Ecuador GIS data

Subcircuit where signs of assault have been found in Quito, period 2018 - 2021

Subcircuit where signs of burglary have been found in Quito, period 2015 - 2021

Subcircuit where signs of robbery have been found in Quito, period 2015 - 2021

Subcircuit where signs of vehicle robbery have been found in Quito, period 2015 - 2021

Subcircuit where there is evidence of total case in Quito by crime (assault, robbery, burglary, theft, vehicle theft), period 2015 - 2021

Statistics of gender violence in Santa Cruz, period 2018-2020

Record of news of sexual crimes against children and adolescents

News registry for the crime of corruption

Crime news registry for violence against women and femicides

Places where crimes have been carried out within Quito, period 2018-2021

Record of aggression against children and adolescents

Record of physical, psychological and sexual domestic violence

Through the tripadvisor platform, information about establishments of tourist attractions until 2021 for the city of Santa Cruz was obtained.

Through the tripadvisor platform, information about hotels establishments until 2021 for the city of Santa Cruz was obtained.

Through the tripadvisor platform, information about restaurants establishments until 2021 for the city of Santa Cruz was obtained.

Ecuador administrative level 0-3 boundaries (COD-AB) dataset. These administrative boundaries were established in: 2017 NOTE: ADM4 feature names are not yet availalble. (Currently ADM4_PCODE is copied into ADM4_ES as a proxy.) No lines layer is yet availalbe. The live geoservices do not reflect the new ADM4 level. This COD-AB was most recently reviewed for accuracy and necessary changes in December 2024. The COD-AB does not require any update. Sourced from INEC - Instituto Nacional de Estadística y Censos Vetting by Information Technology Outreach Services (ITOS) with funding from USAID. This COD-AB is suitable for database or GIS linkage to the Ecuador COD-PS. No edge-matched (COD-EM) version of this COD-AB has yet been prepared. Please see the COD Portal. Administrative level 1 contains 26 feature(s). The normal administrative level 1 feature type is ""currently not known"". Administrative level 2 contains 223 feature(s). The normal administrative level 2 feature type is ""currently not known"". Administrative level 3 contains 1,044 feature(s). The normal administrative level 3 feature type is ""currently not known"". Recommended cartographic projection: South America Albers Equal Area Conic This metadata was last updated on January 9, 2025.

This dataset shows road access constraints in Ecuador after the earthquake that occurred on 17 April 2016

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

Archivos con la información de las construcciones mapeadas en el mapatón de Ecuador del 2025

**ADAM ID: **sm\_us7000pv06 Magnitude 6.3 earthquake at 35.0 depth occurred on Apr 25 2025 20km NE of Esmeraldas. The epicentre of the earthquake was located at latitude 1.1049 longitude -79.5345. It impacted 81316 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us7000pahy Magnitude 5.5 earthquake at 10.0 depth occurred on Jan 31 2025 42km NW of Tena. The epicentre of the earthquake was located at latitude -0.8239 longitude -78.1491. It impacted 160248 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us6000srr2 Magnitude 5.4 earthquake at 118.545 depth occurred on Apr 21 2026 25km W of Ibarra. The epicentre of the earthquake was located at latitude 0.3666 longitude -78.3421. It impacted 360812 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us6000rik7 Magnitude 5.5 earthquake at 69.758 depth occurred on Oct 22 2025 25km SE of Santa Rosa. The epicentre of the earthquake was located at latitude -3.6247 longitude -79.8229. It impacted 547397 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us7000qu91 Magnitude 5.4 earthquake at 0.0 depth occurred on Sep 05 2025 34km NW of Ayangue. The epicentre of the earthquake was located at latitude -1.8831 longitude -81.0479. It impacted 346460 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Ecuador **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.2421 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0415 million km (17.1237%) - Unpaved roads: 0.0592 million km (24.4697%) About 0.1414 million km (58.4066%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0013 million km (0.9017%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

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This dataset contains building footprints mapped in OpenStreetMap as part of the LATAM Regional Mapatón 2026 in Ecuador. The data were generated by volunteers participating in the regional mapping campaign conducted from 13 June to 31 July 2026. The mapping focused on priority areas identified according to disaster risk exposure and vulnerability. The resulting open geospatial data can support disaster preparedness, risk analysis, local planning and climate change adaptation.

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Number of male and female students and their totality by educational institution, area, type of education, regime, modality, access in the period (2020-2021)

Number of students promoted, not promoted and dropouts by educational institution in which their regime, area and grade are broken down in the period (2019-2020)

Number of male and female students and their totality by educational institution, area, type of education, regime, modality, access in the period (2019-2020)

Number of students promoted, not promoted and dropouts by educational institution in which their regime, area and grade are broken down in the period (2018-2019)