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24 results for “urban indicators”
Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Indicative distribution map for Ecosystem Functional Group T7.4 Urban and industrial ecosystems
<p>This archive contains indicative distribution maps and profiles for <strong>T7.4 Urban and industrial ecosystems</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
SESMG model scenarios of the study "Indicators for the optimization of sustainable urban energy systems based on energy system modeling"
<p>This folder contains the model scenarios belonging to the publication "<strong>Indicators for the optimization of sustainable urban energy systems based on energy system modeling</strong>" (<a href="https://doi.org/10.1186/s13705-021-00323-3">https://doi.org/10.1186/s13705-021-00323-3</a>).</p> <p>The individual scenarios can be executed and evaluated with the <strong>Spreadsheet Energy System Model Generator (<a href="https://github.com/chrklemm/SESMG">SESMG</a>)</strong> <a href="https://doi.org/10.5281/zenodo.5412027">v0.0.4</a>, respectively <a href="https://doi.org/10.5281/zenodo.5520513">v0.2.0</a>.</p> <p>The file names are to be understood as follows:</p> <p><em>"scenario name"_"(dispatch) optimization criterion"_"scenario concretization"_"further scenario concretization"_"associated program version"</em>.xlsx.</p> <p>For example, the title name "<em>Scenario3_C_4MW_Biogas_SESMGv0.0.4.xlsx</em>" contains the following information:<br> - This file belongs to scenario 3 (see main publication for details).<br> - Dispatch optimized according to energy costs C (see main publication for details).<br> - The scenario contains 4 MW biogas CHP capacity (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.0.4.</p> <p>Another example. The title name "<em>optimization_C_80PercentDemand_70PercentEmissions_SESMGv0.1.1.xlsx</em>" contains the following information:<br> - This file belongs to the optimization scenario (see main publication for details).<br> - The primary optimization criterion is energy costs C (see main publication for details).<br> - Energy demand was capped at 80 percent and emissions at 70 percent of baseline (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.1.1.<br> </p> <p><strong>Acknowledgements:</strong></p> <p>The authors would like to thank Prof. Dr. Peter Vennemann (Münster University of Applied Sciences) for the constructive discussion regarding this article. This research has been conducted within the R2Q project, funded by the German Federal Ministry of Education and Research (BMBF) - grant number 033W102A and the junior research group energy sufficiency funded by the German Federal Ministry of Education and Research (BMBF) as part of its Social-Ecological Research funding priority, funding number 01UU2004A. </p>
SiEUGreen_Dataset_for_Monitoring_the_contribution_of_urban_agriculture_to_urban_sustainability:_an_indicator-based_framework
<p>The data was collected for scientific publication: Tapia, C., Randall, L., Wang, S.; Borges, L. A. (2021): Monitoring the contribution of urban agriculture to urban sustainability: an indicator-based framework. <em>Sustainable Cities and Society</em>. In press, <a href="https://doi.org/10.1016/j.scs.2021.103130">https://doi.org/10.1016/j.scs.2021.103130</a></p> <p>This dataset includes the data from the survey in Brabrand Fallaesgartneriet, which is the study case reported in the article.</p>
Resilience indicators used in the multidimensional characterization of the resilience of urban areas prone to flash floods in the region of Castilla y León (Spain)
<p>Database containing resilience indicators used in the characterization of all dimensions of resilience (social, economic, ecosystemic, physical, institutional and cultural) in those municipalities susceptible to flash floods in the region of Castilla y León (Spain). The database includes a total of 191 resilience indicators, of which 48 correspond to social resilience, 32 to economic resilience, 34 to ecosystem resilience, 44 to physical resilience, 27 to institutional resilience and 6 to cultural resilience.</p> <p>The Excel (.xlsx) file is organized by dimensions, where the prefix "SOC_" corresponds to the social dimension, "ECON_" to the economic dimension, "ECOS_" to the ecosystemic dimension, "PHY_" to the physical dimension, "INS_" to the institutional dimension and "CUL_" to the cultural dimension. Each dimension of resilience occupies two tabs: the first tab (suffixes "_data") contains the description of the indicators included (i.e., indicator code, unit of measurement, year of information, source of information, direct link to the information and bibliographic references that support the consideration of the different indicators); while the second tab (suffixes "_variables") contains the values of the different indicators (identified by their codes, which appear in the first tab) for each unit of analysis. </p>
Measures of urban form and mobility energy use indices for each census tract in the United States
