The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

The world is a crowded place, with more than 7 billion people on the planet as of 2014. About half of this population lives in urban areas, and ongoing migration into city centers has given rise to the megacity—a metropolitan area with 10 million people or more. This story map was produced by Esri's story map team. It is a customization of the Esri Story Map Journal app, and was created in collaboration with the Smithsonian Institution. This story map was also published on Smithsonian.com:https://www.smithsonianmag.com/science-nature/make-cities-explode-size-these-interactive-maps-180952832/

Urban open space (UOS) plays an important role, especially in areas characterized by intense social and economic activity. However, UOS mapping products for major cities around the world are lacking. To fill this gap, we used a deep learning–based method based on a tiny-manual annotation strategy to map UOS and produced a 1.19 m resolution UOS map of 2021 in urban areas of global 169 megacities with populations of over 3,000,000 people (namely the OpenspaceGlobal product). It contains five urban open space categories, namely "park and green spaces," "outdoor sports spaces," "transportation spaces," "water body spaces", and "background". The OpenspaceGlobal product can promote a better understanding of human-made space surfaces in major cities worldwide and facilitate functions such as urban livability estimation, urban planning, and sustainable development. The OpenspaceGlobal product is free to use for non-commercial forms including scientific research and science promotion under proper citation.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

A city with a population in excess of 10 million is defined by the UN as a Megacity. The International Hydrological Programme of UNESCO has established the Megacities Alliance for Water and Climate Change to support the exchange of knowledge and best practices in delivering sustainable water and sanitation services in view of Climate Change. Creative Cities, intend to foster international cooperation with and between cities committed to investing in creativity as a driver for sustainable urban development, social inclusion and cultural vibrancy. Inclusive and Sustainable Cities are interested in sharing experiences in order to improve their policies to fight racism, discrimination, xenophobia and exclusion; and Learning cities provide inspiration, know-how and best practices in matter of international policy. The map presents the Cities UNESCO’s Sectors are supporting in their efforts to enhance knowledge exchange and achieve the SDGs of the 2030 Sustainable Agenda.

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

Link travel speeds in road networks are fundamental data in many research areas of traffic, transportation, and logistics. To support the research in these areas, we develop a dataset, containing the travel speeds on each road link and in different time periods together with the real road network map. The dataset is collected from a representative megacity in Western China, Chengdu. The road network of this city involves different urban road network structures. The dataset shows the realistic variations and randomness of urban link travel speeds. This enables the research of real data-driven decision-making problems in traffic, transportation and logistics areas.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

Landsat Thematic Mapper (TM) images were processed by semi-automatic classification on QGIS to extract the water bodies and built-up land information for Jakarta , Metro Manila and Istanbul. OpenStreetMap (OSM) data and local historical road network maps were collected to obtain road networks. DEM images were used to obtain slope (in percentage) and hillshade maps. OpenQuake Engine was used to obtain seismic hazard maps (PGA in g) for 10% and 2% probabilities of exceedance. Please see ReadMe files for re-use of data.

Urban Informal Settlements (UIS) denote densely populated areas, which often exhibit a mixture of rural and urban, and are characterized by insufficient urban infrastructure standards. However, UIS mapping products for major cities in China are lacking. To fill this gap, we developed a methodology incorporating very high spatial resolution remote sensing images close to 2013 and 2021 of 37 Chinese megacities and deep learning-based models to map UIS areas (namely the ChinaUIS product). The ChinaUIS product can help promote a new understanding of urban and rural development in major cities of China and facilitate applications such as urban poverty estimation, urban\rural planning, and urban sustainability. The ChinaUIS product is free to use for non-commercial forms including scientific research and science promotion under proper citation.

The dataset contains Interferometric Synthetic Aperture Radar (InSAR)-derived Vertical Land Motion (VLM) measurements and building damage risk maps for five rapidly growing Indian megacities: New Delhi, Mumbai, Bengaluru, Chennai, and Kolkata. Researchers can visualize and extract values, including latitude and longitude information, using ArcGIS, QGIS, or any programming language that supports the ESRI shapefile format.

The geospatial distribution of post-earthquake human casualties is essential for urban planning and seismic rescue operations. This study assesses the distribution of post-earthquake injuries and fatalities in Chengdu, a major Chinese megacity, by integrating multiple big data sources, including Baidu heat map data, building vector data, point-of-interest data, and census data. The resulting dataset includes casualty distributions under seismic intensities VIII, IX, and X. It is structured in 200 m x 200 m grids with one-hour temporal resolution over a one-week period. The dataset is validated using census population data, and its accuracy is quantitatively evaluated. This high-resolution dataset can provide valuable insights for government agencies by supporting spatial pattern recognition and enhancing emergency response strategies.

