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]. [1] https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO?hl=es-419 & https://www.worldclim.org 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 Variable Category Code Accessibility index (Indice de accesibilidad)[1] Anthropogenic ia Social lag index (Indice de rezago social)[2] Anthropogenic irs Marginalization index (Indice de marginación)[3] Anthropogenic im Enviromental quality index (indice de calidad del entorno)[4] Anthropogenic ice Population[5] Anthropogenic pop Dinamyc Habitat Indices[6] Anthropogenic dhi Human Foot Print[7] Anthropogenic hfp Dengue temperature suitability[8] climatic Sui IndexP (2022)[9] Entomological indexp [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 [2] https://www.coneval.org.mx/Medicion/IRS/Paginas/Indice_Rezago_Social_2020.aspx [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 [5] https://landscan.ornl.gov [6] https://silvis.forest.wisc.edu/data/DHIs-clusters/ [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 [8] https://www.nature.com/articles/s41467-025-58609 [9] https://www.nature.com/articles/s41597-023-02170-7 Table 3. Climatic and anthropogenic layers of 2023 and 2024 (current climate). Category Category Code Earth Engine Snippet[1] 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 [1] https://earthengine.google.com