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556 results for “infrastructure”
Infrastructure Climate Resilience Assessment Data Starter Kit for Senegal
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Infrastructure Climate Resilience Assessment Data Starter Kit for Zambia
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Infrastructure Climate Resilience Assessment Data Starter Kit for Kenya
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Infrastructure Climate Resilience Assessment Data Starter Kit for Tanzania
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Infrastructure Climate Resilience Assessment Data Starter Kit for Ghana
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Derived daily timeseries of weather, soil moisture and temperature, flow and nitrogen species (nitrate and nitrite, ammonium) concentrations data for the North Wyke Farm Platform National Biosciences Research Infrastructure, England
<p>For a selection of catchments from the North Wyke Farm Platform in southwest England, where land use conversions have been introduced, daily time series data covering weather conditions (minimum temperature, maximum temperature, total rainfall, wind speed and solar radiation), near-surface soil status (moisture content and temperature), flow and concentrations of key nitrogen species (nitrate and nitrite, ammonium) have been filtered based on attached data quality tags . The datasets run between 2013 and March 2024. For the main climate variables, data gaps were infilled with preceding- and following-on daily data, observations from a nearby weather station or existing national datasets to generate a continuous data series for modelling. For the other data series, annual and seasonal summary statistics on data coverage are provided. Information on significant field events, such as ploughing, drilling and harvest, fertiliser applications and manure spreading were also tabulated.</p>
The Carbon Footprint of Astronomical Research Infrastructures
<p><strong>Content of the directory</strong></p> <p>This record contains code and data that were used for the paper</p> <p><strong>Knoedlseder, J., et al. Estimate of the carbon footprint of astronomical research infrastructures, Nature Astronomy, in press</strong>.</p> <p><strong>Data</strong></p> <p>The file <em>ri-carbon-footprint.xls</em> contains all data that were used for the analysis in the paper.</p> <p>The file is an excel file containing the following tabs:</p> <ul> <li>Description - a description of the excel file</li> <li>Summary - summary of the findings of the carbon footprint estimates</li> <li>Carbon footprint (ground) - Master table for carbon footprint of ground-based observatories</li> <li>Carbon footprint (space) - Master table for carbon footprint of space missions</li> <li>Emission factors - Collection of emission factors used as input to the study</li> <li>Active infrastructures - List of astronomical facilities that were active worldwide in 2019</li> <li>Community - Astronomical community and IAU members for several countries (Ahn, S.H., Economic Power, Population, and Size of Astronomical Community, JKAS, 52, 159 (2019).</li> </ul> <p><strong>Code</strong></p> <p>The record contains three Python scripts.</p> <p><strong><em>adsquery.py</em></strong></p> <p>Script to query the ADS database to extract number of publications for a given facility. The script also determines the number of unique authors. It returns global numbers since the start of the mission or observatory operations, and numbers restricted to IRAP.</p> <p><em><strong>bootstrap.py</strong></em></p> <p>Script to bootstrap the considered facilities to extrapolate the carbon footprint to all infrastructures that exist worldwide. The script also produces Figure 1 of the paper.</p> <p><strong><em>carbonintensity.py</em></strong></p> <p>Script to generate various figures from the excel data, and in particular the carbon intensity Figure 2 of the paper. Running the script requires the "xlrd" Python module that can be installed via conda.</p>
ams-icdd-usecases: Use cases for employing ICDD containers for infrastructure asset management
