05-summary.Rmd

# Limitations and Future Directions {#limitations}

The utility-based accessibility metrics we present and apply in this paper are
evaluated from a discrete choice model estimated on simulated decision makers
constructed from a third-party passive origin-destination matrix. This
methodological choice has some strengths: Foremost among these is the ability to
readily and affordably construct a large dataset on an infrequent trip purpose.
Most destination choice and activity location models are estimated on
small-sample household travel surveys. Securing sufficient responses to estimate
a rich behavioral model on a trip purpose as infrequent as parks has proven
prohibitively expensive outside of extensive research activities [e.g.,
@Kaczynski2016]. Using passive data sets to increase the effective sampling rate
possible in a discrete choice model is a potentially powerful strategy, and its
application here is an important contribution of our work.

At the same time, passive data sets available from commercial providers do not
reveal any details about the specific trip makers beyond what can be learned
from their residence block group. In this research we were able to determine
whether a device resided in a block group with a high proportion of low-income
households, but could not have confidence that a particular device belonged to a
member of such a household. Similarly, there is no information on what kind of
trip the device-holder actually accomplished at each park. These limitations
combined mean that it would likely be infeasible to directly observe devices
that traveled to the converted streets during the COVID-19 lockdowns. The ideal
dataset for estimating individual park activity location choices generally and
in special situations remains a high-quality, large-sample household survey of
real individuals.

The individual-level demographic data would also be valuable in understanding
more clearly the observed heterogeneity in response among different income or
ethnic groups.  The trends and correlations revealed in the presented models may
reflect situational inequities rather than true preferences. For example, the
distinct observed parameters on size and distance for block groups with high
minority populations may indicate that areas with large minority populations
tend to have smaller parks that are more geographically distributed relative to
other areas of the region. This interpretation could also explain some of the
non-intuitive response observed in our models, especially in regards to
playgrounds.

We limited our analysis to home locations and parks in Alameda County,
California. It is possible that some Alameda residents visit parks in
neighboring counties, just as it is possible that parks in Alameda County
attract trips from outside the county borders. This is most likely for block
groups and parks on the north and south borders of the county. The scope of this
analysis was determined by the passive data set available for the research, but
the county boundaries are not a general requirement for all studies of this
kind.

The distance to a park was represented in this study using a walk network
retrieved from the OpenStreetMap project. Though perhaps superior to a Euclidean
distance, this measure still has many limitations. First, we were unable to
verify the integrity of the underlying network information; based on our prior
experience, it is likely that some broken or improperly connected links
artificially inflated the measured distance for an unknown number of park /
block group pairs. A more serious limitation, however, is that experienced
travel distances are a function of the transport mode employed by the traveler.
Using bare distances does not provide any detail on how access to parks might be
increased with improved transit service, for example. Using a mode-choice model
logsum as a multi-modal impedance term in the activity location choice model
would enable this kind of analysis.

The monetary benefits we present in this analysis are heavily dependent on
two separate assumptions. First, reasonable researchers might have selected 
different values of time or cost coefficients. Second, the decision to assign
one benefit to each household could also have been made
differently. A change in either assumption would lead to a highly different 
total benefits estimate, but it would not change the distribution of the 
benefits, which is the objective of this study. At some level, converting the
esoteric measure of choice model logsums into a unit that can be conveniently
compared against other policies is desirable to help the public and policy
makers evaluate such decisions. Further research should establish guidelines and
practices for applying accessibility logsums in monetary cost-benefit analyses.

Of course, COVID-19 led to the closure of some park facilities --- playgrounds,
pavilions, and in some cases entire parks --- that were not captured in this
analysis. These closures would lead to a decrease in the consumer surplus for
park access, which might overwhelm or at least change the distribution of
positive benefits we measured here.

# Conclusions {#conclusions}

Converting city roadways into pedestrian-oriented public spaces was in some ways
an obvious response to the COVID-19 pandemic: Vehicle traffic demand was down,
and there was a also critical need for pedestrian-oriented open spaces in many
communities. The research we present here suggests that this policy had
measurable and meaningful benefits to neighborhoods in Alameda County,
California, and that these benefits were distributed in an equitable manner in the sense that the distributin favored marginalized populations. The total benefit to households in the community is estimated
at approximately \$1 million with disproportionately high benefits going to
Black, Hispanic and low-income neighborhoods. There is, however, a
disproportionately low benefit to neighborhoods with high Asian populations that
might be addressed were the policy to continue, be repeated, or made permanent
in some way.

In estimating these benefits, we applied an emerging technique to estimate park
choice preferences and utility from passive mobile device data. This technique
allowed a more nuanced measure of access that allowed us to consider the
converted streets as providing quantitatively different amenities than other city
parks. A policy of permanently closing these streets to vehicle traffic may or
may not have negative effects on transportation access that would need to be
considered against the benefits we measured in this research. But utility-based
access measures provide a mechanism to weigh the benefits of access against the
costs of travel in a theoretically coherent manner. Adopting such flexible
methods of measuring access will help transportation and land use planners
better understand the nuances and tradeoffs inherent in a wide range of policy
proposals.



# Acknowledgments {-}

We are grateful for the comments of two anonymous reviewers. Graphics and tables
were developed using several open-source R packages [@ggspatial; @modelsummary;
@wesanderson].