This database presents population and other demographic estimates and projections from 1960 to 2050, covering more than 200 economies. It includes population data by various age groups, sex, urban/rural; fertility data; mortality data; and migration data.

Use this country model layer when performing analysis within a single country. This layer displays a single global land cover map that is modeled by country for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create these predictions. Variable mapped: Projected land cover in 2050. Data Projection: Cylindrical Equal Area Mosaic Projection: Cylindrical Equal Area Extent: Global Cell Size: 300m Source Type: Thematic Visible Scale: 1:50,000 and smaller Source: Clark University Publication date: April 2021What you can do with this layer? This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 WorldLand Cover 2050 RegionalLand Cover 2050 CountryLand Cover Vulnerability Change 2050 WorldLand Cover Vulnerability Change 2050 RegionalLand Cover Vulnerability Change 2050 CountryWhat these layers model (and what they don’t model) The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many. The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use. Quantitative Variables used to create Models Biomass Crop Suitability Distance to Airports Distance to Cropland 2010 Distance to Primary Roads Distance to Railroads Distance to Secondary Roads Distance to Settled Areas Distance to Urban 2010 Elevation GDP Human Influence Index Population Density Precipitation Regions Slope Temperature Qualitative Variables used to create Models Biomes Ecoregions Irrigated Crops Protected Areas Provinces Rainfed Crops Soil Classification Soil Depth Soil Drainage Soil pH Soil Texture Were small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change. Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050. 39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset: The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland 2 Grassland, Scrub, or Shrub 3 Mostly Deciduous Forest 4 Mostly Needleleaf/Evergreen Forest 5 Sparse Vegetation 6 Bare Area 7 Swampy or Often Flooded Vegetation 8 Artificial Surface or Urban Area 9 Surface Water 10 Permanent Snow and Ice

According to the United Nations, 54% of the world’s population resides in urban areas in the year 2014. It is projected that by 2050 this number will increase by 12%. The direct effect of this urban drift has had profound effects on social, economic and ecological systems, causing stresses on the environment and society. The social and economic implications include impacts from human activities such as transport, industrialization, combustion, construction etc., all of which have a direct or indirect bearing on the environment. These pollution sources have led to release of pollutants such as Nitrogen dioxide (NO2), Particulate Matter (PM) and Sulphur dioxide (SO2) into the atmosphere. It is believed that air pollution is influenced by urban dynamics.In this project, we present a method for predicting historical air quality (as measured by daily median PM25 concentration) for locations where no ground-based sensors are present, by using weather data and remote sensing data from sources like the Sentinel 5P satellite. Air quality data is obtained for 555 cities and supplemented by satellite and weather data. This is then used to build a model to predict the air quality for a given date and location. A competition hosted by Zindi was used to crowd-source the creation of the model used, with the winning code forming the basis of our modelling approach.We use the trained model to create a new dataset of historical air quality predictions for cities across Africa, available at https://github.com/johnowhitaker/air_quality_prediction. For access to the original data see https://search.datacite.org/works/10.15493/sarva.301020-2.