<p>This repository provides two use cases for infrastructure asset management using information containers according to the <a href="https://www.iso.org/standard/74389.html">Information Container for linked Document Delivery standard (ISO 25197)</a>. Use Case 1 demonstrates the data preparation and result collection of bridge visual inspection with requirement- and delivery container. Use Case 2 demonstrates the pavement maintenance plan based on the existing condition data. Therefore, an additional connection to a relational database in the information container is provided in Use Case 2, which is registered within the container using an extension <a href="https://icdd.vm.rub.de/ontology/icdd/ExtendedDocument/">EXDOC:Extension for document types for the ISO 21597 ICDD Part 1 Container ontology</a>.</p> <p><strong>Full Changelog</strong>: <a href="https://github.com/RUB-Informatik-im-Bauwesen/ams-icdd-usecases/commits/v0.1">https://github.com/RUB-Informatik-im-Bauwesen/ams-icdd-usecases/commits/v0.1</a></p>
Will it float? Exploring the social feasibility of floating solar energy infrastructure in the Netherlands
<p>Floating photovoltaic (FPV) is emerging as a promising renewable energy concept in which solar panels are installed on floating infrastructure to enable the production of renewable energy on water. While the body of knowledge on technical, financial and environmental aspects is expanding steadily, so far the societal implications of FPV remain largely unstudied. Here, we investigate public attitudes to a FPV pilot project at the Oostvoornse lake, the Netherlands. We conducted interviews with stakeholders to explore how the local community with high interest and involvement in the lake perceives the pilot project. Thereupon, we conducted a field survey with recreational users of the lake and carried out a random forest regression analysis to examine what factors shape recreationists’ support or opposition. Interview results show that the diversity of stakeholders and their diverging use of the Oostvoornse lake leads to a broad variety of concerns about how the pilot project could affect their activities and interests. Particularly the uncertainty on possible impacts due to the newness of FPV was a reason for stakeholders to take a reluctant stance towards the pilot. In contrast, our quantitative results show that recreationists were highly supportive of the project, mainly due to their positive attitudes towards local authorities and the broader societal benefits the pilot project is perceived to generate. Landscape alteration was identified to be by far the most important objection, which indicates that negative implications from a recreation perspective could be largely accommodated through appropriate siting decisions or other measures that mitigate visibility.</p>
The location and vegetation physiognomy of ecological infrastructures determine bat activity in Mediterranean floodplain landscapes
<p>Ecological infrastructures (EI), defined as natural or semi-natural structural elements, are important to support biodiversity and could play a crucial role in counteracting the well-known impacts of intensive agriculture. Yet, the importance of EI remains largely unexplored in Mediterranean agricultural landscapes and for species providing essential ecosystem services such as bats. Here, we evaluated the role of different EI types – in terms of location (riparian vs terrestrial) and vegetation physiognomy (woody vs non-woody) – in shaping bat guild activity in crop fields located in the floodplains of the Iberian Peninsula. We recorded 60,732 bat sequences in 96 crop fields and characterized 106 EI patches via an adaptation of the Biodiversity Potential Index (BPI). We found that the activity of mid-range echolocators (MRE) and long-range echolocators (LRE) was twofold higher when the nearest EI patch was riparian (i.e., contiguous to a watercourse) than when it was terrestrial. When assessing changes in bat activity in crop fields in relation to a gradient distance from EI types, our results revealed both distinct and similar effects of the location and vegetation physiognomy of the EI on bat guilds. For instance, while only the LRE guild positively responded to the proximity of woody EI, both MRE and LRE showed a marked increase of activity when increasing distances to non-woody EI, thus suggesting low bat activity levels near these features. Our habitat quality assessment also revealed that woody EI and riparian EI had higher biodiversity potential and related habitat quality, thus contributing to our understanding of bat responses to EI type in crop fields. As riparian areas are rarely targeted in biodiversity-friendly measures in farmland, we strongly recommend including riparian EI (especially the woody type) in conservation planning as they are crucial for both biodiversity conservation and ecosystem functioning.</p>
Future water level, discharge, and flood maps under climate change and infrastructure impacts along the Cambodian Mekong.
