 convenience stores that are also electronics stores), and some places may have labels that are incorrect (e.g. a park field labeled as a gym). Additionally, the dataset is not dynamic and some places are mislabeled as operational when they are closed (especially given widespread closures during the COVID-19 pandemic). Nevertheless, the dataset is widely thought to have more extensive coverage and higher accuracy than related datasets (e.g. OpenStreetMaps, Foursquare) \cite{safegraph,comparison}.

In a setting where many businesses do not have business licenses and so are not registered with official directory of establishments \cite{straulino2021uncovering}, the Google Places dataset has clear advantages over the official registry (i.e. the directory of establishments held by the local chamber of commerce). For example, just 1165 firms are registered as retail firms not at a residential address (sector: ``Retail trade in non-specialized stores" (CIIU 471)), yet there are 8,240 convenience stores in the Places database within the Bogot\'a municipality. For this reasons, we consider the Places dataset to be more representative than official data at the local level of commercial business activity (``street commerce"). 

\subsection{Socioeconomic stratum}
In order to proxy for socio-economic status, we use granular data on socio-economic `strata' \cite{DANE} constructed by the national government via surveys of urban residential dwelling conditions. This data is provided in the form of survey indices for each census block, with values ranging from 1 to 6: 1 denotes very poor quality housing (often informal), a majority of residents live in mid-stratum areas 2-4, while 5 and 6 include the most expensive areas of the city. Stratum tends to be highly spatially autocorrelated, owing to socio-economic segregation \cite{morales2008quality}. Strata are assigned to census blocks, but we use geographically weighted spatial averaging to estimate average strata at the zone level.

\subsection{Mobility survey}
In order to study the effects of amenities on residents' trip behaviour, we utilise the 2019 Encuesta de Movilidad survey which is a resident/household-level survey of 21,208 respondent households throughout the metropolitan area. The dataset covers a total of 134,498 trips and includes information on trip purpose, modality, origin/destination zone, time of day, time of week, and time elapsed. 

We use the survey data in two instances. First, we use the trip survey to estimate the spatial distribution of homes and jobs as in \cite{guzman2018accessibility}. With regards to jobs, this has an advantage over official employment statistics in that the survey data encompasses both formal and informal jobs. Because the sampling and weighting procedures may not accurately quantify the number of homes or jobs precisely at the zonal level, we aggregate these numbers to the cell level. At this level, we geolocate over 9.5 million homes and 2.5 million jobs across the study area. 

Secondly, we use the survey data to analyze effects on travel behavior for amenity-related trips originating from the respondents' home zones, focusing on trip duration and trip modality. Trip modality can broadly be distinguished as walking, cycling, driving (car/motorcycle), bus (including many different bus categories), taxi, informal transport, and a few other categories. The survey includes information related to trip purpose and mode. These purposes include work, work-related business, school/university, visiting friends/family, returning home, looking for someone, looking for a job, helping someone - and what we consider as amenity-related travel: receiving health attention, looking for something, going out to eat/drink, shopping, errands, recreation/culture, religious activities, and exercise/sports. Finally, because the survey is matched to respondent/household level characteristics, we can control for individual-level demographic factors, including socioeconomic stratum, age, gender, and whether the household has a motorized vehicle. 

\begin{figure}
    \includegraphics[width=\textwidth]{new_figs/dv_summary.pdf}
    \centering
    \caption{{\bf Summary of dependent trip behaviour variables.} Subfigure A displays the distribution of time per trip across the three types of modes we study. For instance, about half of car trips take less than or equal to half an hour, while only about $20\%$ of bus trips are this short. Subfigure B displays the distribution of trip modes for the particular non-work trip purposes we study.}
    \label{figdv}
\end{figure}

