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88 results for “polygon”
Ocean Basin and Lake Polygons for Sea Level Grids
<p>This collection of files contains geographically registered polygons defining the shape of ocean basins and large lakes across the globe, designed for use in gridding and analysis of satellite-based observations of sea level.<span> </span>In particular, this data set was produced as part of the NASA-SSH (<a href="https://podaac.jpl.nasa.gov/NASA-SSH">https://podaac.jpl.nasa.gov/NASA-SSH</a>) effort, whose aim is to deliver continuously updated, climate-quality, global observations of sea level derived from radar altimeter observations.<span> </span></p> <p> </p> <p>The polygons were derived using products from free vector and raster map data Natural Earth (<a href="https://naturalearthdata.com">https://naturalearthdata.com</a>).<span> </span>Ocean basins were derived from version 5.1.0 of the 1:10m Marine Areas:</p> <p><a href="https://www.naturalearthdata.com/downloads/10m-physical-vectors/">https://www.naturalearthdata.com/downloads/10m-physical-vectors/</a></p> <p>and Lakes were derived from version 5.0.0 of the 1:50m Lakes and Rivers polygons:</p> <p><a href="https://www.naturalearthdata.com/downloads/50m-physical-vectors/">https://www.naturalearthdata.com/downloads/50m-physical-vectors/</a></p> <p> </p> <p>For the lakes, only the largest 26 lakes and inland seas were retained, as most smaller lakes are not typically sampled by traditional nadir altimeters.<span> </span>For the marine basins, some polygons were joined or sometimes split in order to simplify grouping altimeter data by regions where it is expected to be geographically correlated.<span> </span>For example, southern sections were split from the South Pacific and South Atlantic Oceans to simplify separation of these basins across the South American Peninsula.<span> </span></p> <p> </p> <p>In addition to the polygons themselves, a table listing connections between polygons is also provided.<span> </span>This allows users to select observations in regions that are likely to be correlated over time scales of days to weeks or longer. Each polygon carries a unique numerical identifier (the Arctic Ocean is 1, the Southern Ocean is 2, etc…).<span> </span>For each identifier, the connection table lists all of the other identifiers that polygon is connected to.<span> </span>This is used in the NASA-SSH gridding process to down-select data used to estimate sea level at a specific location.</p> <p> </p> <p>Both the ocean and lake polygons themselves, and the connection table can be easily visualized in Google Earth (or other geographic mapping software) using the KMZ file provided.<span> </span>The connection table provides sets of polygons connected to each individual feature.<span> </span>For example, the Arctic Ocean polygon is connected to the Beaufort Sea, the Greenland Sea, the Barents Sea, etc.<span> </span>These can be easily visualized by turning on subsets of features in the Basin Connections folder within the KMZ file.</p> <p> </p> <p>Files contained in this dataset include:</p> <p>basin_files.tar.gz – a tar gzip file that contains a .dbf, .prj, .shx, and .shp Shape file that can be loaded into a geographic mapping program such as QGIS or Google Earth.<span> </span>This contains the polygon definitions, including their names.</p> <p>basin_name_table.txt – an ascii text file containing a list of all the basin ID numbers and simplified version of the basin names, separated by a colon “:”.<span> </span>A few basins were created for this dataset and do not have common geographic names.<span> </span>These are given names based on their ID number for example “Feature ID: 240”.<span> </span></p> <p>basin_connection_table.txt – an ascii text file containing a list of all the basin ID numbers that are geographically connected to a given basin ID.<span> </span>The basin ID number in question is listed first on each row, and the connected ID numbers follow a colon “:”, in a comma separated list.