It is about updating to GIS information database, Decision Support Tool (DST) in collaboration with IWMI. With the support of the Fish for Livelihoods field team and IPs (MFF, BRAC Myanmar, PACT Myanmar, and KMSS) staff, collection of Global Positioning System GPS location data for year-1 (2019-20) 1,167 SSA farmer ponds, and year-2 (2020-21) 1,485 SSA farmer ponds were completed with different GPS mobile applications: My GPS Coordinates, GPS Status & Toolbox, GPS Essentials, Smart GPS Coordinates Locator and GPS Coordinates. The Soil and Water Assessment Tool (SWAT) model that integrates climate change analysis with water availability will provide an important tool informing decisions on scaling pond adoption. It can also contribute to a Decision Support Tool to better target pond scaling. GIS Data also contribute to identify the location point of the F4L SSA farmers ponds on the Myanmar Map by fiscal year from 1 to 5.

Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

GIS database (points) with the location of the documented villages

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. One hundred and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

GIS database with the vector drawings of the structures documented.

Due to a coding error when we created the newer versions of this database, the record does not link to the new versions. You should use them in all cases:

Guth, P. (2025). DEMIX GIS Database Version 4 (Version 4) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17538186

This database supports the work of the Digital Elevation Model Intercomparison eXperiment (DEMIX) working group (Strobl and others, 2021; Guth and others, 2021; Bielski and others, 2023, 2024). The two files have the database in CSV format, and a metadata file describing the contents of each field in the database.

To understand the use of the database, see the prepint (Bielski and others, 2023).

Changes to version 2 which is the only version you should use:

1. Added 2 new areas, Stateline and Canary Islands East which should have minimal differences between the DSM and the DTM and no significant changes over the last 20 years.

2. Added the country to the database

3. Added a number of areas in France

4. Added some additional tiles for a few areas

5. Total number of tiles almost doubled

6. Now using GDAL to compute the datum shift, horizontal and vertical, for USGS 3DEP

7. Fixed some anomalies computing pixel-is-area DEMs

8. Recomputed all the reference data and the version 1.0 GIS database (Guth, 2022)

9. New file naming conventions

References:

Bielski, C.; López-Vázquez, C.; Guth. P.L.; Grohmann, C.H. and the TMSG DEMIX Working Group, 2023. DEMIX Wine Contest Method Ranks ALOS AW3D30, COPDEM, and FABDEM as Top 1” Global DEMs: https://arxiv.org/pdf/2302.08425.pdf

Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth. P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; Reuter, H.; Strobl, P., 2024. Novel approach for ranking DEMs: Copernicus DEM improves one arc second open global topography. IEEE Transactions on Geoscience & Remote Sensing. vol. 62, pp. 1-22, 2024, Art no. 4503922, https://doi.org/10.1109/TGRS.2024.3368015

Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. Digital Elevation Models: Terminology and Definitions. Remote Sens. 2021, 13, 3581. https://doi.org/10.3390/rs13183581

Strobl, P.A.; Bielski, C.; Guth, P.L.; Grohmann, C.H.; Muller, J.P.; López-Vázquez, C.; Gesch, D.B.; Amatulli, G.; Riazanoff, S.; Carabajal, C. The Digital Elevation Model Intercomparison eXperiment DEMIX, a community based approach at global DEM benchmarking. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, XLIII-B4-2021, 395–400. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021

A new relationship-estimation model to perform a frequency-dispersion-normalized estimation and reduce the unwanted effects of ecological errors, Ecologically Corrected Spatial Relationship Estimator (ECSRE).

