Original data can be downloaded from here. Another online version of the data can be found HERE.This version presented and hosted by CPAWS-NL allows for data extraction and analysis within ArcGIS Online Map Viewer."Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbor-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of St. Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of theses taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass." (DOI: 10.17632/dtk86rjm86.2)

We introduce a method to calculate and store approximately 1.2 million surname distributions calculated for surnames found in Great Britain for six years of historic population data and 20 years of contemporary population registers compiled from various consumer sources. We subsequently show how this database can be incorporated into an interactive web-environment specifically designed for the public dissemination of detailed surname statistics. Additionally, we argue that the database can be used in the quantitative analysis of surnames in Great Britain and potentially offer valuable insights into processes of contagious and hierarchical diffusion of populations as well as the regional distinctiveness of demographic change and stasis.

Statistics of pastoralist population kernel density maps disaggregated by time point.

Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbour-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of Saint Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of these taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass.

Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbour-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of St. Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of theses taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass.

Functional trait space analyses are pivotal to describe and compare organisms’ functional diversity across the tree of life. Yet, there is no single application that streamlines the many sometimes-troublesome steps needed to build and analyze functional trait spaces. To fill this gap, we propose funspace, an R package to easily handle bivariate and multivariate (PCA-based) functional trait space analyses. The six functions that constitute the package can be grouped in three modules: ‘Building and exploring’, ‘Mapping’, and ‘Plotting’. The building and exploring module defines the main features of a functional trait space (e.g., functional diversity metrics) by leveraging kernel density-based methods. The mapping module uses general additive models to map how a target variable distributes within a trait space. The plotting module provides many options for creating flexible and high-quality figures representing the outputs obtained from previous modules. We provide a worked example to dem..., , , # funspace - Creating and representing functional trait spaces

Estimation of functional spaces based on traits of organisms. The package includes functions to impute missing trait values (with or without considering phylogenetic information), and to create, represent and analyse two dimensional functional spaces based on principal components analysis, other ordination methods, or raw traits. It also allows for mapping a third variable onto the functional space.

Description of the Data and file structure

We provide the package as a .tar file (filename: funspace_0.1.1.tar). Once the package has been downloaded, it can be directly uploaded in R from Packages >> Install >> Install from >> Package Archive File (.zip, .tar.gz). All the functions and example datasets included in funspace and that are necessary to reproduce the worked example in the paper will be automatically uploaded. Functions and example datasets can be then accessed using the standard syntax fu...

This is visualized and calculated data of Year of gross forest cover loss event (lossyear) from Global Forest Change, University of Maryland by using Kernel Density: Kernel Density calculates the density of point features in a neighborhood around features. Weighted to the recent tree cover loss point with higher value (score as actual number of year) and calculate the density per square kilometer.

Significant Benthic Areas are defined in DFO’s Ecological Risk Assessment Framework (ERAF) as “significant areas of cold-water corals and sponge dominated communities”, where significance is determined “through guidance provided by DFO-lead processes based on current knowledge of such species, communities and ecosystems”. Here we provide maps of the location of significant concentrations of corals and sponges on the east coast of Canada produced through quantitative analyses of research vessel trawl survey data, supplemented with other data sources where available. We have conducted those analyses following a bio-regionalization approach in order to facilitate modelling of similar species, given that many of the multispecies surveys do not record coral and sponge catch at species level resolution. The taxa analyzed are sponges (Porifera), large and small gorgonian corals (Alcyonacea), and sea pens (Pennatulacea). We applied kernel density estimation (KDE) to create a modelled biomass surface for each of those taxa, and applied an aerial expansion method to identify significant concentrations, following an approach first applied in 2010 to this region. We compared our results to those obtained previously. KDE uses only geo-referenced biomass data to identify “hot spots”. The borders of the areas so identified can be refined using knowledge of null catches and species distribution models that predict species presence-absence and/or biomass, both incorporating environmental data. We present such predictive models produced using a random forest machine-learning technique, and in one region compare the biomass random forest models for sea pens to those produced by generalized additive models (GAMs). Together, these distribution maps can be used to identify significant concentrations of corals and sponges in eastern Canada; an essential first step in the identification of Sensitive Benthic Areas.

The mxd_file folder contains the maps for the empirical analysis. The transect_addin folder contains the add-in transect tools that can be installed within ArcGIS. The transect_data folder contains the data used for making the maps in the mxd_file.

