Organisation: Witnessism (South Korea)
Publication Date: 04/14/2016
Size of team/newsroom:small
DescriptionThis project was designed to shed light on notable incidents and cases due to migrant crisis caused by Civil War chiefly with two focuses. 1.How serious migrant situation has been ongoing and what kinds of efforts have been made international community in a balanced manner? Analysis on UNHCR’s data about refugees enabled us to see imbalanced acceptance of displaced people from 2011 until now. Especially, more than 80% of refugees have found new destinations of Turkey, Lebanon and Jordan within Middle East region, which permitted viewers to find out social and economic issues from severe government expenditure. This issue is not only related to each nation’s difficulty but also to refugees’ difficulty to keep their quality of living. However, other Gulf States of which economic standard is much higher have been reluctant to incorporate themselves into migrant crisis. 2.Which players have been inflicting threats on Syrians within civil war? It is striking fact that the largest cause of the crisis is civil war. We could visualize the relevance between each data and number of fatalities on a monthly basis and also cases of civilian damages, refugees and etc., which further facilitated how deeply certain groups have involved in each incident. The fundamental intention of visualization is supposed to raise the question for the international community which questions we should voice up to relieve migrant crisis.
What makes this project innovative? What was its impact?This project compromises innovation by looking at Syrian crisis from inside, not from outside. The first issue we would like to raise is “How do Syrian people look at this civil war?” and therefore we hope to hear from what they really want to say. It encourages us to search Syria-based data source of Syriantracker.com and timeline postings on Facebook page of Syrian Network for Human Rights. These data include reports directly posted by local residents, while data mining enables us to extract repeated patterns from 2011 until now. Collected data set facilitated analysis on the types of civilian damages and the relevance with certain groups which caused those incidents. Using more than 5,000 samplings, we could also visualize civil war situation – it allows viewers to find out in which way the number of refugees and fatalities have been fluctuating along the war.
Technologies used for this project:python/google translator API/R/D3/Jquary We activated python and R to define and analyze data with d3 visualizing tool. Files of .csv format are provided by Syrian tracker, while web crawling was applied to timeline postings of Syrian Network For Human Rights Facebook page. Since these data were not organized with a defined way, data cleansing was firstly mandated. Ahead of data cleansing, translation was first agenda that Arabic text files in high-capacity be translated through Google translator API. Afterwards, words were being sorted by mentioned frequency to accelerate process of eliminating needless words. Keywords were selected in this process so as to indicate relevant groups with civilian assaults and war. By using sampled keywords, two types of data set were designed with its relation to civil war: frequency of civil damage by sampling on a monthly basis within whole civil war period and; frequency of reports which mention both certain groups and types of damage. Based on the ratio of two data sets, it is available to show visualized chart on ongoing situation of civil war and results including numbers of refugees and mortality, and finally the relation between each type of damage and each group.
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