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508 results for “Malawi”
Malawi probabilistic seismic hazard analysis (PSHA) using the Malawi Seismogenic Source Model (MSSM). Supplementary Files v1.1
<p>Updated (October 2022) version of supplementary files for running probabilistic seismic hazard analysis (PSHA) MATLAB codes for Malawi. The PSHA codes themselves (v1.0) are available at: https://doi.org/10.5281/zenodo.7265781and the most recent version will be available on GitHub at: https://github.com/jack-williams1/Malawi_PSHA. Note the variables stored here are not stored on GitHub due to the file size.</p> <p>Includes both input files for performing PSHA and output ground motions for plotting PSHA results.</p> <p>Files are:</p> <ul> <li>malawi_Vs30_active.txt: Input USGS slope-based Vs30 values for Malawi (Wald and Allen 2007)</li> <li>EQCAT_comb.mat: MSSM Direct catalog for all possible rupture weightings (stored as MATLAB variable)</li> <li>GM_MSSM_em_20221027: Ground motions for plotting PSHA maps (stored as MATLAB variable)</li> <li>GM_MSSM_20221021.mat: Ground motions needed for plotting PSHA-site analysis figures (stored as MATLAB variable)</li> <li>mssm_comb.mat: Matlab file for combined MSSM Direct and Adapted MSSM catalogs (stored as MATLAB variable)</li> <li>MSSM_Catalog_Adapted_em.mat: Adapated MSSM event catalog (stored as MATLAB variable)</li> <li>syncat_bg.mat: Areal source stochastic event catalog (stored as MATLAB variable)</li> </ul> <p>Further descriptions of these files and how to use them are provided on Github. An open-access manuscript describing the PSHA is available at: </p> <p>Williams J. N., Werner M. J., Goda K., Wedmore L. N. J., De Risi R., Biggs J., Mdala H., Dulanya Z., Fagereng Å, Mphepo F., Chindandali P. (2023). Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi, Geophysical Journal International, Volume 233, Issue 3, June 2023, Pages 2172–2206, <a href="https://doi.org/10.1093/gji/ggad060">https://doi.org/10.1093/gji/ggad060</a></p> <p>Please reference this publication along with this repository when using these data.</p> <p>USGS vs30 value compilation described in:</p> <p>Allen, T. I., and Wald, D. J., 2009, On the use of high-resolution topographic data as a proxy for seismic site conditions (Vs30), Bulletin of the Seismological Society of America, 99, no. 2A, 935-943.</p> <p> </p>
Scarp height data and topographic profiles from the Zomba Graben, southern Malawi
<p>Measurements of fault scarp height from five faults in the Zomba Graben, southern Malawi (Table S1-S6). Topographic profiles used to measure the height of the scarp are in Tables S7-S11.</p> <p>For more details of this dataset please refer to Wedmore, L. N. J., Biggs, J., Williams, J. N., Fagereng, Å. Dulanya, Z., Mphepo, F., & Mdala, H. (2020). Active fault scarps in southern Malawi and their implications for the distribution of strain in incipient continental rifts. <em>Tectonics</em>, 39(3), <a href="https://doi.org/10.1029/2019TC005834">https://doi.org/10.1029/2019TC005834</a></p> <p> </p> <p>Please contact the author, Luke Wedmore (luke.wedmore@bristol.ac.uk) for more details.</p>
Fault scarp and structural measurements along the Thyolo Fault, southern Malawi
<p>Measurements of fault scarp height, topographic profiles used to measure fault scarp and metamorphic foliation measurements along the Thyolo Fualt, southern Malawi.</p> <p>This dataset is used in Wedmore, LNJ, Williams, JN, Biggs, J, Fagereng, Å, Mphepo, F, Dulanya, Z, Willoughby, J, Mdala, H, Adams, BA. 2020. Structural inheritance and border fault reactivation during active early-stage rifting along the Thyolo fault Malawi. <em>Journal of Structural Geology</em>, 139, 104097. <a href="https://doi.org/10.1016/j.jsg.2020.104097">https://doi.org/10.1016/j.jsg.2020.104097</a></p> <p>For more information please contact luke.wedmore@bristol.ac.uk</p>
A Map of Land Use and Land Cover in Southern Malawi Derived from Sentinel-2 Data (2023)
