The datasets are described in the associated manuscript submitted to PLOS ONE. The LIDSAT, LID, and SAT files are structured the same way, with each row representing a Public Land Survey System (PLSS) section and each column representing a response variable or remote sensing predictor. The first column (“section_id”) indicates the PLSS section ID. The next six columns (“received_6” to “received_1”) represent the number of transmitted signals received by 5, 4, 3, 2, 1, and 0 stationary goTennas, respectively, and the “tot_trans” column represents the total number of signals transmitted by the mobile goTenna in the section. The next six columns (“Con_6_obs” to “Con_1_obs”) represent the proportion of transmitted signals received by 5, 4, 3, 2, 1, and 0 stationary goTennas (i.e., the six connectivity levels). These were calculated by dividing the respective “received” columns by the “tot_trans” column (e.g., Con_6_obs = received_6/tot_trans, etc.). Because Dirichlet regression cannot handle zero values, zeroes were imputed as described in the manuscript in order to derive the next six columns (“Con_6” to “Con_1”). These columns correspond to the compositional response variables used to develop the Dirichlet regression models and represent the proportion of time 5, 4, 3, 2, 1, and 0 stationary goTennas were connected to the mobile goTenna, respectively. All remaining columns after “Con_1” correspond to either a lidar- or satellite-derived metric calculated for each section, according to the descriptions and variable keys located in the manuscript. The LIDSAT, LID, and SAT datasets have identical response variables and the only difference between them is the inclusion of different remote sensing predictors. The LIDSAT dataset contains all of the lidar- and satellite-derived predictors, the LID dataset only contains the lidar-derived predictors, and the SAT dataset only contains the satellite-derived predictors. The ATAK_Full_RS_Metrics_MaxMinValues dataset contains the maximum and minimum values for each remote sensing predictor variable which were used to normalize the variables as described in the manuscript. The first column contains the remote sensing predictor variable name and matches the remote sensing variable names in the LIDSAT, LID, and SAT datasets. The next two columns list the minimum and maximum values of the corresponding predictor.

File Directory:
LIDSAT.csv:
LID.csv:
SAT.csv:
ATAK_Full_RS_Metrics_MaxMinValues.csv: Contains the maximum and minimum values for each remote sensing predictor variable which were used to normalize the variables as described in the manuscript.
Header Key:
[Column 1]: Contains the remote sensing predictor variable name and matches the remote sensing variable names in the LIDSAT, LID, and SAT datasets.
min: Minimum values of the corresponding predictor.
Max: Maximum values of the corresponding predictor.
readme.txt