1)Note that since the individual responds only to the nearest recognized tree (of type depending on the discrimination level, see ‘Methods’), the effective ‘infinity’ is achieved by setting a perceptual range larger than the largest nearest neighbour distance between the recognized trees.

This geospatial layer is a spatial index for the CED (Conservation Efforts Database https://conservationefforts.org/), serving as a spatial framework for summary reports by area (a.k.a. polygon). In addition, this SRU (Sagebrush Reporting Unit) data is an option for data providers to provide spatial ambiguity to alleviate concerns of too much spatial detail representing private landowners’ efforts efforts and to protect Personally Identifiable Information. This option allows CED data providers to pick a predetermined SRU instead of submitting the explicit effort boundary. These SRUs are large enough to provide spatial ambiguity and obscure private landowner locations. This SRU data is in the format of a GIS polygon layer and is an aggregate of USGS partner’s lek cluster layer, BLM HAF data modified by Oregon, Idaho layers, and CED development team modification for CED purposes.

Table of class and type representation in spatial units.

The following data can be retrieved: * State * Political district * Court district * Municipality * Postal codes * Location In the case of multilingual places, the corresponding designation Assignment Postcodes - Places In addition to this data, the hierarchical relationship between the records is stored, i.e. which political districts a state comprises, which judicial districts a political district, etc. A record, as returned by the search as a result, includes all the information listed above. For example, if you search for a municipality, you will find as many result records as places or postal codes are assigned to that municipality, even if the search does not cover all these data fields. This can be prevented by specifying in the search that duplicates should be removed from the search result.

Units of Spatial Distribution of Forests of the Slovak Republic - Spatial Data. The forest spatial division unit is the basic unit for forest condition detection, management planning, forest economic record keeping and management control.

Abstract The words "environment" and "space" demonstrate distinct spatial units. It must be questioned whether the internal space, seen as an analytical subcategory of space, adds specificities of this type of designation. Therefore, if indoor is a subcategory of space, then its characteristics and types of representation must be observed and analyzed considering aspects of space. The purpose of this article is to present the characteristics of the indoor space unit as a subcategory of space. The “space” terminology applied to specify the indoor spatial unit has some features of spatial analysis that allow a broader and deeper spectrum as an object of study. Compared to space, the "environment" proves to be limited to represent the characteristics of the indoor. The intern must be understood as a space within a space, inserting a subcategory of the urban space, however, it is never seen as in its entirety. The totality does not observe space as it is, but everything within it. Space, as a creation of man, allows the creation of subspaces with no connection to the outside, in the category called indoor contributing to the analysis procedures based on the understanding of their relationships.

Spatial Data Modeller, SDM, is a collection of tools for use with GIS software for adding categorical maps with interval, ordinal, or ratio scale maps to produce a predictive map of where something of interest is likely to occur. The tools include the data-driven methods of Weights of Evidence, Logistic Regression, and two supervised and one unsupervised neural network methods, and categorical tools for a knowledge-driven method Fuzzy Logic. All of the tools have help files that include references to publications discussing the applications of the methods implemented in the tool. Several of the tools create output rasters, tables, or files that require the user to enter a name. Default values are provided in most cases to serve as suggestions of the style of naming that has been found useful. These names, following ArcGIS conventions, can be changed to meet the user’s needs. To make all of the features of SDM work properly it is required that several Environment parameters are set. See the discussion of Environment Settings below for the details. The Weights of Evidence, WofE, and Logistic Regression, LR, tools addresses area as the count of unit cells. It is assumed in the WofE and LR tools that the data has spatial units of meters. If your data has other spatial units, these WofE and LR tools may not work properly.

