Data from: Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety
	DOI: 10.7923/6XRT-QB81

Data, code and/or products within this dataset support the following manuscript:
	Manuscript Title: Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety
	Journal: PLOS ONE
	DOI: 10.1371/journal.pone.0278645
	The code associated with this analysis is available from the author upon request.

Data Use:
	License: CC-BY | https://creativecommons.org/licenses/by/4.0/legalcode
	Citation: Zimbelman, E., & Keefe, R. (2022). Data from: Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety (Version 1.0) [Data set]. University of Idaho. https://doi.org/10.7923/6XRT-QB81

Data/Code Files: 
	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

Description/Abstract:
	Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3) assess the relative importance of the predictive metrics. A large field experiment was conducted to test the connectivity of a network of one mobile and five stationary goTenna Pro mesh radios on 24 Public Land Survey System sections in northern Idaho. Dirichlet regression was used to predict connectivity using 1) both lidar- and satellite-derived metrics (LIDSAT); 2) lidar-derived metrics only (LID); and 3) satellite-derived metrics only (SAT). On average the full network was connected only 32.6% of the time and the mobile goTenna was disconnected from all other devices 18.2% of the time. RMSE for the six connectivity levels ranged from 0.101 to 0.314 for the LIDSAT model, from 0.103 to 0.310 for the LID model, and from 0.121 to 0.313 for the SAT model. Vegetation-related metrics affected connectivity more than topography. Developed models may be used to predict the expected performance of real-time mesh networks to support wildland firefighting, forestry, and public safety. However, safety professionals should be aware of the impacts of vegetation on connectivity.

Resource URL: https://data.nkn.uidaho.edu/dataset/data-lost-woods-forest-vegetation-and-not-topography-most-affects-connectivity-mesh-radio

Creator(s):	
	1. Full Name: Eloise G. Zimbelman
		Unique identifier: https://orcid.org/0000-0001-6859-400X
		Affiliation(s): University of Idaho
	2. Full Name: Robert F. Keefe
		Unique identifier: https://orcid.org/0000-0003-0136-1951
		Affiliation(s): University of Idaho

Other Contributor(s): NULL

Publisher: University of Idaho

Publication Year: 2022
	
Language(s): American English

Subject(s):
	4. Agricultural sciences
		4.1 Agriculture, Forestry, and Fisheries
	3. Medical and Health sciences
		3.3 Health sciences
		
Keywords/Tags: wildland firefighting; public safety; mesh network; location sharing; Dingell Act; resource tracking

Resource Type General: Dataset
		
Dates: 2022-05-23 (Submitted to PLOS ONE)

Date available for the public: 2022-05-19

Sizes: 1.2 MB
 
Format(s): csv, txt
 
Version: 1.0

Funding References: This work was supported in part by the Idaho Forest Utilization Research (FUR) program and the U.S. Centers for Disease Control and Prevention (CDC) National Institute for Occupational Safety and Health (NIOSH) grant number 5 U01 OH010841 at the University of Idaho.
 
Spatial/Geographical Coverage Location:
	Study Area Description: northwestern Idaho, USA
	Geospatial Extent:
		Easting: -115.57 W
		Westing: -116.61 W
		Northing: 46.91 N
		Southing: 45.28 N
	Point Location(s): NULL
 
Temporal Coverage:
	Start Date: 2016-10-29 (lidar acquisition); 2019-06-13 (satellite data acquisition); 2019-06-14 (field sampling)
	End Date: 2016-11-13 (lidar acquisition); 2019-10-12 (satellite data acquisition); 2019-11-01 (field sampling)
		 
Granularity of the Data: Each row in the LIDSAT, LID, and SAT datasets represents data summarized for a Public Land Survey System (PLSS) section, each of which are approximately one square mile in size.
 
Contact Info:
	Contact Name: Eloise Zimbelman
	Contact Email: eloisez@uidaho.edu

Related Content: 
	Peer-Reviewed Manuscript-PLOS ONE | https://doi.org/10.1371/journal.pone.0278645