Description This dataset contains both tabular and geospatial data of eight great powers' overseas military bases, including China, the United States, the United Kingdoms, Russia, Japan, India, the United Arab Emirates, and France up until November 2020. An interactive view of this dataset: Link Source All data were collected from multiple public sources and specified in each data point in the Excel file and Shapefile. For metadata, such as data description and available methods for geospatial data processing, please read the readme.pdf. Terms of use This dataset features in a collection of geospatial data "Geo-mapping databases for the Belt and Road Initiative". To cite this work, available citation styles can be found here: https://doi.org/10.6084/m9.figshare.c.6076193

India Biodiversity Portal (IBP) is repository of information on biodiversity in India. The portal provides geospatial data on biodiversity by the following themes: Biogeography, Abiotic, Demography, Species, Administrative Units, Land Use Land Cover, Conservation, Threats.As well as for the following geographies: India (national), Uttaranchal, Nilgiri Biosphere Reserve, Papagni, Andhra Pradesh, Western Ghats, BR Hills, Karnataka, Vembanad, Kerala, Satkoshia, Orissa, North East Area, Agar, Madhya Pradesh, Mandla, Madhya Pradesh, Pench, Madhya Pradesh, Bandipur, Karnataka, Kanakapura.

Covid19Kerala.info-Data is a consolidated multi-source open dataset of metadata from the COVID-19 outbreak in the Indian state of Kerala. It is created and maintained by volunteers of ‘Collective for Open Data Distribution-Keralam’ (CODD-K), a nonprofit consortium of individuals formed for the distribution and longevity of open-datasets. Covid19Kerala.info-Data covers a set of correlated temporal and spatial metadata of SARS-CoV-2 infections and prevention measures in Kerala. Static releases of this dataset snapshots are manually produced from a live database maintained as a set of publicly accessible Google sheets. This dataset is made available under the Open Data Commons Attribution License v1.0 (ODC-BY 1.0).

Schema and data package Datapackage with schema definition is accessible at https://codd-k.github.io/covid19kerala.info-data/datapackage.json. Provided datapackage and schema are based on Frictionless data Data Package specification.

Temporal and Spatial Coverage

This dataset covers COVID-19 outbreak and related data from the state of Kerala, India, from January 31, 2020 till the date of the publication of this snapshot. The dataset shall be maintained throughout the entirety of the COVID-19 outbreak.

The spatial coverage of the data lies within the geographical boundaries of the Kerala state which includes its 14 administrative subdivisions. The state is further divided into Local Self Governing (LSG) Bodies. Reference to this spatial information is included on appropriate data facets. Available spatial information on regions outside Kerala was mentioned, but it is limited as a reference to the possible origins of the infection clusters or movement of the individuals.

Longevity and Provenance

The dataset snapshot releases are published and maintained in a designated GitHub repository maintained by CODD-K team. Periodic snapshots from the live database will be released at regular intervals. The GitHub commit logs for the repository will be maintained as a record of provenance, and archived repository will be maintained at the end of the project lifecycle for the longevity of the dataset.

Data Stewardship

CODD-K expects all administrators, managers, and users of its datasets to manage, access, and utilize them in a manner that is consistent with the consortium’s need for security and confidentiality and relevant legal frameworks within all geographies, especially Kerala and India. As a responsible steward to maintain and make this dataset accessible— CODD-K absolves from all liabilities of the damages, if any caused by inaccuracies in the dataset.

License

This dataset is made available by the CODD-K consortium under ODC-BY 1.0 license. The Open Data Commons Attribution License (ODC-By) v1.0 ensures that users of this dataset are free to copy, distribute and use the dataset to produce works and even to modify, transform and build upon the database, as long as they attribute the public use of the database or works produced from the same, as mentioned in the citation below.

Disclaimer

Covid19Kerala.info-Data is provided under the ODC-BY 1.0 license as-is. Though every attempt is taken to ensure that the data is error-free and up to date, the CODD-K consortium do not bear any responsibilities for inaccuracies in the dataset or any losses—monetary or otherwise—that users of this dataset may incur.

India's elevation data as a single TIFF file. See https://github.com/dilawar/map-india-center for more details.MD5 checksum: 97dcbee8b20f3b4de3036cfb9701a5e7 india.clipped.tif# CreditsFile india-composite.geojson is from datameet repository https://github.com/datameet/maps/tree/master/Country (Release under http://creativecommons.org/licenses/by-sa/2.5/in/ )

This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate.

v1.0: India Flood Inventory (IFI) 1967-2016.

v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area.

v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district.

IFI v1.0 publication: Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. https://doi.org/10.1007/s11069-021-04698-6

IFI v2.0 publication: Saharia, Manabendra, et al. A District Level Flood Severity Index for India. arXiv:2405.01602, arXiv, 1 May 2024. arXiv.org, https://doi.org/10.48550/arXiv.2405.01602. [Under Review]

Morph_CNeT” (Morphometric Channel Network Extraction Tool) can facilitate extraction of the topology based new morphometric attributes by processing DEM datasets within GIS framework. Morph_CNeT tool is used to create a repository named as Morph_CNeT-India of topological catchment attributes for 1749 gauging stations maintained by the Central Water Commission (CWC) across 22 River basin Systems of India.