<p>Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario.</p> <p> </p> <p>A full description of the methods and results can be found in the article: </p> <p>Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. <em>Nat. Hazards Earth Syst. Sci.</em></p>
What energy infrastructure to support 1.5°C scenarios? - Scenario data
<p>This excel file summarizes the main assumptions considered for the analysis " What energy infrastructure to support 1.5°C scenarios?", prepared by Artelys on behalf of the European Climate Foundation in 2020. The report is available here: https://www.artelys.com/wp-content/uploads/2020/12/Artelys-2050EnergyInfrastructureNeeds.pdf</p>
Global patterns of current and future road infrastructure - Supplementary spatial data
<p><strong>Global patterns of current and future road infrastructure - Supplementary spatial data</strong></p> <p><strong>Authors:</strong> Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper</p> <p><strong>Research paper summary: </strong>Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted <em>R</em>2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.</p> <p><strong>Contents:</strong> The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a <a href="https://creativecommons.org/publicdomain/zero/1.0/deed.en">Creative Commons License (CC-0)</a> and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the <a href="https://www.globio.info/global-patterns-of-current-and-future-road-infrastructure">paper by Meijer et al (2018)</a>. Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.</p> <p>Regional coding of the other vector datasets in shapefile and ESRI fgdb format:</p> <ul> <li>Region 1: North America</li> <li>Region 2: Central and South America</li> <li>Region 3: Africa</li> <li>Region 4: Europe</li> <li>Region 5: Middle East and Central Asia</li> <li>Region 6: South and East Asia</li> <li>Region 7: Oceania</li> </ul> <p>Road density raster data:</p> <ul> <li>Total density, all types combined</li> <li>Type 1 density (highways)</li> <li>Type 2 density (primary roads)</li> <li>Type 3 density (secondary roads)</li> <li>Type 4 density (tertiary roads)</li> <li>Type 5 density (local roads)</li> </ul> <p><strong>Keyword:</strong> global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios</p>
Rebuilding green infrastructure in boreal production forest given future global wood demand
<p>Global policy for future biodiversity conservation is ultimately implemented at landscape and local scales. In parallel, green infrastructure (GI) planning needs to account for socio-economic dynamics at national and global scales. Progress towards policy goals must, in turn, be evaluated at the landscape scale. Evaluation tools are often environmental quality objectives (EQO) indicators.</p> <p>We present three management scenarios for a 100,000 hectare boreal forest landscape in Sweden in the coming 100 years. The scenarios optimize financial returns and account for downscaled projected global demand of wood given a middle-of-the road Shared Socioeconomic Pathway (SSP2). We contrast a <em>reference</em> scenario meeting the wood demand against an <em>economy</em> scenario with no upper harvest limit, and a <em>green infrastructure</em> (<em>GI</em>) scenario optimizing the levels of four EQO indicators (the area of old forest, the area of mature broadleaf-rich forest, the amount of deadwood and the density of large trees).</p> <p>EQO indicators generally reached the highest levels in the <em>GI</em> scenario and the lowest levels in the <em>economy</em> scenario. Most indicators increased further in set-asides. The financial profit was 14% lower in the <em>GI</em> and 2% higher in the <em>economy</em> than in the <em>reference</em> scenario.</p> <p>These scenarios were used in the associated publication to evaluate the future response of eleven model species from three different species groups with widely differing habitat requirements. The studied species were four bird species, six wood-decaying fungi and one lichen, all either of conservation concern or considered indicator species for forest of high conservation value. Models and data for the birds and fungi have been published previously. The model for the lichen <em>Lobaria pulmonaria</em> was created for this study; the underlying data is therefore presented here as well.</p> <p>Our study has shown that effects of global SSPs can be downscaled and accounted for in planning landscape-scale forest and conservation management. Accounting for EQO indicators in the management optimization was found to be an effective approach to reveal scenarios for reaching targets on both revenue and conservation. Rebuilding green infrastructure in the production forest is possible at a relatively minor economic cost and to the benefit of species of conservation concern.</p>
Maps of ecosystem multifunctionality and ecological connectivity for identifying Green Infrastructure networks in the European Alps
