Migration Analysis of the Syrian Refugee Population
Team Members: Justin Brown, Ben Diego, Marissa Ferrante, Sven Schmit, and Laura Zehender
Location: Stanford, CA
Introduction and Background
In spring of 2011, a popular uprising in Syria against the government of Bashar al-Assad, as part of the wider Middle Eastern protest movement known as the Arab Spring, grew into a full-fledged civil war. According to the United Nations High Commissioner for Refugees (UNCHR), more than 100,000 people have been killed in the Syrian conflict thus far. As a result, about one in four Syrians have left their homes, with over two million refugees fleeing into neighboring countries and more than four million internally displaced within Syria. As of October 29, 2013, over 800,000 registered Syrian refugees were residing in Lebanon and nearly 100,000 more were awaiting registration (Syria Regional Refugee, 2013).
The crisis in Syria demands global attention. It is being hailed by many as “one of the worst humanitarian crises in recent decades, if not the worst since the Balkans war and the [Rwandan genocide]” (Georgieva, 2013). The UNHCR and its partners have been working since the start of the crisis to provide humanitarian aid to displaced Syrians. In order to better understand the experience of the Syrian refugees and to identify their needed resources, the UNHCR and other agencies have created many spatio-analytical representations of Syrian populations in other countries.
Our project requires the creative integration and visualization of extant data on the crisis in Syria. In particular, it aims to analyze the spatial correlation between Syrian migration data with the cause and effect data for those migrations. We hope that our research and analysis will lead to a better understanding of the situations of Syrian refugees affected by the crisis.
What are the migration patterns of Syrian refugees into Lebanon?
For instance, do refugees from the same place in Syria travel to the same location in Lebanon?
To answer this, we will need to examine:
What factors and events have influenced these migration patterns?
Is there a noticeable change in the movements of certain demographics of refugees after a particular kind of event?
Factors we will consider include:
This project requires the compilation of both quantitative and qualitative data from across a number of sources. UNHCR has already provided our team with extensive data on registered Syrian refugees in Lebanon. This data includes the arrival dates of Syrian refugees to Lebanon, but not their travel times, methods, or start dates. This data could be important to understanding the causal factors affecting refugee trajectories (e.g., travelling by car could correlate to a greater distance travelled). According to Foreign Affairs, most refugees travel by foot (Georgieva); using a distance calculator, we will interpolate the average travel times for refugee travel to a specific destination from a point of origin. Then, using the ‘Query’ and ‘FieldCalculator’ functions in ArcGIS, we will subtract refugees’ estimated average times of travel from their recorded dates of arrival in order to determine their respective approximate start dates. These start dates could be referenced to our event data so as to determine the relationship between the date and location of an exit and the date and location of an event.
A major part of our project requires the derivation of event data tracking the dates, locations, and magnitudes (in terms of death toll) of incidents of armed conflict in Syria. We are in the process of compiling this data from literature tracking the development of the Syrian Civil War. We have set a threshold magnitude of 5,000 per incident, since incidents of lesser magnitude might be less likely to affect refugee migrations to the same extent. After our data is visualized, we will use a hot spot, or cluster, analysis to determine the areas most vulnerable to conflict and the relationship between event type and affected demographic (e.g., do families with children leave areas of conflict before single adults?).
The thematic classes of our project will be based on data drawn from a number of sources. First, we will have the refugees layers. From the dataset provided by the UNHCR, we can create a origin layer with points for each refugee’s place of origin and a destination layer with points for each place of arrival, and we can link the two through refugee identification numbers. From a base map of the governorate and country boundaries of the region we are researching, we can add demographic data for each area, such as average socioeconomic information.
Another dataset will be events; we will create a layer containing data that has attributes including the event type (such as a conflict, a change in border control policies, etc.) and the date of the event. This data will be compiled manually through research and text mining. We will also have a location layer in which we will record where there are places relevant to refugees such as refugee housing, border crossings, or food distribution sites.
Locator Map of Research Area