</p> <p>NASA-SSH Basins.kmz – This KMZ file contains all of the polygon definitions, along with the set of polygons for each basin that shows which basins it is connected to.<span> </span>If loaded into Google Earth, it will create a folder in the Google Earth “Places” panel called “NASA-SSH Basins”.<span> </span>Below this, two subfolders will be created, one will be called “All Basin Polygons” and will contain all of the basins polygons colored red.<span> </span>Clicking on any one of the polygons will show the Basin ID number and name of this polygon.<span> </span>The second subfolder is called “Basin Connections” and contains a list of subfolders, one for each Basin ID.<span> </span>These can be turned on 1 at a time and will show a given basin polygon and all of the polygons it is connected to.</p> <p> </p> <p>If you use these data please cite:</p> <p>Willis, J.K., J. Sanchez, R. Santos, and S. Fournier,<span> </span>Ocean Basin and Lake Polygons for Sea Level Grids, at DOI:10.5281/zenodo.13910542.</p>
Figure 16 in Support polygons and symmetrical gaits in mammals
Figure 16. Possible adaptive value of diagonal-sequence walking gaits in primates. At the moment of forefoot touchdown, when weight is about to be transferred to a new and untested substrate, the line of gravity (grey arrow: the vertical through the body's centre of mass, estimated here as the vertical through the midpoint of an ischium-to-occiput line) passes approximately through the supporting hindfoot (grey tone) in the D-S walk of the baboon (top), but not in the L-S walk of the horse (bottom). In primates or other arboreal animals with marked grasping specializations of the hind foot, the primate support pattern allows the animal to draw back or regain its balance if the new support breaks or bends precipitously.
Figure 11 in Support polygons and symmetrical gaits in mammals
Figure 11. Modified Hildebrand diagram for the walking gaits (average duty factor ≥ 50) in our data set. The theoretically relevant duty factor in each case – Sh for diagonal-sequence walks, Sf for lateral-sequence walks – is plotted against diagonality. The diagonal lines show the distribution predicted by the support-polygon model as initially formulated.
Figure 12 in Support polygons and symmetrical gaits in mammals
Figure 12. Modified Hildebrand diagram for our data set: revised support-polygon model. The relevant duty factor in each case – which is now Sf for L-S, D-C gaits (Rule 2: D = 100 – Sf) and Sh for the others – is plotted against diagonality. The diagonal lines show the distribution predicted for walking gaits by the revised support-pattern model. Compare Figs 3 and 11.
Figure 8 in Support polygons and symmetrical gaits in mammals
Figure 8. Values of diagonality predicted from the support-polygon model. Duty factors and graphic conventions as in Figure 4. To minimize overall bipedality while maximizing diagonal bipedality ('2-diag'), a quadruped walking with a lateral-sequence gait (A) should adopt a Dvalue equal to 100 minus the forelimb duty factor (100 − Sf: horizontal arrows). A quadruped using a diagonal-sequence walk (B) should adopt a D-value equal to the hindlimb duty factor (Sh: horizontal arrows). In A, this entails that forefoot liftoffs and ipsilateral hind footfalls should be simultaneous; in B, fore footfalls and ipsilateral hindfoot liftoffs should be simultaneous (vertical arrows). In both A and B, diagonal bipedality ('2-diag') comprises the minimal percentage (64%) of the stride period possible for these duty factors. The animal is supported on 3 legs ('3-ped') for the remaining 36% of the stride period.
Figure 15 in Support polygons and symmetrical gaits in mammals
Figure 15. Values of diagonality predicted for lateralsequence, lateral-couplets walks from the revised supportpolygon model. Duty factors and graphic conventions as in Figure 4. To minimize overall bipedality while maximizing unilateral bipedality ('2-unilat'), a quadruped walking with an L-S, L-C gait should adopt a D-value equal to the hindlimb duty factor minus 50 (horizontal arrows). This entails that fore footfalls and contralateral hind liftoffs should be simultaneous (vertical arrows). Compare Figure 8.
