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)

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.

As part of the cultural landscape, administrative toponyms do not only reflect natural and sociocultural phenomena, but also help with related management and naming work. Historically, county-level administrative districts have been stable and basic administrative regions in China, playing a role in the country’s management. We explore the spatio-temporal evolutionary characteristics of the county-level administrative toponyms cultural landscape in China’s eastern plains areas. A Geographical Information System (GIS) analysis, Geo-Informatic Tupu, Kernel Density Estimation, and correlation coefficients were conducted. We constructed a GIS database of county-level administrative toponyms from the Sui dynasty onward using the Northeast China, North China, and Yangtze Plains as examples. We then summarized the spatio-temporal evolutionary characteristics of the county-level administrative toponyms cultural landscape in China’s eastern plains areas. The results indicate that (1) the number of toponyms has roughly increased over time; (2) toponym densities on the three plains are higher than the national average in the corresponding timeframe since the Sui; and (3) county-level administrative toponyms related to mountains and hydrological features accounted for more than 30% of the total in 2010. However, the percentage of county-level administrative toponyms related to natural factors on the three plains has decreased since the Sui. To explore the factors influencing this spatio-temporal evolution, we analyzed the correlations between the toponyms and natural factors and human/social factors. The correlation degree between toponym density and population density is the highest, and that between toponym density and Digital Elevation Model (DEM) the lowest. Temperature changes were important in toponym changes, and population changes have influenced toponym changes over the last 400 years in China.

This data gathers socioeconomic drivers at 1km2 grid cell spatial resolution to predict forest fires in a region in Spain. The response variable was fire density by grid cell from kernel density methods. It was produced under the Firemap project (https://geogra.uah.es/firemap/) by using GIS, spatial and statistical data sources at regional level in Spain. The resulting work was published at Vilar del Hoyo L, Martín Isabel MP, Martínez Vega FJ. 2011. Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data. European Journal of Forest Research. 130:983-96. doi: 10.1007/s10342-011-0488-2

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.

Analysis condicted by ABR Inc.–Environmental Research & Services.Data from ADFG/BLM/NSB and ConocoPhillips Alaska Inc.For Brownian Bridge Movement Models - Conducted dynamic Brownian Bridge Movement Models (dBBMM) to delineate movmeents on seasonal herd ranges. dBBMM models were run using the move package for r using the following methods.1. Locations within 30 days of first collaring were removed from the analysis. 2. Selected females from PTT and GPS collars during the date range July 1 2012–June 30 2017 and individuals having more than 30 locations per season.3. ran a dBBMMM model for each individual during each season using 1 km pixels. 4. Calculate the 95% isopleth for each individual.5. Overlap all 95% isopleths and calculate the proportion of animals using (as defined by 95% isopleth) each pixel. Value shown is proportion times 1000. Seasons used: Winter (Dec 1-Apr 15); Spring (Apr 16-May 31); Calving (June 1-15); postcalving (June 16-30); Mosquito (July 1-15); Oestrid Fly (July 16-Aug 7); late summer (August 8-Sept 15); Fall Migration (Sept 16-Nov 30). For Kernel Density Estimates - Conducted Kernel Density Estimation (KDE) to delineate seasonal herd ranges. Kernels were run using the ks package for r and the plugin bandwidth estimator. 1. Locations within 30 days of first collaring were removed from the analysis. 2. The mean latitiude and longitude for each animal was calculated for each day.3. A KDE utilization distribution was calculated for Julian day of the season (all years combined). 4. The daily KDE uds were averaged across the season. This method accounts for individual's movements during the seasons without the overfitting that results from using autocorrelated lcoations from individuals.Seasons used: Winter (Dec 1-Apr 15); Spring (Apr 16-May 31); Calving (June 1-15); postcalving (June 16-30); Mosquito (July 1-15); Oestrid Fly (July 16-Aug 7); late summer (August 8-Sept 15); Fall Migration (Sept 16-Nov 30).

This shapefile contains four levels of occurrence (frequent, common, occasional, and rare range) for the Florida black bear (Ursus americanus floridanus) throughout the state of Florida. Range extent and levels of occurrence were created using research, management, and public-generated location data of black bears from 2009-2018. The four levels of occurrence were estimated by 90% kernel density estimator (KDE) isopleth (range of frequent occurrence), 97.5% KDE isopleth (common), concave hull model (occasional), and the remainder of Florida (rare).

description: 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; abstract: 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 study uses the methods of kernel density analysis and service area analysis in GIS to quantify the accessibility of public service facilities for residents within 15-minute living circles. This study takes the Ciqikou block as an example to analyze the distribution of public facilities and the polarization of services in historical blocks, quantifies and evaluates the accessibility of public facilities and the influencing factors of accessibility for residents' walking, and makes an assessment contribution to the sustainable renewal of social transportation and the maintenance of social equity in historical blocks in the later stage, with the aim of providing a useful reference for sustainable urban renewal.

