Compare frequency of armed conflicts with national indexes like GDP and education rate to see if there is a correlation between the two on the map.
For correlation analysis and/or trend prediction, refer to Rate-of-Change charts that compare ROC's of indexes in various countries.
The backbone of any project is always accurate and trustworthy data, and ACAP only uses reputable sources in data collection, all of which will be listed for the user's reference.
In 2016, InfoSeeking Lab students began a draft for a report that outlined the trends between armed conflicts in the Middle East in the efforts to predict how future conflicts would pan out.
Picking up a year old report outline created within the same lab, five students at the InfoSeeking Lab at Rutgers revamped the these to sketch out an idea for a full-scale database that could allow diplomats, historians, and politicians to draw their own conclusions about what factors affect war. After carefully discussing what features would be most useful to people conducting research as well as studying what other databases were lacking, the students knew their idea was something worth pursuing. In Late July, the team visited the UN and introduced their idea to the Data Analytics branch, and from there, ACAP was born.
The Kenyan general elections were scheduled to be held early August 2017, and many government and humanitarian agencies, including the United Nations, were trying to anticipate the liklihood for the elections to result in large-scale violence. So when the UN reached out to ICT4SD collaborating universities to assist them with the project, the ACAP team conducted independent research and data analytics to create a report outlining the results it found.
With the green light from the UN, the ACAP team is currently in the process of coding the collected data into a user-friendly interface. Progress can be track through our Github, where team members store all our data and platform code. In addition to computer science work, the ACAP team is also in the process of conducting historical research to give users more background on the trends our maps show.
A user can choose from a variety of variables, such as GDP, poverty rates, or education rates, and see them visualized by region as a color coded map. On top of that, the map shows the frequency of armed conflict in the regions to allow the user to find and support patterns between these variables and the commonality of war.
Check out our partially dynamic prototype to get an idea for how we bring multi-variable analysis to the user.
Jhanvi Virani is an undergraduate student at the Honors College at Rutgers University- New Brunswick. Currently pursuing majors in History and Computer Science, she enjoys extracurricular activities like being a part of the Rutgers Entrepreneurial Society, writing a hip-hop column for The Daily Targum, and working as a research assistant.
Fancy is a graduate student at Rutgers studying information technology. He worked for 4 years as Software Engineer in Fixed Income valuation at BNP Paribas ISPL in the Corporate and Investment Banking Department. He enjoys expanding his skillset and exploring the fields of Software Engineering.
Nrithya is an aspiring Data Analyst, and is currently enrolled in the Master of Information program at Rutgers University, with a concentration in data science. She was previously worked for Tata Consultancy Services Ltd., India, as a System Engineer. There she was the team lead for the Operations and Business Support Systems Lab, which focused on providing Enterprise solutions for Telecom Service Providers.
Chirag Shah is an Associate Professor of Information and Computer Science at Rutgers University. He directs the Center for Data Science and Social Systems (CDS3) and the InfoSeeking Lab. When he is not doing Data Science or Information/Computer Science stuff, he likes to go camping, hiking, and ballroom dancing.
Niranjana Ganesh is an undergraduate honors student in the Rutgers School of Engineering. She is double majoring in computer science and computer engineering. Her extracurricular pursuits include Enactus, singing, and Sanskrit.
Rajvi Mehta is an undergraduate student at Rutgers University - New Brunswick majoring in Computer Science and Math. She is involved with the Rutgers Mobile App Development Club (RuMAD) and the Rutgers Veg Society. In her free time, she like to run, sing, and play the piano.
Wei Shi is a masters student graduate from Rutgers University where she majored in Information with a concentration on data science. Before graduating in August, Wei worked as a research assistant in the Info seeking lab. With strong interests on data science and marketing, she is seeking opportunities in marketing research and analysis. In her spare time, Wei enjoys jogging, yoga and photography.
Twisha Ajwani is a graduate student at Rutgers Business School concentrating in Data Analytics. Passionate about deriving insights out of huge chunks of data and aiding strategic level business planning, she has significant research expertise in statistics, business forecasting, data visualisation and highly developed quantitative and mathematical skills. She will graduate in December 2017. During her free time, she enjoys reading books and doing yoga.
Soumik is a PhD student in the department of Library and Information Science in the School of Communication and Information, Rutgers -New Brunswick. His research interests involve information retrieval, computational linguistics and data science. In his spare time, he enjoys cycling, swimming and photography.