OVERVIEW This dataset contains data from a survey of low income households in four cities across south India. This fileset includes a guidance document on how the data was collected and how to interpret and use the data. The survey data was collected between April-June 2019. A team of 11 survey enumerators and researchers were involved in the data collection which was collected through a collaboration between the University of Cambridge and the Indian Institute for Human Settlements. Data collection for this project received ethical approval from both the Department of Engineering, University of Cambridge and Indian Institute for Human Settlements. This anonymised dataset is being released to allow full use by others.

DATASET CONTENTS This dataset contains the following files: - Indian_Low_Income_Household_Energy_Survey_Codebook.pdf - south_indian_household_energy_survey_19.csv - south_indian_household_energy_survey_19.Rda - README.txt Data contained in the csv files is the same as data contained in the Rda file.

HOW TO USE All csv files can be opened using any appropriate software. Rdata script files must be opened and run using R. We recommend using RStudio and R version 3.5.1 (“Feather Spray”) or later.

This survey followed the same methodology and as an earlier survey of low-income households in Bangalore, India. The dataset from this earlier survey can be found at: https://doi.org/10.17863/CAM.59870

This dataset was used as external validation dataset for a microsimulation of cooking fuel use in India cities. Code for the microsimulation model can be found in the following GitHub repository: github.com/anetobradley/urban_energy_microsimulation_india

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live includes:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

GapMaps curates up-to-date and high-quality GIS Data tracking store openings and closures for leading retail brands across Asia and MENA. Get the insights you need to make more accurate and informed business decisions.

Data and methods

On 3 October 2023, the collapse of a frozen lateral moraine into South Lhonak Lake triggered a multi-hazard cascade in the Sikkim Himalaya, India (Sattar et al, 2025). This repository contains two DEMs derived from Pléiades satellite images that were acquired before and after the event.

• A post-event DEM computed from a pair of PHR1A stereoscopic images acquired on 2023-10-29 (20231029.tif)
• A pre-event DEM computed from a triplet of PHR1A stereoscopic images acquired on 2022-10-18 (20221018.tif)

The DEMs (height above the WGS84 ellipsoid) were posted at 1.0 m with UTM 45N projection (EPSG:32645) and coregistered to the GLO30 Copernicus DEM. They were computed with the MicMac software in the DSM-OPT service using the following configuration: correlation window size: 3x3, regularization factor: 0.05, vertical uncertainty: 0.1, potential accuracy threshold: 0.4 (Rupnik et al. 2017).

These DEMs allow the analysis of topographic changes in the lake area. We included examples of such analysis with the addition of the High Mountain Asia 8 m DEM (Shean 2017).

Acknowledgements

Pléiades images were acquired and processed thanks to the CIEST2 service developed and performed with the French Space Agency (CNES) by FormaTerre, Solid Earth component of the Data Terra Research Infrastructure.

References

GapMaps Store Location Data uses known population data combined with billions of mobile device location points to provide highly accurate demographics insights at 150m grid levels across Asia and MENA. Understand who lives in a catchment, where they work and their spending potential.

The series of indian Remote sensing satellites like IRS-1A,IRS-1B,IRS-1C,IRS-1D,IRS-P4,IRS-P6,IRS-P5 with spatial resolution ranging from 360m to 2.5m and also with pancromatic and multispectral imaging capability,catering to the needs of the country in managing its natural resources. Today, IRS data is being used for a diverse range of applications such as crop acreage and production estimation of major crops, drought monitoring and assessment based on vegetation condition, flood risk zone mapping and flood damage assessment, hydro-geo-morphological maps for locating underground water resources, irrigation command area status monitoring, snowmelt run-off estimation, land use and land cover mapping, urban planning, biodiversity characterization, forest survey, wetland mapping, environmental impact analysis, mineral prospecting, coastal studies, integrated surveys for developing sustainable action plans and so on.

Description

The global pedogenon map classifies soil units based on similarities in their formation processes while excluding anthropogenic effects, at a high spatial resolution of 90 metersin the Equator.

This repository contains data from a scientific work that was presented in the Digital Soil Mapping & GlobalSoilMap conference in Bengalore, India, 2025 (Francos et al., 2025):

• The global pedogenon map in 90-meters resolution: GPM_mosaic_17062025.tif
• The centroids of the pedogenon classes: RGB_CENTROIDS_04062025.csv
• Sampling points after data processing and before classification: POINTS_04062025.csv

Format:

• Raster map in GeoTIFF format (*.tif).
• Spatial resolution: 90 meters in the Equator.
• Coordinate reference system: EPSG:4326 (WGS 84).

Reference:

Francos, N., McBratney, A., 2025. The Global Pedogenon Map. In: Soil Mapping for Sustainable Land UsePlanning, 3rd Joint Workshop of the IUSS Working Groups on Digital Soil Mapping & GlobalSoilMap, 21st–24th January 2025, Bengaluru, India.