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"

This layer shows the elevation (in meters) for Kalimantan. 90 meter resolution. Data was prepared by the World Resources Institute for use in the Suitability Mapper (2012).

Map illustrating hydrology in Indonesia.This web map was created by Esri for training purposes and includes data from the Indonesia - Small Hydro GIS Database. It was 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.

Indonesia District Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Badan Pusat Statistik. These were published in December 2022. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country Provinsi Kabupaten Subdistrict

This layer shows the soil drainage, based on result of a classification established from Kalimantan RePPProT dataon 'SL_drain1' field (1990, 1:250,000 scale) . This data was provided and processed by Daemeter Consulting and was prepared by the World Resources Institute for use in the Suitability Mapper (2012). Data separated into categories: stagnant; very poor; poor; moderately good; good; excessive; very excessive.

Coastal flood inundation layers for Indonesia. The inundation estimates were computed using a GIS-based planar approach, which uses extreme water levels from the DIVA model and a Digital Elevation Model (DEM) as inputs. Extreme water levels and inundation extends are available for return periods of 10, 100 and 1000 years. The unit of the data is inundation depth in cms. The data resolution is 30 arc seconds (approximately 1km at the equator). The data are provided in GeoTIFF raster file format ‘.tif’, with the geographic projection EPSG:4326 WGS84.

Maps with wind speed, wind rose and wind power density potential in Indonesia. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

This map shows the purchasing power per capita in Indonesia in 2022, in a multiscale map (Country, Province, County, District, and Subdistrict). Nationally, the purchasing power per capita is 36,387,408 Indonesian rupiah. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Indonesian rupiah (IDR) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaPurchasing power per capita indexCounts of population by education levelThe Purchasing Power Index compares the demand for a specific purchasing category in an area, with the national demand for that product or service. The index values at the national level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the national average; an index of 80 implies that demand is 20 percent lower than the national average.The source of this data is Michael Bauer Research. The vintage of the data is 2022. This item was last updated in November, 2022 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

The development success promoted by an area depends on the size of the obstacles of crime. Society must have been very familiar with the figure of a group of teenagers who called himself a motorcycle gang. They like artists who are and are always rising in the art of crime. They regard evil as the beauty of their loyalty to fellow motorcycle gang members. Solidarity, more intimate friendship is considered harmful by society. The purpose of this study is to evaluate whether there is a relationship between the level of crime with the density and the number of people living in a particular area in Makassar. By using survey method and data collection technique that is snowball sampling, then for analysis of distribution pattern of hotspot criminal using ArcGIS software aid with Density Mapping method, while to see spatial relationship pattern of criminality to the amount and population density using SPSS software and overlay method. The results show that the design of distribution of criminal hotspots in 2015 and 2016 with the design of hotspots clusters that spread in areas of high and medium population density. Then there is a significant relationship between population density and criminal hotspots. In 2015, the distribution pattern of crime hotspots concentrated in areas with high population density, but by 2016 the distribution pattern of crime hotspots spread evenly not only in high population density areas but has spread to the medium population density area.

Indonesia airport from Ministry of Transportation (Kementerian Perhubungan). Extracted from MoT ArcGIS REST Services: https://portal-gis.dephub.go.id/server/rest/services/Tematik_Perhubungan_KementerianLembaga/FeatureServer/0

This data is about the daily weather forecast produced by the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG). The data is available in ArcGIS REST Services: https://gis.bmkg.go.id/arcgis/rest/services/prakiraan_cuaca_harian/prakHujanHarian/MapServer/0 and http://gis.bmkg.go.id/arcgis/home/webscene/viewer.html?layers=2d271294bc454394ad4aa62571c3759d. The daily weather forecast report (in PDF format and Indonesia language) also available: https://www.bmkg.go.id/cuaca/prakiraan-cuaca-tigaharian.bmkg

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 1.8582 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.1766 and 0.1278 (in million kms), corressponding to 9.5052% and 6.877% respectively of the total road length in the dataset region. 1.5538 million km or 83.6178% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0237 million km of information (corressponding to 1.5266% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

The U.S. Geological Survey (USGS) has compiled a geodatabase containing mineral-related geospatial data for 10 countries of interest in Southwest Asia (area of study): Afghanistan, Cambodia, Laos, India, Indonesia, Iran, Nepal, North Korea, Pakistan, and Thailand. The data can be used in analyses of the extractive fuel and nonfuel mineral industries and related economic and physical infrastructure integral for the successful operation of the mineral industries within the area of study as well as the movement of mineral products across domestic and global markets. This geodatabase reflects the USGS ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports for the countries in the area of study. The geodatabase contains data feat ...