Grid-based building morphological parameters with global coverage at 30-arc second spatial resolution are currently available in GeoTIFF format. Provided datasets contains three-building morphological parameters (the mean building height Have, plan area density PAD and frontal area density FAD) and two-aerodynamic parameters (aerodynamic roughness length z0 and zero-place displacement d) and sky-view factor (svf).The building morphological datasets were estimated from the global databases such as population, nighttime light, impervious surface area and gross domestic products. Two aerodynamic parameters and sky-view factors are calculated using the empirical equations discussed by Kanda et al. (2013) and Kanda et al. (2005), respectively.1. Raster files: (parameter name)_2013.tifFormat: GeoTIFFProjection: WGS 1984 World Mercator projectionSpatial resolution: 30-arc secondData list: Have_2013.tif, PAD_2013.tif, FAD_2013.tif, d_2013.tif, z0_2013.tif, svf_2013.tif2. Building Original DataFormat: Microsoft Excel WorkbookOriginal_building_data.xlsx contains observed building morphological parameters calculated from three- and two-dimensional building databases, and global databases (impervious surface area ISA and population density adjusted by nighttime light PopdenVIIRS) at each grid code.Validation_analysis.xlsx contains building morphological parameters calculated from three-dimensional building database (observed) and parameters estimated from global databases (predicted) at one-km spatial resolution in Berlin, Singapore and Osaka.Additional_validation_UScities.xlsx contains building morphological parameters at one-km resolution by NUDAPT database (observed) and estimated from global databases (predicted) for 42 US cities. We used this data in the Supplementary Discussion. Megacities_statistic.xlsx contains GDPcity, the maximum, minimum, mean value and standard deviation of each predicted building morphological parameters at 37 megacities. 3. Source CodeProgramming language 1: Python site package in ArcGIS v10.3.1Calculate_parameters.py contains code for calculating observed building morphological parameters from grid-based two- and three-dimensional building database input. We recommend using this script after using the Split By Attributing Tools to convert a fishnet building footprint map into multiple grids.Modifying_population_by_nightlight.py contains code for adjusted population density by nighttime light at each grid.Programming language 2: Python v2.7Converting_grids.py contains code for converting grid-based population density adjusted by nighttime light into a global map. This source code is used after running Modifying_population_by_nightlight.py.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

A dynamic data storage containing the atmospheric concentration/and or emission map data presented in the Emission Observatory website (https://www.emissionobservatory.org). The data updates every time new locations or gas concentration/emission maps are available/uploaded. Please note that the data are provisional and could be updated after quality control. The data storage system is Amazon Web Service (AWS) Simple Storage Service (S3 bucket). Specific information about the data can also be found from the Emission Observatory website https://www.emissionobservatory.org/. The data are presented as showcases of applications of satellite-based The dataset includes: - Nitrogen oxide (NOx) annual emission maps over 16 cities and megacities in Africa based on TROPOMI/S5p NO₂ observations - SO2 concentration maps and annual SO₂ emissions over the Tsumeb (Namibia) copper smelter based on OMI and TROPOMI/S5p SO₂ observations - Nitrogen oxide (NOx) annual emission maps over 6 coal burning power plants and the Secunda synthetic fuel plant in South Africa based on TROPOMI/S5p NO₂ observations - 6 OCO-3 Snapshot Area maps (SAMs) including enhancements of column-averaged dry air mole fraction of carbon dioxide (XCO₂) overlapped to the TROPOMI/S5p NO₂ tropospheric columns - TROPOMI/S5p methane (CH₄) vertical column density 4 background-removed maps over the Hassi Massaoud oil & gas field during the 2020 methane leak - Annual mean of TROPOMI observations of the tropospheric vertical column density (VCD) of nitrogen dioxide (NO₂) License and credits: Satellite images contain modified Copernicus Sentinel-5P/TROPOMI data and NASA OCO-3 and OMI/Aura data processed by the Finnish Meteorological Institute.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities. This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum. The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently. This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

To add this story map into a gallery, use this entry in AGOL: https://story.maps.arcgis.com/home/item.html?id=7d0b278827944efab4a692b1d6509e46not the one you are looking at. The one you are looking at is the underlying application, but we applied some customizations and the the final URL for the customized map journal is the AGOL entry above.