Figure 7 in Support polygons and symmetrical gaits in mammals
Figure 7. Curves of unilateral bipedality (Bu) and diagonal bipedality (Bd) against diagonality (D) for the general case where the duty factors of the hind- and forelimbs (Sh, Sf) are unequal. Kh = Sh − 50; Kf = Sf − 50. All variables represent durations expressed as percentages of the stride period (time between two successive falls of the same foot). See text for explanation.
Figure 6. Diagrammatic quadruped seen from above. Points A-D in Support polygons and symmetrical gaits in mammals
Figure 6. Diagrammatic quadruped seen from above. Points A-D represent the animal's four feet; point G represents the vertical drawn through its centre of gravity. The animal will be more nearly balanced when standing on two diagonally opposite feet (A, C) than when standing on two ipsilateral feet (A, D), as long as G is closer to line AC than to line AD – which will be the case in most situations.
Figure 2 in Support polygons and symmetrical gaits in mammals
Figure 2. Plot of diagonality against (average) duty factor: the Hildebrand diagram. Positions of some mammalian gait types (two-letter codes) are plotted on the diagram. DS, diagonal-sequence walks; LS, lateral-sequence walks; RP, running pace; RT, running trot; WP, walking pace; WT, walking trot.
Figure 10 in Support polygons and symmetrical gaits in mammals
Figure 10. Running gaits (Sh <50> Sf) in our data, plotted on the Hildebrand diagram. The arrow ('trot line') indicates the diagonality predicted for running gaits (D = 50).
Coordinates of CHP and non-CHP configurations in regular polygons
<p>We report the coordinates of the CHP configurations in several regular polygons, for different numbers of shells.</p>
Text-fig. 7. Scanning electron microscope (SEM, a–c, e, f) and synchrotron radiation X-ray tomographic microscopy (SRXTM, d) images of fruits and pollen grains of Hedyflora crystallifera; Catefica locality, Portugal. a, b) Lateral view of fruits showing the remains of apical tepals and remains of the hypanthium, which is thicker on the edges of the fruit in (b) (arrows); c) Detail of hypanthium surface between the thickenings on the edges of the fruit showing polygonal cells with a central papilla; d) Transverse section (orthoslice xy0600) of fruit and seed showing the finely crystalliferous endotesta (arrowhead) and sclerified outer tegmen (asterisk); e) Distal view of pollen grain from surface of fruit showing the poorly defined tetrachotomocolpate aperture (daggers indicating the four arms of the aperture) and semi-tectate, reticulate tectum; f) Detail of pollen wall showing narrow muri with indistinctly beaded surface ornamentation of minute verrucae in poorly defined rows and supported by long columellae. Specimens, Catefica 49-S172313 (a), Catefica 49-S153159 (b), Catefica 49-S172324 (c), Catefica 49-S172325 (d), Catefica 50-S170453 (e, f). Scale bars = 300 Μm (a, b), 100 Μm (d), 50 Μm (c), 6 Μm (e), 1.5 Μm (f). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 7. Scanning electron microscope (SEM, a–c, e, f) and synchrotron radiation X-ray tomographic microscopy (SRXTM, d) images of fruits and pollen grains of Hedyflora crystallifera; Catefica locality, Portugal. a, b) Lateral view of fruits showing the remains of apical tepals and remains of the hypanthium, which is thicker on the edges of the fruit in (b) (arrows); c) Detail of hypanthium surface between the thickenings on the edges of the fruit showing polygonal cells with a central papilla; d) Transverse section (orthoslice xy0600) of fruit and seed showing the finely crystalliferous endotesta (arrowhead) and sclerified outer tegmen (asterisk); e) Distal view of pollen grain from surface of fruit showing the poorly defined tetrachotomocolpate aperture (daggers indicating the four arms of the aperture) and semi-tectate, reticulate tectum; f) Detail of pollen wall showing narrow muri with indistinctly beaded surface ornamentation of minute verrucae in poorly defined rows and supported by long columellae. Specimens, Catefica 49-S172313 (a), Catefica 49-S153159 (b), Catefica 49-S172324 (c), Catefica 49-S172325 (d), Catefica 50-S170453 (e, f). Scale bars = 300 Μm (a, b), 100 Μm (d), 50 Μm (c), 6 Μm (e), 1.5 Μm (f).
