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8 results for “mapping on demand”
Adaptation and energy demand systematic mapping of the literature dataset
<p>Those files form a database of all the informations extracted during the associated systematic review : <a href="https://doi.org/10.1088/1748-9326/abc044">When adaptation increases energy demand: a systematic map of the literature</a></p> <p>ReadMe.pdf provides a detailled notice of the dataset.</p> <p>This dataset contains 1 SQLite file and for convenience 9 CSV files which are the tables contained in the SQLite file. CSV files are given both in Windows and Linux format in separate folders.</p>
Data collection for article "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar"
<p>This dataset contains supply, demand, and flow maps from nine ecosystem service models (as layer files in .tif format) underlying the publication "Regional scale mapping of ecosystem services supply, demand, flow and mismatches in Southern Myanmar". </p>
Data for article: Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018
<p>The data consists of the indicators for spatio-temporal analysis of wood Ecosystem Service (ES) potential, supply, and demand across Europe between 2008 and 2018. This dataset was used for the analysis of temporal trends of wood ES in the study "Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018".</p> <p>The data are collected and compiled from open access statistical databases. They consist of three parts:</p> <p>1. The PDF file with a detailed description of the data and all the input sources from which it was derived.</p> <p>2. The Zip file with 3 separate Excel files containing the short description of the indicators for mapping spatio-temporal changes, namely wood ES potential, wood ES supply and wood ES demand and their values between 2008 and 2018 at three different levels: continental, national and regional. Note that for the regional level only supply and demand indicators are available.</p> <p>3. The tiff file representing the spatial resolution for visualisation and analysis of the data.</p> <p> </p> <p> </p> <p>Resolution:</p> <p>Data are available for 3 spatial levels, in the temporal dimension between 2008 and 2018 (annually). All 3 indicators are available at continental (European) and national scales. The study area covers 24 countries of the European Union (EU) and Switzerland. Supply and demand are also available at regional level. At the regional scale, we assessed supply and demand using the nomenclature of territorial units for statistics (NUTS 3; n = 1061) and local administrative units (LAU; n = 957) from the year 2016 (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units.">Eurostat, 2016</a>). The visualisation of the resolution is available in the tiff file attached to the data.</p> <p>The data are visualised and analysed in the ETRS 1989 LAEA projection.</p> <p> </p> <p>For more information on the indicators used, data collection and processing, see the supplementary files of the published article.</p> <p> </p> <p> </p> <p><strong>DEFINITIONS of ES mapping indicators used in the study: </strong></p> <p><em>ES potential</em> - the hypothetical maximum yield of services potentially available for supply.</p> <p><em>ES supply</em> – the amount of the mobilized service within the ecosystem capable to provide a service at a given location in a certain time (frequently referred in literature as ES flow).</p> <p><em>ES demand </em>- the need for the ecosystem-based service by the end users. In this study demand is analysed from the perspective of service end-user.</p>
Global heat map of probable importance of terrestrial ecosystems on meeting local demand of freshwater services
<p>This map (raster dataset, single layer) uses existing datasets to map globally “How important point x is likely to be for meeting the demand of a reliable & useable source of water on a scale of 0 to 1?” This relatively simple approach uses estimated water demand in a given basin as weight to identify pressure for flow regulation and water provisioning services. Precipitation and land cover estimates are then combined with it to give some insight into the hydrologic attributes of “location” and “timing” of flow that the ecosystems may influence. The underlying assumption here is that undisturbed ecosystems everywhere are performing the ecohydrological functions leading to freshwater services. The question is more (at the global scale): how dependent are the populations in the basin on the continued functioning of these services.</p> <p><strong>Input datasets:</strong></p> <ol> <li>Annual surface & groundwater (“blue”) water consumption estimates. URL: <a href="http://waterfootprint.org/en/resources/water-footprint-statistics/">http://waterfootprint.org/en/resources/water-footprint-statistics/</a></li> <li>HydroBasins watershed outline.</li> <li>European Space Agency (ESA) global land cover 2015.</li> <li>WorldClim annual average precipitation (Version 2.0).</li> </ol> <p><strong>Process:</strong></p> <p>Step 1: Calculate average annual water consumption estimates over HydroBasin outlines. This step spreads the demand laterally (in case of small basins) and upstream to the headwaters from (typically) downstream consumer concentration.</p> <p>Step 2: Normalize the demand globally and map the normalized values on to “natural” land cover classes from the land cover dataset [forests, grasslands, etc].</p> <p>Step 3: Normalize annual precipitation layer within basins on the scale 0-1 where 1 is the maximum annual precipitation in that basin. This is also mapped on the “natural” land cover. Precipitation is thus acting as ‘weight’ for importance within the basin. Example, upland headwaters will typically receive more rainfall and can be argued to be important for the flow regulation in the basin.</p> <p>Step 4: Combine the layers from 2 and 3.</p> <p><strong>Caveats:</strong></p> <ol> <li>Identification of what constitutes a “natural” land cover is not trivial, especially from global land cover maps. Example: Forests and plantations are hard to distinguish from these products.</li> <li>Improvement of quality of water is assumed to be implicit for functioning ecosystems.</li> </ol>
Supplementary material 2 from: Bicking S, Burkhard B, Kruse M, Müller F (2018) Mapping of nutrient regulating ecosystem service supply and demand on different scales in Schleswig-Holstein, Germany. One Ecosystem 3: e22509. https://doi.org/10.3897/oneeco.3.e22509
Average yield (in t/ha) of agricultural crops in Schleswig-Holstein in 2010 (data from Statistikamt Nord 2010), nitrogen content (in kg N/dt fresh weight) of crop plants (data from DüV 2007) and corresponding estimated nitrogen content per hectare.
Supplementary material 3 from: Bicking S, Burkhard B, Kruse M, Müller F (2018) Mapping of nutrient regulating ecosystem service supply and demand on different scales in Schleswig-Holstein, Germany. One Ecosystem 3: e22509. https://doi.org/10.3897/oneeco.3.e22509
Relevant nitrogen application and input rates considering nitrogen losses (data from Landwirtschaftskammer Niedersachsen 2017b).
Supplementary material 1 from: Bicking S, Burkhard B, Kruse M, Müller F (2018) Mapping of nutrient regulating ecosystem service supply and demand on different scales in Schleswig-Holstein, Germany. One Ecosystem 3: e22509. https://doi.org/10.3897/oneeco.3.e22509
Nitrogen excretion (in kg N/year) of livestock (data from the Ministerium für Landwirtschaft, Umwelt und Verbraucherschutz Mecklenburg-Vorpommern 2008)
Probing the Role of Feature Dimension Maps in Visual Cognition: Impact of Task Demands (Expt 2.1)
ClinicalTrials.gov study NCT06281457. IPD Sharing: YES. Countries: 1. Publications: 14.
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