World Countries is a detailed layer of country level boundaries which is best used at large scales (e.g. below 1:2m scale). For a more generalized layer to use at small-to-medium scales, refer to the World Countries (Generalized) layer. It has been designed to be used as a layer that can be easily edited to fit a users needs and view of the political world. Included are attributes for name and ISO codes, along with continent information. Particularly useful are the Land Type and Land Rank fields which separate polygons based on their areal size. These attributes are useful for rendering at different scales by providing the ability to turn off small islands which may clutter small scale views.This dataset represents the world countries as they existed in January 2015.

EN.POP.DNST. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.

This study is based on the GEE platform, combined with Sentinel-2 satellite data, using image normalization technology to enhance the recognition of smaller islands, and using support vector machine classification algorithm to extract ecological information from global islands. Using global island vector data provided by the United States Geological Survey, the Institute for Environment and Natural Resources, and the United Nations Environment Programme's World Conservation Monitoring Center as island boundaries, a total of 269391 small islands were selected for ecosystem classification. The dataset contains 1599 blocks and a color mapping table. The data size is 16.1GB. Island ecosystems are divided into five categories: water bodies, vegetation, urban areas, shallow reefs, and bare land. However, in this dataset, the water bodies were subjected to null value processing, resulting in only four classifications presented in the dataset. The TIF images extracted from this dataset can be viewed, read, and statistically analyzed using remote sensing software such as ENVI, Arc GIS, QGIS, etc.

This dataset contains the digitized treatments in Plazi based on the original journal article Pall-Gergely, Barna, Hunyadi, Andras, Jochum, Adrienne, Asami, Takahiro (2015): Seven new hypselostomatid species from China, including some of the world's smallest land snails (Gastropoda, Pulmonata, Orthurethra). ZooKeys 523: 31-62, DOI: http://dx.doi.org/10.3897/zookeys.523.6114, URL: http://dx.doi.org/10.3897/zookeys.523.6114

This map layer contains a natural-earth image of Hawaii. The image is land cover in natural colors combined with shaded relief, which produces a naturalistic rendition of the Earth's surface. The data set is in an Albers Equal-Area Conic projection. The Natural Earth data were produced from existing National Atlas land cover, tree canopy, elevation, and satellite view data.

Small-world effect plays an important role in the field of network science, and optimizing the small-world property has been a focus, which has many applications in computational social science. In the present study, we model the problem of optimizing small-world property as a multiobjective optimization, where the average clustering coefficient and average path length are optimized separately and simultaneously. A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. Experimental results have proved that the presented method is capable of solving this problem efficiently, where a uniform distribution of solutions on the Pareto-optional front can be generated. The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. The optimization on networks with the feature of community structure is more remarkable, but community structure has less impact on the optimization when the internal community is triangles-saturated.

Digital line graph (DLG) data are digital representations of cartographic information. DLGs of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1:100,000 are used. Intermediate-scale DLGs are sold in five categories: (1) Public Land Survey System; (2) boundaries; (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG-Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

Political corruption in the land sector is pervasive, but difficult to document and effectively prosecute. This paper provides new evidence on political land corruption in Malta, the European Union’s smallest member state and one of the world’s most densely populated countries. It shows how the country’s highly restrictive zoning laws, along with a de jure independent regulator, have created opportunities for extensive and endemic corruption in the granting of land development permits in zones that are outside development. It provides an example of governments creating institutions as rent-collection instruments – not to correct market failures, but to create opportunities for corruption. The unique underlying dataset was collected through an automated Web-scraping program as the regulator first turned down then ignored freedom of information requests for the data.

Overview: era5.copernicus: surface temperature daily averages from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily surface temperature in degrees Celsius x 10.

Survey of 892 small-scale farm households, which was conducted in 30 regions during May to August 2017, of which 25 in 14 European countries, and 5 in African countries.The characterization of the reference regions can be see here: http://www.salsa.uevora.pt/reference-regions/This data set formed the basis for a set of analyzes published in a SI in the scientific journal Global Food Security. Papers already published:Czekaj, M., Adamsone-Fiskovica, A., Tyran, E., Kilis, E., 2020. Small farms’ resilience strategies to face economic, social, and environmental disturbances in selected regions in Poland and Latvia, Global Food Security, 26, 100416.Guarín, A., Rivera, M., Pinto-Correia, T., Guiomar, N., Šūmane, S., Moreno-Pérez, O. M., 2020. A new typology of small farms in Europe. Global Food Security, 26, 100389.Rivera, M., Guarín, A., Pinto-Correia, T., Almaas, H., Mur, L.A., Burns, V., Czekaj, M., Ellis, R., Galli, F., Grivins, M., Hernández, P., Karanikolas, P., Prosperi, P., Zamora, P.S., 2020. Assessing the role of small farms in regional food systems in Europe: evidence from a comparative study. Global Food Security 26: 100417.Żmija, K., Fortes, A., Tia, M.N., Šūmane, S., Ayambila, S.N., Żmija, D., Satoła, Ł., Sutherland, L.-A., 2020. Small farming and generational renewal in the context of food security challenges. Global Food Security, 26, 100412.