Advancing Research on Nutrition and Agriculture (AReNA) is a 6-year, multi-country project in South Asia and sub-Saharan Africa funded by the Bill and Melinda Gates Foundation, being implemented from 2015 through 2020. The objective of AReNA is to close important knowledge gaps on the links between nutrition and agriculture, with a particular focus on conducting policy-relevant research at scale and crowding in more research on this issue by creating data sets and analytical tools that can benefit the broader research community. Much of the research on agriculture and nutrition is hindered by a lack of data, and many of the datasets that do contain both agriculture and nutrition information are often small in size and geographic scope. AReNA team constructed a large multi-level, multi-country dataset combining nutrition and nutrition-relevant information at the individual and household level from the Demographic and Health Surveys (DHS) with a wide variety of geo-referenced data on agricultural production, agroecology, climate, demography, and infrastructure (GIS data). This dataset includes 60 countries, 184 DHS, and 122,473 clusters. Over one thousand geospatial variables are linked with DHS. The entire dataset is organized into 13 individual files: DHS_distance, DHS_livestock, DHS_main, DHS_malaria, DHS NDVI, DHS_nightlight, DHS_pasture and climate (mean), DHS_rainfall, DHS_soil, DHS_SPAM, DHS_suit, DHS_temperature, and DHS_traveltime.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. An ArcInfo(tm) (ESRI) GIS database was designed for WICA using the National Park GIS Database Design, Layout, and Procedures created by the BOR. This was created through Arc Macro Language (AML) scripts that helped automate the transfer process and ensure that all spatial and attribute data was consistent and stored properly. Actual transfer of information from the interpreted aerial photographs to a digital, geo-referenced format involved two techniques, scanning (for the vegetation classes) and on-screen digitizing (for the land-use classes). Both techniques required the use of 14 digital black-and-white orthophoto quarter quadrangles (DOQQ's) covering the study area. Transferred information was used to create vegetation polygon coverages and ancillary linear coverages in ArcInfo(tm) for each WICA DOQQ. Attribute information including vegetation map unit, location, and aerial photo number was subsequently entered for all polygons.

Data set that contains information on archaeological remains of the pre historic settlement of the Letolo valley on Savaii on Samoa. It is built in ArcMap from ESRI and is based on previously unpublished surveys made by the Peace Corps Volonteer Gregory Jackmond in 1976-78, and in a lesser degree on excavations made by Helene Martinsson Wallin and Paul Wallin. The settlement was in use from at least 1000 AD to about 1700- 1800. Since abandonment it has been covered by thick jungle. However by the time of the survey by Jackmond (1976-78) it was grazed by cattle and the remains was visible. The survey is at file at Auckland War Memorial Museum and has hitherto been unpublished. A copy of the survey has been accessed by Olof Håkansson through Martinsson Wallin and Wallin and as part of a Masters Thesis in Archeology at Uppsala University it has been digitised.

Olof Håkansson has built the data base structure in the software from ESRI, and digitised the data in 2015 to 2017. One of the aims of the Masters Thesis was to discuss hierarchies. To do this, subsets of the data have been displayed in various ways on maps. Another aim was to discuss archaeological methodology when working with spatial data, but the data in itself can be used without regard to the questions asked in the Masters Thesis. All data that was unclear has been removed in an effort to avoid errors being introduced. Even so, if there is mistakes in the data set it is to be blamed on the researcher, Olof Håkansson. A more comprehensive account of the aim, questions, purpose, method, as well the results of the research, is to be found in the Masters Thesis itself. Direkt link http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149265&dswid=9472

Purpose:

The purpose is to examine hierarchies in prehistoric Samoa. The purpose is further to make the produced data sets available for study.

Prehistoric remains of the settlement of Letolo on the Island of Savaii in Samoa in Polynesia

The GIS database has been developed by under the Small Hydropower Mapping and Improved Geospatial Electrification Planning in Indonesia Project [Project ID: P145273].

The scope of the project was to facilitate and improve the planning and investment process for small hydro development both grid and isolated systems through:

• building up a central database on smal hydro at national scale and validating the mapping of small hydro in NTT, Maluku, Maluku Utara and Sulawesi

• improved electrification planning by integrating small hydro potential for the provinces of NTT, Maluku, Maluku Utara and Sulawesi into the planning process.