<p>High resolution raster datasets (20 meters) containing the results of an ecological connectivity and an ecosystem multifunctionality assessment for identifying Green Infrastructure networks in 10 pilot regions of the European Alps, modelled as part of the LUIGI Interreg Alpine Space project. Pilot regions include: department of Isère (FR), departments of Savoie and Haute-Savoie (FR), Munich Metropolitan Region (DE), Central Area of Salzburg (AT), South Burgenland (AT), Goriška region (SI), South Tyrol (IT), canton of Grisons (CH), Metropolitan City of Milan (IT), and Metropolitan City of Turin (IT). For a preview of the data and the results available for each pilot region <a href="https://www.alpine-space.org/projects/luigi/en/project-results/d.t1.2.1-pilot-regions-policy-briefs">click here</a></p> <p>Further information on the LUIGI project is available at: <a href="https://www.alpine-space.org/projects/luigi/en/home">https://www.alpine-space.org/projects/luigi/en/home</a></p> <p><a href="https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf">https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf </a></p> <p>The datasets include:</p> <ul> <li>a map for ecosystem service-based multifunctionality calculated out of the average of 11 standardized ecosystem service indicators: water provision, crop potential, timber production, fodder provision, pollination potential, carbon sequestration, nitrogen retention, natural hazard mitigation, runoff retention, outdoor recreation, and landscape aesthetics.</li> <li>a map of the modelled Ecological Network composed of core areas and ecological corridors. Corridors are modelled for medium-large forest mammal species and represent least-cost pathways connecting core areas. Different classes indicate areas with different levels of current ecological connectivity starting from core areas to areas in cities or anthropized land with no connectivity. Modeled corridors are presented in two classes to mirror different levels of prioritization and management actions.</li> <li>a map of the resistance of the landscape to the movement of forest mammal species. The landscape resistance raster has been developed by reclassifying and aggregating a high resolution (5m) land use and land cover map. Resistance values have been determined in relation to the naturalness of different land use and land cover classes. In this context, land use or landscape resistance is intended as the opposite of habitat suitability.</li> </ul>
Rainfall-induced hydroplaning risk over road infrastructure of the continental USA
<p>The data and codes uploaded here can be used to quantify rainfall-induced hydroplaning risk for road accidents and its temporal evolution on account of variations in spatio-temporal patterns of precipitation under changing climate. The analysis is carried out over all the road sections within the continental USA.</p>
Microclimate simulation output: "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens"
<p>The following microclimate simulation dataset supports the paper "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens" by Mathias Schaefer, published in Urban Ecosystems (2022).</p> <p>"T0Simulation_11082020_output" contains data about the status quo simulation of the area of interest (500 m x 500 m x 60 m), whereas "T1Simulation_11082020_output" shows the results of the Green Infrastructure scenario described in the research article above. Please ensure enough memory space on your device, as both files have a size of approximately 25 GB (unzipped).</p> <p>The output files can be visualized with the ENVI-met Leonardo extension. The ENVI-met LITE-version is freely available and can be downloaded at the <a href="https://envi-met.info/doku.php?id=files:download">ENVI-met homepage</a>. Alternatively, the included .NETCDF files can be imported as a multidimensional raster dataset in ArcGIS Pro.</p> <p>Files in the folder "atmosphere" represent meteorological parameters such as potential air temperature [°C], relative humidity [%], or wind speed [m/s]. Air pollution calculations like particulate matter concentrations [µg/m³] can be found in the folder "pollutants". The folder "buildings" contains building data for 3D visualizations of surface temperatures [°C].</p>
A Dataset for Exploring Wi-Fi Network Diversity in Vehicle-to-Infrastructure Communication
<p><strong>Introduction:</strong></p> <p>This dataset contains space and time-indexed performance data for Wi-Fi communication between a moving vehicle and a set of stationary Access Points (APs). In order to allow comparisons between technologies, 3 different types of Wi-Fi are used in parallel: 800.11n, ac, and ad.</p> <p>For more information, please consult the following article: <a href="https://www.cs.vassar.edu/~rpachecomeireles/research/papers/vnc-2020.pdf"><em>Exploring Wi-Fi Network Diversity for Vehicle-to-Infrastructure Communication</em></a>, Rui Meireles, António Rodrigues, Andrei Stanciu, Ana Aguiar, Peter Steenkiste, in the 2020 IEEE Vehicular Networking Conference (VNC 2020), December 2020, <a href="https://doi.org/10.1109/VNC51378.2020.9318407">doi:10.1109/VNC51378.2020.9318407</a>. Video presentation available <a href="https://youtu.be/IREMIGV4XLc">here</a>.