Abstract
The paper focuses on mobility patterns as experienced in Lombardy peri-urban areas to investigate whether and if so how new processes of urban regionalization, (Soja in New companion to the city. Wiley-Blackwell, Cambridge, pp 679–689, 2011; Brenner in Towards a study of planetary urbanization. Iovis Verlage, Berlin, pp 14–27, 2013a; Brenner in Implosions/explosions: towards a study of planetary urbanization. Iovis Verlage, Berlin, 2013b; Young and Keil in Cities 27:87–95, 2010) can be better understood through a reorganization of mobility practices and the emergence of new geographies of movements. Some research evidence suggests that patterns linked to mobility, consumption and lifestyles in peri-urban areas are changing quickly, challenging the way we conceptualize the relationship that European city central areas entrain with their outer areas and suburbs. In the international literature on the subject, it is widely acknowledged that changing socio-economic conditions are calling for a parallel readjustment of the approaches adopted to assess such phenomena. This to a certain extent has been done; nonetheless, knowledge in the field remains fragmented and scattered across a number of disciplinary domains. In order to make sense of the evidence generated on mobility practices in peri-urban areas, this chapter investigates the diversity of mobility practices characterizing such areas in the Milan urban region (North Italy), highlighting possible transformative scenarios.
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Notes
- 1.
See Intra Urban Dynamics in Espon FOCI Report 2010, Urban Audit.
- 2.
According to Sultana and Weber (2014) study on commuting patterns, peri-urban areas of recent residential growth in American cities have longer commuting times than other parts of the city; furthermore, and parallel with the ageing of this areas, commuting time will go through a predictable commuting transition.
- 3.
Among those, the “retrofitting” of suburbs through the densification of residential areas in and around existing or new public transport hubs.
- 4.
According to an established literature (Urry 2000; Kaufmann 2002; Cresswell 2006; Sheller and Urry 2006), presented also in the first chapter of this book, spatial mobility is a key to understand the transformations in contemporary city, as socio-spatial phenomenon capable of describing urban practices and their rhythms.
- 5.
- 6.
Based on the Regional survey (2014) in Lombardy Region, the commuter flows are 22 % of the daily mobility flows (they were 25 % in 2002). In the last decade, the occasional displacements increased (24 % in 2002; 26 % in 2014) (Regione Lombardia, 2002 and 2015).
- 7.
The daily average trips were 2.65 trips/day in 2002 and 2.5 trips/day in 2014. 91.2 % of the sample performed two displacements sequentially (Regione Lombardia, 2002 and 2015).
- 8.
Commuter flows between 2001 and 2011 boost (+9.2 %), supported by a significant increase in outflows (+19.8 %), compared with a decline of internal flows in each municipality.
- 9.
14 % of the daily commuting in Milan urban region is characterized by a travel time over 60 min (Lombardy regional average is 8.9 %). Of these flows, 75.6 % are outflows (88.2 % in Lombardy). On the contrary, the flows under 15 min of travel time significantly decreased (−42.6 %).
- 10.
This is calculated with self-containment index, as ratio between internal flows and all the employed in each municipality.
- 11.
This trend affects especially the “Pedemontana” area in the North of Milan (along Varesina and Sempione roads), and in the North-East (as Magentino and Castanese), where, in the previous decade, there were high values of integration between home and work places.
- 12.
The commuter flows attracted by Milan from the municipalities in Milan urban region were in decline between 1991 and 2001 (−12.1 %), and steady between 2001 and 2011 (−0.04 %).
- 13.
Milan catchment area—calculated as a percentage of commuter flows to Milan on total outflows generated by each municipality, excluding Milan—indicated in 2001 that 10.6 % of outflows had as destination Milan, as compared with 8.13 % in 2011.
- 14.
Processed index are: Self-containment index calculated as ratio between internal flows and the employee in each municipality; this index shows the level of integration between workplaces and home places at the municipality level. Mobility index is calculated as ratio between internal flows + outflows and the employees in each municipality. This index shows the density of displacements at the municipality level. Dependence index is calculated as ratio between the outflows and the employees in each municipality. This index shows the level of dependence for the offer of employment from other municipalities. The index of attraction is calculated as ratio between the inflows deducting the outflows and the employees in each municipality. This index shows the level of polarization and attraction for the supply of employment at municipality level (Pucci 2010).
- 15.
Industrial activities are being replaced by commercial and business settlement: big commercial centres, settlement of multinational labels and commercial parks, new shopping malls.
- 16.
Two types of mobile phone data are processed: Erlang and O/D Matrix. Erlang is a dimensionless unit used in telecommunications as a measure of the average number of concurrent contacts in a time unit. In our case, the data is a measure of mobile phone traffic intensity every 15 min. They were supplied by Telecom Italia in spatialized form; starting from the traffic recorded by each cell of the network, the provider distributed the measurements, by means of weighted interpolations, throughout a spatial tessellation of the Region in 250 × 250 m2. O/D matrix is an origin–destination datum, derived from the Call Detail Record database provided by Telecom Italia derived from localized and aggregated tracks of anonymized mobile phone users (Pucci et al. 2015).
- 17.
The processed indices are self-containment index, mobility index, dependence index, index of attraction and housing density. For an explanation, see Footnote 14 in this chapter.
- 18.
The socio-economic data processed are: inhabitants by sex and age; socio-professional profile of population; foreign population; commuter flows; employees and economical activities. In addition: land-use and buildings uses, age of the buildings.
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Pucci, P. (2016). Mobility Practices in Peri-Urban Areas: Understanding Processes of Urban Regionalization in Milan Urban Region. In: Pucci, P., Colleoni, M. (eds) Understanding Mobilities for Designing Contemporary Cities. Research for Development. Springer, Cham. https://doi.org/10.1007/978-3-319-22578-4_13
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