Acción contra el hambre - GIS4tech

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

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.

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 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 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 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 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.

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.

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 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.

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 walk from a municipality to the nearest Higher Education Centre (University). 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.

The total volume of physical productive assets available in a municipality is represented by the total stock of capital in the rural population. 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, barracks, command post, etc.), using a motor vehicle. 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.), using a motor vehicle. 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 health centre (clinics, hospitals, outpatient clinics, health centres, etc.) using a motor vehicle. 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 Higher Education Centre (University) by motor vehicle. 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.

Sum of the lengths of all roads in the locality divided by the km2 of territory of each municipality, in order to know the road connectivity and local economic development. Data obtained from HumData and transformed by GIS4Tech. 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.

Number of public order buildings per 100,000 inhabitants based on territorial analysis with GIS (own elaboration). Data source: Humdata June 2020 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.

Information on protected areas in Central America is expressed in square kilometres. Categorized by country, departments 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.

Calculation of the percentage of elderly people in relation to the total population of each municipality using data extracted from Facebook. Data have been transformed by GIS4Tech. For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.