## 2 Materials and Methods
### Measures
Table 2 shows the conditions for categorization of the stops into the activity location types \"home\", \"other residential area\", \"residential and shopping area\", \"shopping area\", \"workplace\", \"small green area\", \"larger green area\", \"sports facilities\" and \"other\". Since we had only access to data on land use and buildings of the Netherlands, stops with their center of gravity located outside the boundaries of the Netherlands were excluded from analyses (approximately 4% of the data). For this purpose, the centers of gravity of trips were also calculated: only trips with their center of gravity within the Netherlands were included for analyses. \begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} **Activity** \\ **Locations (AL)** \\ \end{tabular} & **AND/OR** & **Classification Conditions** \\ \hline \multirow{4}{*}{Home} & \multirow{4}{*}{OR} & AL within 25 meter buffer from home address, obtained \\  & & through questionnaire. \\ \cline{1-1}  & & AL within 25 meter buffer from home address, obtained by using \\ \cline{1-1}  & & GPS-data: if the first and last coordinates of multiple days were \\ \cline{1-1}  & & identical, these coordinates were considered the home \\ \cline{1-1}  & & location—only when the home address missed or was incomplete \\ \hline \multirow{4}{*}{Other residential area} & \multirow{4}{*}{AND} & \(\text{BF}_{25}\) = residence \\ \cline{1-1}  & & AND & \(\text{BF}_{25}\) \(\
eq\) shops or foodservice industry \\ \cline{1-1}  & & AND & \(\text{LU}_{25}\) = residences \textgreater{} 70\% \\ \hline \multirow{4}{*}{Residential \& & \(\text{BF}_{25}\) = shops or foodservice industry \\ \cline{1-1}  & & AND & \(\text{BF}_{25}\) = residence \\ \cline{1-1}  & & AND & \(\text{BF}_{25}\) \(\
eq\) other functions \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Categorization of stops into various activity locations. **Background variables. ** A questionnaire was used to collect information on home address, details on the home location (e.g., having a garden), having a car, work address, health status (SF36), and socio-demographic factors. Socio-demographic factors were age, gender, education (low, middle, high), employment (_i.e.,_ yes, no), household structure (_i.e.,_ having a partner, having children), neighborhood, and ethnicity (_i.e.,_ autochthonous, western-, and non-western immigrants). Non-western immigrants are people who were born and/or of whom at least one parent was born in Turkey, an African country, a country in Latin-America, or in a country in Asia (except for Japan and Indonesia). Western immigrants are people who were born and/or of whom at least one parent was born in Japan, Indonesia, a European country (except for Turkey), a country in North-America, or a country in Oceania. **Meteorological data. ** Data on daily temperature (\({}^{\circ}\)C), sunshine (hours), and average wind speed (m/s) were obtained from Royal Dutch Meteorological Institute measurement stations in Maastricht and Rotterdam [35]. Dummy variables were created to take these variables into account in analyses. Cut-points for four equal groups were obtained using descriptive statistics in SPSS.