This dataset corresponds to land area polygons of Australian coastline and surrounding islands. It was generated from 10 m Sentinel 2 imagery from 2022 - 2024 using the Normalized Difference Water Index (NDWI) to distinguish land from water. It was estimated from composite imagery made up from images where the tide is above the mean sea level. The coastline approximately corresponds to the mean high water level.

This dataset was created as part of the NESP MaC 3.17 northern Australian Reef mapping project. It was developed to allow the inshore edge of digitised fringing reef features to be neatly clipped to the land areas without requiring manual digitisation of the neighbouring coastline. This required a coastline polygon with an edge positional error of below 50 m so as to not distort the shape of small fringing reefs.

We found that existing coastline datasets such as the Geodata Coast 100K 2004 and the Australian Hydrographic Office (AHO) Australian land and coastline dataset did not meet our needs. The scale of the Geodata Coast 100K 2004 was too coarse to represent small islands and the the positional error of the Australian Hydrographic Office (AHO) Australian land and coastline dataset was too high (typically 80 m) for our application as the errors would have introduced significant errors in the shape of small fringing reefs. The Digital Earth Australia Coastline (GA) dataset was sufficiently accurate and detailed however the format of the data was unsuitable for our application as the coast was expressed as disconnected line features between rivers, rather than a closed polygon of the land areas.

We did however base our approach on the process developed for the DEA coastline described in Bishop-Taylor et al., 2021 (https://doi.org/10.1016/j.rse.2021.112734). Adapting it to our existing Sentinel 2 Google Earth processing pipeline. The difference between the approach used for the DEA coastline and this dataset was the DEA coastline performed the tidal calculations and filtering at the pixel level, where as in this dataset we only estimated a single tidal level for each whole Sentinel image scene. This was done for computational simplicity and to align with our existing Google Earth Engine image processing code. The images in the stack were sorted by this tidal estimate and those with a tidal high greater than the mean seal level were combined into the composite.

The Sentinel 2 satellite follows a sun synchronous orbit and so does not observe the full range of tidal levels. This observed tidal range varies spatially due to the relative timing of peak tides with satellite image timing. We made no accommodation for variation in the tidal levels of the images used to calculate the coastline, other than selecting images that were above the mean tide level. This means tidal height that the dataset coastline corresponds to will vary spatially. While this approach is less precise than that used in the DEA Coastline the resulting errors were sufficiently low to meet the project goals.

This simplified approach was chosen because it integrated well with our existing Sentinel 2 processing pipeline for generating composite imagery.

To verify the accuracy of this dataset we manually checked the generated coastline with high resolution imagery (ArcGIS World Imagery). We found that 90% of the coastline polygons in this dataset have a horizontal position error of less than 20 m when compared to high-resolution imagery, except for isolated failure cases.

During our manual checks we identified some areas where our algorithm can lead to falsely identifying land or not identifying land. We identified specific scenarios, or 'failure modes,' where our algorithm struggled to distinguish between land and water. These are shown in the image "Potential failure modes":
a) The coastline is pushed out due to breaking waves (example: western coast, S2 tile ID 49KPG).
b) False land polygons are created because of very turbid water due to suspended sediment. In clear water areas the near infrared channel is almost black, starkly different to the bright land areas. In very highly turbid waters the suspended sediment appears in the near infrared channel, raising its brightness to a level where it starts to overlap with the brightness of the dimmest land features. (example: Joseph Bonaparte Gulf, S2 tile ID 52LEJ). This results in turbid rivers not being correctly mapped. In version 1-1 of the dataset the rivers across northern Australia were manually corrected for these failures.
c) Very shallow, gentle sloping areas are not recognised as water and the coastline is pushed out (example: Mornington Island, S2 tile ID 54KUG). Update: A second review of this area indicated that the mapped coastline is likely to be very close to the try coastline.
d) The coastline is lower than the mean high water level (example: Great Keppel (Wop-pa) Island, S2 tile ID 55KHQ).

Some of these potential failure modes could probably be addressed in the future by using a higher resolution tide calculation and using adjusted NDWI thresholds per region to accommodate for regional differences. Some of these failure modes are likely due to the near infrared channel (B8) being able to penetrate the water approximately 0.5 m leading to errors in very shallow areas.

