Introduction

• Author: Sermi, F.; Iacus, S.; Vespe, M.; Santamaria, C.; Spyratos, S.; Tarchi, D.
• Pages: 3-4

1 Introduction

In April 2020, the European Commission (EC) asked European Mobile Network Operators

(MNOs) to share fully anonymised and aggregated mobility data in order to support the fight

against COVID-19 (European Commission, 2020a,European Commission, 2020b) with data

driven evidence.

The value of mobile positioning personal data to describe human mobility has been explored

(Csáji et al., 2013) and its potential in epidemiology studies demonstrated (Wesolowski

et al., 2012, Jia et al., 2020, WU et al., 2020, Kraemer et al., 2020) in literature.

The new initiative between the Commission and the European MNOs relies on the effec-

tiveness of using fully anonymised and aggregated mobile positioning data in compliance

with ‘Guidelines on the use of location data and contact tracing tools in the context of the

COVID-19 outbreak’ by the European Data Protection Board (EDPB, 04/2020).

This work introduces an innovative way to map natural human mobility through fully anonymised

and aggregated mobile data. Maps showing natural mobility are based on the usual patterns

of citizens’ mobility and can be compared with maps of administrative areas.

Indeed, the mapping of human mobility patterns has a long tradition in settlement geog-

raphy, urban planning and policy making. The idea behind mobility patterns is the identifica-

tion of a network of aggregated inbound and outbound movements across spatial structures

for a given time scale (for example, daily, intra-weekly, seasonally, etc) according to the

scopes of their use. These patterns have been called in several ways; the followings list

includes just a few variants of the same concept:

—‘commuting regions’: the identification of relatively closed regions of daily moves

of residing population based on commuting data from censuses (Casado-Díaz, 2000,

Van der Laan, 1998).

—‘functional regions’: a tool used to target areas of specific national and European poli-

cies (OECD, 2002). There are several natural areas of application of functional regions

including employment and transportation policies, environmentally sustainable spatial

forms, reforms of administrative regions, strategic level of urban and regional plan-

ning and a wide range of geographical analyses (migration, regionalisation, settlement

system hierarchisation) (Andersen, 2002,Ball, 1980, Casado-Díaz, 2000,Van der Laan,

1998).

—‘functional urban areas’: cities with their commuting zone (Eurostat, 2106, Dijkstra

et al., 2019). They are generally identified by a densely inhabited city, together with

a less densely populated commuting zone whose labour market is highly integrated

with that of the city.

—‘overlapping functional regions’ (Killer and Axhausen, 2010).

The most common data sources for the above-mentioned studies are by far the population

censuses and ad hoc pilot surveys.

This study proposes an alternative method to define highly-interconnected spatial regions

(i.e., forming dense sub-networks); only fully-anonymised and aggregated mobility data

are used to this end. The data-driven regions identified through the proposed method are

referred to as ‘Mobility Functional Areas’ (MFA).

Although mobile data has been used in the past in a pilot-study on mobility in Estonia

(Novak et al., 2013), the present study adopts a new technique to define mobility functional

areas (MFA), which is based only on aggregated data, and extends the research to 15

European countries (14 member states: Austria, Belgium, Bulgaria, Czechia, Denmark,

Estonia, Spain, Finland, France, Greece, Croatia, Italy, Sweden, Slovenia, plus Norway).

In a policy making perspective, especially related to the COVID-19 pandemic, the insights

resulting from this analysis may help governments and authorities at various levels:

a) to limit all non-essential movements across specific geographic areas, especially in the

initial phase of a future outbreak of the virus, to limit spread while also limiting the

economic impact of such measures outside the MFA;

b) to apply different physical distancing policies in different areas, according to their

specific epidemiological situation.

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