Closed timcdlucas closed 5 years ago
library(disaggregation) library(raster) library(dplyr) source("setUserInfo.R") # define paths PR_path <- Z('GBD2017/Processing/Stages/04b_PR_DB_Import_Export/Verified_Outputs/2018_02_15/pfpr_dump.csv') API_path <- Z('GBD2017/Processing/Stages/04c_API_Data_Export/Checkpoint_Outputs/subnational.csv') pop_path <- Z('GBD2017/Processing/Stages/03_Muster_Population_Figures/Verified_Outputs/Output_Pop_At_Risk_Pf_5K/ihme_corrected_frankenpop_All_Ages_3_2015_at_risk_pf.tif') shapefile_path <- Z('master_geometries/Admin_Units/Global/GBD/GBD2017_MAP/GBD2017_MAP_MG_5K/MG_5K_GBD2017_ADMIN3.shp') cov_raster_paths <- c( Z('mastergrids/MODIS_Global/MOD11A2_v6_LST/LST_Day/5km/Synoptic/LST_Day_v6.Synoptic.Overall.mean.5km.mean.tif'), Z('mastergrids/MODIS_Global/MCD43D6_v6_BRDF_Reflectance/EVI_v6/5km/Synoptic/EVI_v6.Synoptic.Overall.mean.5km.mean.tif'), #Z('mastergrids/Other_Global_Covariates/TemperatureSuitability/TSI_Pf_Dynamic/5km/Synoptic/TSI-Martens2-Pf.Synoptic.Overall.Mean.5km.Data.tif'), #Z('GBD2017/Processing/Static_Covariates/MAP/other_rasters/accessibility/accessibility.5k.MEAN.tif'), Z('mastergrids/Other_Global_Covariates/Elevation/SRTM-Elevation/5km/Synoptic/SRTM_elevation.Synoptic.Overall.Data.5km.mean.tif'), Z('mastergrids/MODIS_Global/MOD11A2_v6_LST/LST_Day/5km/Synoptic/LST_Day_v6.Synoptic.Overall.SD.5km.mean.tif'), #Z('mastergrids/MODIS_Global/MCD43B4_BRDF_Reflectance/TCB/5km/Synoptic/TCB.Synoptic.Overall.mean.5km.mean.tif'), #Z('mastergrids/Other_Global_Covariates/NightTimeLights/VIIRS_DNB_Composites/5km/Annual/VIIRS-SLC.2016.Annual.mean.5km.median.tif'), #Z('mastergrids/Other_Global_Covariates/UrbanAreas/Global_Urban_Footprint/From_86m/5km/Global_Urban_Footprint_5km_PropUrban.tif'), Z('mastergrids/MODIS_Global/MCD43D6_v6_BRDF_Reflectance/TCW_v6/5km/Synoptic/TCW_v6.Synoptic.Overall.mean.5km.mean.tif') ) shps <- shapefile(shapefile_path) covs <- lapply(cov_raster_paths, raster) cov_stack <- stack(covs) cov_stack <- crop(cov_stack, extent(c(40, 52, -26, -10))) api <- read.csv(API_path) api <- api %>% filter(iso3 == 'MDG', year == 2013) shps <- shps[shps$area_id %in% as.character(api$shapefile_id), ] shps$inc <- api$api_mean_pf[match(shps$area_id, as.character(api$shapefile_id))] pop_ras <- raster(pop_path) pop_ras <- crop(pop_ras, extent(c(40, 52, -26, -10))) cov_stack <- crop(cov_stack, pop_ras) dis_data <- prepare_data(shps, cov_stack, aggregation_raster = pop_ras, id_var ='area_id', response_var = 'inc', ncores = 8) dis_data$covariate_data[is.na(dis_data$covariate_data)] <- 0 dis_data$aggregation_pixels[is.na(dis_data$aggregation_pixels)] <- 0 m <- fit_model(dis_data, family = 'gaussian', link = 'identity', field = FALSE) mpred <- predict_model(m) munc <- predict_uncertainty(m, N = 40, predict_iid = F) munc2 <- predict_uncertainty(m, N = 40, predict_iid = T)
Error in p$rasterize(nrow(r), ncol(r), as.vector(extent(r)), values, background) :
Not very minimal. I'm going to look at this. But thought I'd document it here in case I get pulled away.
Not very minimal. I'm going to look at this. But thought I'd document it here in case I get pulled away.