<h3><strong>Overview</strong></h3> <p>The land use and land cover map comprises the Mulanje and Phalombe districts, in Southern Malawi. It includes five classes: forest, natural vegetation, cropland, wetland, and other lands. The map is derived from Sentinel-2 mosaics, resulting in a spatial resolution of 10 meters, for 2023. </p> <p> </p> <h3><strong>Map Accuracy</strong></h3> <p>The land use and land cover map achieves an overall accuracy of 89%. Details of user and producer accuracies are provided in Table 1.</p> <p>Table 1: Land use and land cover classification validation,including overall, producer (PA) and user (UA) accuracies values for each class.</p> <div> <table> <tbody> <tr> <td> <p><strong>Class </strong></p> </td> <td> <p><strong>Producer Accuracy</strong></p> </td> <td> <p><strong>User Accuracy</strong></p> </td> </tr> <tr> <td> <p>Cropland</p> </td> <td> <p> 93%</p> </td> <td> <p> 85%</p> </td> </tr> <tr> <td> <p>Wetland</p> </td> <td> <p> 100%</p> </td> <td> <p> 100%</p> </td> </tr> <tr> <td> <p>Other Lands</p> </td> <td> <p> 90%</p> </td> <td> <p> 95%</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p> 79%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p>Natural Vegetation</p> </td> <td> <p> 90%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p><strong>Overall Accuracy</strong></p> </td> <td><br> <p><strong> 89%</strong></p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3><strong>Files descripion</strong></h3> <ul> <li>MLW_Sentinel_LULC_2023.tif / .qml: land use and land cover map and QGIS style file</li> <li>training_samples.gpkg: training samples with class labels</li> <li>validation_samples.gpkg: validation samples with class labels</li> </ul>
Dataset and code to reproduce analysis on the impact of indoor residual spraying (IRS) on malaria at Illovo Nchalo, Malawi
<p><strong>V3 edit: </strong>The latest R file contains extra lines of code to produce prediction intervals. </p> <p> </p> <p><strong>The repository contains:</strong></p> <p>- Excel sheets for each round of indoor residual spraying from 2014 - 2018 for villages based on the Illovo Nchalo Estate (provided by public health officer)</p> <p>- Weather data for 1999 - 2019 downloaded from Sasri Weather web for Malawi - Illovo Nchalo (Open access after signing up)</p> <p>- Explanation of variables downloaded from Sasri Weather Web</p> <p>- Expected population: number of residents living in Illovo clinic's catchment areas based on 2016 and 2019 census. Linear interpolation for the other years</p> <p>- Malaria data per month per clinic from the public health officer's records at Illovo Nchalo for 7 clinics for 2014 - 2018</p> <p>- Malaria data downloaded and selected from DHIS2 (access upon request and approval)</p> <p>- R file to reproduce figures, tables, and results for the paper under submission for PLOS GPH</p> <p>- Geopackages of data that is not open-source already to reproduce the map in figure 1</p> <p> </p> <p><strong>Description of IRS data:</strong></p> <p>- Village: Name of the villages based at Illovo being targeted for IRS</p> <p>- Target_spray: Number of structures within the village targeted for spraying</p> <p>- Sprayed: Number of structures actually sprayed</p> <p>- Date_start: Start date of the IRS campaign in a village</p> <p>- Date_end: End date of the IRS campaign in that village</p> <p>- Coverage_p: Percentage of structures sprayed calculated from "target_spray" and "sprayed"</p> <p> </p> <p><strong>Notes on reconciling the different years of IRS:</strong></p> <p>1. Post office and D. compound have been added to Nkombedzi</p> <p>2. B compound has been added to Riverside/Mess</p> <p>3. The following villages attend the following clinics</p> <p> </p> <p><strong>The following villages attend the assigned clinics:</strong><br>- Mess and Bonksville -> Factory<br>- Mlambe and Paxman -> Mangulenje<br>- Sande Ranch -> Lengwe<br>- Mechanical Pool -> Mwanza</p> <p> </p> <p><strong>Description of the malaria data:</strong></p> <p>- Date, month, year</p> <p>- Time_dummy: 1 to 48, over the study period</p> <p>- Village: The name of the village the clinic is based in. In further analyses, this is referred to as "clinic" instead to avoid confusion.