</p> <p><strong>Experiment description:</strong></p> <ul> <li> <p>The AP was placed at the corner of a residential area intersection while the mobile client drove a circuit around it. The mobility pattern is shown in the animated file <code>vehicle-movement.gif</code>.</p> </li> <li> <p>The data is divided into traces, gathered on different dates, using different vehicles to support the AP on the roof, as shown below:</p> </li> </ul> <table> <tbody><tr> <th>trace nr</th> <th>date</th> <th>start time</th> <th>n(n)</th> <th>n(ac)</th> <th>n(ad)</th> <th>AP vehicle</th> <th>n(clients)</th> </tr> </tbody><tbody> <tr> <td>302</td> <td>2019-08-20</td> <td>10:28:45</td> <td>3262</td> <td>2787</td> <td>423</td> <td>2001 Honda Civic sedan</td> <td>1</td> </tr> <tr> <td>303</td> <td>2019-08-20</td> <td>11:26:23</td> <td>3374</td> <td>3027</td> <td>312</td> <td>-</td> <td>2</td> </tr> <tr> <td>304</td> <td>2019-08-20</td> <td>12:39:46</td> <td>1711</td> <td>216</td> <td>14</td> <td>-</td> <td>3 (n & ac) 2 (ad)</td> </tr> <tr> <td>401</td> <td>2019-08-22</td> <td>10:19:24</td> <td>1685</td> <td>1681</td> <td>545</td> <td>2003 Peugeot Partner</td> <td>1</td> </tr> <tr> <td>402</td> <td>2019-08-22</td> <td>10:48:26</td> <td>2859</td> <td>2827</td> <td>764</td> <td>-</td> <td>2</td> </tr> <tr> <td>403</td> <td>2019-08-22</td> <td>11:39:36</td> <td>135</td> <td>135</td> <td>116</td> <td>-</td> <td>2</td> </tr> <tr> <td>404</td> <td>2019-08-22</td> <td>11:42:50</td> <td>114</td> <td>114</td> <td>53</td> <td>-</td> <td>2</td> </tr> <tr> <td>405</td> <td>2019-08-22</td> <td>11:45:07</td> <td>2019</td> <td>2019</td> <td>507</td> <td>-</td> <td>2</td> </tr> </tbody> </table> <ul> <li><strong>APs:</strong> all positioned at coordinates {lat : 41.111879, lon : -8.631146}</li> </ul> <table> <tbody><tr> <th>ap</th> <th>device</th> <th>802.11 type</th> <th>channel</th> <th>cntr. freq (MHz)</th> <th>bw (MHz)</th> </tr> </tbody><tbody> <tr> <td>unifi-003</td> <td>ubiquiti ac lite</td> <td>n</td> <td>6</td> <td>2437</td> <td>20</td> </tr> <tr> <td>unifi-001</td> <td>-</td> <td>ac</td> <td>40</td> <td>5200</td> <td>40</td> </tr> <tr> <td>tp-01</td> <td>tp-link talon ad7200*</td> <td>ad</td> <td>1</td> <td>60480</td> <td>2160</td> </tr> </tbody> </table> <p>*running tp-link's original firmware, not OpenWrt</p> <ul> <li><strong>Main clients:</strong> all positioned in the moving vehicle's roof, a vw golf mk3</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link archer t4uh</td> <td>2</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-03)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <ul> <li><strong>Background clients:</strong> the purpose is to increase channel util.</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> <th>position</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w2</td> <td>fixed, ~2m away from AP</td> </tr> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w2</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-04)</td> <td>-</td> <td>macbook</td> <td>stopped vehicle's roof</td> </tr> </tbody> </table> <ul> <li><strong>Monitor nodes:</strong> all positioned in the moving vehicle's roof</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link talon ad7200 (tp-02)</td> <td>8</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-02)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <p>Dataset structure:</p> <p>All the packet captures are already digested and ready to use in the file <code>wifi-exp-log-summary.csv</code>. An explanation of the fields below:</p> <ul> <li><strong>systime</strong> : system time (1 Hz resolution) that this row refers to. All node clocks were synchronized through NTP.</li> <li><strong>traceNr</strong> : nr. of the trace the row belongs to.</li> <li><strong>lon</strong> : longitude (in degrees) reported by the receiver's GPS at <code>systime</code></li> <li><strong>lat</strong> : latitude reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverAlt</strong> : altitude (in meters) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverX</strong> : x coordinate of the receiver's position when space is discretized as a Cartesian plane and the sender is set to be the origin of the coordinate system. The x axis corresponds to east-west (positive values are east, negative values are west). Unit is meters.</li> <li><strong>receiverY</strong> : y coordinate of the receiver's position when space is discretized as a Cartesian plane</li> <li><strong>receiverDist</strong> : distance (in meters) of receiver to ap(s)</li> <li><strong>receiverSpeed</strong> : speed (in m/s) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverId</strong> : system-specific id for the client (in the vehicle)</li> <li><strong>senderId</strong> : system-specific id for the ap serving the client (side of the road)</li> <li><strong>isIperfOn</strong> : 1 if row's <code>systime</code> corresponds to a period where our UDP packet consumer application is known to have been running on the receiver side.</li> <li><strong>isInLap</strong> : 1 if this row's systime has been marked as being part of a time period where clients were doing laps around the APs, 0 otherwise.