The attraction of tourism resources is very important to promote the sustainable development of tourism industry. This study takes China’s world cultural and natural heritage as the research object, and constructs an attractiveness evaluation system for China’s world cultural and natural heritage tourism resources by collecting user feedback data from three major travel OTA platforms. At the same time, ArcGIS 10.7 software was used for spatial autocorrelation analysis and kernel density analysis to explore the spatial distribution pattern of tourism resource attraction. The results show that China’s world cultural and natural heritage can be subdivided into 5 main categories and 10 sub-categories. From the perspective of spatial aggregation, only the Moran’s I index of tourist resource points showing a significant spatial aggregation feature. This study is helpful to reveal the weaknesses of tourism resource points and provide reference for sustainable development of attraction and optimization of tourism planning and management.

The Kernel Density tool calculates the density of features in a neighborhood around those features.Kernel Density calculates the density of point features around each output raster cell. Conceptually, a smoothly curved surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance from the point, reaching zero at the Search radius distance from the point. Only a circular neighborhood is possible. The volume under the surface equals the Population field value for the point, or 1 if NONE is specified. The density at each output raster cell is calculated by adding the values of all the kernel surfaces where they overlay the raster cell center. This layer is included in a storymap about the Panama City crayfish, a species listed as Threatened under the Endangered Species Act in 2022. Storymap link: https://fws.maps.arcgis.com/home/item.html?id=a791906fe3f8433eabadda5898184372

description: 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.Link to the Dataset - ftp://ftp.dfg.ca.gov/R7_MR/NONCONSUMPTIVE/NCCSR/Photo.zip; abstract: 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.Link to the Dataset - ftp://ftp.dfg.ca.gov/R7_MR/NONCONSUMPTIVE/NCCSR/Photo.zip

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.Link to the Dataset - ftp://ftp.dfg.ca.gov/R7_MR/NONCONSUMPTIVE/NCCSR/Cars.zip

Mule deer populations continue to decline across much of the western United States due to loss of habitat, starvation, and severe climate patterns, such as drought. In order to track the home range size and ecological preferences of mule deer, an important species for culture, economy, and ecosystems, the New Mexico Bureau of Land Management Taos Field Office captured mule deer, attached collars to them, and released them into Rio Grande del Norte National Monument. Collected from 2015-2017, each unique entry is one deer during one year, for a total of 23 entries. The point data was then intersected with vegetation data in the area, and the density of points was determined through Kernel Density Estimation (KDE). Reclassified BLM Vegetation Treatment data was used for zonal statistics on the KDE data and offered insights into mule deer response to treatments. This project was conducted as a joint project between the NMBLM TFO, Fort Collins USGS Science Center, and Kent State University’s Biogeography & Landscape Dynamics lab. This dataset includes all spatial data (CPG, DBF, XLSX, PRJ, SBN, SBX, SHP, and SHX) files for the comprehensive location fix shapefile, the convex hulls, the reclassified LANDFIRE EVT raster, the analysis area, the reclassified BLM Vegetation Treatment groups, the Kernel Density Estimation result, and the hill shade and state boundary data.

Predator-prey interactions can be profoundly influenced by vegetation conditions, particularly when predator and prey prefer different habitats. Although such interactions have proven challenging to study for small and cryptic predators, recent methodological advances substantially improve opportunities for understanding how vegetation influences prey acquisition and strengthen conservation planning for this group. The California Spotted Owl (Strix occidentalis occidentalis) is well-known as an old-forest species of conservation concern, but whose primary prey in many regions – woodrats (Neotoma spp.) – occurs in a broad range of vegetation conditions. Here, we used high-resolution GPS tracking coupled with nest video monitoring to test the hypothesis that prey capture rates vary as a function of vegetation structure and heterogeneity, with emergent, reproductive consequences for Spotted Owls in Southern California. Foraging owls were more successful capturing prey, including woodrats, in taller multilayered forests, in areas with higher heterogeneity in vegetation types, and near forest-chaparral edges. Consistent with these findings, Spotted Owls delivered prey items more frequently to nests in territories with greater heterogeneity in vegetation types and delivered prey biomass at a higher rate in territories with more forest-chaparral edge. Spotted Owls had higher reproductive success in territories with higher mean canopy cover, taller trees, and more shrubby vegetation. Collectively, our results provide additional and compelling evidence that a mosaic of large tree forests with complex canopy and shrubby vegetation increases access to prey with potential reproductive benefits to Spotted Owls in landscapes where woodrats are a primary prey item. We suggest that forest management activities that enhance forest structure and vegetation heterogeneity could help curb declining Spotted Owl populations while promoting resilient ecosystems in some regions. Methods See README DOCUMENT Naming conventions *RSF or prey refers to prey capture analysis *delivery in a file name refers to delivery rate analysis *repro in a filename means that file is for the delivery rate analysis

Setup *files with vegetation data should work with minimal alteration(will need to specify working directory) with associated R code for each analysis *Shapefiles were made in ArcGIS pro but they can be opened with any GIS software such as QGIS.

