were born a certain location from Syria (from the list used at the previous analysis) and currently live at a target location in Lebanon. Such a search is constructed as https://www.facebook.com/search/people/?q=people%20from%20homs%20in%20beirut and it is illustrated in Figure 13.

Fig.13. Snippet of browser results returned by Facebook Graph Search for the query “people from Homs in Beirut”.

While this toll can be very powerful, Facebook restricts its use as an API, therefore a browser automation was necessary to collect the relevant data. We achieved this using a Microsoft Excel macro written in VBA, automating the Internet Explorer browser. On a computer with 4GB of RAM, we could load the first 1800-2000 hits, until the memory got full – but most of the time this was not necessary as the search was exhaustive after a few hundred hits. The HTML source of these findings was later processed in Python to yield the Facebook version of the distribution of the source-target location pairs (Fig. 14).

Fig.14. Distribution of the people flow to destination locations in Lebanon, for each source location in Syria, as returned by

Facebook. Distributions are measured by people flow.

Of course, there are several limitations to the Facebook-model.

It does not subtract the diaspora already living in Lebanon before the crisis

It only considers public Facebook profiles

It uses data only from the Facebook-population

Hometown and current location are self-declared fields on Facebook

Declared locations are often mistyped

We only used the English denomination, while many places names can be in native Arabic

However, upon visual inspection, we can see that there is some correlation between the results of the gravity and Facebook models, this is only a partial match for most of the distributions. The large cities have a better fit, probably because of the largest Facebook user base. Because of the poor quality of similarity, we decide not to include comparative statistics in this section. Although it is important to point out that for the large cities of Homs, Damascus and Aleppo, the two distributions match remarkably well – therefore hinting that this might be just a data availability problem on the Facebook side. But, we must also not forget that the Facebook data – where available in abundance – probably paints a picture closer to reality than the one provided by the gravity model, as it is self-reported. Also, the errors of the gravity model were found to be quite high for the locations with smaller people flows.

Both of models confirm that the people flows yield heavy-tail distribution, usually emerging from a power law. This is known phenomenon in population distributions and it is referred to as the city rank-size rule, a derivation of Zipf’s law (1946).

VI. CONCLUSIONS

Our goal for this paper was to model migration of Syrian refugees to target location in Lebanon, in order to better understand the reason and motive for each source location in Syria for the refugees. We found that these motives change with distance from the place of origin and depends on many factors, economic status of the current location, health, education, family size and ages. However, the strongest correlation was discovered to be protection – understandably explained by the war situation at the source.

We have fitted a gravity migration model to the UNHCR data and contrasted this with data obtained through social media harvesting from Facebook. We have found a good correlation of the two for large cities and pointed out that the distribution of destinations follows a power law, under both models.

Future work

There are several directions this work can be expanded into in the future. By adjusting the analysis for the data of another country, we can make a step further towards the generalization of the patterns found. Also, with a more detailed regression analysis we can expand the need priorities to further cover the maslovian needs.

With the implemented Facebook data-harvester it is possible to map the existing Syrian diaspora, as well as its demographics, and subsequently, upon comparing this with pre-conflict census data we can gain a lot of insight into motives to flee – as the search results information blocks oftentimes contain data about the person’s job, education and marital status as well.

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