detailed_results to
spatial_availability(), used to specify whether results
should be aggregated by origin-destination pair or by origin. When
aggregation by origin-destination pair, the output also includes the
demand, impedance and combined balancing factors used to calculate
spatial availability.concentration_index() and
theil_t().palma_ratio() and gini_index())
and poverty (fgt_poverty()).spatial_availability() and
balancing_cost().cost_to_closest() parameter n now accepts
a numeric vector, instead of being restricted to a single number.cumulative_cutoff() parameters cutoff and
travel_cost now accepts a numeric and a character vector,
respectively, instead of being restricted to a single
number/string.cumulative_interval() parameter interval
now accepts a list of numeric vectors, instead of being restricted to a
single vector.decay_stepped(), both steps and
weights can take a list of
numeric vectors as input, instead of being restricted to a
single numeric vector each).cost_to_closest() would return
NA values when filling missing ids (which should be filled
with Inf, since they cannot reach any opportunities). This
was also responsible for the warning reported in #27, which was also
fixed.The package has been to tremendous changes. Basically, there’s not a single part of it that remained untouched: documentation, vignettes, function names, parameter names, extra functionality, performance improvements, etc. While it is impossible to highlight everything that has been done, we’ll try to summary some of the key points in the following topics.
data. Now they require two input datasets:
travel_matrix and land_use_data.time_to_closest() ->
cost_to_closest()cumulative_time_cutoff() ->
cumulative_cutoff()cumulative_time_interval() ->
cumulative_interval()gravity_access() -> gravity()opportunity_col -> opportunitytravel_cost_col -> travel_costby_col -> activecost_to_closest(): n_opportunities
-> ncumulative_interval(): stat ->
summary_functionfloating_catchment_area():
population_col -> demandfloating_catchment_area(): fca_metric
-> methodactive now takes a logical, instead of a
string (which by_col previously took).cumulative_interval(): summary_function
now takes a function, instead of a string (which
stat previously took).decay_stepped().interval_increment to
cumulative_interval(), used to specify how many travel cost
units separate the cutoffs used to calculate the accessibility estimates
which will be used to calculate the summary estimate within the
specified interval.group_by
parameter, that allows accessibility estimates to be grouped by one or
more columns present in travel_matrix.cumulative_interval())
gained a fill_missing_ids parameter, that includes in the
results origins whose accessibility would be 0 but, due to some commonly
overlooked implementation details, are usually left out from the output.
cumulative_interval() doesn’t have this parameter because
its result will always include all origins, otherwise the summary
measure wouldn’t be calculated properly.