</p> <p>- Total_cases: total number of cases testing positive for malaria by RDT, or in a very small percentage of cases microscopy (only used when RDT gives inconclusive or conflicting results, or when symptoms persist with negative RDT). Cases_on + cases_off = total_cases</p> <p>- Cases_on: Number of malaria cases from residents of villages located within the boundaries of the Illovo estate</p> <p>- Cases_off: Number of malaria cases from residents of villages located (just) outside the boundaries of the Illovo estate</p> <p>- Total_patients: Total number of patients attending the clinic that month</p> <p> </p> <p>From the selected control clinics only "WHO NMCP P Confirmed malaria cases" was used to indicate the number of malaria cases and "CMED Total Population" to indicate the clinic catchment population. Further info on DHIS2 website. </p> <p> </p> <p>For further information don't hesitate to contact Remy Hoek Spaans. </p> <p> </p> <p> </p> <p> </p>
LukeWedmore/malawi_seismogenic_source_model: Malawi Seismogenic Source Model v1.2
<p>The Malawi Seismogenic Source Model (MSSM) is a geospatial database that documents the geometry, slip rate and seismogenic properties (ie earthquake magnitude and frequency) of active faults in Malawi. Each geospatial feature represents a potential earthquake rupture of 'source' and is classified based on its geometry into one of three types:</p> <ul> <li>section</li> <li>fault</li> <li>multi-fault</li> </ul> <p>Source types are mutually exclusice, and so if incorporated into a PSHA, they should be assigned relative weightings.</p> <p>The MSSM is the first seismogenic source database in central and northern Malawi, and represents an update of the South Malawi Seismogenic Source Database (SMSSD; <a href="https://doi.org/10.5194/se-12-187-2021">Williams et al., 2021a</a>) because it incorporates new active fault traces (Kolawole et al., 2021; Williams et al., <a href="https://zenodo.org/record/5507190#.YrxpuFl7mN8">2021b</a>; <a href="https://doi.org/10.1029/2022GC010425">2022</a> - <a href="https://doi.org/10.5281/zenodo.5507190">MAFD</a>), new geodetic data (<a href="https://doi.org/10.1029/2021GL093785">Wedmore et al., 2021</a>) and a statistical treatment of uncertainty, within a logic tree approach.</p> <p>The seismogenic sources in this model are adapted from the faults in the Malawi Active Fault Database (<a href="https://doi.org/10.5281/zenodo.5507190">Williams et al., 2021b</a>; <a href="https://doi.org/10.1029/2022GC010425">2022</a>).</p> <p>Prior to publication please cite this database using the following two references:</p> <p>Williams, JN, <strong>Wedmore, LNJ</strong>, Fagereng, Å, Werner, MJ, Mdala, H, Shillington, DJ, Scholz, CA, Folawole, F, Wright, LJM, Biggs, J, Dulanya, Z, Mphepo, F, Chindandali, P. 2022. Geologic and geodetic constraints on the magnitude and frequency of earthquakes along Malawi’s active faults: the Malawi Seismogenic Source Model (MSSM). <em>Natural Hazards and Earth Systems Science</em>, 22, 3607-3639, <a href="https://doi.org/10.5194/nhess-22-3607-2022">https://doi.org/10.5194/nhess-22-3607-2022</a></p> <p>Williams, Jack N., Wedmore, Luke N. J., Fagereng, Åke, Werner, Maximilian J., Biggs, Juliet, Mdala, Hassan, Kolawole, Folarin, Shillington, Donna J., Dulanya, Zuze, Mphepo, Felix, Chindandali, Patrick R. N., Wright, Lachlan J. M., & Scholz, Christopher A. (2021). Malawi Seismogenic Source Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5599616</p> <p>Database Design and File Formats</p> <p>The MSSM is a geospatial database that consists of two separate components:</p> <ol> <li>A 3D geometrical model of fault seismogenic sources in Malawi</li> <li>The mapped trace of each source in a GIS vector format, with associated source attributes (Data Table).