\section{Discussion}

In this paper, we investigate the role of socio-economic status in the relationship between local amenities and mobility patterns. In particular, we leverage data from a large internet repository of location data on amenities, and use this to define an amenity accessibility measure. Our results show that on the whole, having more amenities nearby leads to a modest reduction in car and bus travel distance and increases residents' tendency to walk, in line with other recent work on amenities in Sweden \cite{haugen2013divergent, ellder2020kind, ellder2020local}. However, SES seems to modulate this relationship in an important and hitherto unrecognized manner. Specifically, we find that the presence of amenities increases walking trip propensity and decreases car travel duration for the wealthiest group only. Effects are either insignificant or very small in magnitude for the lower and middle SES groups. On the other hand, amenities tend to reduce bus trip duration (which has been studied less in this literature) for just the low and middle income SES groups.

We suspect that different socioeconomic groups make travel mode decisions based on different factors, particularly in the case of walking. In general, low income people are thought to choose walking for different reasons than wealthy people \cite{boarnet2001travel, mondschein2018persistent}. For example, lower income people may see the cost of driving or taking a bus as a more costly alternative, and factor this cost more heavily into their decision to walk. A longer walking trip may be more preferable than paying a bus fare or paying for gas. By contrast, this cost weighs less heavily on the wealthy, and their decision to walk may depend less on cost and more on proximity. Wealthier suburban residents - with relatively few amenities nearby - can much more efficiently get what they need by driving than by walking. By contrast, inner-city high SES residents live in amenity-rich ``walkable'' neighbourhoods and are hence much likelier to walk. This narrative is consistent with our results which highlight the much stronger effect of amenities on the tendency of the wealthy to walk relative to the low and middle SES groups.

Turning to car trips, we also found that wealthier residents reduced the duration of their trips in response to local amenities. We suspect this result may have a related explanation to that of walking. That is, high SES motorists may seek primarily to minimise time in traffic, whereas lower SES motorists may have have other motivations. In particular, we suspect that this result may be related to a mismatch between amenities in lower SES areas and the needs of the people living there. Our measure only captures average accessibility across amenities, but low income people may be more separated from the specific amenities that they need or wish to access. For instance, previous research has shown that poorer neighbourhoods have lower accessibility to banking and more access to alternative financial institutions (e.g. payday lenders) in the US \cite{small2021banks}, so residents needing, for example, mortgage advice would need to travel further. Additionally, it is well known that poorer areas are often associated with more fast food and fewer supermarkets \cite{sharkey2009association}, and so local residents in amenity-rich areas may be travelling further for their own needs. 

Overall, our results suggest that poorer people's walking and driving behaviour seems to depend less on their local environment relative to wealthy residents. This result somewhat contrasts previous characterisations of the poor as being more connected to their local environment \cite{marquet2017local}. For instance, in the context of urban health, many studies show that the relationship between environment and health is stronger for poorer people \cite{vallee2021everyday}.

Our measure can be adapted to measure proximity to amenities at various spatial scales. A priori we might expect that closeby amenities would influence the propensity to walk whereas further away amenities might impact car trip duration. However, in our analysis we find similar behaviour with regards to spatial scale for all three modes studied. Specifically, the coefficients peak in a short range of 0.5-1km, and then typically level off. We could interpret this as suggesting, for example in the case of car trip duration, is that what matters is not so much the presence of further away amenities, but the absence of nearby amenities. This topic merits further study and perhaps more sophisticated framework for amenity accessibility that takes into account local vs regional amenities as well (similar to \cite{haugen2013divergent}).

Overall, our findings generate important nuance in the debate on the development of more walkable neighbourhoods via bringing residents closer to amenities ("15 minute cities'' or "walkable mixed use communities''). While our study is cross-sectional\footnote{Many studies suggest residential self-selection may limit the inference of cross-sectional studies on transport behaviour - however, recent work suggests it to be relatively minor in importance \cite{naess2014tempest}}, and limited to just one aspect of walkability, our results suggest that adding amenities to a low income vs. a wealthy neighbourhood may have different effects on residents' mobility behaviour. In a wealthy area, amenities alone may drive residents to walk much more, but other neighbourhood improvements may be