ged_sb_best_sum_nokgi
ged2_pgm
Variable "best" from Davies et al. 2022. Organized violence 1989-2021 and drone warfare. Journal of Peace Research 59(4); Sundberg & Melander .2013. Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research 50(4).
Aggregation to priogrid level, grouped by type of violence, Nokgi = No imputation (single or multiple) was made for the known geographic imprecision problem, i.e. events that do not have precise locations recorded by UCDP (see Croicu and Hegre, 2019).
The best (most likely) estimate of the total number of battle-related deaths (BRDs) from state-based conflict as per the UCDP definition.
The best (most likely) estimate of the total number of battle-related deaths (BRDs), disaggregated for state-based conflict per the Uppsala Conflict Data Program (UCDP) definitions in the UCDP Georeferenced Events Dataset (UCDP GED) and the UCDP Candidate Events Dataset (UCDP Candidate).
Monthly
ged_os_best_sum_nokgi
ged2_pgm
UCDP Georeferenced Events Dataset (UCDP GED) and UCDP Candidate Events Dataset (UCDP Candidate), variable best.
Davies et al. 2022. Organized violence 1989-2021 and drone warfare. Journal of Peace Research 59(4); Sundberg & Melander. 2013. Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research 50(4).
Aggregation to priogrid level, grouped by type of violence, Nokgi = No imputation (single or multiple) was made for the known geographic imprecision problem, i.e. events that do not have precise locations recorded by UCDP (see Croicu and Hegre, 2019).
The best (most likely) estimate of the total number of fatalities from one-sided violence as per the UCDP definition.
The best (most likely) estimate of the total number of fatalities, disaggregated for one-sided violence per the Uppsala Conflict Data Program (UCDP) definitions in the UCDP Georeferenced Events Dataset (UCDP GED) and the UCDP Candidate Events Dataset (UCDP Candidate).
Monthly
ged_os_best_count_nokgi
ged2_pgm
Variable "best" from Reference: Davies et al. 2022. Organized violence 1989-2021 and drone warfare. Journal of Peace Research 59(4); Sundberg & Melander .2013. Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research 50(4)
aggregation to priogrid level, grouped by type of violence, Nokgi = No imputation (single or multiple) was made for the known geographic imprecision problem, i.e. events that do not have precise locations recorded by UCDP (see Croicu and Hegre, 2019).
Best estimate of event counts of one-sided violence
The best (most likely) estimate of total events for one-sided conflict per the Uppsala Conflict Data Program (UCDP) definitions in the UCDP Georeferenced Events Dataset (UCDP GED) and the UCDP Candidate Events Dataset (UCDP Candidate).
Annual
ged_ns_best_sum_nokgi
ged2_pgm
UCDP Georeferenced Events Dataset (UCDP GED) and UCDP Candidate Events Dataset (UCDP Candidate), variable best.
Davies et al. 2022. Organized violence 1989-2021 and drone warfare. Journal of Peace Research 59(4); Sundberg & Melander. 2013. Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research 50(4).
aggregation to priogrid level, grouped by type of violence, Nokgi = No imputation (single or multiple) was made for the known geographic imprecision problem, i.e. events that do not have precise locations recorded by UCDP (see Croicu and Hegre, 2019).
The best (most likely) estimate of the total number of fatalities from non-state conflict as per the UCDP definition.
The best (most likely) estimate of the total number of fatalities, disaggregated for non-state conflict per the Uppsala Conflict Data Program (UCDP) definitions in the UCDP Georeferenced Events Dataset (UCDP GED) and the UCDP Candidate Events Dataset (UCDP Candidate).
Monthly
ged_ns_best_count_nokgi
ged2_pgm
Variable "best" from Davies et al. 2022. Organized violence 1989-2021 and drone warfare. Journal of Peace Research 59(4); Sundberg & Melander .2013. Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research 50(4)
aggregation to priogrid level, grouped by type of violence
best estimate of events for non-state conflict
The best (most likely) estimate of total event counts for non-state conflict per the Uppsala Conflict Data Program (UCDP) definitions in the UCDP Georeferenced Events Dataset (UCDP GED) and the UCDP Candidate Events Dataset (UCDP Candidate).
Monthly
pop_gpw_sum
priogrid_year
Center for International Earth Science Information Network (CIESIN) and Centro Internacional de Agricultura Tropical (CIAT) (2005). Gridded Population of the World, Version 3 (GPWv3): Population Count Grid. Palisades, NY. doi:10.7927/H4639MPP; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The sum of pixel values (number of persons) within the grid cell.