</li> </ol> <p>Each fault is associated with a source in the 3D geometrical model that is listed in a comma-separated-values (csv) file. The sections, faults and multi-faults that make up the individual seismogenic sources are described in separate geospatial files that describe the map-view geometry and metadata that control each sources earthquake magnitude and frequency for seismic hazard purposes.</p> <p>The sections, faults and multi-faults in this database are provided in a variety of GIS vector file formats. <a href="http://geojson.org/">GeoJSON</a> is the version of record, and any changes should be made in this version before they are converted to other file formats using the script in the repository that uses the <a href="https://gdal.org/">GDAL</a> tool <a href="https://gdal.org/programs/ogr2ogr.html">ogr2ogr</a> (the script is adapted from <a href="https://github.com/cossatot/central_am_carib_faults/blob/master/convert.sh">https://github.com/cossatot/central_am_carib_faults/blob/master/convert.sh</a> - we thank Richard Styron for making this publicly available). The other versions available are <a href="https://support.esri.com/en/white-paper/279">ESRI ShapeFile</a>, <a href="https://earth.google.com">KML</a>, <a href="https://www.generic-mapping-tools.org/">GMT</a>, and <a href="https://www.geopackage.org/">GeoPackage</a>.</p> <p> </p> <table> <caption>List and brief description of the fault geometry, slip rate estimates and earthquake source attributes in the GIS vector format files that make up the MSSM.</caption> <tbody> <tr> <td>Attribuge</td> <td>Type</td> <td>Description</td> <td>Notes</td> </tr> <tr> <td>MSSM_ID</td> <td>integer</td> <td>Unique numerical reference ID for each seismic source</td> <td>ID 00-300 is section rupture<br> ID 300-500 is fault rupture<br> ID 600-700 is a multi-fault rupture</td> </tr> <tr> <td>name</td> <td>string</td> <td> </td> <td>Assigned based on previous mapping or local geographic feature.<br> <br> For sections and faults, the name of the fault (flt_name) and larger multi-fault (mflt_name) system they are hosted on are given respectively.</td> </tr> <tr> <td>basin</td> <td>string</td> <td>Basin that source is located within.</td> <td>Used in slip rate calculations</td> </tr> <tr> <td>class</td> <td>string</td> <td>intrarift or border fault</td> <td> </td> </tr> <tr> <td>length (L<sub>s</sub>)</td> <td>real number</td> <td>straight-line distance in km between fault tips; sum of L<sub>sec</sub> for segmented faults; sum of L<sub>fault</sub> for multi-faults</td> <td>measured in km to 1 decimal place. Must be greater than 5 km (except for linking sections).</td> </tr> <tr> <td>area</td> <td>integer</td> <td>Calculated from L<sub>s</sub> multiplied by Eq. 1 or based on fault truncation.</td> <td>measured in km<sup>2</sup></td> </tr> <tr> <td>strike</td> <td>integer</td> <td>Azimuth of straigth line between the fault tips.<br> azimuth is <180°</td> <td> <p>Used as input for slip rate estimates in Eq. 2</p> </td> </tr> <tr> <td>dip_lower</td> <td>integer</td> <td>lower range of dip value</td> <td>When no previous measurements of dip are available, a nominal value of 45° is used.</td> </tr> <tr> <td>dip_int</td> <td>integer</td> <td>Intermediate dip value</td> <td>In the MSSM geometrical model, only the intermediate measurements is considered. When no previous measurements of are available, a nominal value of 53° is assigned.<br> <br> No dip is assigned for multi-fault sources, as different participating faults may have different dips.</td> </tr> <tr> <td>dip_upper</td> <td>integer</td> <td>Upper range of dip value</td> <td>When no previous measurements of dip are availabe, a nominal value of 65° is used.</td> </tr> <tr> <td>dip_dir</td> <td>string</td> <td>Dip direction: compass quadrant that the fault dips in.