Surface Survey and Legacy Data in the Upper Plain of the Heraion of Perachora: The Perachora Peninsula Archaeological Project 2020 [Digital Supplement with updated polygons from 2020]
<p><strong>The digital Supplement </strong>contains figures in original size (TIFF) & digital data (SHP, CSV). The data can be also found in the linked GitHub repository <a href="https://github.com/sdam-au/perachora-medit-arch/">https://github.com/sdam-au/perachora-medit-arch/</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The Perachora Peninsula Archaeological Project held its first season of intensive surface survey from 12 January to 2 February 2020, during which time we focused on the region called the Upper Plain, situated above the sanctuary of Hera, which was in use from the 8th to the 2nd century BC. The nature of this area has been disputed, with Humfry Payne seeing it as a substantial town, while Richard Tomlinson referred to it as “a sporadic scatter of structures.” In order to clarify the nature of the settlement we employed a two-pronged effort: first to verify and document the legacy data in Tomlinson’s plan of the area, and second to conduct intensive surface survey from the boundary of the sanctuary to Lake Vouliagmeni. Within three weeks, our teams measured, photographed, and took the coordinates of nearly all the structures on Tomlinson’s plan, which included domestic structures, a small temple, and an extensive waterworks system, and several that had not been previously recorded. Building A 1 and the Fountain House were cleared and recorded using photogrammetry. The survey’s finds informed our legacy data verification efforts. Densities across all land types were high: nearly 23,000 artefacts were counted, and all but nine survey units produced artefacts. A preliminary analysis of the ceramics indicates a consistent habitation from the Archaic through the Classical period. Concentrations of miniature votives and fine wares were also found on hillsides, possibly indicating open-air shrines. Our work also highlights the need to conserve the structures in the Upper Plain.</p>
Key landscapes for the conservation of biodiversity in Sub-Saharan Africa. Proposal for modification of polygons. BIOPAMA.
<p>Key Landscapes for Conservation (KLC) are areas recognised to be of global wildlife importance with intact ecosystems that are capable of sustaining wildlife populations in the face of increasing isolation from other similar areas (Larger Than Elephants - Inputs for EU strategic approach to wildlife conservation in Africa - Regional Analysis, European Commission, 2016).</p> <p>In 2019 and 2020, following discussions with experts, the JRC proposed an update of the layer published in 2016. The 2020 layer was created in QGIS with python (10.5281/zenodo.8207793).</p> <p>The layer is available in three different data formats: ESRI shapefile, geojson and kml.</p> <p>The document KLC_modifications_20200922.pdf illustrates the proposed changes relative to the 2016 version of the polygons.</p> <p> </p>
Legume and ant polygons for range size analysis and visualization
<p>Mutualism improves organismal fitness, but strong dependence on another species can also limit a species' ability to thrive in a new range if its partner is absent. We assembled a large, global dataset on mutualistic traits and species ranges to investigate how multiple plant-animal and plant-microbe mutualisms affect the spread of legumes and ants to novel ranges. We found that generalized mutualisms increase the likelihood that a species establishes and thrives beyond its native range, whereas specialized mutualisms either do not affect or reduce non-native spread. This pattern held in both legumes and ants, indicating that specificity between mutualistic partners is a key determinant of ecological success in a new habitat. Our global analysis shows that mutualism plays an important, if often overlooked, role in plant and insect invasions.</p>
Legume and ant polygons for range size analysis and visualization
Open the record for dataset details and reuse information.
