Small area model-based estimators using big data sources

The timely, accurate monitoring of social indicators, such as poverty or inequality, on a fine- grained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena

Notulae to the Italian alien vascular flora: 11

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, exclusions, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates published elsewhere are provided as Suppl. material 1

The AGILE Mission

AGILE is an Italian Space Agency mission dedicated to observing the gamma-ray Universe. The AGILE's very innovative instrumentation for the first time combines a gamma-ray imager (sensitive in the energy range 30 MeV-50 GeV), a hard X-ray imager (sensitive in the range 18-60 keV), a calorimeter (sensitive in the range 350 keV-100 MeV), and an anticoincidence system. AGILE was successfully launched on 2007 April 23 from the Indian base of Sriharikota and was inserted in an equatorial orbit with very low particle background. Aims. AGILE provides crucial data for the study of active galactic nuclei, gamma-ray bursts, pulsars, unidentified gamma-ray sources, galactic compact objects, supernova remnants, TeV sources, and fundamental physics by microsecond timing. Methods. An optimal sky angular positioning (reaching 0.1 degrees in gamma- rays and 1-2 arcmin in hard X-rays) and very large fields of view (2.5 sr and 1 sr, respectively) are obtained by the use of Silicon detectors integrated in a very compact instrument. Results. AGILE surveyed the gamma- ray sky and detected many Galactic and extragalactic sources during the first months of observations. Particular emphasis is given to multifrequency observation programs of extragalactic and galactic objects. Conclusions. AGILE is a successful high-energy gamma-ray mission that reached its nominal scientific performance. The AGILE Cycle-1 pointing program started on 2007 December 1, and is open to the international community through a Guest Observer Program

Contributi per una flora vascolare di Toscana. XII (739-812)

Vengono presentate nuove località e/o conferme relative a 74 taxa specifici e sottospecifici di piante vascolari della flora vascolare to- scana, appartenenti a 69 generi e 28 famiglie: Bunium, Trinia (Apia- ceae), Nerium (Apocynaceae), Lemna (Araceae), Artemisia, Bidens, Centaurea, Crupina, Gazania, Hieracium, Rhagadiolus, Symphyotri- chum, Tagetes, Tripleurospermum (Asteraceae), Impatiens (Balsami- naceae), Anredera (Basellaceae), Cynoglottis, Phacelia (Boraginaceae), Cardamine, Diplotaxis, Hornungia (Brassicaceae), Campanula, Lobe- lia (Campanulaceae), Cerastium, Dianthus, Polycarpon, Spergularia, Stellaria (Caryophyllaceae), Commelina (Commelinaceae), Fallopia (Convolvulaceae), Sempervivum (Crassulaceae), Dryopteris (Dryopte- ridaceae), Euphorbia (Euphorbiaceae), Lathyrus, Medicago, Ononis, Trigonella (Fabaceae), Geranium (Geraniaceae), Lycopus, Stachys (Lamiaceae), Malva (Malvaceae), Anacamptis, Cephalanthera, Epi- pactis, Orchis (Orchidaceae), Linaria (Plantaginaceae), Ceratochloa, Eragrostis, Festuca, Gastridium, Hyparrhenia, Molineriella, Phalaris, Phyllostachys, Setaria, Sporobolus, Stipellula (Poaceae), Anogramma (Pteridaceae), Anemonoides, Ranunculus (Ranunculaceae), Reseda (Resedaceae), Alchemilla, Kerria, Pyracantha, Rosa, Rubus (Rosa- ceae), Galium, Valantia (Rubiaceae), Thesium (Santalaceae). Infine, viene discusso lo status di conservazione delle entità e gli eventuali vincoli di protezione dei biotopi segnalati

The use of Big Data as covariates in area level small area models

The timely, accurate monitoring of social indicators, such as poverty or inequality, at a fine grained spatial and temporal scale is a challenging task for official statistics, albeit a crucial tool for understanding social phenomena and policy making. Big Data sensed from the digital breadcrumbs that humans leave behind in their daily activities, mediated by the Information Communication Technologies, provide accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential of providing us with a novel microscope for understanding social complexity. We propose a model based area level approach that uses Big Data as auxiliary variables to estimate poverty indicators for the Local Labour Systems of the Tuscany region. This model allows us to take into account the measurement error in the auxiliary variables