Gives the sum of pixel values (number of persons) within the grid cell. To obtain population density estimates, this variable can be divided by landarea.
ttime_mean
priogrid_year
Uchida, Hirotsugu and Nelson, Andrew (2009). Agglomeration Index: Towards a New Measure of Urban Concentration. Background paper for the World Bank’s World Development Report 2009; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The average travel time in minutes to the nearest major city within each cell.
ttime_ is an estimate of the travel time to the nearest major city, derived from a global high-resolution raster map of accessibility developed for the EU. The original indicator is a result of network analysis using a combination of several sources, most collected between 1990 and 2005. The original pixel value is the estimated travel time in minutes by land transportation from the pixel to the nearest major city with more than 50 000 inhabitants. ttime_mean gives the average travel time within each cell.
bdist3
priogrid_year
Weidmann, Nils B., Doreen Kuse & Kristian Skrede Gleditsch (2010) The geography of the international system: The CShapes Dataset. International Interactions, 36(1): 86-106; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The spherical distance (in kilometer) from the cell centroid to the territorial outline of the country the cell belongs to
The spherical distance (in kilometer) from the cell centroid to the territorial outline of the country the cell belongs to. For cells located along a coast and for cells of island states (e.g. New Zealand), bdist3 measures the shortest straight-line distance to international waters.
capdist
priogrid_year
Weidmann, Nils B., Doreen Kuse & Kristian Skrede Gleditsch (2010) The geography of the international system: The CShapes Dataset. International Interactions, 36(1): 86-106; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The spherical distance in kilometers from the cell centroid to the national capital city.
The spherical distance in kilometers from the cell centroid to the national capital city in the corresponding country, based on coordinate pairs of capital cities derived from the cShapes dataset v.0.4-2. It captures changes over time wherever relevant.
imr_mean
priogrid_year
Storeygard, Adam; Deborah Balk, Marc Levy & Glenn Deane (2008) The global distribution of infant mortality: A subnational spatial view. Population, Space and Place, 14(3):209-229; Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Poverty Mapping Project: Global Subnational Infant Mortality Rates. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). doi:10.7927/H4PZ56R2. Accessed 19.05.2006; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The average infant mortality rate within the grid cell.
Infant mortality rate, based on raster data from the SEDAC Global Poverty Mapping project. The original pixel value is the number of children per 10,000 live births that die before reaching their first birthday. This indicator is a snapshot for the year 2000 only. imr_mean gives the average infant mortality rate within the grid cell.
excluded
priogrid_year
Vogt, Manuel, Nils-Christian Bormann, Seraina Rüegger, Lars-Erik Cederman, Philipp Hunziker, and Luc Girardin. 2015. “Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Dataset Family.” Journal of Conflict Resolution, 59(7), 1327-1342. doi:10.1177/0022002715591215; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
Number of excluded groups (discriminated or powerless) settled in the grid cell for the given year.
Counts the number of excluded groups (discriminated or powerless) as defined in the GeoEPR/EPR data on the status and location of politically relevant ethnic groups settled in the grid cell for the given year, derived from the GeoEPR/EPR 2014 update 2 dataset.
nlights_calib_mean
priogrid_year
Elvidge, Christopher D., Feng-Chi Hsu, Kimberly E. Baugh and Tilottama Ghosh (2014). “National Trends in Satellite Observed Lighting: 1992-2012.” Global Urban Monitoring and Assessment Through Earth Observation. Ed. Qihao Weng. CRC Press; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
Average nighttime light emission with values standardized to be between 0 and 1, where 1 is the highest observed value in the time-series, and 0 is the lowest.
Average nighttime light emission from the DMSP-OLS Nighttime Lights Time Series Version 4 (Average Visible, Stable Lights, & Cloud Free Coverages), calibrated to account for intersatellite differences and interannual sensor decay using calibration values from Elvidge et.al. (2013). Values are standardized to be between 0 and 1, where 1 is the highest observed value in the time-series, and 0 is the lowest.
urban_ih
priogrid_year
Meiyappan, Prasanth and Atul K. Jain (2012). Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Frontiers of Earth Science, 6(2), 122-139. doi:10.1007/s11707-012-0314-2; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The percentage area of the cell covered by urban area.
