Raster layers of bioclimatic, environmental, and anthropogenic variables in a metropolitan area of Mexico City
Authors/Creators
Description
Mexico is considered endemic and hyperendemic for arbovirus transmissions. Since the 1970s, the highest dengue transmission burden has been observed in coastal areas, coinciding with vector distribution. Currently, climate change is favoring the establishment of the vector and the transmission of viruses in non-endemic areas with warm temperatures and altitudes above 1800 m above sea level. During the dengue outbreaks caused by serotype 3 in Mexico between 2023 and 2025, temperate localities where dengue transmission was very rare or had never been reported began to experience local transmission of dengue. Local dengue transmission was recently documented in the Mexico City metropolitan area, a decade after the first report of Aedes aegypti invasion in Mexico City.
The generation of raster layers in Mexico City is fundamental to understanding the factors associated with the presence of vectors and imported cases. These layers can also serve as inputs for predicting the probability of the presence of a vector and the number of cases. This repository provides the following environmental layers with a spatial resolution of 100 m, maintained with a geographic coordinate system using the EPSG 4326 code (EPSG:4326).
The bioclimatic variables used were obtained from WorldClim V1 Bioclim (Hijmans, 2005) using the Google Earth Engine. The Bioclim database includes a time series from 1960 to 1991, and its native spatial resolution was 927.67 m. The methodology for accessing the databases was as follows: First, the Google Earth Engine services were authenticated and initialized using the first author's credentials. Second, the image from the WorldClim database (“WORLDCLIM/V1/BIO”) was defined. Third, the area of interest (Mexico City Metropolitan Area) was defined, and its geographic bounding boxes were extracted. Fourth, the Google Earth Engine image was downscaled to 100 m and exported as a locally hosted TIFF file. The same process was followed for the heat islands, NDVI, temperature, altitude, word cover, and cover fraction.
All layers were clipped to the geographic bounding box of the Mexico City Metropolitan Area and resampled using the bilinear method, referencing a BioClim layer (Bio01) that was downscaled to 100 m. This was done to adjust the geographic extent and allow all the layers to be stacked in a single geographic file.
In the case of sociodemographic indices, they were constructed by the first author with a geostatistical model using INLA, projected onto the centroids of a tempered raster layer (bio01), and subsequently converted to raster and saved with tif extension.
Table 1. Bioclimatic variables of WorldClim[1].
|
Variable |
Category |
Code |
|
Annual mean temperature |
Climatic |
Bio1 |
|
Mean diurnal range |
Climatic |
Bio2 |
|
Isothermality |
Climatic |
Bio3 |
|
Temperature seasonality |
Climatic |
Bio4 |
|
Max temperature of the warmest month |
Climatic |
Bio5 |
|
Min temperature of the coldest month |
Climatic |
Bio6 |
|
Annual temperature range |
Climatic |
Bio7 |
|
Mean temperature of wettest quarter |
Climatic |
Bio8 |
|
Mean temperature of driest quarter |
Climatic |
Bio9 |
|
Mean temperature of warmest quarter |
Climatic |
Bio10 |
|
Mean temperature of coldest quarter |
Climatic |
Bio11 |
|
Annual precipitation |
Climatic |
Bio12 |
|
Precipitation of wettest month |
Climatic |
Bio13 |
|
Precipitation of driest month |
Climatic |
Bio14 |
|
Precipitation seasonality |
Climatic |
Bio15 |
|
Altitude |
Climatic |
Altitude |
|
Precipitation of wettest quarter |
Climatic |
Bio16 |
|
Precipitation of driest quarter |
Climatic |
Bio17 |
|
Precipitation of warmest quarter |
Climatic |
Bio18 |
|
Precipitation of coldest quarter |
Climatic |
Bio19 |
Table 2. Anthropogenic, climatic and entomological variables
[1] https://www.datos.gob.mx/dataset/accesibilidad_centros_urbanos, https://www.gob.mx/conapo/documentos/analisis-geoespacial-de-la-accesibilidad-a-centros-urbanos-de-las-localidades-de-mexico
[3] https://www.datos.gob.mx/dataset/indices_marginacion, https://www.gob.mx/conapo/documentos/indices-de-marginacion-2020-284372
[4] https://www.datos.gob.mx/dataset/indice_calidad_entorno, https://www.gob.mx/conapo/documentos/indice-de-calidad-del-entorno?idiom=es
[7]https://www.nature.com/articles/s41597-022-01284-8, https://figshare.com/articles/figure/An_annual_global_terrestrial_Human_Footprint_dataset_from_2000_to_2018/16571064
Table 3. Climatic and anthropogenic layers of 2023 and 2024 (current climate).
|
Category |
Category |
Code |
|
|
Temperature mean |
Climatic |
temp |
LANDSAT/LC09/C02/T1_L2 |
|
Estimated probability of complete coverage by built |
Anthropogenic |
built |
GOOGLE/DYNAMICWORLD/V1 |
|
Estimated probability of complete coverage by trees |
Anthropogenic |
tree |
GOOGLE/DYNAMICWORLD/V1 |
|
Normalized Difference Vegetation Index |
Environmental |
ndvi |
COPERNICUS/S2_SR_HARMONIZED |
|
Palmer Drought Severity Index |
Climatic |
psdi |
IDAHO_EPSCOR/TERRACLIMATE |
|
Precipitation mean |
Climatic |
prcp |
IDAHO_EPSCOR/TERRACLIMATE |
|
average minimum temperature |
Climatic |
tmin |
IDAHO_EPSCOR/TERRACLIMATE |
|
average maximum temperature |
Climatic |
tmax |
IDAHO_EPSCOR/TERRACLIMATE
|
|
Evaporative Demand Drought Index |
Climatic |
eddi |
GRIDMET/DROUGHT |
|
Reative humidity |
Climatic |
rh |
ECMWF/ERA5_LAND/DAILY_AGGR |
|
Urban Heat Island |
Climatic |
uhi |
LANDSAT/LC08/C02/T1_L2, LANDSAT/LC09/C02/T1_L2 |
|
Standardized Urban Heat Island |
Climatic |
suhi |
LANDSAT/LC08/C02/T1_L2, LANDSAT/LC09/C02/T1_L2 |
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