Please note that this data was selected from a larger dataset for use in the San Diego Ocean Planning Partnership, a collaborative pilot project between the California State Lands Commission and the Port of San Diego. For more information about the Partnership, please visit: https://www.sdoceanplanning.org/ The data was retrieved in May 2018 from OceanSpaces.org and is now available at https://data.cnra.ca.gov/dataset/spatial-and-economic-human-uses-california-south-coast-mpa-baseline-study-1992-to-2012 (Chen et al. An Economic and Spatial Baseline of Coastal Recreation in the South Coast of California. OceanSpaces.org. Retrieved May 2018).These data are a Kernel Density layers produced using ArcGIS. The Kernel analysis is a nonparametric statistical method for estimating probability densities from a set of points. Conceptually, a smooth raster surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance (i.e.search radius), eventually reaching zero. A default value is calculated by the analysis tool for the search radius based on the input data; increasing the radius has little affect on the density value. Although more points will fall inside a larger search radius the number will be divided by a larger area when calculating density resulting in a more generalized output raster. The volume under the surface equals the weighted value for the point. The weights were created by Knowledge Networks and applied to the points based on demographics. The density of the output raster is calculated by adding the values of all the individual surfaces where they overlap. The point's weighted value determines the number of times to count the point. For example, a weighted value of 1.5 would cause the point to be counted one and half times. The resulting dataset is a smooth raster surface depicting the intensity use or density of an activity. Based on previous experience and after conducting some tests, all of the activity datasets were given a search radius of one mile. In discussing a similar project with the Oregon Department of Parks and Recreation we discovered that most visitors to the coast stay within a mile of their activity location. This distance also proved to be a good match to the mapped activities.

This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Additional file 2. Contains all data sets (SIGMAR1, HRH1, HERG,and DRD5) retrieved from ChEMBL23, their computed NAMS similarities and R source code for analysis and inference of molecular activity spaces.

Auxiliary Data.gdb: Land_use: original land use data POI_name: interests-point-data from the Amap platform (name indicates category)

New_gridded_population_dataset(.gdb): experimental result data, i.e., a gridded population map of mainland China with a resolution of 100 meters

New_minus_WorldPop_PopulationResidual(.gdb): pixel-level residuals of the new gridded population dataset with the Worldpop dataset

POI_Correlation_Coefficient: Zonal statistical output of POI kernel density values: summary of various POI kernel densities in residential areas of administrative units Summary of POI Pearson correlation coefficients: sum of Pearson's correlation coefficients for 13 types of POIs at a certain bandwidth

PopulationData_AdministrativeUnitLevel.gdb: Population_data_mainlandChina_level3: population data at the district and county level in mainland China Population_data_Name_level4_Table: township and street-level population data for provinces and municipalities

Note: Due to the storage space limitation, 3D building, nighttime light, and WorldPop datasets have not been uploaded. To access these publicly available data, please visit the official website via the "Related links" at the bottom. In addition, we are not authorized to share data for the fourth level of administrative boundaries, so we only share the corresponding population data in tabular form.

This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This repository contains the supplementary materials (Supplementary_figures.docx, Supplementary_tables.docx) of the manuscript: "Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France". This repository also provides the R codes and datasets necessary to run the analyses described in the manuscript.

The R datasets with suffix "_a" have anonymous spatial coordinates to respect confidentiality. Therefore, the preliminary preparation of the data is not provided in the public codes. These datasets, all geolocated and necessary to the analyses, are:

• Attack_sf_a.RData: 19,302 analyzed wolf attacks on sheep
  • ID: unique ID of the attack
  • DATE: date of the attack
  • PASTURE: the related pasture ID from "Pasture_sf_a" where the attack is located
  • STATUS: column resulting from the preparation and the attribution of attacks to pastures (part 2.2.4 of the manuscript); not shown here to respect confidentiality
• Pasture_sf_a.RData: 4987 analyzed pastures grazed by sheep
  • ID: unique ID of the pasture
  • CODE: Official code in the pastoral census
  • FLOCK_SIZE: maximum annual number of sheep grazing in the pasture
  • USED_MONTHS: months for which the pasture is grazed by sheep
• Removal_sf_a.RData: 232 analyzed single wolf removal or groups of wolf removals
  • ID: unique ID of the removal
  • OVERLAP: are they single removal ("non-interacting" in the manuscript => "NO" here), or not ("interacting" in the manuscrit, here "SIMULTANEOUS" for removals occurring during the same operation or "NON-SIMULTANEOUS" if not).
  • DATE_MIN: date of the single removal or date of the first removal of a group
  • DATE_MAX: date of the single removal or date of the last removal of a group
  • CLASS: administrative type of the removal according to definitions from 2.1 part of the manuscript
  • SEX: sex or sexes of the removed wolves if known
  • AGE: class age of the removed wolves if known
  • BREEDER: breeding status of the removed female wolves, "Yes" for female breeder, "No" for female non-breeder. Males are "No" by default, when necropsied; dead individuals with NA were not found.
  • SEASON: season of the removal, as defined in part 2.3.4 of the manuscript
  • MASSIF: mountain range attributed to the removal, as defined in part 2.3.4 of the manuscript
• Area_to_exclude_sf_a.RData: one row for each mountain range, corresponding to the area where removal controls of the mountain range could not be sampled, as defined in part 2.3.6 of the manuscript