The percentage area of the cell covered by urban area, based on ISAM-HYDE landuse data. Following the land cover classification system used by ISAM-HYDE and aggregated to the category “Urban” (landuse class “Urban”). In PRIO-GRID, this indicator is available for the years 1950, 1960, 1970, 1980, 1990, 2000, and 2010.
gcp_mer
priogrid_year
Nordhaus, William D. (2006) Geography and macroeconomics: New data and new findings. Proceedings of the National Academy of Sciences of the USA, 103(10): 3510- 3517; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
Indicates the gross cell product, measured in USD.
Indicates the gross cell product, measured in USD, based on the G-Econ dataset v4.0, last modified May 2011. The original G-Econ data represent the total economic activity at a 1x1 degree resolution. In border areas, the G- Econ cells might overlap with PRIO-GRID cells allocated to a neighboring country. To minimize bias, PRIO-GRID only extracts G-Econ data for cells that have the same country code as the G-Econ cell represents. This variable is only available for five-year intervals since 1990.
mountains_mean
priogrid_year
Blyth, Simon, Brian Groombridge, Igor Lysenko, Lera Miles, and Adrian Newton (2002). Mountain Watch: environmental change & sustainable development in moun- tains. UNEP-WCMC Biodiversity Series 12. ISBN: 1-899628-20-7; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The proportion of mountainous terrain within the cell based on elevation, slope and local elevation range.
The proportion of mountainous terrain within the cell based on elevation, slope and local elevation range, taken from a high-resolution mountain raster developed for UNEP’s Mountain Watch Report. The original pixel values are binary, capturing whether the pixel is a mountain pixel or not based on the seven different categories of mountainous terrain in the report.
agri_ih
priogrid_year
Meiyappan, Prasanth and Atul K. Jain (2012). Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Frontiers of Earth Science, 6(2), 122-139. doi: 10.1007/s11707-012-0314-2; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The percentage area of the cell covered by agricultural area.
The percentage area of the cell covered by agricultural area, based on ISAM-HYDE landuse data. In PRIO-GRID, this indicator is available for the years 1950, 1960, 1970, 1980, 1990, 2000, and 2010.
barren_ih
priogrid_year
Meiyappan, Prasanth and Atul K. Jain (2012). Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Frontiers of Earth Science, 6(2), 122-139. doi: 10.1007/s11707-012-0314-2; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The percentage area of the cell covered by barren area.
The percentage area of the cell covered by barren area, based on ISAM-HYDE landuse data. Aggregated using the following lansuse classes: “Tundra”, “Desert”, “PdRI”. In PRIO-GRID, this indicator is available for the years 1950, 1960, 1970, 1980, 1990, 2000, and 2010.
forest_ih
priogrid_year
Meiyappan, Prasanth and Atul K. Jain (2012). Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Frontiers of Earth Science, 6(2), 122-139. doi: 10.1007/s11707-012-0314-2; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The percentage area of the cell covered by forest area.
The percentage area of the cell covered by forest area, based on ISAM-HYDE landuse data. Aggregated to the category “Total forest” (landuse classes “TrpEBF”, “TrpDBF”, “TmpEBF”, “Tm- pENF”, “TmpDBF”, “BorENF”, “BorDNF”, “SecTrpEBF”, “SecTrpDBF”, “SecTmpEBF”, “SecTm- pENF”, “SecTmpDBF”, “SecBorENF”, “SecBorDNF”). In PRIO-GRID, this indicator is available for the years 1950, 1960, 1970, 1980, 1990, 2000, and 2010.
pasture_ih
priogrid_year
Meiyappan, Prasanth and Atul K. Jain (2012). Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Frontiers of Earth Science, 6(2), 122-139. doi: 10.1007/s11707-012-0314-2; PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The percentage area of the cell covered by pasture area.
The percentage area of the cell covered by pasture area, based on ISAM-HYDE landuse data. Aggregated to the category “Total pastureland” (landuse classes “C3past”, “C4past”). In PRIO-GRID, this indicator is available for the years 1950, 1960, 1970, 1980, 1990, 2000, and 2010.
savanna_ih
priogrid_year
PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo
The percentage area of the cell covered by grasslands.
The percentage area of the cell covered by grasslands, based on ISAM-HYDE landuse data. Aggregate to the category “Savanna” (landuse class “Savanna”). In PRIO-GRID, this indicator is available for the years 1950, 1960, 1970, 1980, 1990, 2000, and 2010.
shrub_ih
priogrid_year
PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo.
The percentage area of the cell covered by shrublands.