<p>This dataset contains data on urban form (the configuration of the built environment) for each census tract in the United States, encompassing density (destination access), land use diversity (entropy), road network properties, road network capacity relative to the surrounding population, and public transit access. Metrics are measured around the centroid of each census tract in multiple given radii. The data also contain other publicly available metrics for each census tract that may be helpful, such as each tract's associated city, zipcode, and county name, area and water area, and centroid coordinates. Certain measures resemble those available in the U.S. Environmental Protection Agencies' Smart Location database or were derived from them, while others were compiled using additional data sources and the statistical model presented in the associated main article. Specifically, the data presented here contain travel energy use indices for each census tract, reflecting the estimated difference in daily land-based mobility energy use per capita relative to the baseline (the U.S. average) as a result of that environment's particular urban form. </p>
Measures of urban form and mobility energy use indices for each census tract in the United States
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Urban Heat: Forward-Looking Climate Modelling for West-Africa: extra indicator
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. This dataset serves as a supplement to the previous two datasets: https://zenodo.org/doi/10.5281/zenodo.11085333 and https://zenodo.org/doi/10.5281/zenodo.11073297. It contains an extra indicator Heat Index (HI) based on the apparent temperature (AT) for 12 cities in west Africa. </p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for HI across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>The indicator is available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats. It is named as HIAT in the file name with extra information such as scenario, resolution, projection, etc. </li> <li>The indicator is calculated at a resolution from <strong>100 m </strong>to <strong>200 m </strong>(depending on the size of the city). Additionally, downscaled versions of the indicator is provided at a resolution of <strong>30 m</strong>.</li> <li>The Heat Index is calculated as the <strong>yearly average number of days when apparent temperature reaches 105 F</strong>. More information regarding the definition and calculation of HI can be found: Rohat, G., Flacke, J., Dosio, A., Dao, H., & Van Maarseveen, M. (2019). Projections of human exposure to dangerous heat in African cities under multiple socioeconomic and climate scenarios. <em>Earth's Future</em>, <em>7</em>(5), 528-546.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format are available. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_HIAT.zip </strong>(for the cities with future projection) or <strong>{city}</strong><strong>_present_HIAT.zip </strong>(for those cities without future projection).</li> <li>More information about other indicators, including the simulation, methodology, all available data list, contact information, etc. can be found in the other two datasets.</li> </ul>
Emergency-line calls as an indicator to assess human-wildlife interaction in urban areas
<p><span>Human-wildlife interactions (HWI) are increasingly common as human disturbance and development continue to remove wildlife habitats. Documenting HWIs is critical for management agencies to develop strategies and management decisions that meet the needs of both people and wildlife. However, evaluating the frequency and types of HWI at broad spatial scales (e.g., national or regional level) can be costly and difficult to implement by managers. In this study, we apply a novel method for evaluating the patterns of HWI in urban areas using publicly available data from emergency calls placed by inhabitants of Romania's >300 urban areas. We used information from 4,601 emergency calls (Romanian National Emergency Call System 112), consisting of (1) wildlife species, (2) spatial location, (3) date and time, and (4) a short description of the emergency. Out of the 318 analyzed cities, 300 cities documented emergency calls on HWI between 2015–20120, with roe deer and brown bear as the most frequent species. There was an increasing trend in HWI-related emergency calls in 73% of the urban areas. We mapped the large-scale distribution of HWI by species and type of interactions, capturing variations at the national level, and further, we analyze social and biophysical factors influencing the occurrence and frequency of HWI. The results showed that social factors have the same positive or negative effect on all species, while the effect of the biophysical factors varied between species. Particularly, the presence of large natural habitats, represented by forests, influenced the number of calls only for brown bears. Seminatural landscapes with agricultural land have a different influence in terms of effect and significance for the considered species. Our results suggest that publicly available data from emergency calls can be used for the rapid assessment of HWI and for evaluating trends and predictors of HWI at broad spatial scales.</span></p>
Urban soil quality is being deteriorated even with low heavy metal levels: An arthropod-based multi-indices approach
