These data were automated to provide an accurate high-resolution historical shoreline of Duncan Canal, Alaska suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

City Limits of Nome, Alaska, based on boundaries on record at the State of Alaska Local Boundary Commission. Boundaries downloaded from DNR site did not reflect official boundary on record with LBC. Redrawn by Duncan GIS using Certificate on file with LBC, dated November 26, 1982. When calculating Lat-long coordinates, assumed WGS72 due to date of Certificate preceding WGS84.

This collection contains 1 2021 1-centimeter natural color orthorectified image of Duncan Saddle, Idaho. This study area is approximately one hour south of Mountain Home, Idaho in the Big Jacks Creek Wilderness Area (Owyhee County). These data were acquired August 20, 2021. These data are sourced from US NSF Idaho EPSCOR.These data are part of a larger collection (README.txt) of UAS imagery data and data products collected at Duncan Saddle, approximately one hour south of Mountain Home, Idaho. We used a DJI Mavic 2 Pro with Map Pilot Pro software to capture imagery over the area of interest. The imagery was collected in a crossgrid pattern at 44m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The images were processed and the products created in Agisoft Metashape Pro. All products are georectified and in WGS84 UTM Zone 12 N.Recommended Citation: Roser, A., Marie, V., Olsoy, P., Delparte, D., & Caughlin, T. T. (2022). Unoccupied aerial systems imagery from Duncan Saddle Idaho (Version 1.0) [Data set]. University of Idaho. https://doi.org/10.7923/S6Q0-1V41Individual image tiles can be downloaded using the Idaho Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Data are sourced from: https://doi.org/10.7923/S6Q0-1V41

Dead Run RHESSys Workflow with supplied GIS data preparation

RHESSysWorkflows provides a series of Python tools for performing RHESSys data preparation workflows. These tools build on the workflow system defined by EcohydroLib and RHESSysWorkflows. This notebook assumes data steps 1 to 13 have already been prepared and uploaded as a HydroShare resource.

This notebook focuses on general steps 14 to 19 using the Dead Run catchment. 14 Generate template 15 Create world 16 Create flow table 17 Initializing vegetation carbon and nitrogen stores 18 Creating a RHESSys TEC file 19 Running RHESSys models

Users interested in seeing step outputs, remove output = from the command line.

Scores for English territorial waters marine bird species’ population risk due to displacement by offshore wind farms, ranked by species score.

A python script model to be used with ArcGIS (10.5.1) to produce statistics based on stream riparian buffers and projected hemlock losses (USFS 2018).

Long-standing tensions between Protestant and Catholic communities in Northern Ireland have led to high levels of segregation. This article explores the spaces within which residents of north Belfast move within everyday life and the extent to which these are influenced by segregation. We focus in particular on the role that interconnecting tertiary streets have on patterns of mobility. We adapt Grannis’s (1998) concept to define T-communities from sets of interconnecting tertiary streets within north Belfast. These are combined with more than 6,000 Global Positioning System (GPS) tracks collected from local residents to assess the amount of time spent within different spaces. Spaces are divided into areas of residents’ own community affiliations (in-group), areas not clearly associated with either community (mixed), or areas of opposing community affiliation (out-group). We further differentiate space as being either within a T-community or along a section of main road. Our work extends research on T-communities by expanding their role beyond exploring residential preference, to explore, instead, networks of (dis)connection through which social divisions are expressed via everyday mobility practices. We conclude that residents are significantly less likely to move within mixed and out-group areas and that this is especially true within T-communities. It is also evident that residents are more likely to travel along out-group sections of a main road if they are in a vehicle and that women show no greater likelihood than men to move within out-group space. Evidence from GPS tracks also provides insights into some areas where mixing appears to occur. Key Words: GIS, Northern Ireland, postconflict, segregation, T-communities.

Scores for species’ population vulnerability to collision mortality at offshore wind turbines, with species ranked by overall score.

a = score for highest percent of biogeographic population in England in any season;b = adult survival score;c = UK threat status score;d = Birds Directive score;e = estimated percentage at blade height;f = flight manoeuvrability;g = percentage of time spent flying;h = nocturnal activity;i = disturbance susceptibility;j = habitat specialization.Scores used in assessing sensitivity of seabird species to collision and displacement/disturbance risks from offshore wind farms in English territorial waters.

This layer was created by Duncan Smith and based on work by the European Commission JRC and CIESIN. A description from his website follows:--------------------A brilliant new dataset produced by the European Commission JRC and CIESIN Columbia University was recently released- the Global Human Settlement Layer (GHSL). This is the first time that detailed and comprehensive population density and built-up area for the world has been available as open data. As usual, my first thought was to make an interactive map, now online at- http://luminocity3d.org/WorldPopDen/The World Population Density map is exploratory, as the dataset is very rich and new, and I am also testing out new methods for navigating statistics at both national and city scales on this site. There are clearly many applications of this data in understanding urban geographies at different scales, urban development, sustainability and change over time.

This digital dataset shows quadrangles published by the Oklahoma Geological Survey (OGS) and was subjectively altered from the original datasets where there were overlaps, gaps, and errant polygons. The central Oklahoma dataset represents a composite of 25 geologic quadrangles showing the outcrop of rock units that comprise the Garber Sandstone, Wellington Formation, Hennessey and Duncan Formations as well as alluvium and terrace deposits. The dataset for western, north-central, and south-central Oklahoma represents a composite of 20 geologic quadrangles showing the outcrop of rock units that comprise the aquifers in this region of the state.