The percentage area of the cell covered by shrublands, based on ISAM-HYDE landuse data. Aggregated to the category “Total shrubland”(landuse classes “Denseshrub”, “Openshrub”). In PRIO-GRID, this indicator is available for the years 1950, 1960, 1970, 1980, 1990, 2000, and 2010.
dist_diamsec_s_wgs
priogrid
diamsec_s and diamsec_y from PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo. For citation also use: Gilmore, Elisabeth, Nils Petter Gleditsch, Päivi Lujala & Jan Ketil Rød, 2005. Con- flict Diamonds: A New Dataset, Conflict Management and Peace Science 22(3): 257– 292; Lujala, Päivi, Nils Petter Gleditsch & Elisabeth Gilmore, 2005. A Diamond Curse? Civil War and a Lootable Resource. Journal of Conflict Resolution, 49(4): 538–562.
Distance to nearest primary diamond resource
Captures the distance from the grid cell to the nearest secondary diamonds resource, referring to the World Geodetic System (WGS84)
dist_petroleum_s_wgs
priogrid
petroleum_s and petroleum_y from PRIO- GRID version 2.0, please see Tollefsen, Andreas Forø; Håvard Strand & Halvard Buhaug (2012) PRIO-GRID: A unified spatial data structure. Journal of Peace Research, 49(2): 363-374. doi: 10.1177/0022343311431287 and Tollefsen, Andreas Forø, Karim Bahgat, Jonas Nordkvelle and Halvard Buhaug (2015). PRIO-GRID v.2.0 Codebook. Peace Research Institute Oslo. For citation also use: Lujala, Päivi, Jan Ketil Rød & Nadia Thieme, 2007. Fighting over Oil: Introducing A New Dataset. Conflict Management and Peace Science, 24(3), 239-256.
Distance to nearest petroleum resource
Captures the distance from the grid cell to the nearest petroleum resource (static data, and only onshore production), referring to the World Geodetic System (WGS84)
tlag1_dr_mod_gs
hack_temp_escwa_pgm_pgm
Standardized Precipitation Evapotranspiration Index (Vicente-Serrano et al., 2010).
Occurrence of a modest drought during the growing season (SPEI value lower than -0.5), lagged by one month.
Modest, low-intensity drought occurring in a grid-month during which the growing season is ongoing. ‘Modest’ corresponds to values of the SPEI (Standardized Precipitation Evapotranspiration Index, Vicente-Serrano et al., 2010) between -0.5 and -1. Lagged by 1 month.
Difference between agricultural SPEI during current growing season and its avarage ten previous years (spei1_gs_prev10_anom)
spei1_gs_prev10_anom
hack_temp_escwa_pgm_pgm
Computation from SPEI Drought Monitor (Vicente-Serrano et al., 2010).
Difference between current value of agricultural SPEI during the growing season and its average value for the ten previous years.
Difference between the current value of agricultural SPEI (during the growing season) and the average value of SPEI during the growing season months in the previous 10 years.
tlag_12_crop_sum
hack_temp_escwa_pgm_pgm
Mapspam (International Food Policy Research Institute 2019).
Harvest quantity of the main crops, capturing harvest failure. Lagged by 12 months.
Total harvests of the main crops cultivated for each priogrid-cell. Lagged by 12 months.
spei1gsy_lowermedian_count
hack_temp_escwa_pgm_pgm
Computation from SPEI Drought Monitor (Vicente-Serrano et al., 2010).
Consecutive number of years in which drought exceeds median value for sample.
Count of the previous years (up to 5) during which spei1 gsm is lower than the median value for the reference period (1990-2010).
count_moder_drought_prev10
hack_temp_escwa_pgm_pgm
Computation from Vesco, P. (2021). A Climate of War or Peace? The Effect of Droughts on Conflict Dynamics.
Count of the months experiencing a moderate drought in the previous 10 years.
Count of the months experiencing a moderate drought in the previous 10 years.
Proportion of month in a year for which the growing season is ongoing, lagged by 12 months (cropprop)
cropprop
hack_temp_escwa_pgm_pgm
Mapspam (International Food Policy Research Institute 2019).
Proportion of months in a year for which the growing season is ongoing, lagged by 12 months.