Indonesia flight routes from Ministry of Transportation (Kementerian Perhubungan). Extracted from MoT ArcGIS REST Services: https://portal-gis.dephub.go.id/server/rest/services/Tematik_Perhubungan_KementerianLembaga/FeatureServer/3

Indonesia sea routes from Ministry of Transportation (Kementerian Perhubungan). Extracted from MoT ArcGIS REST Services: https://portal-gis.dephub.go.id/server/rest/services/Tematik_Perhubungan_KementerianLembaga/FeatureServer/8

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['amenity'] IN ('kindergarten', 'school', 'college', 'university') OR tags['building'] IN ('kindergarten', 'school', 'college', 'university')

Features may have these attributes:

• name
• name:en
• amenity
• building
• operator:type
• capacity:persons
• addr:full
• addr:city
• source
• name:id

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This layer shows 2017 land cover, classified by type. The data is sourced from 2017 Ministry of Environment & Forestry data (1:250,000 scale). The World Resources Institute reclassified the original land cover categories from the Ministry of Environment & Forestry dataset for use in the Suitability Mapper (2012), into the following categories:Primary Forest: Primary dry land forest, primary mangrove forest, primary swamp forestSecondary Forest: Secondary dry land forest, secondary mangrove forest, secondary swamp forestPlantation Forest: Plantation forestGrass Land: Bush/Shrub, SavannahCropland: Estate crop plantation, dryland agriculture, shrub-mixed dryland farm, rice fieldOther Land: Bare land, fish pond, airport/ harbor, mining areaSettlement: Transmigration area, settlement areaWetland: Swamp, swamp shrubUnknown: CloudBodies of Water: Bodies of waterOriginal data available at http://geoportal.menlhk.go.id/arcgis/rest/services/KLHKEN under “LandCover_2017."

DATASET: Alpha version 2010, 2012, 2015, 2020, 2025, 2030, and 2035 estimates of numbers of live births per grid square, with national totals adjusted to match UN national estimates on numbers of live births (http://esa.un.org/wpp/). REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated births per grid square MAPPING APPROACH: Tatem AJ, Campbell J, Guerra-Arias M, de Bernis L, Moran A, Matthews Z, 2014, Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births, International Journal of Health Geographics, 13:2 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AZE2010adjustedBirths.tif = Azerbaijan (AZE) births count map for 2010 adjusted to match UN national estimates on numbers of live births. DATE OF PRODUCTION: May 2014

Indonesia Kabupaten Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Badan Pusat Statistik. These were published in December 2022. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country Provinsi District Subdistrict

Landslides are defined as the movement of soil and rocks that form slopes. Landslides can cause environmental damage, property losses, and deaths for people in disaster-prone areas. This study aims to review and compare landslide risk management patterns in China and Indonesia from research conducted in 2019-2023. The method used in this study is a Systematic Literature Review (SLR). While searching for literature using Scopus, Mendeley has a publication period of 2019-2023. The research findings show that disaster risk management also focuses on more than community knowledge in disaster emergency response. However, other elements need attention, namely road sections most vulnerable to landslides, slope conditions, river density, land use, GIS, resources, community participation, and training. In Fengjie County, China, landslide vulnerability is a significant problem, with about 70% of areas in the vulnerability zone very high. In Pengasih Sentolo district, Indonesia, nine villages are included in the very high-risk site, showing significant landslide vulnerability. The integration and application of GIS technology have greatly assisted in assessing landslide susceptibility and identifying high-risk zones. Conclusion: The case study in Fengjie County, China and the study in Pengasih Sentolo District, Kulon Progo, Indonesia, emphasize the importance of using geospatial techniques, particularly GIS, for landslide risk assessment.