![]() Kontur Population became one of the daily topics of #30DayMapChallenge on Twitter. Population data is applicable for visualization and insight extraction in various domains. In addition, data on the population of the area surrounding the city is also important since the fringe areas may become part of the city. When planning fire services, it is crucial to know the total population and its distribution within a city. We used Kontur Population as the baseline dataset to create a Fire Service Scarcity Risk map to help cities visualize their ability to protect citizens from fire. Read how we used Machine Learning algorithms to detect building footprints from satellite imagery and create a custom population dataset for a site selection project in Abu Dhabi. Reliable population data is a crucial part of such analysis. Using geospatial data analysis significantly reduces the time it takes to find the best location when starting a new business. We created a bivariate choropleth map to visualize the waste bin availability along with population density within one layer. We teamed up with volunteers in Batumi to help them approach the problem of litter that pollutes streets, parks, rivers, and beaches within the city. Kontur team wanted to apply our geospatial expertise to solving local environmental challenges. Humanitarian OSM Summit 2020: Kontur Population & Disaster.Ninja Waste management optimization Read how data correlated with global population density helps support the rapid deployment of emergency mapping campaigns for Humanitarian OpenStreetMap Team. ![]() The tool is actively used by humanitarian mappers to take action confidently based on data correlated with world population density. ![]() The dataset was primarily designed to support visualization behind Disaster Ninja. The latest version of Kontur Population is available at. Non-integer population counts are rounded to integers. 25 km²) are spread out to neighboring cells to satisfy constraints. While the population total is accurate, extremely populated cells (i.e., 500,000 people in. Lakes, rivers, glaciers, sands, forests, and other alike land uses are marked as unpopulated. Quarries and big roads are marked as unpopulated, as they are often falsely detected as populated in GHSL. Building presence, or otherwise built-up area, implies there’s someone on the ground, which is often missed in Facebook Africa data. Known artifacts of GHSL and HRSL datasets are constrained using OpenStreetMap data. Microsoft Building Footprint, Land Information New Zealand, and Copernicus Global Land Service data are used to improve distribution accuracy. GHSL data is overlaid with Facebook population data (HRSL) where available. Population calculations are based on the Global Human Settlement Layer (GHSL) – a framework relying on a large set of sensors, including radar and optical public and commercial missions. This property greatly simplifies performing analysis and smoothing over gradients. ![]() The reason why we use H3 grid instead of the common square grid is that unlike squares, hexagons have equal distances between a hexagon centerpoint and the centers of neighboring cells. Kontur Population dataset is represented by H3 hexagons with population counts at 400m resolution. The world population density map shows the distribution of people across the globe, with higher population densities typically concentrated in urban areas and lower densities in rural areas.
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