</li> <li><strong>rssiMean</strong> : the mean of the RSSI (Received Signal Strength Indicator) values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>snrMean</strong> : SNR (signal to noise ratio) retrieved from 802.11ad sectore sweep frames.</li> <li><strong>channelFreq</strong> : center frequency of the WiFi channel used, in MHz.</li> <li><strong>channelBw</strong> : bandwidth of the WiFi channel used, in MHz.</li> <li><strong>channelUtil</strong> : percentage of time the wireless medium was sensed to be busy during the 1-second period systime period the row refers to. <strong>In traces 40x, the 802.11n and ac routers didn't log channel busy time, and as such we had to approximate channel util. based on x,y coordinates and nr. of active clients.</strong></li> <li><strong>wifiType</strong> : 802.11 type (e.g., n, ac or ad).</li> <li><strong>nrClients</strong> : nr. of parallel clients operating in <code>wifiType</code> mode, on the same channel and bandwidth as <code>receiverId</code>.</li> <li><strong>dataRateMedian</strong> : the median of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>dataRateMean</strong> : the mean of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>nBytesReceived</strong> : total number of bytes received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>tghptConsumer</strong> : throughput reported by the UDP packet consumer application, during the 1-second period systime period the row refers to.</li> <li><strong>nRetries</strong> : nr. of WLAN-level re-transmissions on 1 second period</li> <li><strong>meanBeaconRssi</strong> : mean RSSI measured from beacons in 1 second period. nan values are filled with -100 dBm.</li> <li><strong>meanInterBeaconTime</strong> : mean interval between consecutive beacons, within 1 second period. nan values are filled with 1 sec.</li> <li><strong>nBeacons</strong> : total nr. of beacons received by client within 1 second period.</li> </ul>
Differential equity in access to public and private coastal infrastructure in the Southeastern United States
<p>Despite the ubiquity of coastal infrastructure, it is unclear what factors drive its placement, particularly for water access infrastructure (WAI) that facilitates entry to coastal ecosystems such as docks, piers, and boat landings. The placement of WAI has both ecological and social dimensions, and certain segments of coastal populations may have differential access to water. In this study, we employed an environmental justice framework to assess how public and private WAI in South Carolina, USA is distributed with respect to race and income. Using publicly available data from state agencies and the US Census Bureau, we mapped the distribution of these structures across the 301 km of the South Carolina coast. Using spatially explicit analyses with high resolution, we found that census block groups with lower income contain more public WAI, but racial composition has no effect. On the other hand, private docks showed the opposite trends, as the abundance of docks is significantly, positively correlated with census block groups that have greater percentages of White residents, while income has no effect. Under a "need-based" model of equity, we argue that WAI are not equitably distributed in South Carolina and constitute an environmental justice issue. We contend that the racially unequal distribution of docks is likely a consequence of the legacy of Black land loss, especially of waterfront property, throughout the coastal Southeast over the past half-century. Knowledge of racially inequitable distribution of WAI can guide public policy to rectify this imbalance and support advocacy organizations working to promote public water access. Our work also points to the importance of considering race in ecological research, as the spatial distribution of coastal infrastructure both directly affects ecosystems through the structures themselves and regulates which groups access water and what activities they can engage in at those sites.</p>
Biodiversity and infrastructure interact to drive tourism to and within Costa Rica
Significance Tourism accounts for roughly 10% of global gross domestic product, with nature-based tourism its fastest-growing sector in the past 10 years. Nature-based tourism can theoretically contribute to local and sustainable development by creating attractive livelihoods that support biodiversity conservation, but whether tourists prefer to visit more biodiverse destinations is poorly understood. We examine this question in Costa Rica and find that more biodiverse places tend indeed to attract more tourists, especially where there is infrastructure that makes these places more accessible. Safeguarding terrestrial biodiversity is critical to preserving the substantial economic benefits that countries derive from tourism. Investments in both biodiversity conservation and infrastructure are needed to allow biodiverse countries to rely on tourism for their sustainable development.
ScienceDex guides
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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.