Please refer to the country project page for additional outputs and reports: http://esmap.org/re_mapping_indonesia

The GIS database contains the following datasets:

• SHP(promising sites)
• Admin Divisions
• Topomas_grid
• Rivers, Geology
• Forest_areas
• Roads
• RainfallGauges
• RunoffGauges
• ElectricSystem,
  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: Indonesia - Small Hydro GIS Atlas, 2017, https://energydata.info/dataset/indonesia-small-hydro-gis-database-2017"

National Address DatabaseThis National Geospatial Data Asset (NGDA) dataset, shared as a U.S. Department of Transportation (USDOT) feature layer, displays address data in the United States. Per USDOT, "The U.S. Department of Transportation (USDOT) and its partners from all levels of government recognize the need for a National Address Database (NAD). Accurate and up-to-date addresses are critical to transportation safety and are a vital part of Next Generation 9-1-1. They are also essential for a broad range of government services, including mail delivery, permitting, and school siting. To meet this need, USDOT partners with address programs from state, local, and tribal governments to compile their authoritative data into the NAD."District of Columbia (DC) Residential AddressesData currency: Current federal service (Address Points from National Address Database)NGDAID: 196 (National Address Database (NAD))For more information: Getting to know the National Address Database (NAD); National Address DatabaseFor feedback, please contact: Esri_US_Federal_Data@esri.com NGDA Data SetThis data set is part of the NGDA Transportation Theme Community. Per the Federal Geospatial Data Committee (FGDC), Transportation is defined as the "means and aids for conveying persons and/or goods. The transportation system includes both physical and non-physical components related to all modes of travel that allow the movement of goods and people between locations". For other NGDA Content: Esri Federal Datasets

The Digital Data Series encompasses a broad range of digital data, including computer programs, interpreted results of investigations, comprehensive reviewed data bases, spatial data sets, digital images and animation, and multimedia presentations that are not intended for printed release. Scientific reports in this series cover a wide variety of subjects on all facets of U.S. Geological Survey investigations and research that are of lasting scientific interest and value. Releases in the Digital Data Series offer access to scientific information that is available in digital form; the information is primarily for viewing, processing, and (or) analyzing by computer

Available on CD Rom through the Map and Data Library. CD #008.

This is a link to the Automated Geographic Reference Center (AGRC) that houses GIS data for the state of Utah. This includes geoscience, cadastre, elevation and terrain, digital aerial photography, roads, aquifer data, etc. Several GIS datasets used in the Utah FORGE project originated from this site.

This was developed for a forthcoming paper. A reference will be posted here when it is published.

This database supports the work of the Digital Elevation Model Intercomparison eXperiment (DEMIX) working group (Strobl and others, 2021; Guth and others, 2021; Bielski and others, 2024). The four files have the database tables in CSV format.

• Difference distributions for elevation, slope, and surface roughness. The provides continuity with \cite{BielskiOthers2024, GuthOthers2024}; for readers who want, it has the statistics like RMSE and LE90 for elevation and two LSPs, as well as the signed mean and median differences.
• FUV for a mixed suite of LSPs chosen to sample the full range of LSPs calculated from DEMs. These provide a better rankings of the test DEMs, and provides an estimate of the robustness of LSPs and suggest that some should be used with caution.
• FUV for the partial derivatives used for slope, aspect, and curvature.
• FUV for the suite of integrated curvature measures (Minar and others, 2020.

This version adds to CopDEM, ALOS AW3D30, and FABDEM:

• FathomDEM (Uhe and others, 2025)
• EDTM 1.1 (https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif, https://doi.org/10.5281/zenodo.7676373)
• GEDTM 1.2—this is the best version of 1.2 (there are at least two others,which have no scaling for the elevations) (https://s3.opengeohub.org/global/dtm/v1.2/gedtm_rf_m_30m_s_20060101_20151231_go_epsg.4326.3855_v1.2.tif
• GEDTM 1.1—this has 0.9” spacing https://s3.opengeohub.org/global/edtm/gedtm_rf_m_30m_s_20060101_20151231_go_epsg.4326.3855_v20250611.tif, https://doi.org/10.5281/zenodo.15689805
• GEDTM v 0—this has 0.9” spacing https://s3.opengeohub.org/global/edtm/legendtm_rf_30m_m_s_20000101_20231231_go_epsg.4326_v20250130.tif, https://doi.org/10.5281/zenodo.14900181

The database contains 1381 tiles, about 10x10 km, in 140 areas. The tiles are based on the local projected grid, a change from earlier versions of the DEMIX database which used geographic outlines.

