Micro-climate to macro-risk: Mapping fine scale differences in mosquito-borne disease risk using remote sensing

This is the repository used to create West Nile virus risk maps with ECOSTRESS LST measurements.

It is organized as a R project in two parts: LST to air temperature modeling temperature_modeling (data, code, and results/figures), and WNV risk map creation and analysis risk_maps (data, code, and results/figures).

Temperature Modeling

temperature_modeling has data from several sources in the raw_data folder that lives in the data folder:

• CIMIS
  • LatLon.csv: lats and lons of site locations
  • all_points: all available data during study period
• NOAA
  • LatLon.csv: lats and lons of site locations
  • all_points: all available data during study period
• ECOSTRESS
  • cimis_points: all ecostress lst images at the CIMIS locations for Jun-Sept 2018-2020 obtained using AppEARS and CIMIS LatLon.csv file
  • noaa_points: all ecostress lst images at the NOAA locations for Jun-Sept 2018-2020 obtained using AppEARS and NOAA LatLon.csv file
• Landsat
  • cimis_points: all landsat 8 images at the CIMIS locations for Jun-Sept 2018-2020 obtained using AppEARS and CIMIS LatLon.csv file
  • noaa_points: all landsat 8 images at the NOAA locations for Jun-Sept 2018-2020 obtained using AppEARS and NOAA LatLon.csv file

These data are then processed and matched together in the merge_data.R file that can be found in the code folder to create merged_df.RData that can be found in the processed_data folder.

Under results an R markdown file named temp_mod_figures.Rmd (temperature modeling figures) and corresponding html file can be found that describes the modeling approach and houses several figures:

• Figure S1: Distribution of ECOSTRESS images included in the study by month and hour of image acquisition.
• Figure S2: LST and air temperature scatterplot and model.
• Table S1: AIC, adjusted R2, and Breusch-Pagan statistics for models predicting air temperature.
• Figure S3. Effect of fractional vegetation on air temperature prediction using land surface temperature (LST).

Risk Maps

risk_maps has the following raw_data in its data folder:

• Study_extent: shapefiles of the study border created using study_extent.R
• regression.RData: a lm object created in temp_mod_figures.Rmd from the results section in the temperature_modeling portion of the project
• ECOSTRESS:
  • all_ims: all ECOSTRESS images in the study area Jun-Sept 2018-2020
  • filtered_ims: Renamed images with high QC and no cloud cover (see filter_LST.R)
  • chosen_four: four representative images of the same year at night, dawn, midday, and dusk
• HLS Landsat images for four chosen ECOSTRESS images
  • all_ims
  • chosen_four
• Kern_ag_layer
  • original
  • binned_cropped: binned into different categories of interest and cropped to study area using kern_bin.R
• Urban layer
  • original: the 2016 Statewide Crop Mapping GIS Shapefiles from the California Department of Water Resources (https://data.cnra.ca.gov/dataset/statewide-crop-mapping)
  • filtered_cropped: urban layer only cropped to the study extent using urban_filter.R

In the processed_data folder you can find the following:

• ECOSTRESS which holds the corrected air_temperatures and corresponding biting_rate and transmission_rate maps, created in lst_to_air_b_tx.R

• landcover_avgs.RData: a file with the average risk profiles (tx and bite) over the different land cover types of interest, built in landcover_avg.R.

• all_pixels_location_match.RData: a file with every temperature observation and risk profiles (tx and bite) resampled to grid of first image for spatial consistency, built in all_pixels_integrate.R.

• all_pixels_integrate.RData: a file with the average risk profiles (tx and bite) over each pixel (resampled to grid of first image for spatial consistency), calculated from all_pixels_location_match.RData in all_pixels_integrate.R.

• all_pixels_integrate_with_landcover.RData: a file with the average risk profiles (tx and bite) over each pixel (resampled to grid of first image for spatial consistency), labeled by landcover of the pixel, calculated from all_pixels_location_match.RData in all_pixels_integrate.R. Pixels that change lancover from 2018-2020 are excluded. - all_pixels.RData: every pixel within an image land cover types and vegetation, built in all_pixels.R. check if still used - day_pixels.RData: every pixel within a daytime image, built in day_pixels.R. check if still used

• Landcover which holds a shapefile with flattened geometries for the different landcover types of interest, created in flatten_landcovers.R

Under results an R markdown file named risk_map_figures.Rmd and corresponding html file can be found that describes the modeling approach and houses the remaining figures:

• Figure 2. Culex tarsalis biting and West Nile virus transmission rates.
• Figure 3. Air temperature and West Nile virus temperature suitability maps
• Figure 4. Diurnal cycles of air temperature, and resulting biting and transmission rates.
• Figure 5. Air temperature and WNV transmission probability distributions by land cover type.
• Table 1: Using aggregate data results in statistically significant biases when calculating risk.
• various statistical tests referenced in the manuscript