</td> <td> </td> </tr> <tr> <td>slip_type</td> <td>string</td> <td>Source kinematics (e.g. normal, thrust etc).</td> <td>All sources in the MSSM are assumed to be normal faults.</td> </tr> <tr> <td>slip_rate</td> <td>real number</td> <td>Mean value from repeating Eq. 2 in Monte Carlo simulations (see manuscript for details).</td> <td>In mm yr<sup>-1</sup>. All sources in the MSSM are assumed to be normal so is equivalent to dip-slip rate.<br> <br> Reported to two significant figures.</td> </tr> <tr> <td>s_rate_err</td> <td>real number</td> <td>Slip rate error: 1σ error from Monte Carlo slip rate simlations.</td> <td> </td> </tr> <tr> <td>mag_lower</td> <td>real number</td> <td>Lower magnitude estimate.<br> <br> Calculated from Leonard (2010) scaling relationship (Eq. 4) for L<sub>s</sub> or A<sub>s</sub>, and using lower estimates of C<sub>1</sub> and C<sub>2</sub> constants in Leonard (2010).</td> <td>Reported to one decimal place.</td> </tr> <tr> <td>mag_med</td> <td>real number</td> <td>Mean magnitude estimate.<br> <br> Calculated from Leonard (2010) scaling relationship (Eq. 4) for L<sub>s</sub> or A<sub>s</sub>, and using mean estimates of C<sub>1</sub> and C<sub>2</sub> constants in Leonard (2010).</td> <td>Reported to one decimal place.</td> </tr> <tr> <td>mag_upper</td> <td>real number</td> <td>Upper magnitude estimate.<br> <br> Calculated from Leonard (2010) scaling relationship (Eq. 4) for L<sub>s</sub> or A<sub>s</sub>, and using upper estimates of C<sub>1</sub> and C<sub>2</sub> constants in Leonard (2010).</td> <td>Reported to one decimal place.</td> </tr> <tr> <td>ri_lower</td> <td>real number</td> <td>Lower recurrence interval estimate.<br> <br> Calculated as 1σ below the mean of the Monte Carlo simulations (assuming a log normal distribution).</td> <td>Reported to two significant figures.</td> </tr> <tr> <td>ri_med</td> <td>real number</td> <td>Mean recurrence interval.<br> <br> Mean value from log of recurrence interval Monte Carlo simulations.</td> <td>Reported to two significant figures.</td> </tr> <tr> <td>ri_upper</td> <td>real number</td> <td>Upper recurrence interval estimate.<br> <br> Calculated as 1σ above the mean of the Monte Carlo simulations (assuming a log normal distribution).</td> <td>Reported to two significant figures.</td> </tr> <tr> <td>MAFD_id</td> <td>list</td> <td>List of integers of ID of equivalent structures in the <a href="https://doi.org/10.5281/zenodo.5507190">Malawi Active Fault Database</a></td> <td>Multi-fault sources have multiple ID's.</td> </tr> </tbody> </table> <p> </p> <p>Version Control</p> <p>This version is intended to be "Live" and as such we encourage edits of the GeoJSON file and the submission of pull requests. Please contact Jack Williams <a href="mailto:jack.williams@otago.ac.nz">jack.williams@otago.ac.nz</a> Luke Wedmore <a href="mailto:luke.wedmore@bristol.ac.uk">luke.wedmore@bristol.ac.uk</a> or Hassan Mdala <a href="mailto:mdalahassan@yahoo.com">mdalahassan@yahoo.com</a> for information, other requests or if you find any errors within the database.</p> <p>It is the intention that future versions of this database will include fault slip rates that have been determined from direct geological methods (e.g. offset stratigraphy that has been dated) rather than the systems based approach that is currently used.</p> <p> </p> <p>References</p> <p>Kolawole, F., Firkins, M. C., Al Wahaibi, T. S., Atekwana, E. A., & Soreghan, M. J. (2021a). Rift Interaction Zones and the Stages of Rift Linkage in Active Segmented Continental Rift Systems. <em>Basin Research</em>. <a href="https://doi.org/10.1111/bre.12592">https://doi.org/10.1111/bre.12592</a></p> <p>Leonard, M. (2010). Earthquake fault scaling: Self-consistent relating of rupture length, width, average displacement, and moment release. <em>Bulletin of the Seismological Society of America</em>, 100(5A), 1971-1988. <a href="https://doi.org/10.1785/0120090189">https://doi.org/10.1785/0120090189</a></p> <p>Wedmore, L. N. J., Biggs, J., Floyd, M., Fagereng, Å., Mdala, H., Chindandali, P. R. N., et al. (2021). Geodetic constraints on cratonic microplates and broad strain during rifting of thick Southern Africa lithosphere. <em>Geophysical Research Letters</em>. 