wowi: Utilities for detecting statistically significant spatial clusters of high acute malnutrition rates using SaTScan’s Bernoulli spatial-scan model 
Child acute malnutrition can lead to death if not identified and treated in time. Driven by a combination of diverse factors, it often exhibits spatial variation. The levels of acute malnutrition are commonly measured through surveys that are representative of the area of interest. These survey results are then used to inform programme responses — to find and treat affected children. To that end, programme managers require actionable insights on where acute malnutrition is most prevalent. This is essential for prioritising interventions, especially when resources are limited.
wowi - an expression meaning “where” in Elómwè, a local language spoken in central-northern Mozambique - provides convenient utilities for this purpose. It identifies locations across the survey area where acute malnutrition is significantly high (hotspots) or low (coldspots), and unlikely to be due to chance alone.
wowi is a wrapper package built on top of the rsatscan package, which enables the use of the SaTScan software from within R. While rsatscan provides general-purpose functionality, wowi was specifically made for acute malnutrition analysis, tailoring the tools to the needs of nutrition-focused spatial investigations.
To use wowi, you must have SaTScan installed on your machine, along with the mwana R package for preprocessing anthropometric data.
Installation
wowi is not yet on CRAN but can be installed through:
pak::pak(pkg = "nutspatial/wowi")What does wowi do?
It takes a dataset with GPS coordinates (latitude and longitude), scans for clusters of acute malnutrition—either high or low, depending on the user’s specification—across the survey area, and returns three main outputs: (1) an interactive HTML map displaying the detected clusters (previewed below); (2) a .txt file containing the results; and (3) a table with summary statistics and metadata parsed from the .txt file.
This package is particularly handy when working with datasets that span multiple areas or administrative units, enabling consistent, area-wise detection of hotspots and coldspots. It also generates additional GIS-based files (e.g., shapefiles), which can be useful for further geospatial manipulation or integration into other mapping workflows.
A glimpse of the summary table
## # A tibble: 2 × 18
## survey_area nr_EAs total_children total_cases `%_cases` location_ids geo radius span children n_cases expected_cases observedExpected relative_risk
## <chr> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
## 1 District 36 532 104 19 23,24,26,25,3… 13.6… 1.43 … 1.88… 170 4 33.2 0.12 0.085
## 2 District 36 532 104 19 16,20,14,12,1… 13.8… 26.24… 43.5… 258 84 50.4 1.67 4.46
## # ℹ 4 more variables: `%_cases_in_area` <dbl>, log_lik_ratio <dbl>, pvalue <dbl>, ipc_amn <chr>Citation
If you use wowi package in your work, please cite using the suggested citation provided by a call to citation() function as follows:
citation("wowi")
#> To cite wowi in publications use:
#>
#> Tomás Zaba (2025). _wowi: Utilities for detecting statistically
#> significant spatial clusters of high acute malnutrition rates using
#> SaTScan's Bernoulli spatial-scan model_. R package version 0.1.0,
#> <https://nutspatial.github.io/wowi/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {wowi: Utilities for detecting statistically significant spatial clusters of high acute malnutrition rates using SaTScan's Bernoulli spatial-scan model},
#> author = {{Tomás Zaba}},
#> year = {2025},
#> note = {R package version 0.1.0},
#> url = {https://nutspatial.github.io/wowi/},
#> }Community guidelines
Feedback, bug reports and feature requests are welcome; file issues or seek support here. If you would like to contribute to the package, please see the contributing guidelines.