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TongFen (通分) means to convert two fractions to the least common
denominator, typically in preparation for further manipulation like
addition or subtraction. In English, that’s a mouthful and sounds
complicated. But in Chinese there is a word for this, TongFen, which
makes this process appear very simple.
When working with geospatial datasets we often want to compare data
that is given on different regions. For example census data and election
data. Or data from two different censuses. To properly compare this data
we first need to convert it to a common geography. The process to do
this is quite analogous to the process of TongFen for fractions, so we
appropriate this term to give it a simple name. Using the
tongfen package, preparing data on disparate
geographies for comparison by converting them to a common geography is
as easy as typing tongfen.
TongFen home page and reference guide
install.packages("tongfen")The latest development version can be installed from GitHub.
remotes::install_github("mountainmath/tongfen")
library(tongfen)The get_tongfen_census_ct and
get_tongfen_census_ct_from_da methods make use of the
StatCan correspondence files. To speed up this process it is useful to
permanently cache these files instead of having to download them
repeatedly. If caching is desired, set either
options("tongfen.cache_path"="<your local cache path>")Sys.setenv("tongfen.cache_path"="<your local cache path>")options("custom_data_path"="<your local cache path>")in your .Rprofile or .Renviron file.
The tongfen package is build around the following basic
TongFen workflow:
estimate_tongfen_correspondence.meta_for_additive_variables function
does this for additive variables.tongfen_aggregate.A convenience function to validate geographic TongFen fit via area
comparison is available via check_tongfen_areas, it allows
to explore and deal with spatial mismatches during TongFen.
Finding a common tiling of several different yet congruent
geographies is only one part of the problem TongFen addresses,
aggregating up the variables is the other part. The tongfen
package deals with this using a metadata table that specifies
how variables should be aggregated. In it’s simplest form values are
simply added up. The meta_for_additive_variables
convenience function builds the metadata for additive variables.
Metadata for non-additive variables like averages, ratios or percentages
needs more care to build, it requires additional information on the
parent variable that specifies the denominator of the
average, ratio or percentage. Other data, like medians, can’t be
aggregated up, although tongfen can provide estimates of
medians on aggregated geographies by treating them as averages.
The package ships with a subset of voting data from Elections Canada for the 42nd and 43rd federal elections as well as the polling district geographies for the 42nd and 43rd. This facilitates running the example vignette on polling districts without having to download external data. Both are available as open data covered under the Open Government Licence - Canda.
The need for TongFen comes up frequently with certain types of geographies. Census geographies is one such example. In some cases these data sources come with their own correspondence files that go beyond geographic matchup but also join regions to alleviate data integrity problems like geocoding issues.
In such cases it can be worthwhile to wrap data acquisition and TongFen into one convenience function, and also extend the TongFen method parameter to allow for external correspondence files to be used.
The package is well-integrated to work with Canadian census data in
two essential ways. * meta_for_ca_census_vectors builds
rich metadata for a given list of Canadian census variables by utilizing
the metadata available via CensusMapper. In particular, this
automates the proper aggregation of non-count variables like averages,
ratios and percentages. * get_tongfen_ca_census wraps the
process of data acquisition (via CensusMapper and the cancensus
package and tongfen into one convenience function. At the same time
it adds the TongFen method = "statcan" option that uses the
Statistics Canada correspondence files to build the common geography. *
The get_tongfen_correspondence_ca_census function breaks
out the correspondence generation to aid the process of accessing the
Statistics Canada correspondence files (and better integration of
generating correspondences for Canadian census geographies in general)
to facilitate mixing in non-census data coming on census geographies,
like for example CMHC
data.
get_tongfen_us_census integrates the data acquisition
(via the tidycensus
package) with TongFen, and adds the tongfen
method = "census.gov" to use the US Census Bureau
correspondence files for matching.The tongfen package is open to add extensions for other
specialized data sources, as well as extensions of existing ones.
When geographies aren’t sufficiently congruent or the target
geography is fixed, we won’t be able to use the tongfen
methods to compute the data on a common geography but have to instead
rely on estimates. The tongfen_estimate makes no assumption
on the underlying geographies and returns estimates of the data on the
target geography. It uses area-weighted interpolation to achieve this,
and can be refined to dasymmetric estimates using the
proportional_reaggregate function.
This method has the example that it works independent of the nature of the underlying geographies, but comes at the heavy price of only being an estimate. To be useful for research purposes we also need methods to estimate the errors this introduces and the effects this has on subsequent analysis results.
Methods to facilitate this are still under active development.
If you wish to cite tongfen:
von Bergmann, J. (2021). tongfen: R package to Make Data Based on Different Geographies Comparable. v0.3.3.
A BibTeX entry for LaTeX users is
@Manual{,
author = {Jens {von Bergmann}},
title = {tongfen: R package to Make Data Based on Different Geographies Comparable},
year = {2021},
note = {R package version 0.3.3},
url = {https://mountainmath.github.io/tongfen/},
}These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.