Locational data files

NOTE LOCATIONAL DATA IS SHIFTED AND ROTATED FROM THE ORIGINAL -due to the sensitive nature of this species. The locational_data includes: * All_2021_owls_shifted * Point file showing all GPS tag locations for prey capture analysis * Attributes include: * TERRITORY ID: Numerical identifier for each bird * Year: year GPS tag was recorded * Month: month GPS tag was recorded * Day: Day GPS tag was recorded * Hour: Hour GPS tag was recorded * Minute: minute GPS tag was recorded * All_linked_polygons_shifted * Polygon file showing capture polygons for prey capture analysis * Attributes include * Territory ID: numerical identifier for each bird * Polygon id: numerical identifier for each capture polygon for each bird * Shape area: area of each polygon * SBNF_camera_nests_shifted * Point file showing spotted owl nests for prey capture analysis * Attributes include * Territory id: numerical identifier for each bird * C95_KDE_2021_socal_shifted * Polygon file of owls 95% kernel density estimate for prey delivery rate analysis * Attributes include * Id: numerical identifier for each territory(bird) * Area: area of each polygon * San_bernardino_territory_centers * Point file showing Territory centers for historical SBNF territories – shifted for repro success analysis * Attributes include * Repro Territory id: unique identifier for each territory in broader set of territories

Besides the sifted locational data we have included - For the Resource selection function vegetation data, for the delivery analysis we have included an overview of prey deliveries by territory and vegetation data used, and for the reproductive analysis we have again included vegetation data as well as an overview of reproductive success. these are labled as follows:

Files for the prey capture analysis

Socal_RSF_data.txt

*description: Text file with vegetation data paired with capture locations both buffered polygons used in prey capture analysis and the unbuffered ones which were not used.(Pair with Socal_rsf_code R script) *format: .txt *Dimensions: 2641 X 35

*Variables: *ORIG_fid: completely unique identifier for each row *unique_id: unique identifier for each capture polygon(shared between a buffered capture location and its unbuffered pair) *territory_id: unique numerical idenifier of territory *Polygon_id: within territory unique prey capture polygon id *buff: bianary buffered or unbuffered (1=buffered, 0=unbuffered) *used: bianary used=1 available=0 *prey_type: prey species associated with polygon unkn:unknown, flsq:flying squirel, wora:woodrat, umou:mouse, pogo:pocketgopher, grsq: grey squirel, ubrd: unknown bird, umol:unknown mole, uvol, unknown vole. *area_sqm: area of polygon in square meters *CanCov_2020_buff: average canopy cover in polygon *CanHeight_2020_buff: average canopy height in polygon *Canlayer_2020_buff: average number of canopy layers in polygon *Understory_density_2020_buff: average brushy vegetation density in polygon *pix_COUNT: count of pixels in polygon (not needed for analysis) *p_chaparral: percent of polygon comprised of chaparral habitat
*p_conifer: percent of polygon comprised of conifer habitat *p_hardwood: percent of polygon comprised of hardwood habitat *p_other: percent of polygon comprised of other habitat types *Calveg_cap_CHt_gt10_CC_30to70_intersect_buff: percent of polygon comprised of trees taller than 10m with 30-70percent canopy cover (used to check data) *Calveg_cap_CHt_gt10_CCgt70_intersect_buff: percent of polygon comprised of trees taller than 10m with greater than 70percent canopy cover (used to check data) *Calveg_cap_CHt_lt10_intersect_buff:percent of polygon comprised of trees less than 10m (used to check data)
*p_sm_conifer: percent of polygon comprised of conifer trees less than 10m (used to calculate diversity)
*p_lrg_conifer_sc: percent of polygon comprised of conifer forests >10m tall with sparse canopy(used to calculate diversity) *p_large_conifer_dc: percent of polygon comprised of conifer forests greater than 10m tall with dense canopy (used to calculate diversity) *p_sm_hard: percent of polygon comprised of hardwood trees less than 10m (used to calculate diversity) *p_lrg_hard_sc: percent of polygon comprised of hardwood forests greater than 10m with sparse canopy(used to calculate diversity)
*p_lrg_hard_dc: percent of polygon comprised of hardwood forests greater than 10m dense canopy (used to calculate diversity) *p_forests_gt10_verysparse_CC: percent of polygon comprised of trees less than 10m with very sparse canopies (used to calculate diversity) *primary_edge: total distance in meters of primary edge in a polygon
*normalized_by_area_primary_edge: total distance in m of primary edge in a polygon divided by the area of the polygon
*secondary_edge: total distance in meters of secondary edge in a polygon *normalized_by_area_secondary_edge:total distance in m of secondary edge in a polygon divided by the area of the polygon *coarse_diversity: shannon diversity in each polygon (see methods below) *fine_diversity: shannon diversity in each polygon (see methods below) *nest_distance: distance from polygon center to nest for each polygon in meters