McMurdo Dry Valleys Anhydrobiosis Soils Polygon Study
In the Antarctic Dry Valleys, soil polygons are prominent features of the landscape and may be key units for scaling local ecological information to the greater region. We examined polygon soils in each of the 3 basins of Taylor Valley, Antarctica. Our objectives were to characterize variability in soil biogeochemistry and biodiversity at local to regional scales, and to test the influence of soil properties upon invertebrate communities. We found that soil biogeochemical properties and biodiversity vary over multiple spatial scales from fine (greater than 10 m) to broad (less than 10 km) scales. Differences in biogeochemistry were most pronounced at broad scales among the major lake basins of Taylor Valley corresponding to differences in geology and microclimate, while variation in invertebrate biodiversity and abundance occurred at landscape scales of 10-500 m, and within individual soil polygons. Variation in biogeochemistry and invertebrate communities across these scales reflects the influence of physical processes and landscape development over ecosystem structure in the dry valleys. The development of soil polygons influences the spatial patterning of soil properties such as soil organic matter, salinity, moisture, and invertebrate habitat suitability. Nematode abundance and life history data indicate that polygon interiors are more suitable habitats than soils in the troughs at the edges of polygons. These data suggest that physical processes (i.e. polygon development) and biogeochemistry are an important influence on the spatial variability of biotic communities in dry valley soil ecosystems.
McMurdo Dry Valleys Polygon Study: Variability in soil biogeochemistry and biodiversity
In the Antarctic Dry Valleys, soil polygons are prominent features of the landscape and may be key units for scaling local ecological information to the greater region. We examined polygon soils in each of the 3 basins of Taylor Valley, Antarctica. Our objectives were to characterize variability in soil biogeochemistry and biodiversity at local to regional scales, and to test the influence of soil properties upon invertebrate communities. We found that soil biogeochemical properties and biodiversity vary over multiple spatial scales from fine (less than 10 m) to broad (greater than 10 km) scales. Differences in biogeochemistry were most pronounced at broad scales among the major lake basins of Taylor Valley corresponding to differences in geology and microclimate, while variation in invertebrate biodiversity and abundance occurred at landscape scales of 10-500 m, and within individual soil polygons. Variation in biogeochemistry and invertebrate communities across these scales reflects the influence of physical processes and landscape development over ecosystem structure in the dry valleys. The development of soil polygons influences the spatial patterning of soil properties such as soil organic matter, salinity, moisture, and invertebrate habitat suitability. Nematode abundance and life history data indicate that polygon interiors are more suitable habitats than soils in the troughs at the edges of polygons. These data suggest that physical processes (i.e. polygon development) and biogeochemistry are an important influence on the spatial variability of biotic communities in dry valley soil ecosystems.
The data generated or compiled in the study of "Diverse polygonal patterned grounds in the northern Eridania basin, Mars: Possible origins and implications"
<p>The data that we generated or compiled, that is used to make a figure (e.g. geologic maps (Figure 2), mapped ridges (included in Figure 2), tables of measurements underlying the histograms (Figures 10 and 18), crater counts (Figure 12)), are placed in a public data repository outside of the JGR paywall. They have been introduced in the following publication, where more details can be found:</p> <p> </p> <p>Y. Dang, F. Zhang, J. Zhao, J. Wang, Y. Xu, T. Huang, and L. Xiao</p> <p>Diverse Polygonal Patterned Grounds in the Northern Eridania Basin, Mars: Possible Origins and Implications.</p> <p>Journal of Geophysical Research: Planets, 2020,125, e2020JE006647. https://<br> doi.org/10.1029/2020JE006647</p> <p> </p> <p>The file format is "Shp" processed in the software ArcGIS 10.6. The detailed description and information can be found in the title of each file below, and also their corresponding figure caption in the paper after it is formally published.</p>
Repository: Irregular Polygonal Ridge Networks in Ancient Noachian Terrain on Mars
<p>This repository contains:</p> <ol> <li>Shapefiles of irregular polygonal ridge networks within mapping extent.</li> <li>MOLA elevation and slope data extracted over mapped ridge networks.</li> </ol>
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