The sedtR package allows R programmers to easily interact with Urban’s
Spatial Equity Data
Tool (SEDT) public
application programming
interface
(API).
The SEDT enables local, state, and federal leaders, as well as the general public, to upload their own point spatial data and quickly assess whether place-based programs and resources – such as parks, libraries, wi-fi hotspots or electric vehicle charging stations – are equitably distributed across neighborhoods and demographic groups.
The tool – launched in 2020 and expanded in 2021 – has been used by a wide variety of experts and nonexperts alike to assess equity in the distribution of local programs and resources. For example, the Bloomington Pedestrian and Bicycle Safety Commission analyzed sidewalk funding allocations, and advocates in Cincinnati identified economic and racial inequities in car crash locations.
Through engagements with local government and nonprofit users, we
identified a key barrier. Organizations wanted to embed the SEDT within
their own tools and data workflows and thus make the Tool a core part of
institutional processes. However, before the API’s release in March of
2024, Tool users would have had to visit Urban’s website and use the GUI
interface to upload one dataset at a time whenever new analyses were
needed. The release of the API overcomes this barrier by allowing
programmatic access to the Tool. We wrote sedtR to wrap the public
API’s endpoints in user-friendly R code. We are hopeful that sedtR
makes it easy for researchers, analysts, and policymakers to incorporate
SEDT calculations into R workflows.
You can install the development version of sedtR from GitHub with:
# install.packages("devtools")
devtools::install_github("UrbanInstitute/sedtR")The following example illustrates using the call_sedt_api() function
on Minneapolis, MN bikeshare data stored on the Urban Institute’s Data
Catalog.
#> Loading sedtR - using staging API
#> [1] "getting output file"
call_sedt_api() returns a list object that contains a sf object
storing the geographic disparity scores:
sedt_response$geo_bias_data |>
head()
#> prop_cb_renter_hh diff_no_internet prop_pov diff_pov
#> 1 0.00049 -0.006 0.00345 -0.003
#> 2 0.00937 -0.008 0.01036 -0.005
#> 3 0.00158 -0.007 0.00721 -0.007
#> 4 0.00316 -0.007 0.00515 -0.005
#> 5 0.00024 -0.006 0.00160 -0.002
#> 6 0.00487 0.001 0.00384 -0.001
#> disp_name sig_diff_seniors diff_seniors diff_pop
#> 1 Census Tract 1.01, Minneapolis, MN TRUE -0.007 -0.008
#> 2 Census Tract 1.02, Minneapolis, MN FALSE -0.004 -0.007
#> 3 Census Tract 3, Minneapolis, MN TRUE -0.008 -0.008
#> 4 Census Tract 6.01, Minneapolis, MN TRUE -0.015 -0.011
#> 5 Census Tract 6.03, Minneapolis, MN TRUE -0.007 -0.008
#> 6 Census Tract 11, Minneapolis, MN FALSE 0.000 -0.002
#> GEOID prop_children prop_pop diff_children diff_cb_renter_hh data_prop
#> 1 27053000101 0.01043 0.00786 -0.010 0.000 0.00000
#> 2 27053000102 0.01378 0.01152 -0.009 -0.004 0.00506
#> 3 27053000300 0.00746 0.00753 -0.007 -0.002 0.00000
#> 4 27053000601 0.01193 0.01091 -0.012 -0.003 0.00000
#> 5 27053000603 0.00486 0.00761 -0.005 0.000 0.00000
#> 6 27053001100 0.00533 0.00493 -0.002 -0.002 0.00253
#> prop_no_internet diff_under_200_poverty_line prop_seniors
#> 1 0.00552 -0.008 0.00684
#> 2 0.01252 -0.010 0.00938
#> 3 0.00728 -0.006 0.00799
#> 4 0.00735 -0.009 0.01453
#> 5 0.00622 -0.002 0.00700
#> 6 0.00198 -0.002 0.00330
#> prop_under_200_poverty_line sig_diff_cb_renter_hh sig_diff_children
#> 1 0.00809 FALSE TRUE
#> 2 0.01458 FALSE TRUE
#> 3 0.00563 FALSE TRUE
#> 4 0.00855 FALSE TRUE
#> 5 0.00243 FALSE TRUE
#> 6 0.00517 FALSE FALSE
#> sig_diff_no_internet sig_diff_pop sig_diff_pov
#> 1 FALSE TRUE FALSE
#> 2 FALSE TRUE FALSE
#> 3 FALSE TRUE TRUE
#> 4 TRUE TRUE TRUE
#> 5 TRUE TRUE FALSE
#> 6 FALSE TRUE FALSE
#> sig_diff_under_200_poverty_line weighted_counts
#> 1 TRUE 0
#> 2 TRUE 2
#> 3 TRUE 0
#> 4 TRUE 0
#> 5 TRUE 0
#> 6 FALSE 1
#> geometry
