DOI: 10.5281/zenodo.1218209
title: Cm-Rwl1-O15S16 Daily Refugee Arrivals And Weather Data, Italy/Central Med., Oct.2015 - Sept.2016

description: ========================================================

       <strong>Dataset: CM-RWL1-O15S16</strong>

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

    Copyright (c) 2018 by Harris V. Georgiou

========================================================<br>
   Release:     Apr 14, 2018

   - Version:  1.1a<br>
   - Format:   .xlsx/.csv/.txt<br>
========================================================

<br>
This file contains important information about the<br>
current version of the dataset package.

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

We welcome your comments and suggestions.

_______________________________________________<br>
WHAT'S IN THIS PACKAGE?

-  Overview<br>
-  Available file formats<br>
-  Files and Datasets<br>
-  License Agreement

_______________________________________________<br>
OVERVIEW

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

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

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

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

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

_______________________________________________<br>
AVAILABLE FILE FORMATS

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

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

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

_______________________________________________<br>
FILES AND DATASETS

Root folder:    CM-RWL1-O15S16\

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

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

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

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

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

<br>
_______________________________________________<br>
LICENSE AGREEMENT

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

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

<br>
--
description: 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) 2016 by Harris V. Georgiou (MSc,PhD) -- http://xgeorgio.info