These datasets were used to run the following analyses codes:

• Code 1 : The file Kernel_wolf_culling_attacks_p.R contains the before-after analyses.
  • We start by delimiting the spatio-temporal buffer for each row of the "Removal_sf_a.RData" dataset.
    • We identify the attacks from "Attack_sf_a.RData" within each buffer, giving the data frame "Buffer_df" (one row per attack)
    • We select the pastures from "Pasture_sf_a.RData" within each buffer, giving the data frame "Buffer_sf" (one row per removal)
  • We calculate the spatial correction
    • We spatially slice each buffer into 200 rings, giving the data frame "Ring_sf" (one row per ring)
    • We add the total pastoral area of the ring of the attack ("SPATIAL_WEIGHT"), for each attack of each buffer, within Buffer_df ("Buffer_df.RData")
  • We calculate the pastoral correction
    
    • We create the pastoral matrix for each removal, giving a matrix of 200 rows (one for each ring) and 180 columns (one for each day, 90 days before the removal date and 90 day after the removal date), with the total pastoral area in use by sheep for each corresponding cell of the matrix (one element per removal, "Pastoral_matrix_lt.RData")
    • We simulate, for each removal, the random distribution of the attacks from "Buffer_df.RData" according to "Pastoral_matrix_lt.RData". The process is done 100 times (one element per simulation, "Buffer_simulation_lt.RData").
  • We estimate the attack intensities
    • We classified the removals into 20 subsets, according to part 2.3.4 of the manuscript ("Variables_lt.RData") (one element per subset)
    • We perform, for each subset, the kernel estimations with the observed attacks ("Kernel_lt.RData"), with the simulated attacks ("Kernel_simulation_lt.RData") and we correct the first kernel computations with the second ("Kernel_controlled_lt.RData") (one element per subset).
    • We calculate the trend of attack intensities, for each subset, that compares the total attack intensity before and after the removals (part 2.3.5 of the manuscript), giving "Trends_intensities_df.RData". (one row per subset)
    • We calculate the trend of attack intensities, for each subset, along the spatial axis, three times, one for each time analysis scale. This gives "Shift_df" (one row per ring and per time analysis scale.
• Code 2 : The file Control_removals_p.R contains the control-impact analyses.
  • It starts with the simulation of 100 removal control sets ("Control_sf_lt_a.RData") from the real set of removals ("Removal_sf_a.RData"), that is done with the function "Control_fn" (l. 92).
  • The rest of the analyses follows the same process as in the first code "Kernel_wolf_culling_attacks_p.R", in order to apply the before-after analyses to each control set. All objects have the same structure as before, except that they are now a list, with one resulting element per control set. These objects have "control" in their names (not to be confused with "controlled" which refers to the pastoral correction already applied in the first code).
  • The code is also applied again, from l. 92 to l. 433, this time for the real set of removals (l. 121) - with "Simulated = FALSE" (l. 119). We could not simply use the results from the first code because the set of removals is restricted to removals attributed to mountain ranges only. There are 2 resulting objects: "Kernel_real_lt.RData" (observed real trends) and "Kernel_controlled_real_lt.RData" (real trends corrected for pastoral use).
  • The part of the code from line 439 to 524 relates to the calculations of the trends (for the real set and the control sets), as in the first code, giving "Trends_intensities_real_df.RData" and "Trends_intensities_control_lt.RData".
  • The part of the code from line 530 to 588 relates to the calculation of the 95% confidence intervals and the means of the intensity trends for each subset based on the results of the 100 control sets (Trends_intensities_mean_control_df.RData, Trends_intensities_CImin_control_df.RData and Trends_intensities_CImax_control_df.RData). This will be used to test the significativity of the real trends. This comparison is done right after, l. 595-627, and gives the data frame "Trends_comparison_df.RData".
• Code 3 : The file Figures.R produces part of the figures from the manuscript:
  • "Dataset map": figure 1
  • "Buffer": figure 2 (then pasted in powerpoint)
  • "Kernel construction": figure 5 (then pasted in powerpoint)
  • "Trend distributions": figure 7
  • "Kernels": part of figures 10 and S2
  • "Attack shifts": figure 9 and S1
  • "Significant": figure 8

This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

description: This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).; abstract: This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

description: This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).; abstract: This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).