#> 1 -93.31950, -93.31419, -93.30431, -93.29919, -93.29921, -93.29906, -93.30668, -93.31943, -93.31943, -93.31944, -93.31950, 45.05125, 45.05124, 45.05118, 45.05114, 45.04392, 45.03766, 45.03935, 45.04217, 45.04397, 45.04760, 45.05125
#> 2 -93.29919, -93.29409, -93.29153, -93.28223, -93.28083, -93.28343, -93.28321, -93.28270, -93.28673, -93.28811, -93.28811, -93.29041, -93.29403, -93.29489, -93.29906, -93.29921, -93.29919, 45.05114, 45.05113, 45.05113, 45.05116, 45.04357, 45.03536, 45.03345, 45.03243, 45.03151, 45.03151, 45.03272, 45.03473, 45.03646, 45.03674, 45.03766, 45.04392, 45.05114
#> 3 -93.31879, -93.30856, -93.30848, -93.30833, -93.31343, -93.31853, -93.31862, -93.31870, -93.31879, 45.03546, 45.03545, 45.03132, 45.02409, 45.02410, 45.02409, 45.02772, 45.03134, 45.03546
#> 4 -93.26411, -93.26238, -93.26210, -93.25998, -93.25992, -93.24985, -93.24739, -93.24218, -93.23711, -93.23711, -93.23712, -93.23712, -93.23711, -93.24748, -93.24734, -93.26071, -93.26311, -93.26269, -93.26411, 45.02760, 45.02760, 45.02944, 45.03051, 45.03554, 45.03555, 45.03555, 45.03558, 45.03561, 45.02766, 45.02405, 45.02397, 45.02044, 45.02038, 45.01677, 45.01682, 45.01682, 45.02101, 45.02760
#> 5 -93.23712, -93.23711, -93.23711, -93.22693, -93.22690, -93.22690, -93.22688, -93.22687, -93.22686, -93.22686, -93.22961, -93.23328, -93.23712, -93.23712, 45.02405, 45.02766, 45.03561, 45.03567, 45.02773, 45.02771, 45.02410, 45.02047, 45.01951, 45.01892, 45.01922, 45.02226, 45.02397, 45.02405
#> 6 -93.24748, -93.23711, -93.23713, -93.23715, -93.24225, -93.24734, -93.24734, -93.24748, 45.02038, 45.02044, 45.01682, 45.01320, 45.01318, 45.01316, 45.01677, 45.02038A data frame with demographic information used in the analysis is also returned:
sedt_response$demo_bias_data |>
head()
#> census_var data_value summary_value diff_data_city
#> 1 pct_no_internet 8.78435 7.752 1.03235
#> 2 pct_under_200_poverty_line 37.70731 33.240 4.46731
#> 3 pct_all_other_races 6.23852 6.280 -0.04148
#> 4 pct_less_hs_diploma 10.10677 9.295 0.81177
#> 5 pct_under18_unins 4.50332 3.074 1.42932
#> 6 pct_under18_pov 19.22721 21.193 -1.96579
#> data_value_sd summary_value_margin sig_diff geo geo_fips geo_display
#> 1 0.54109 0.508 FALSE city 2743000 Minneapolis, MN
#> 2 0.74891 0.955 TRUE city 2743000 Minneapolis, MN
#> 3 0.32612 0.395 FALSE city 2743000 Minneapolis, MN
#> 4 0.62023 0.532 FALSE city 2743000 Minneapolis, MN
#> 5 1.49243 0.495 FALSE city 2743000 Minneapolis, MN
#> 6 2.10830 1.697 FALSE city 2743000 Minneapolis, MN
#> geo_mo baseline_pop
#> 1 null total_pop
#> 2 null total_pop
#> 3 null total_pop
#> 4 null total_pop
#> 5 null under18_pop
#> 6 null under18_popThe package allows users to visualize the geographic disparity scores
and demographic disparity scores with create_map() and
create_demo_chart(), respectively. By default, these functions do not
save the resulting images, but both provide arguments (save_map and
save_chart) which take logicals and allow a user to save the outputted
visualization. Like the interactive visualizations on the [SEDT
website] (https://apps.urban.org/features/equity-data-tool/), these
functions allow users to visualize different geographic or demographic
disparity scores. create_map() allows interactive or static
visualizations.
create_map(sedt_response$geo_bias_data,
interactive = FALSE, # optional, defaults to TRUE
save_map = FALSE, #optional, defaults to FALSE. If TRUE, provide file_path
col_to_plot = "diff_pop") #optional, default is "diff_pop"
#> tmap mode set to plotting
create_demo_chart(sedt_response$demo_bias_data,
group = "total", # optional, default is "total"
save_chart = FALSE) # optional, default is FALSE. If TRUE provide file_path
The Spatial Equity Data Tool has comprehensive documentation in the form
of an online book.
Most notably, there is a chapter specifically devoted to the
API.
It provides more in-depth sedtR code and outputs.
Other particularly relevant chapters cover common API errors and warnings, how to interpret Tool results, and a description of data appropriate for the Tool.
Please provide feedback by opening GitHub Issues or contacting us at sedt@urban.org.