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.
Nicaragua administrative level 0-2 boundaries, gazetteer, and geoservices.
COD-EM datasets do not replace the authoritative COD-AB available here; however COD-EM datasets may be preferred for cartographic purposes. See caveats.
These boundaries are NOT suitable for database or GIS linkage to the Nicaragua - Subnational Population Statistics tables, which do not have compatible P-coding.
Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
Nicaragua administrative level 0 (country), 1 (department or autonomous region, departamento o región autónoma), and 2 (municipality, municipio) boundaries polygons
These layers were altered in November 2023. "Región Autónoma Atlántico Norte" [NI91] and "Región Autónoma Atlántico Sur" [NI93] were renamed to "Region Autonoma Costa Caribe Norte" and "Region Autonoma Costa Caribe Sur", respectively. The Nicaragua - Subnational Edge-matched Administrative Boundaries edge-matched layers were also made available.
Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
These boundaries are NOT suitable for database or GIS linkage to the Nicaragua - Subnational Population Statistics tables, which do not have compatible P-coding.
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.
Number of major markets per 100,000 inhabitants from Humdata data. Data are categorized from 0-1 to greater than 20.
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 health centres per 100,000 inhabitants based on Humdata data from June 2020. The data have been elaborated 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.
The dataset contains the number of educational centres per 100,000 inhabitants based on territorial analysis with GIS. The data have been elaborated and transformed by GIS4Tech. Categorized by country, department and municipality. Data source: Humdata June 2020
For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.
Percentage of areas covered by bodies of water that are found on the land surface or in the subsoil, whether natural or artificial, and may be fresh, brackish or salt water. Data source: OpenStreetMap.
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 a column with the percentage of 3G telephony coverage based on data from www.gsma.com (Global Telephone Company Legal Representative). 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.
Nicaragua population density for 400m H3 hexagons.
Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.
Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.
Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.
"Internally displaced persons - IDPs" refers to the number of people living in displacement as of the end of each year.
"Internal displacements (New Displacements)" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.
Contains data from IDMC's Global Internal Displacement Database.
This table contains subnational multidimensional poverty trends data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2023).
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.
20+ Downloads
This dataset updates: Every week
This dataset is part of the data series [?]: IFRC - Appeals