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This data collection comprises spatial data for all accounts. It includes the 250 m resolution BSU in GDA94 / Australian Albers (EPSG:3577) projection, the coverage fraction of each pixel of the Ecosystem Accounting Area (EAA), and a “terrestrial” mask derived from the national land use data. A BSU is a geometric construct that represents the minimum unit of spatial aggregation for accounting. It was devised in agreement with ABS to create national ecosystem accounts. See methods report in the Related Links for more detail on the BSU. Lineage: Refer to methods (see Related Links): Liu N, Newnham G, Richards AE, Pascoe S, Tetreault Campbell S and Schmidt RK (2024) Methods for developing the 250 m resolution Basic Spatial Units (BSU) for national ecosystem accounts. A report for the National Ecosystem Accounting Project. CSIRO, Australia. https://doi.org/10.25919/3r5x-pg80

To examine potential influence of tributaries on riparian habitat complexity along ~216 km of the Colorado River in Utah and ~300km of the Dolores River in Colorado and Utah, we first classified fluvial features and land cover of the bottomland on remotely sensed imagery. We then examined riparian and geomorphic patterns within the near channel zone with variably-sized spatial units. We used supervised image classification to create a 2-m resolution map of the primary land cover types within bottomlands of the Colorado and Dolores rivers, including two anthropogenic classes, four vegetation classes, bare ground, water and shadow. We selected these cover classes as major vegetation and land cover types that could be discerned from imagery. Our minimum mapping unit was 16m2. We were unable to map channel areas with flowing or standing water using supervised image classification, so we hand digitized channels based on a visual inspection of 2-m resolution imagery. We classified 6 channel classes based on their geomorphic characteristics and location within the river network (i.e., tributary vs. primary channel) or relation to the primary channel (e.g., split flow channels and secondary channels) and converted these to a 2-m resolution image (adapted from Moore et al 2012). We then combined land cover and channel classes to produce a single map representing both cover types along the Colorado and Dolores rivers. Our classification was based on 2-m resolution, multi-spectral (RGB NIR) aerial photographs for September 2013 and 2014 from the USDA National Agriculture Imagery Program (NAIP; http//www.fsa.usda.gov). We identified tributary junctions using the National Hydrography Dataset Plus Version 2 (NHDPlus V2) using the medium resolution (1:100,000 scale) National Hydrography Dataset (NHD) (http://nhd.usgs.gov/). To more accurately locate tributary junctions, we extracted flowlines corresponding to tributaries and converted each flowline to a point located at the terminus proximal to the channel centerline. We manually corrected tributary junction point locations with the NAIP images. We defined the near channel zone as within 20 meters of the edge of the Dolores low flow channel and within 100 meters of the edge of the Colorado low flow channel. These distances represented the average widths of the low flow channel for the two rivers. We assumed that habitat conditions closer to the channel would be more strongly influenced by fluvial processes and less strongly influenced by land management (e.g., farming, road development). We created spatial units for analysis within the near channel zone with Thiessen polygons - a polygon containing a point and defining an area closest to the point relative to all other systematically placed points (Fortin and Dale 2005). Beginning at the upstream study site boundary for each river, we placed regularly spaced points at three intervals: 10-, 25-, and 100-m to capture patterns for different sized spatial units around tributary junctions. For each point, we created a Thiessen polygon. Our use of Thiessen polygons as spatial units followed the example of other researchers (Alber and Piegay 2011). This data release includes shapefiles and associated metadata for: land and channel cover types along both rivers; tributary junction locations along both rivers; and the 10-, 25-, and 100-m Thiessen polygons along both rivers. Alber A., and Piégay H., 2011, Spatial disaggregation and aggregation procedures for characterizing fluvial features at the network-scale: application to the Rhône basin (France): Geomorphology, v. 125, p. 343-360. Fortin M.J., and Dale M.T., 2005, Spatial analysis: a guide for ecologists: Cambridge, Cambridge University Press, 365 p. Moore K., Jones K., Dambacher J., and Stein C., 2012, Aquatic inventories project methods for stream habitat surveys: Corvallis, OR, Conservation and recovery program, Oregon Department of Fish and Wildlife, 74 p.

We aimed to evaluate the role of spatial units with different shapes and sizes on road-kill modeling for small vertebrate species. We used the road-kill records of two reptiles, water snake (Helicops infrataeniatus) and D’Orbigny's slider turtle (Trachemys dorbigni), and three mammals, white-eared opossum (Didelphis albiventris), coypu (Myocastor coypus) and Molina's Hog-nosed skunk (Conepatus chinga). Hierarchical partitioning was used to evaluate the independent influence of different land-use classes on road-kill by varying the shape and size of the spatial units. Variables that most explained road-kill were consistent over the different spatial unit types. The standard size seemed to be a reasonable solution for these species. Prior analysis with several sizes and shapes is needed to identify the appropriate spatial unit to model road-kill occurrence for larger vertebrates with different history traits.

