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	   Dataset: CM-RWL1-O15S16

	Daily refugee arrivals and weather data
        Italy/Central Med., Oct.2015 - Sept.2016
 
	        Release Notes

	Copyright (c) 2018 by Harris V. Georgiou

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   Release:     Apr 14, 2018

   - Version:  1.1a
   - Format:   .xlsx/.csv/.txt
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This file contains important information about the
current version of the dataset package.

Downloading and using this material hints that you
accept the EULA/Terms-of-Use (please read carefully).

We welcome your comments and suggestions.

_______________________________________________
WHAT'S IN THIS PACKAGE?

-  Overview
-  Available file formats
-  Files and Datasets
-  License Agreement

_______________________________________________
OVERVIEW

Since early January 2015, Europe has witnessed an unprecedented influx of refugees
from regions of war and conflict in the Middle East, primarily Syria, Afghanistan 
and Iraq. The rapid allocation of proper resources is the most critical factor in 
the success or failure of any rescue and relief operations, especially in the "hot"
zones. In order to do so, proper tools of predictive analytics mus be available,
specifically for forecasting the intensity and, if possible, the location of the
next refugee influx waves, so that the rescue elements and the logistical support
is properly prepared beforehand.

This package contains a set of data regarding daily refugee arrivals at the general
area of the Central Mediterranean Sea, more specifically towards Italy, for the most
intense period of influx waves, from the beginning of October 2015 until the end of
September 2016 (one full year).

The sources of the data are:
1) For daily arrivals (Italy):
	UNHCR Refugees/Migrants Emergency Response (Data mashups)
	http://data2.unhcr.org/en/situations/mediterranean/location/5205
2) For weather: 
	Weather Underground Database (mashup of NOAA, aviation, local)
	https://www.wunderground.com/about/data

The datasets from (1) have already been used in various publications describing 
such predictive analytics models. Detailed description and related conclusions
can be found at:

* Harris V. Georgiou, "Identification of refugee influx patterns in Greece via 
model-theoretic analysis of daily arrivals" (9-May-2016),
https://arxiv.org/abs/1605.02784

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AVAILABLE FILE FORMATS

The datasets are available in the following formats (included):

*.xlsx	: MS-Excel/LibreOffice native spreadsheets
*.csv	: comma-separated plaintext spreadsheets
*.txt	: raw plaintext files with full column headers

These data formats are equivalent, i.e., they contain the exact same
sets of data. Normally, at least one of them should be compatible
with any major programming platform (e.g. Matlab, Octave, R) or any
native programming language for arbitrary handling (e.g. C, Java).

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FILES AND DATASETS

Root folder:	CM-RWL1-O15S16\

Dataset 1:	Arrivals\(csv,xlsx)
	 "Italy-DailyArrivals-Oct2015Sept2016.*"
		: Complete data series for refugee influx arrivals for Italy

Dataset 2:	Weather\(xlsx,txt)
	Habib Bourguiba, Tunisia
	Sfax El-Maou, Tunisia
	Lampedusa, Italy
	Luqa, Malta
	Tripoli Mitiga, Libya
		: Weather data (temp,wind,gust,w.dir,...) at local airports

The Mitiga airport station at Tripoli, Libya, is the most critical regarding
the construction of analytics and predictive modeling of the daily influx series
towards Italy. However, due to adverse conditions and lack of maintenance, 
there are several blocks of consecutive days with missing weather data. Thus,
the other four reliable weather stations in the area should be used to build
regression models for filling-in these gaps.

The satellite map(*) in the \Suppl folder shows the situation of Search & Rescue
(SAR) operations, density of shipwrecks by the end of Sept. 2015, as well as 
the location of these weather stations and how these relate spatially to the
target area of Tripoli, which is still the departing spot with the highest 
density of boats.

(*) SAR map source:  https://blamingtherescuers.org/report/


_______________________________________________
LICENSE AGREEMENT

This program was produced primarily for academic research and educational purposes. 
Downloading and using this material implies acceptance of the Creative Commons
License: Attribution-NonCommercial-ShareAlike 4.0 International (BY-NC-SA), 2016.
* http://creativecommons.org/licenses/by-nc-sa/4.0/

Copyright (c) 2018 by Harris V. Georgiou (MSc,PhD) -- http://xgeorgio.info


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