Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case study
Citation: Musah A, Browning E, Aldosery A, Valerio Graciano Borges I, Ambrizzi T, Tunali M, Basibüyük S, Yenigün O, Moreno GMM, de Lima CL, da Silva ACG, dos Santos WP, Massoni T, Campos LC and Kostkova P. (2023). Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case study. Front. Trop. Dis 4:1039735. doi: 10.3389/fitd.2023.1039735
This paper has been featured as an original research article in Frontiers in Tropical Diseases. This repository contains all processed datasets used to reproduce the results featured in the above titled research article. Please refer to the R script labelled as "R code for MAXENT analysis and outputs.R" in order to reproduce the analysis of this paper in RStudio. Please note that all datasets used for this analysis are open source. If you have any questions - feel free to ask by sending an email on a.musah@ucl.ac.uk. Please note that all links and references to the datasets used for this analysis were explicitly mentioned in our research article. Nevertheless, we provide the location(s) of where they can be accessed and downloaded:
1.) Level 0: Country's border for Brazil (gadm36_BRA_0.shp)
2.) Level 1: Boundaries for the 27 states in Brazil (gadm36_BRA_1.shp)
The shapefiles can be downloaded from Global Administrative Areas Database (GADM). All shapefile data for Brazil are contained in the zipped folder "Shapefile.zip".
3.) Tiled Shapefiles (Tiled_Region_Brazil.shp)
IMPORTANT NOTE: The tiled shapefiles are squared polygons used to clip large rasters to a tenable size that is acceptable for extractions and downloading via Google Earth Engine. The square polygons for Brazil were based on dimensions of the downloadable raster tiles from Global Forest Change 2000–2021 Data. Please refer to its usage in the important note section below under NDVI and consult the text file labelled "Python code for extracting NDVI via GEE.txt"
All gridded datasets have been prepared, resampled and standardised to a uniform resolution (i.e., 2.5 arcmins (equivalent to 4.5 km)).
1.) Brazilian population density in 2013 (Brazil Population Density 2013.tif)
2.) Brazilian urbanisation in 2013 (Brazil Urbanisation 2013.tif)
3.) Levels of natural lighting in Brazil (Brazil Natural Lighting.tif)
These three datasets can be downloaded from WorldPop database
4.) Averaged annual precipitation in Brazil in 2013 (Brazil Annual Precipitation 2013.tif)
5.) Averaged annual temperature in Brazil in 2013 (Brazil Annual Temperature 2013.tif)
These two datasets can be downloaded from historical monthly data from the WorldClim database. Note: Annual temperature was based on the monthly measures for maximum temperature only.
6.) Land surface elevation in Brazil (Brazil Natural Lighting.tif)
This dataset was downloaded from the STRM 90.0m DEM Digital Elevation Database
7.) Averaged normalised differenced vegetation index (NDVI) for Brazil in 2013 (Brazil NDVI 2013.tif)
We used the MOD13A1.061 Terra Vegetation Indices 16-Day Global 500m to compute the NDVI estimates for Brazil in 2013 using USGS Earth Explorer.
IMPORTANT NOTE: Extracting the NDVI data was a highly involved process especially for a large region. It is highly recommended to perform the extraction through Google Earth Engine using bespoke Python code. In addition, for large regions, it is best to extract the NDVI data on a tile-by-tile basis in a recursive loop. Please refer to the Python script (i.e., see text file labelled "Python code for extracting NDVI via GEE.txt") for extracting NDVI via Google Earth Engine and use the "Tiled_Region_Brazil.shp" shapefile (see zipped folder labelled "Tiled Shapefiles.zip"). Note that you will need to create a Google Account to implement the code in an code editor hosted on Google Earth Engine. The downloads will send the tiles as .tif files to your Google Drive which you can download locally to your computer. Finally, you will need to stitch the tiled .tif files back accordingly to form the country in GIS software (e.g., R, QGIS, ArcGIS etc.,).
All socioeconomic, environmental and climate-based raster datasets for Brazil are contained in the zipped folder "Gridded Datasets.zip".
1.) Aedes aegypti occurrence dataset for Brazil in 2013 (Aedes Occurrences in 2013.csv)
2.) Random generated pseudo-background point dataset was used as controls (Background points.csv)
The occurrence dataset used in this paper was originally from the Global Compendium of the Aedes species project which is open source database and accessible via Global Biodiversity Information Facility (GBIF)
1.) R code for preparing all spatial datasets (RStudio)
2.) Python code for extracting NDVI via Google Earth Engine (Python and GEE Code Editor)
3.) R code for MAXENT analysis and outputs (RStudio)
The first two scripts are available for users for perusal to see what was behind the data cleaning and extraction processes in RStudio and GEE. The raw datasets were no provided as their storage size exceeds 150MB. Processed datasets have been included to this repository - please follow the instructions in the third script to reproduce the results shown in our research article in RStudio.
This research was conducted under the project titled: Mosquito populations modelling for early warning system and rapid public health response (MEWAR). This research through the Belmont Forum was supported in the United Kingdom by UKRI NERC under the grant NE/T013664/1, and in Turkey by TÜBITAK under the grant 119N373. This work was supported in Brazil by FAPESP under the grants 2019/23553-1 and 2020/11567-5, and by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.
Shield:
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