The spatial data set delineates areas with similar environmental properties regarding soil, terrain morphology, climate and affiliation to the same administrative unit (NUTS3 or comparable units in size) at a minimum pixel size of 1km2. The scope of developing this data set is to provide a link between spatial environmental information (e.g. soil properties) and statistical data (e.g. crop distribution) available at administrative level. Impact assessment of agricultural management on emissions of pollutants or radiative active gases, or analysis regarding the influence of agricultural management on the supply of ecosystem services, require the proper spatial coincidence of the driving factors. The HSU data set provides e.g. the link between the agro-economic model CAPRI and biophysical assessment of environmental impacts (updating previously spatial units, Leip et al. 2008), for the analysis of policy scenarios. Recently, a statistical model to disaggregate crop information available from regional statistics to the HSU has been developed (Lamboni et al. 2016). The HSU data set consists of the spatial layers provided in vector and raster format as well as attribute tables with information on the properties of the HSU. All input data for the delineation the HSU is publicly available. For some parameters the attribute tables provide the link between the HSU data set and e.g. the soil map(s) rather than the data itself. The HSU data set is closely linked the USCIE data set.

The districts are subunits of the residential districts. Each residential district with the exception of Klybeck and Kleinhüningen is divided into 2 to 8 districts. A total of 69 districts are distinguished across the entire cantonal area. Statistical numbering: The numbering of the districts consists of the two-digit residential district number and the single-digit district number (separated by a point in the label): — Example of the Flora District (1) in the residential district of Matthew (17): BEZ_ID 171, the name (BEZ_label) is 17.1 — Example of the residential district Kleinhüningen without districts (19.0): BEZ_ID 190, the name (BEZ_Label) is 19.0

Spatial dependence exists whenever the expected utility of one unit of analysis is affected by the decisions or behavior made by other units of analysis. Spatial dependence is ubiquitous in social relations and interactions. Yet, there are surprisingly few social science studies accounting for spatial dependence. This holds true for settings in which researchers use monadic data, where the unit of analysis is the individual unit, agent, or actor, and even more true for dyadic data settings, where the unit of analysis is the pair or dyad representing an interaction or a relation between two individual units, agents, or actors. Dyadic data offer more complex ways of modeling spatial-effect variables than do monadic data. The commands described in this article facilitate spatial analysis by providing an easy tool for generating, with one command line, spatial-effect variables for monadic contagion as well as for all possible forms of contagion in dyadic data.

This shapefile describes the outputs from the application of a decision support tool (Rohweder and others 2015) used to assist the Northern Missouri Private Lands Program make thoughtful and strategic choices about where to spend its limited management resources. It incorporates landscape and management unit features to help prioritize management on 17 oak savanna and open woodland restoration and enhancement project areas. The private lands program and other stakeholders can use this information to prioritize and target management. This shapefile contains the relevant input criteria attributes used in the development of station objective models.

Spatial planning regions. Map types: Lines, Choropleths. Spatial extent: Switzerland. Time: 2017. Spatial unit: Communes

This metadata record describes model outputs and supporting model code for the Data-Driven Drought Prediction project of the Water Resources Mission Area Drought Program. The data listed here include outputs of multiple machine learning model types for predicting hydrological drought at select locations within the conterminous United States. The child items referenced below correspond to different models and spatial extents (Colorado River Basin region or conterminous United States). See the list below or metadata files in each sub-folder for more details. 1. Daily streamflow percentile predictions for the Colorado River Basin region — Outputs from long short-term memory (LSTM) deep learning models corresponding to selected stream gage locations.

Zhukov, Byers, Davidson, and Kollman, "Integrating Data Across Misaligned Spatial Units," Political Analysis (conditionally accepted 10/2022)

Agricultural geospatial data of Hesse:Basic spatial unit for the management and geographical location of agricultural parcels. May contain one or more agricultural parcels declared in IACS and be managed by one or more farmers (or producer associations).

Mountain regions. Map types: Lines, Choropleths. Spatial extent: Switzerland. Time: 2020. Spatial unit: Communes