Table 4 Lasso regression for Italian cities. Each cell contains the coefficient estimated by Lasso regression. Statistical significance is reported as \(\mbox{(***)} = p < 0.001\), \(\mbox{(**)} = p < 0.01\), \(\mbox{(*)} = p < 0.05\), \(\mbox{(.)} = p < 0.1\)
From: Weak signals in the mobility landscape: car sharing in ten European cities
Predictors | Coefficients | ||
|---|---|---|---|
Milan | Rome | Turin | |
Total population | – | – | – |
# unmarried | – | – | – |
# married | – | – | – |
# separated | – | – | – |
# widows | – | – | – |
# divorced | – | – | – |
Age <5 | – | – | – |
Age 5–9 | – | – | – |
Age 10–14 | – | – | – |
Age 15–19 | – | – | – |
Age 20–24 | – | – | – |
Age 25–29 | – | – | – |
Age 30–34 | – | – | – |
Age 35–39 | – | – | – |
Age 40–44 | – | – | – |
Age 45–49 | – | – | – |
Age 50–54 | – | – | – |
Age 55–59 | – | – | – |
Age 60–64 | – | – | – |
Age 65–69 | – | – | – |
Age 70–74 | – | – | – |
Age >74 | – | – | – |
# with university degree | 0.22315 (***) | 0.13065 (***) | 0.22787 (***) |
# with high school degree | – | – | – |
# with middle school diploma | – | – | – |
# with primary school diploma | – | −0.10589 (.) | −0.08575 (***) |
# literate | – | – | – |
# illiterate | – | – | – |
# employed | – | – | – |
# unemployed | – | – | – |
# stay-at-home | – | – | – |
# students | – | – | – |
# other situations outside workforce | – | – | – |
# commuting inside the municipality | – | – | – |
# commuting outside the municipality | −0.1054700 (***) | −0.02632 (*) | – |
# getting money | – | – | – |
# PoIs | – | – | – |
PoIs entropy | – | 0.19928 (***) | – |
# Arts & Entertainment | – | – | – |
# College & University | – | – | 0.04625 |
# Food | – | 0.24642 (*) | – |
# Nightlife Spot | 0.2224 (**) | 0.13826 (.) | 0.28672 (***) |
# Outdoors & Recreation | 0.17047 (***) | 0.08842 | 0.04746 |
# Professional & Other Places | 0.17197 (*) | 0.26091 | 0.26261 |
# Residence | 0.12921 (**) | – | – |
# Shop & Service | – | – | – |
# Travel & Transport | – | – | 0.02488 |