<p><span>Urban-induced habitat conversion drastically changes soil life in a variety of ways. Soil sealing, human disturbance, habitat fragmentation, industrial and vehicular pollution are the main causes of urban soil degradation. Soil arthropods, as the most abundant and diverse group of soil fauna, are involved in many soil processes that are of great importance in maintaining soil health and multifunctionality. Nevertheless, soil quality is still mainly characterized by physical, chemical, and microbiological parameters.</span></p> <p><span>Here, we assessed and compared the biological soil quality in woody (REF: reference forest, REM: remnant forest) and non-woody (TURF: public turfgrass, and RUD: ruderal habitat) types of urban green spaces along a disturbance and management intensity gradient in the Budapest metropolitan area (Hungary), using community metrics and soil arthropod-based indicators. Vegetation cover and landscape characteristics of study sites were quantified through vegetation and urbanization indices, respectively. Basic soil properties, total and bioavailable concentrations of the main heavy metals (Cd, Co, Hg, Ni, Zn) were also measured. </span></p> <p><span>Acari, Collembola, and Hymenoptera (mainly Formicidae) were the most abundant groups. Litter-dweller taxa, particularly Protura, proved to be the most sensitive to urban disturbance. Representatives of Hemiptera, Diptera, Symphyla, and Pauropoda were common in low densities. Soil arthropod assemblages in RUD and TURF were more diverse taxonomically than in REM and REF sites. Although the integrated faunal indices showed no differences among soil habitat types, they provided different responses and, consequently, different information. Our findings demonstrated that the biological quality and arthropod community structure of soils were strongly impacted by soil C/N and heavy metal contamination. </span></p> <p><span>We found that low and moderate levels of pollution have adverse effects on edaphic fauna, suggesting biological degradation of soils, even below pollution limits. Nevertheless, more disturbed urban green spaces have been shown to play a significant role in maintaining belowground biodiversity, thereby soil functions.</span></p>
Constructing an Indicator System for Cultural Sustainability in Chinese Cities under the Objective of Urban Renewal and Capability Measurement
<p>This paper adopts the top-down approach based on the interpretation and connotation of the seven first-level and 22 second-level indicators formulated above. At the same time, we are designing the tertiary indicator database in strict accordance with the principles of scientific, systematic, and adaptive construction of the indicator system. After deliberation and modification according to the actual situation, 179 three-level indicators are formulated in Appendix 1 for details. </p>
Data from: Fine-scale flight strategies of gulls in urban airflows indicate risk and reward in city living
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Urban soil quality is being deteriorated even with low heavy metal levels: An arthropod-based multi-indices approach
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Emergency-line calls as an indicator to assess human-wildlife interaction in urban areas
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Stress in the city: meta-analysis indicates no overall evidence for stress in urban vertebrates
<p>As cities continue to grow it is increasingly important to understand the long-term responses of wildlife to urban environments. There have been increased efforts to determine whether urbanization imposes chronic stress on wild animals, but empirical evidence is mixed. Here we conduct a meta-analysis to test whether there is, on average, a detrimental effect of urbanisation based on baseline and stress-induced glucocorticoid levels of wild vertebrates. We found no effect of urbanisation on glucocorticoid levels, and neither sex, season, life stage, taxon, size of the city nor methodology accounted for variation in the observed effect sizes. At face value our results suggest that urban areas are no more stressful for wildlife than rural or non-urban areas, but we offer a few reasons why this conclusion could be premature. We propose that refining methods of data collection will improve our understanding of how urbanization affects the health and survival of wildlife.</p>
Indicators to evaluate ecosystem services of urban rivers.
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Supplementary material 3 from: Zulian G, Marando F, Mentaschi L, Alzetta C, Wilk B, Maes J (2022) Green balance in urban areas as an indicator for policy support: a multi-level application. One Ecosystem 7: e72685. https://doi.org/10.3897/oneeco.7.e72685
Setting the context at local level
Supplementary material 1 from: Zulian G, Marando F, Mentaschi L, Alzetta C, Wilk B, Maes J (2022) Green balance in urban areas as an indicator for policy support: a multi-level application. One Ecosystem 7: e72685. https://doi.org/10.3897/oneeco.7.e72685
Tutorial
Supplementary material 4 from: Zulian G, Marando F, Mentaschi L, Alzetta C, Wilk B, Maes J (2022) Green balance in urban areas as an indicator for policy support: a multi-level application. One Ecosystem 7: e72685. https://doi.org/10.3897/oneeco.7.e72685
Greenness and changes in vegetation cover in not densely built areas
Supplementary material 2 from: Zulian G, Marando F, Mentaschi L, Alzetta C, Wilk B, Maes J (2022) Green balance in urban areas as an indicator for policy support: a multi-level application. One Ecosystem 7: e72685. https://doi.org/10.3897/oneeco.7.e72685
Setting the context at European Level
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.