48(17), e2021GL093785. <a href="https://doi.org/10.1029/2021GL093785">https://doi.org/10.1029/2021GL093785</a></p> <p>Williams, J. N., Mdala, H., Fagereng, Å., Wedmore, L. N. J., Biggs, J., Dulany, Z., et al. (2021). A systems-based approach to parameterise seismic hazard in regions with little historical or instrumental seismicity: Active fault and seismogenic source databases for southern Malawi. Solid Earth, 12(1), 187–217. <a href="https://doi.org/10.5194/se-12-187-2021">https://doi.org/10.5194/se-12-187-2021</a></p> <p><strong>V1.1 Updates</strong></p> <p>Updated seismic source files and model parameters. Changes are:</p> <ul> <li> <p>Adding lower and upper dip estimates for sources (following a reviewer comment). This should be equivalent to Table 1 in the revised manuscript.</p> </li> <li> <p>Cleaning up the GIS files. In the old file there were some duplicate GIS features that are now removed</p> </li> <li> <p>Changing the name and acronyms from Malawi Seismogenic Source Database (MSSD) to Malawi Seismogenic Sources Model (MSSM).</p> </li> </ul> <p> </p> <ul> <li> <p>Included a basic Matlab script to plot the MSSM geometrical polygons</p> </li> </ul> <p><strong>V1.2 Updates</strong></p> <p>Updated fault source geometry .csv file due to compiling error.</p>
National Checklists 2017: Malawi 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 Malawi collected using effechecka and geonames polygons
National Checklists 2019: Malawi 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 Malawi collected using effechecka and geonames polygons
qdgc Malawi
<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> Within each geopackage file you will find a number of tables with these names:<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> The attributes for each table are:<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> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<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> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<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.<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and receicved 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> 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> Ragnvald Larsen<br> Trondheim 20th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Malawi
<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, 2025)</li> <li>railways (OpenStreetMap, 2025)</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 (2025) 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>
malawi-village
<h4><strong>Source of original data</strong></h4><ul><li><a href="http://www.sociopatterns.org/datasets/contact-patterns-in-a-village-in-rural-malawi/">Contact patterns in a village in rural Malawi</a></li></ul><h4><strong>References</strong></h4><p>If you use this data, please cite the following paper:</p><ul><li><a href=" https://doi.org/10.1140/epjds/s13688-021-00302-w">Using wearable proximity sensors to characterize social contact patterns in a village of rural Malawi</a>. Ozella et al., EPJ Data Science 10, 46 (2021).</li></ul><p> </p>
Transport Starter Data Kit: Historical socio-transport data for Malawi
<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>
National Checklists: Malawi 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 2 in Two new species of Cavernocepheus (Acari, Oribatida, Otocepheidae) from Malawi
Figure 2 Cavernocepheus (Paracavernocepheus) hlavaci n. sp., adult: a – leg I, right, antiaxial view; b – leg II, right, antiaxial view; c – leg III, left, antiaxial view; d – leg IV, left, antiaxial view. Scale bar 50 μm.
