Acción contra el hambre - GIS4tech

Flood risk has been assessed based on data obtained from European Commission's Joint Research Centre. In this analysis, the amount of surface for each municipality that is in flood risk areas has been compiled. The hazard classification is determined using Jenks Natural Breaks Classification (or Optimization) system. The information is showed by country, department and municipality of Central America (Nicaragua, Honduras, Guatemala, El Salvador). 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.

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

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 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.

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.

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.

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 hunger 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 was 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 model trains with the actual data of the variables to be predicted and uses their correlation with agroclimatic indicators, biomass, and violence to predict said variable where there is no actual data, at the level of Commune/Municipality for all of Senegal and Mauritania. This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence. For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel.

The model trains with the actual data of the variables to be predicted and uses their correlation with agroclimatic indicators, biomass, and violence to predict said variable where there is no actual data, at the level of Commune/Municipality for all of Senegal and Mauritania. This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence. For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel.

The model trains with the actual data of the variables to be predicted and uses their correlation with agroclimatic indicators, biomass, and violence to predict said variable where there is no actual data, at the level of Commune/Municipality for all of Senegal and Mauritania. This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence. For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel.

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.

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. This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence. For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel.

Information on nightlights in Senegal and Mauritania. This measurement of visible and near-infrared nighttime lights generated by human activity has been used to create the nighttime light indices, and it is categorized by country, departments, and municipalities. This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence. For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel

Information on biomass in Senegal and Mauritania contains the Gross Primary Productivity index (GPP), which indicates the kilograms of carbon dioxide stored by plants in one square meter, and it is categorized by country, departments, and municipalities. This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence. For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel

Information of protected areas in Senegal and Mauritania is expressed in square kilometres, and categorized by country, departments and municipality. This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence. For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel

Dataset containing information related to precipitation indices useful for analyzing and comparing variations in precipitation over time and across different geographical regions, alongside indices used in the analysis of vegetation and crops to monitor their activity and detect their presence in the territory. Key variables include the Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Structure Insensitive Pigment Index (SIPI) for monitoring vegetation. For rainfall information, the Standardized Precipitation Index (SPI1, SPI3, SPI6, SPI9, and SPI12) is used, where SPI1 to SPI3 are indicators for immediate impacts and SPI6 to SPI12 measure medium-term impacts. Each index is based on data that can be collected through remote sensing, such as satellite imagery, and is designed to measure different characteristics of vegetation and crops. The data are categorized by country, department, and municipality and by year and month, referring to the monthly average. This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence. For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel.

Volcanic activity risk has been assessed based on data obtained from the Ministry of Environment of El Salvador. In this analysis, circular areas have been delineated around the location of each active volcano. The hazard classification is determined by the proximity of a specific area to a volcano. The information is showed by country, department and municipality of Central America (Nicaragua, Honduras, Guatemala, El Salvador) For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.

This indicator shows the length of natural and artificial watercourses per municipality area, based on information from 4 typologies (canals, streams, riverside and rivers). Source: OpenStreetMap. Categorized 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

Data collected in the field in 2020 and 2021 grouped with artificial intelligence in order to find groups of respondents sharing common characteristics. Categorized 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.

PMA data (2020 telephone surveys) clustered with artificial intelligence in order to find groups of respondents sharing common characteristics. For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.

Average time needed to get from a municipality to the nearest health centre (clinics, hospitals, outpatient clinics, health centres, etc.) on foot. Data source: Humdata June 2020. Categorized 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.

Average time needed to get from a municipality to the nearest law enforcement building (police station, police station, barracks, command, etc.) on foot. Data source: Humdata June 2020. Categorized 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.

Average time needed to get from a municipality to the nearest main market (supermarket, hypermarket, market, municipal market, grocery shop etc.) on foot. Data source: Humdata June 2020. Categorized 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.