Creative Commons Attribution for Intergovernmental Organisations
[19]
ODbL
[1]
Open Database License (ODC-ODbL)
[14]
Other
[22]
Public Domain
[1]
Public Domain / No Restrictions
[7]
UN-Habitat’s urban datasets are made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/
[1]
This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University
Projects proposed, in progress, or completed as part of the annual Honduras Humanitarian Response Plans (HRPs) or other Humanitarian Programme Cycle plans. The original data is available on https://hpc.tools
Important: some projects in Honduras might be missing, and others might not apply specifically to Honduras. See Caveats under the Metadata tab.
Evolution over time of basic food prices in Guatemala, Nicaragua and Honduras expressed in US dollars (USD) according to different sources of information.
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.
Database containing information related to precipitation indices useful for analysing and comparing variations in precipitation over time and in different geographical regions and indices used in the analysis of vegetation and crops to monitor their activity and detect their presence in the territory. Key variables: Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Vegetation (NDVI), Enhanced Vegetation Index (EVI) and Structure Insensitive Pigment Index (SIPI) for rainfall information and Standardized Precipitation Index (SPI1, SPI3, SPI6, SPI9 and SPI12) for vegetation information. The indicators between SPI1 - SPI3 refer to short time periods (indicator for immediate impacts) while SPI3 - SPI12 is for more medium-term impact measurements. Each vegetation index is based on a set of data that can be collected through remote sensing, such as satellite imagery, and is designed to measure different vegetation and crop characteristics. The data are categorised by country, department and municipality and by year and month. The indices refer to the monthly average. Vegetation and precipitation data have been produced and transformed by GIS4Tech.
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform: https://predisan.gis4tech.com/ca4
Data collated by UNHCR, containing information about forcibly displaced populations and stateless persons, spanning across more than 70 years of statistical activities. The data includes the countries / territories of asylum and origin. Specific resources are available for end-year population totals, demographics, asylum applications, decisions, and solutions availed by refugees and IDPs (resettlement, naturalisation or returns).
A summary table is established at the municipal level with the list of agroclimatic hazards calculated from the agroclimatic indicators shown in the Vulnerability panel. For each column, the threat category in which the municipality would be found is defined (No risk, Mild threat, Moderate threat, Severe threat), and the final column represents the general summary of global threats, obtained using the 20% rule. The calculation made within each category is established through a matrix of crossed conditions.
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.
The dataset contains information on the Household Dietary Diversity for different countries in Central America. In the categorical column Attribute we have three possibilities: Crisis, Accentuated and None. The value column shows the percentage of the sample that falls under a certain Attribute category. The data are collected since December 2020 and are categorised by country, department and municipality.
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.
The dataset contains information on the Livelihood Coping Strategies for different countries in Central America. In the categorical column Attribute we have four possibilities: No strategy, Stress strategies, Crisis strategies and Emergency strategies. The value column shows the percentage of the sample that falls under a certain Attribute category. The data are collected since December 2020 and are categorised by country, department and municipality.
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.
The dataset contains information on the hungry scale for different countries in Central America. In the categorical column Attribute we have three possibilities: Low incidence, Moderate incidence and Severe incidence. The value column shows the percentage of the sample that falls under a certain Attribute category. The data are collected since December 2020 and are categorised by country, department and municipality.
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.
The dataset contains information on the food consumption score for different countries in Central America. In the categorical column Attribute we have three possibilities: Poor, Acceptable, Borderline. The value column shows the percentage of the sample that falls under a certain Attribute category. The data are collected since December 2020 and are categorised by country, department and municipality.
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.
The dataset contains information on the reduced coping strategy for different countries in Central America. In the categorical column Attribute we have three possibilities: Phase 1. None, Phase 2. Accentuated, Phase 3, 4 or 5. The value column shows the percentage of the sample that falls under a certain Attribute category. The data are collected since December 2020 and are categorised by country, department and municipality.
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.
The International Federation of Red Cross and Red Crescent Societies (IFRC) is the world’s largest humanitarian network. Our secretariat supports local Red Cross and Red Crescent action in more than 192 countries, bringing together almost 15 million volunteers for the good of humanity.
We launch Emergency Appeals for big and complex disasters affecting lots of people who will need long-term support to recover. We also support Red Cross and Red Crescent Societies to respond to lots of small and medium-sized disasters worldwide—through our Disaster Response Emergency Fund (DREF) and in other ways.
There is also a global dataset.
60+ Downloads
This dataset updates: Every week
This dataset is part of the data series [?]: IFRC - Appeals
A summary table is established at the municipal level with the list of threats per product calculated from the product price indicators shown in the Vulnerability panel. For each column, the threat category in which the municipality would find itself is defined (No risk, Mild threat, Moderate threat, Severe threat), and the final column represents the general summary of the global threats, obtained using the 20% rule. The calculation within each category is established through a cross-conditional matrix.
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.
The IPC Acute Food Insecurity (IPC AFI) classification provides strategically relevant information to decision makers that focuses on short-term objectives to prevent, mitigate or decrease severe food insecurity that threatens lives or livelihoods. This data has been produced by the National IPC Technical Working Groups for IPC population estimates since 2017. All national population figures are based on official country population estimates. IPC estimates are those published in country IPC reports.
This dataset contains agency- and publicly-reported data for events in which an aid worker was killed, injured, kidnapped, or arrested (KIKA). Categorized by country.
Please get in touch if you are interested in curated datasets: info@insecurityinsight.org
Contains data from the DHS data portal. There is also a dataset containing Honduras - National Demographic and Health Data on HDX.
The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
Contains data from the DHS data portal. There is also a dataset containing Honduras - Subnational Demographic and Health Data on HDX.
The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.
The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.
The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.