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82 results for “Djibouti”
National Checklists 2017: Djibouti Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Djibouti collected using effechecka and geonames polygons
National Checklists 2019: Djibouti Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Djibouti collected using effechecka and geonames polygons
Fig. 1 in A preliminary catalogue of the Hymenoptera (Insecta) of the Republic of Djibouti
Fig. 1: Hemiscorpius sp. (Scorpionides: Hemiscorpiidae) and its prey Anochetus sp. (Formicidae) at Dimbiya near Karta (Photo: M.A. Jäch, 27.01.2016).
qdgc Djibouti
<p>QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> <br> <br> The attributes for each table are:<br> <br> <br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 23rd of January, 2021<br> <br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Transport Starter Data Kit: Historical socio-transport data for Djibouti
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Djibouti
<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)</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> 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=11539">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> 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> 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> 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>
National Checklists: Djibouti Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Figure 113 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figure 113. Confirmed geographic distribution of scorpions in Djibouti. Maps created in Google Maps (2022).
Figures 100–111 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 100–111: Figures 100–110: Hemiscorpius lipsae sp. n. female holotype, pedipalp (100–109) and telson lateral (110). Figures 100– 109. Chela, dorsal (100), external (101), and ventral (102) views. Patella, dorsal (103), external (104) and ventral (105) views. Femur and trochanter, dorsal (106), and ventral (107) views. Movable (108) and fixed (109) fingers dentation. The trichobothrial pattern indicated in Figures 100–106 by white circles. Figure 111. Hemiscorpius tellini, female holotype, pedipalp chela dorsal.
Figures 95–99 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 95–99. Hemiscorpius lipsae sp. n. female holotype, carapace and tergites I–III (95), sternopectinal region and sternites III–IV (96), metasoma and telson, lateral (97), dorsal (98), and ventral (99) views. Scale bar: 10 mm (73–75).
Figures 79–82 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 79–82: Morphometric comparisons of Orthochirus species. Figures. 79–80. Rank ordered horizontal bar plots of adult male carapace lengths of 42 species (79), and adult male total lengths of 40 species (= anterior margin of carapace to tip of aculeus of extended telson) (80). Figure 81. Bivariate scatter plot of adult male total length vs. adult male carapace length for 39 species. Dark brown bars and symbols highlight the species O. afar; orange bars and symbols highlight the species chosen by Lourenço & Ythier (2021: 345) to represent "moderate-size Orthochirus species". Figure 82. Horizontal bar plot of mean punctae diameters on ventral and lateral surfaces of metasoma IV, expressed as percentage of metasoma IV length, in six females of Orthochirus. Dark brown bars: specimens assigned to the species O. afar Kovařík & Lowe, 2016 (or a closely similar species); green bars: specimens assigned to the species O. olivaceus (Karsch, 1881). The dark female from Nubia (leg. Brignoli, 1975) is statistically grouped with O. afar. In all figures, plotted values are either single measurements or means, and error bars are standard deviations (SD).
Figures 33–40 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 33–40: Compsobuthus vannii, Djibouti. Figures 33, 35. Male from Tadjourah Province, Day, 11.7813°N 42.6408°E, 1490 m a. s. l., carapace and tergites I–III (33), sternopectinal region and sternites (35). Figures 34, 37–40. Female from Tadjourah Province, Abourma, 11.8941°N 42.4877°E, 800 m a. s. l., carapace and tergites I–III (34), sternopectinal region and sternite III (36), and left legs I–IV, retrolateral aspect (37–40).
Figures 73–76 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 73–76: Neobuthus ferrugineus, Djibouti, Barra Yer, 11.31°N 42.71°E, 585 m a. s. l. Figures 73–74. Male, dorsal (73) and ventral (74) views. Figures 75–76. Female, dorsal (75) and ventral (76) views. Scale bar: 10 mm.
Figures 71–72 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 71–72. Microbuthus litoralis, female from Djibouti, Arta Province, Djalelo, 11.3652°N 42.8404°E, 690 m a. s. l. in dorsal (71) and ventral (72) views. Scale bar: 10 mm.
Figures 25–32 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 25–32: Compsobuthus vannii, Djibouti. Figures 25, 27–29. Male from Tadjourah Province, Day, 11.7813°N 42.6408°E, 1490 m a. s. l., telson lateral (25), metasoma and telson, lateral (27), dorsal (28), and ventral (29) views. Figures 26, 30–32. Female from Tadjourah Province, Abourma, 11.8941°N 42.4877°E, 800 m a. s. l., telson lateral 26), metasoma and telson, lateral (30), dorsal (31), and ventral (32) views. Scale bars: 10 mm (27–29, 30–32).
Figures 5–12 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 5–12: Buthus awashensis, Djibouti, Arta Province, Arta, 11.5286°N 42.8508°E, 690 m a. s. l. Figures 5, 7–9. Male, telson lateral (5), metasoma and telson, lateral (7), dorsal (8), and ventral (9) views. Figures 6, 10–12. Female, telson lateral (6), metasoma and telson, lateral (10), dorsal (11), and ventral (12) views. Scale bars: 10 mm (7–12).
Figures 77–78 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 77–78. Orthochirus afar, male from Djibouti, Tadjourah Province, Ditillou, 11.7811°N 42.6934°E, 665 m a. s. l. in dorsal (77) and ventral (78) views. Scale bar: 10 mm.
Figures 65–70 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 65–70. Hottentotta polystictus, female from Djibouti, Tadjourah Province, Medeho, 11.9086°N 43.1356°E, 30 m a. s. l., carapace and tergites I–IV (65), sternopectinal region and sternites (66), telson lateral (67), metasoma and telson, lateral (68), dorsal (69), and ventral (70) views. Scale bar: 10 mm (68–70).
Figures 1–4 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 1–4: Buthus awashensis, Djibouti, Arta Province, Arta, 11.5286°N 42.8508°E, 690 m a. s. l. Figures 1–2. Male, dorsal (1) and ventral (2) views. Figures 3–4. Female, dorsal (3) and ventral (4) views. Scale bar: 10 mm.
Figures 21–24 in Scorpions of the Horn of Africa (Arachnida Scorpiones) Part XXVIII Scorpions of Djibouti
Figures 21–24: Compsobuthus vannii, Djibouti. Figures 21–22. Male from Tadjourah Province, Day, 11.7813°N 42.6408°E, 1490 m a. s. l., dorsal (21) and ventral (22) views. Figures 23–24. Female from Tadjourah Province, Abourma, 11.8941°N 42.4877°E, 800 m a. s. l., dorsal (23) and ventral (24) views. Scale bars: 10 mm (21–22, 23–24).
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