Proportion of the year for which the growing season is ongoing in that grid-cell, for the main crops.
growseasdummy
hack_temp_escwa_pgm_pgm
Portmann, F.T., S. Siebert, and P. Döll. 2010. MIRCA2000 - Global monthly irrigated and rainfed crop area around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Global Biogeochemical Cycles, Vol. 24, GB1011, doi: 10.1029/2008GB003435, 2010
Binary variable indicating if the growing season is ongoing for each month and grid-cell.
Binary variable indicating if the growing season is ongoing for each month and grid-cell. Growing season is defined on the basis of the three most cultivated crops in that priogrid-cell and extracted from MIRCA data; lagged by 12 months.
spei1_gsm_cv_anom
hack_temp_escwa_pgm_pgm
Computation from SPEI Drought Monitor (Vicente-Serrano et al., 2010).
transformation of resolution level from 1x1 to 0.5x0.5
Difference between the temporal coefficient of variation of spei1 gsm along the current year, and the average variation of spei1 gsm over the period 1990-2010.
Difference between the coefficient of variation of SPEI value for the months in which the growing season is ongoing along the current year, and the average coefficient of variation of SPEI during the growing season for the reference period (1990-2010).
spei1_gsm_detrend
hack_temp_escwa_pgm_pgm
Computation from SPEI Drought Monitor (Vicente- Serrano et al., 2010)
transformation of resolution level from 1x1 to 0.5x0.5
SPEI value for the months in which the growing season is ongoing. Detrended.
spei1_gsm. SPEI value for the months in which the growing season is ongoing. Detrended.
spei_48_detrend
hack_temp_escwa_pgm_pgm
Computation from SPEI Drought Monitor (Vicente-Serrano et al., 2010).
transformation of resolution level from 1x1 to 0.5x0.5
Long-term drought (4-year average SPEI) detrended.
SPEI (Standardized Precipitation Evapotranspiration Index) at 48- month scale, detrended.
tlag1_dr_moder_gs
hack_temp_escwa_pgm_pgm
Standardized Precipitation Evapotranspiration Index (Vicente-Serrano et al., 2010).
temporal lag, removed missing values
Occurrence of a moderate drought during the growing season (SPEI value lower than -1.5), lagged by one month.
Moderate drought occurring in a grid-month during which the growing season is ongoing. ‘Moderate’ corresponds to values of the SPEI (Standardized Precipitation Evapotranspiration Index, Vicente-Serrano et al., 2010) between -1 and -1.5. Lagged by 1 month.
tlag1_dr_sev_gs
hack_temp_escwa_pgm_pgm
Standardized Precipitation Evapotranspiration Index (Vicente-Serrano et al., 2010).
Occurrence of a severe drought during the growing season (SPEI value lower than -2), lagged by one month.
Severe drought occurring in a grid-month during which the growing season is ongoing. ‘Severe’ corresponds to values of the SPEI (Standardized Precipitation Evapotranspiration Index, Vicente-Serrano et al., 2010) lower than -2. Lagged by 1 month.
tlag1_spei1_gsm
hack_temp_escwa_pgm_pgm
SPEI Drought Monitor (Vicente-Serrano et al., 2010).
temporal lag, removed missing values
Agricultural drought, proxied by the SPEI value during the growing season months. Lagged by 1 month.
spei1_gsm. SPEI value for the months in which the growing season is ongoing. Computation from SPEI Drought Monitor. Lagged by 1 month.
tlag_12_harvarea_maincrops
hack_temp_escwa_pgm_pgm
Mapspam (International Food Policy Research Institute 2019).
temporal lag, removed missing values
Harvested area of the main crops in the priogrid-cell. Lagged by 12 months.
Harvested area of the main crops cultivated in that grid-cell. Lagged by 12 months.
Binary variable indicating whether the main crops are irrigated, lagged by 12 months (tlag_12_irr_maincrops)
tlag_12_irr_maincrops
hack_temp_escwa_pgm_pgm
temporal lag, removed missing values
Dummy indicating whether the main crops are irrigated, lagged by 12 month.
Binary variable indicating whether the main crops are rainfed, lagged by 12 months (tlag_12_rainf_maincrops)
tlag_12_rainf_maincrops
hack_temp_escwa_pgm_pgm
temporal lag, removed missing values
Dummy indicating whether the main crops are rainfed, lagge dby 12 months.
wdi_nv_agr_totl_kd
wdi_cy
World Development Indicators (World Bank)
Value added from agriculture in constant 2015 U.S. dollars.
Agriculture, forestry, and fishing corresponds to ISIC divisions 01-03 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. Data are in constant 2015 prices, expressed in U.S. dollars