For the Delivery analysis

note: For information on determining average prey biomass see methods as well as zulla et al 2022 for flying squirels and woodrat masses Zulla CJ, Jones GM, Kramer HA, Keane JJ, Roberts KN, Dotters BP, Sawyer SC, Whitmore SA, Berigan WJ, Kelly KG, Gutiérrez RJ, Peery MZ. Forest heterogeneity outweighs movement costs by enhancing hunting success and fitness in spotted owls. doi:10.21203/rs.3.rs-1370884/v1. PPR:PPR470028.

prey_deliveries_byterritory.csv *Description: overview file of prey delivered to each nest *format: .csv *dimensions:332 x 8

*Variables: *SITE: Unique numerical identifier for each territory *DATE: date prey was delivered (in UTC) *CAMERA TIME: time in UTC prey was delivered *VIDEO TIME: time on video prey was delivered - unrelated to real time just original file
*PREY ITEM: prey species delivered to nest unkn:unknown, uncr: unknown if delivery(removed from eventual analysis due to

description: 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; abstract: 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 study employs a range of analytical techniques, including the geographical detector, kernel density estimation, imbalance index, geographical concentration index, and nearest neighbor index, all integrated with ArcGIS 10.8, to examine and illustrate the spatial distribution of 757 traditional villages across Guizhou, revealing an aggregated spatial distribution pattern of traditional villages, i.e., “one highly concentrated area and two secondary density clusters.” This pattern is influenced by both natural and socio-cultural factors, with socio-cultural elements such as road network density, GDP, and ethnic minority populations playing a more significant role than natural environmental factors. The results of geodetector analysis indicate that the interaction between these factors generally shows a nonlinear enhancement effect. Based on these findings, this study proposes four main strategies to preserve and enhance traditional villages: (1) establishing regional identities that reflect local ethnic characteristics; (2) improving village infrastructure to enhance accessibility; (3) implementing targeted protection and utilization strategies based on local conditions; and (4) industrial linkage, combining protection and development.

Calculated using CrimeStat Dual Kernel Density. Note: Burglary data during the end of 2015 and the first half of 2016 (10/30/2015 - 7/7/2016) only from APD. Household data estimates for 2015 from Esri Demographics. Relative Burglary Risk is the natural log (Ln) of the kernel density of burglarties g(x) divided by the kernel density of households g(y). **Zoom in to view more detail including the statistics layer (Stat. - mean and std. deviations for relative burglary densities)More information at: http://www.unm.edu/~lspear/other_nm.html.

This is a heatmap (a graphical representation of data where the individual values contained in a matrix are represented as colors) of 2013 deer hunt kills within the California Department of Fish & Wildlife (CDFW) North Central Region (Region 2). The data was compiled from 2013 CDFW Automated Licensing Data System (ALDS) tables. Text descriptions from hunters were approximated and placed with geographic coordinates. The resulting point data was converted to a heatmap using Kernel Density Tool in ArcGIS 10.1

Snake River Plain Play Fairway Analysis - Phase 1 CRS Raster Files. This dataset contains raster files created in ArcGIS. These raster images depict Common Risk Segment (CRS) maps for HEAT, PERMEABILITY, AND SEAL, as well as selected maps of Evidence Layers. These evidence layers consist of either Bayesian krige functions or kernel density functions, and include: (1) HEAT: Heat flow (Bayesian krige map), Heat flow standard error on the krige function (data confidence), volcanic vent distribution as function of age and size, groundwater temperature (equivalue interval and natural breaks bins), and groundwater T standard error. (2) PERMEABILTY: Fault and lineament maps, both as mapped and as kernel density functions, processed for both dilational tendency (TD) and slip tendency (ST), along with data confidence maps for each data type. Data types include mapped surface faults from USGS and Idaho Geological Survey data bases, as well as unpublished mapping; lineations derived from maximum gradients in magnetic, deep gravity, and intermediate depth gravity anomalies. (3) SEAL: Seal maps based on presence and thickness of lacustrine sediments and base of SRP aquifer. Raster size is 2 km. All files generated in ArcGIS.