MLGlobalHealth/Realtime-SAE-Zimbabwe
Domain:
socioeconomicRecord type:
dataset
Creator:
MLG
Host:
# Realtime-SAE-Zimbabwe
This is a repository for the work "Real-time small area estimation of Food security in Zimbabwe: integrating mobile-phone and face-to-face surveys using joint multilevel regression and poststratification." (arXiv).
## Structure of the repository
The repo should have a `data` folder and a sub-folder `raw` with the following datasets:
* Mobile-phone data: In our analysis, WFP's mVAM survey for Zimbabwe conducted within Real-time Monitoring system
* Face-to-face data: In our data analysis, this is The Rural Zimbabwe Vulnerability Assessment Committee (ZimVAC) survey. See Food and Nutrition Council Zimbabwe for the related reports. We used Household-level data (2023 survey for modelling, 2024 survey for validation).The aggregated data (2021 and 2022 survey) are used only for reference.
Both data should have the same structure, e.g.
| household_id | adm1_name| adm2_name | obs_month | fcs | covariate_1 | covariate_2| $\dots$ |
| -----------: | --------:| --------: | -----: | ----------: | ----------:| ------: |------: |
| 1 | Matabeleland South | Beitbridge | 2023-05-01 | 40 | A | B |
In addition, we will also need
* Data from the 2015 Zimbabwe DHS survey which can be can be requested here.
* Zimbabwe MICS6 SPSS Datasets. This can be requested and downloaded from here.
* Nigeria's shapefile: `Zimbabwe_adm2_uscb_2022.shp` can be downloaded from here. This is modified so that the `adm2_name` matches those from mVAM surveys, and saved to `processed` as `shapefile.rds`
* Zimbabwe 2022 census: `n_hh_by_district_and_ward.xlsx` should contain information extracted from section 5 of the report.
The data should be organized in the following tree structure:
```
├── data
| ├── raw
| │ ├── mVAM
| | | ├── WFP_mvam_weights_pop.xlsx
| | | └── mVAM.csv
| │ ├── ZimVAC
| | | ├── zimvac2024_survey.csv.
| | | ├── zimvac2023_survey.csv
| | | ├── zimvac2022_agg.csv
| | | └── zimvac2021_agg.csv
| │ ├── zimbabwe-adm1 …