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By systematically comparing the predictive performance of different levels of predictor variables and spatial units for data aggregation, this thesis contributed to the understanding of how these components influence conflict prediction. It has been shown that vast amounts of freely available open geodata can be incorporated into complex deep learning models, delivering an edge over classical linear regression models. The occurrence of violent conflict is inherently a time-series problem and treating it as such provides high accuracies even in the absence of additional predictors. The utility of the inclusion of socio-economic and environmental variables into deep learning models shows a dependence on the definition of the outcome class. For some types of violent conflicts, the selected predictors do not decrease the prediction error. For other classes, absolute gains in performance remain low. The role the natural environment plays in the occurrence of violent conflict is still an open debate in the scientific community. The proposed methodology has shown that, due to the increased availability of dense time-series, incorporating a high number of environmental variables in prediction models is feasible. The decision on how to aggregate available predictors substantially affects the prediction outcome. While the presented results do not allow for conclusive assessments, there are indications that aggregating environmental variables based on sub-basin watersheds decreases the prediction error. This comes at the cost of less familiarity with the spatial pattern of the prediction outcome. However, depending on the conflict class, the absolute gains can be quite substantial compared to more familiar sub-national administrative districts. Focusing on gridded data sets allows for almost arbitrary spatial aggregation, opening up distinct research opportunities in the field of conflict prediction. With the recently growing public focus on climate change’s social consequences, evaluating its impact on conflict risk is a crucial component in ensuring sustainable development. After all, human lives are at risk and increasing our understanding of how we can prevent their losses is of uttermost importance. Prediction is a way to contribute to both supporting conflict prevention efforts and advancing the scientific understanding of the relationship between the natural environment and conflict. The usage of modern deep learning frameworks and the vast availability of open geodata allows for comprehensive spatiotemporal research designs adding value to the analysis of the complex process of violent conflict. Leveraging this potential to create impactful scientific findings and recommendations for action is a primary mandate of applied conflict research in the near future.
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 10 (buster)
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.7.9.2 rgdal_1.5-18 countrycode_1.2.0 welchADF_0.3.2
[5] rstatix_0.6.0 ggpubr_0.4.0 scales_1.1.1 RColorBrewer_1.1-2
[9] latex2exp_0.4.0 cubelyr_1.0.0 gridExtra_2.3 ggtext_0.1.1
[13] magrittr_2.0.1 tmap_3.2 sf_0.9-7 raster_3.4-5
[17] sp_1.4-4 forcats_0.5.0 stringr_1.4.0 purrr_0.3.4
[21] readr_1.4.0 tidyr_1.1.2 tibble_3.0.6 tidyverse_1.3.0
[25] huwiwidown_0.0.1 kableExtra_1.3.1 knitr_1.31 rmarkdown_2.7.3
[29] bookdown_0.21 ggplot2_3.3.3 dplyr_1.0.2 devtools_2.3.2
[33] usethis_2.0.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.0 workflowr_1.6.2
[4] lwgeom_0.2-5 splines_3.6.3 crosstalk_1.1.0.1
[7] leaflet_2.0.3 digest_0.6.27 htmltools_0.5.1.1
[10] memoise_1.1.0 openxlsx_4.2.3 remotes_2.2.0
[13] modelr_0.1.8 prettyunits_1.1.1 colorspace_2.0-0
[16] rvest_0.3.6 haven_2.3.1 xfun_0.21
[19] leafem_0.1.3 callr_3.5.1 crayon_1.4.0
[22] jsonlite_1.7.2 lme4_1.1-26 glue_1.4.2
[25] stars_0.4-3 gtable_0.3.0 webshot_0.5.2
[28] car_3.0-10 pkgbuild_1.2.0 abind_1.4-5
[31] DBI_1.1.0 Rcpp_1.0.5 viridisLite_0.3.0
[34] gridtext_0.1.4 units_0.6-7 foreign_0.8-71
[37] htmlwidgets_1.5.3 httr_1.4.2 ellipsis_0.3.1
[40] pkgconfig_2.0.3 XML_3.99-0.3 dbplyr_2.0.0
[43] tidyselect_1.1.0 rlang_0.4.10 later_1.1.0.1
[46] tmaptools_3.1 munsell_0.5.0 cellranger_1.1.0
[49] tools_3.6.3 cli_2.3.0 generics_0.1.0
[52] broom_0.7.2 evaluate_0.14 yaml_2.2.1
[55] processx_3.4.5 leafsync_0.1.0 fs_1.5.0
[58] zip_2.1.1 nlme_3.1-150 xml2_1.3.2
[61] compiler_3.6.3 rstudioapi_0.13 curl_4.3
[64] png_0.1-7 e1071_1.7-4 testthat_3.0.1
[67] ggsignif_0.6.0 reprex_0.3.0 statmod_1.4.35
[70] stringi_1.5.3 ps_1.5.0 desc_1.2.0
[73] lattice_0.20-41 Matrix_1.2-18 nloptr_1.2.2.2
[76] classInt_0.4-3 vctrs_0.3.6 pillar_1.4.7
[79] lifecycle_0.2.0 data.table_1.13.2 httpuv_1.5.5
[82] R6_2.5.0 promises_1.1.1 KernSmooth_2.23-18
[85] rio_0.5.16 sessioninfo_1.1.1 codetools_0.2-16
[88] dichromat_2.0-0 boot_1.3-25 MASS_7.3-53
[91] assertthat_0.2.1 pkgload_1.1.0 rprojroot_2.0.2
[94] withr_2.4.1 parallel_3.6.3 hms_1.0.0
[97] grid_3.6.3 minqa_1.2.4 class_7.3-17
[100] carData_3.0-4 git2r_0.27.1 base64enc_0.1-3