Figure 3 in Two new species of Cavernocepheus (Acari, Oribatida, Otocepheidae) from Malawi
Figure 3 Cavernocepheus (Paracavernocepheus) mulanjensis textbfn. sp., adult: a – dorsal view (legs omitted); b – ventral view (legs omitted); c – lateral view (legs omitted). Scale bar 100 μm.
Figure 1 in Two new species of Cavernocepheus (Acari, Oribatida, Otocepheidae) from Malawi
Figure 1 Cavernocepheus (Paracavernocepheus) hlavaci n. sp., adult: a – dorsal view (legs omitted); b – ventral view (legs omitted); c – lateral view (legs omitted). Scale bar 100 μm.
Fig. 2 in A first report of PSeUDOSUCCInea COlUMella (Say, 1817), an alien intermediate host for liver fluke, in Malawi
Fig. 2 Conchological and anatomical comparison of Pseudosuccinea columella (top row) and Radix natalensis (bottom row). a–d P.columella conchology (a, b), shell microsculpture of the black square hatched area (c) and radular teeth (d) e–h R. natalensis conchology (e, f), shell microsculpture of the black square hatched area (g) and radular teeth (h). Although there is minor variation in the shape of the inner cusp of the first lateral teeth, the discriminatory feature is the periostracum's spiral ridges
Fig. 1 in A first report of PSeUDOSUCCInea COlUMella (Say, 1817), an alien intermediate host for liver fluke, in Malawi
Fig. 1 Sketch maps of the distribution of Pseudosuccinea columella in Mangochi (a), Chikwawa (b) and Nsanje (c) Districts, southern Malawi. Red circles indicate HUGS survey sites where P. columella was found; grey circles are surveyed sites where this snail was not found. The locations are: Mangochi 1 (− 14.31373°, 35.14174°); Chikwawa 1 (− 16.03759°, 34.84091°); Nsanje 4 (− 16.88780°, 35.27475°); Nsanje 5 (− 16.92985°, 35.26552°) with corresponding location photograph. Note that the panorama image of Mangochi 1 clearly shows the stream, flowing left to right, directly connected to Lake Malawi. HUGS, Hybridisation in UroGenital Schistosomiasis (project)
Potential Natural Vegetation of Eastern Africa (Burundi, Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia): raster and vector GIS files for each country
<p>The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia.</p> <p>The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate.</p> <p>Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fspecies.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=aeHdnF4n19CbTTznMdObr91vfZys%2FY1PrK1OxI%2BHif0%3D&reserved=0">https://vegetationmap4africa.org/species.html</a>)</p> <p>The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fvegetation_map.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=VKVkjZ8lTKMyoU9luZLAYFDwY5sbwDrGXceVEQAeGIQ%3D&reserved=0">https://vegetationmap4africa.org/vegetation_map.html</a>) and for PDF versions of the map, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocumentation.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=FIsoem3dYG4%2FIQFMPlM8B2Vf9Doqf2CS7p2fevpAwx0%3D&reserved=0">https://vegetationmap4africa.org/documentation.html</a>). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocs%2Fversionhistory%2F&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=J1aJt1D0dUhDd2fF9uEo8k1uu%2F7josYZCnQG%2FXWj5Ks%3D&reserved=0">https://vegetationmap4africa.org/docs/versionhistory/</a>). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level.</p> <p>When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI <<span><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a></span>>.</p> <p>The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen</p> <p>If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository <<span><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a></span>></p> <p>A simplified version of the map can be found on <u>Figshare <https://doi.org/10.6084/m9.figshare.1306936.v1>. </u>That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.</p> <p> </p>
The Challenges of Implementing Digital Learning Platforms in the Ministry of Information and Digitalization in Malawi
<p>This dataset was collected as part of a study exploring the implementation challenges and opportunities of digital learning platforms within the Ministry of Information and Digitalization in Malawi. The study employs a mixed-methods approach to reveal significant barriers such as internet connectivity issues, technological access limitations, and insufficient support that hinder the effective utilization of these platforms. The data includes responses from ministry personnel on their experiences with digital learning platforms, focusing on factors like support availability, time management, and motivation.</p>
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.