Matrici del pendolarismo
Italian
Italy, Rome
del lavoro

数据描述

Matrici del pendolarismo

The dataset, known as the Commuting Matrix 2011 (Matrice del pendolarismo), contains origin-destination data on daily commuting for work or study reasons, based on the population residing in households or communal living arrangements as recorded by the 15th General Population Census on October 9, 2011. It details the number of people moving between or within municipalities, classified by the reason for travel, sex, mode of transport used, departure time slot, and trip duration. The baseline consists of 28,871,447 individuals who declared traveling daily from their residence to their habitual study or work location. A methodological document accompanying the matrix explains its data structure and provides guidance on variables sampled by survey, such as transport mode and travel duration, while additional classifications and questionnaires are included to aid correct data interpretation. This matrix was specifically utilized to define the 2011 local labor systems.

www.istat.it
IP: 193.204.90.6
访问数据源
加载中...

相关论文

3

Transferable human mobility network reconstruction with neuroGravity

Jinming YangShaoyu HuangZongyuan HuangYaohui JinXiaokang Yang7
Nature Computational Science
2026
2026/6/12
00 p.1-12
Accurate modeling of human mobility is critical for tackling urban planning and public health challenges. In undeveloped regions, the absence of comprehensive travel surveys necessitates reconstructing mobility networks from publicly available data. Here we develop neuroGravity, a physics-informed d...
Computational scienceGeographySociety
10.1038/S43588-026-01003-Y
ISSN:2662-8457

The geography of COVID-19 spread in Italy and implications for the relaxation of confinement measures

Enrico BertuzzoLorenzo MariDamiano PasettoStefano MiccoliRenato Casagrandi7
Nature Communications
2020
2020/8/26
Vol.11 No.1 p.1-11
The pressing need to restart socioeconomic activities locked-down to control the spread of SARS-CoV-2 in Italy must be coupled with effective methodologies to selectively relax containment measures. Here we employ a spatially explicit model, properly attentive to the role of inapparent infections, c...
Computational modelsEpidemiologyViral infection
10.1038/S41467-020-18050-2
ISSN:2041-1723

The epidemicity index of recurrent SARS-CoV-2 infections

Lorenzo MariRenato CasagrandiEnrico BertuzzoDamiano PasettoStefano Miccoli7
Nature Communications
2021
2021/5/12
Vol.12 No.1 p.1-12
Several indices can predict the long-term fate of emerging infectious diseases and the effect of their containment measures, including a variety of reproduction numbers (e.g. $${{\mathcal{R}}}_{0}$$ ). Other indices evaluate the potential for transient increases of epidemics eventually doomed to dis...
Applied mathematicsComputational modelsEcological epidemiologyViral infection
10.1038/S41467-021-22878-7
ISSN:2041-1723