Some additional failures include:
- Interpreting jetties as land
- Interpreting oil rigs as land
- Bridges being interpreted as land, cutting off rivers


Methods:

The coastline polygons were created in four separate steps:
1. Create above mean sea level (AMSL) composite images.
2. Calculate the Normalized Difference Water Index (NDWI) and visualise as a grey scale image.
3. Generate vector polygons from the grey scale image using a NDWI threshold.
4. Clean up and merge polygons.

To create the AMSL composite images, multiple Sentinel 2 images were combined using the Google Earth Engine. The core algorithm was:
1. For each Sentinel 2 tile filter the "COPERNICUS/S2_HARMONIZED" image collection by
- tile ID
- maximum cloud cover 20%
- date between '2022-01-01' and '2024-06-30'
- asset_size > 100000000 (remove small fragments of tiles)
2. Remove high sun-glint images (see "High sun-glint image detection" for more information).
3. Split images by "SENSING_ORBIT_NUMBER" (see "Using SENSING_ORBIT_NUMBER for a more balanced composite" for more information).
4. Iterate over all images in the split collections to predict the tide elevation for each image from the image timestamp (see "Tide prediction" for more information).
5. Remove images where tide elevation is below mean sea level.
6. Select maximum of 200 images with AMSL tide elevation.
7. Combine SENSING_ORBIT_NUMBER collections into one image collection.
8. Remove sun-glint and apply atmospheric correction on each image (see "Sun-glint removal and atmospheric correction" for more information).
9. Duplicate image collection to first create a composite image without cloud masking and using the 15th percentile of the images in the collection (i.e. for each pixel the 15th percentile value of all images is used).
10. Apply cloud masking to all images in the original image collection (see "Cloud Masking" for more information) and create a composite by using the 15th percentile of the images in the collection (i.e. for each pixel the 15th percentile value of all images is used).
11. Combine the two composite images (no cloud mask composite and cloud mask composite). This solves the problem of some coral cays and islands being misinterpreted as clouds and therefore creating holes in the composite image. These holes are "plugged" with the underlying composite without cloud masking. (Lawrey et al. 2022)

Next, for each image the NDWI was calculated:
1. Calculate the normalised difference using the B3 (green) and B8 (near infrared).
2. Shift the value range from between -1 and +1 to values between 1 and 255 (0 reserved as no-data value).
3. Export image as 8 bit unsigned Integer grey scale image.

During the next step, we generated vector polygons from the grey scale image using a NDWI threshold:
1. Upscale image to 5 m resolution using bilinear interpolation. This was to help smooth the coastline and reduce the error introduced by the jagged pixel edges.
2. Apply a threshold to create a binary image (see "NDWI Threshold" for more information) with the value 1 for land and 2 for water (0: no data).
3. Create polygons for land values (1) in the binary image.
4. Export as shapefile.

Finally, we created a single layer from the vectorised images:
1. Merge and dissolve all vector layers in QGIS.
2. Perform smoothing (QGIS toolbox, Iterations 1, Offset 0.25, Maximum node angle to smooth 180).
3. Perform simplification (QGIS toolbox, tolerance 0.00003).
4. Remove polygon vertices on the inner circle to fill out the continental Australia.
5. Perform manual QA/QC. In this step we removed false polygons created due to sun glint and breaking waves. We also removed very small features (1 – 1.5 pixel sized features, e.g. single mangrove trees) by calculating the area of each feature (in m2) and removing features smaller than 200 m2.

15th percentile composite:

The composite image was created using the 15th percentile of the pixels values in the image stack. The 15th percentile was chosen, in preference to the median, to select darker pixels in the stack as these tend to correspond to images with clearer water conditions and higher tides.

High sun-glint image detection:

Images with high sun-glint can lead to lower quality composite images. To determine high sun-glint images, a land mask was first applied to the image to only retain water

The GIS database has been developed under the project "Renewable Energy Mapping: Small Hydro Tanzania". This study is part of a technical assistance project, ESMAP funded, being implemented by Africa Energy Practice of the World Bank in Tanzania which aims at supporting resource mapping and geospatial planning for small hydro. Please refer to the country project page for additional outputs and reports: http://esmap.org/re_mapping_TNZ The GIS database contains the following datasets: Administrative Boundaries Hydrology Protected Areas Satellite Imagery Land Cover Geology Topography Population Infrastructure: Power/ Transport each accompanied by a metadata file Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Tanzania Small Hydro GIS Atlas, 2018, https://energydata.info/dataset/tanzania-small-hydro-gis-database-2018"