It does not consider the low altitude coastal DEMs; for those use version 3 (https://zenodo.org/records/13331458 ).

References:

Bielski, C.; López-Vázquez, C.; Grohmann, C.H.; Guth. P.L.; Hawker, L.; Gesch, D.; Trevisani, S.; Herrera-Cruz, V.; Riazanoff, S.; Corseaux, A.; Reuter, H.; Strobl, P., 2024. Novel approach for ranking DEMs: Copernicus DEM improves one arc second open global topography. IEEE Transactions on Geoscience & Remote Sensing. vol. 62, pp. 1-22, 2024, Art no. 4503922, https://doi.org/10.1109/TGRS.2024.3368015

Guth, P.L.; Trevisani, S.; Grohmann, C.H.; Lindsay, J.; Gesch, D.; Hawker, L.; Bielski, C. Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation. Remote Sens. 2024, 16, 3273. https://doi.org/10.3390/rs16173273

Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. Digital Elevation Models: Terminology and Definitions. Remote Sens. 2021, 13, 3581. https://doi.org/10.3390/rs13183581

Minár, J., Ian S. Evans, Marián Jenčo, 2020, A comprehensive system of definitions of land surface (topographic) curvatures, with implications for their application in geoscience modelling and prediction, Earth-Science Reviews, Volume 211, 103414, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2020.103414

Strobl, P.A.; Bielski, C.; Guth, P.L.; Grohmann, C.H.; Muller, J.P.; López-Vázquez, C.; Gesch, D.B.; Amatulli, G.; Riazanoff, S.; Carabajal, C. The Digital Elevation Model Intercomparison eXperiment DEMIX, a community based approach at global DEM benchmarking. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, XLIII-B4-2021, 395–400. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021

Uhe, P., Lucas, C., Hawker, L., Brine, M., Wilkinson, H., Cooper, A., & Sampson, C. (2025). FathomDEM: an improved global terrain map using a hybrid vision transformer model. Environmental Research Letters, 20(3), 034002. https://doi.org/10.1088/1748-9326/ada972

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

This archive contains all the spatial data of the Interactive Forest Atlas of Cameroon in 2016 as well as administrative documents (Decree of classification, Notice to the public, Development plan, final agreements, temporary agreements

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Using the National Vegetation Classification System (NVCS) developed by Natureserve, with additional classes and modifiers, overstory vegetation communities for each park were interpreted from stereo color infrared aerial photographs using manual interpretation methods. Using a minimum mapping unit of 0.5 hectares (MMU = 0.5 ha), polygons representing areas of relatively uniform vegetation were delineated and annotated on clear plastic overlays registered to the aerial photographs. Polygons were labeled according to the dominant vegetation community. Where the polygons were not uniform, second and third vegetation classes were added. Further, a number of modifier codes were employed to indicate important aspects of the polygon that could be interpreted from the photograph (for example, burn condition). The polygons on the plastic overlays were then corrected using photogrammetric procedures and converted to vector format for use in creating a geographic information system (GIS) database for each park. In addition, high resolution color orthophotographs were created from the original aerial photographs for use in the GIS. Upon completion of the GIS database (including vegetation, orthophotos and updated roads and hydrology layers), both hardcopy and softcopy maps were produced for delivery. Metadata for each database includes a description of the vegetation classification system used for each park, summary statistics and documentation of the sources, procedures and spatial accuracies of the data. At the time of this writing, an accuracy assessment of the vegetation mapping has not been performed for most of these parks.

The Gravity model toolbox, a programmed ArcGIS tool to map and prioritize the potential corridors of urban green space.

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

"Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

Abstract:

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

This database was compiled from the following sources:

1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

source: https://glad.earthengine.app

1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

source: https://doi.org/10.6084/m9.figshare.9828827.v2

1. POPULATION. Total Population in Sweden. Statistics Sweden.

source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

To learn more about the GRASS GIS database structure, see:

https://grass.osgeo.org/grass82/manuals/grass_database.html