406_Conflict_db_analyses3
Analyses after meeting 23rd March 2023
- Proximate drivers
- Most common drivers
- How do drivers vary spatially?
- Which drivers co-occur?
- How are drivers and conflict types related?
- Drivers -> conflict number - with an eye towards potential lumping
(Devlin et al. -> drivers of conflict)
- Distal drivers
- Most common drivers
- How do drivers vary spatially?
- Which drivers co-occur?
- How are drivers and conflict types related?
- Approach:
Focus (so far in this doc): proximate drivers to conflict types, and how
Alternative to the clustering approach:
Calculate number of occurences for combinations of proximate drivers and conflict types
Make a “Sankey” plot to show connections drivers -> conflict types
Doesn’t work as well for actors - there are many combinations (even after lumping different ‘government’ and ‘ngo’ groups together). Perhaps rather use these as predictors for the different confict types directly (not as combinations)
Will try same approach for violence/illegality and for resolution
Data
Main data (ce)
[1] 1089 111Checking data
[1] 235[1] 49$`Article title (article_titl)`
[1] "Coast Guard Arrests 'Notorious' Toothfish Poacher, Minister Ziankahn Confirms"
$`Author (author) \r\nIf no author, leave blank.`
[1] "-999"
$`Source (source)`
[1] "AllAfrica"
$`Year (yr)`
[1] 2018
$`Month (month)`
[1] 3
$`Day\r\n(day)`
[1] 22
$`Language (lang)`
[1] "E"
$`ISO3 country code (iso3_code)`
[1] "LBR"Extract short names
Make ID column
- conf_event_id has some duplicates
- make a new version
==== conf_event_id ==== The following numbers should be 0 if there are no duplicates: [1] 0.02159624[1] 23==== conf_event_id2 ==== The following numbers should be 0 if there are no duplicates: [1] 0[1] 0Get relatively binary variables
Plot tables
Pick columns
- Not including ‘Resolution’ variables
- Data ‘ce2’
Make violence binary
Remove rows with piracy
Type = piracy: 137 rows will be removed Actor = pirates: 4 rows will be removed Function for summarizing columns
Proximate drivers
Pick variables
[1] "conf_event_id2" "benef_dist_driv" "acut_food_short"
[4] "nat_dis" "trad_v_new" "bord_disp_prox"
[7] "inad_gov_prox" "lack_reg_prox" "pol_insta_prox"
[10] "res_cond_hum_prox" "res_cond_nonhum_prox" "ill_res_use_prox"
[13] "iuu_fish_prox" "piracy_prox" "plan_dev" Summarize combinations
- Summarise the number of rows with a given variable or combination of variables (drivers in this case)
- I.e., the number of cases where the value of that variable equals 1
- I.e., the number of cases where the value of that variable equals 1
- Sort from the most common to least common combination of variables
- Make a new text variable which summarizes the names of the variables
- Make new data set with the new text variable and number of rows, add cumulative percentage
Plot
[1] "benef_dist_driv: Distribution of benefits (benef_dist_driv)"
[2] "bord_disp_prox: New or recent border dispute with neighbors (bord_disp_prox)"
[3] "inad_gov_prox: Recent change resulting in inadequate governance or enforcement due to a lack of capacity or resources or to a dispute (inad_gov_prox)"
[4] "lack_reg_prox: Recent change resulting in inadequate, lacking, or conflicting regulation (lack_reg_prox)"
[5] "res_cond_hum_prox: Recent change resulting in poor resource condition due to human activity (e.g. recent bout of overfishing or pollution) (res_cond_hum_prox)"
[6] "res_cond_nonhum_prox: Recent change resulting in poor resource condition due to non-human factors, excluding natural disasters (res_cond_nonhum_prox)"
[7] "ill_res_use_prox: Specific instance(s) of illegal use of a resource (e.g. illegal mangrove harvesting), excluding IUU fishing (ill_res_use_prox)"
[8] "iuu_fish_prox: Specific instance(s) of IUU fishing (iuu_fish_prox)"
[9] "plan_dev: Planned development (plan_dev)" Summarize columns but not rows
Distal drivers
Pick variables
[1] "conf_event_id2" "poverty" "soc_eco_ineq"
[4] "food_insec_dist" "clim_chang" "bord_disp_dist"
[7] "ethnic_div_dist" "cons_dem" "pop_inc"
[10] "inad_gov_dist" "pol_insta_dist" "poor_res_hum_dist"
[13] "poor_res_no_hum_dist" "marit_crime_dist" "ill_res_use_dist"
[16] "iuu_fish_dist" Summarize combinations
- Summarise the number of rows with a given variable or combination of variables (drivers in this case)
- I.e., the number of cases where the value of that variable equals 1
- I.e., the number of cases where the value of that variable equals 1
- Sort from the most common to least common combination of variables
- Make a new text variable which summarizes the names of the variables
- Make new data set with the new text variable and number of rows, add cumulative percentage
Plot
[1] "poverty: Poverty (poverty)"
[2] "clim_chang: Climate change (clim_chang)"
[3] "bord_disp_dist: Ongoing border dispute with neighbors (bord_disp_dist)"
[4] "inad_gov_dist: Ongoing inadequate governance or enforcement (inad_gov_dist)"
[5] "pol_insta_dist: Ongoing political instability (pol_insta_dist)"
[6] "poor_res_hum_dist: Poor resource condition due to human activity, ongoing (poor_res_hum_dist)"
[7] "poor_res_no_hum_dist: Poor resource condition due to non-human factors, ongoing (poor_res_no_hum_dist)"
[8] "marit_crime_dist: Ongoing illegal activity or maritime crime, excluding piracy, illegal use of resources, or IUU fishing (marit_crime_dist)"
[9] "ill_res_use_dist: Systemic illegal use of a resource (e.g. illegal mangrove harvesting), excluding IUU fishing (ill_res_use_dist)"
[10] "iuu_fish_dist: Systematically occurring IUU fishing (iuu_fish_dist)" Conflict types
Pick variables
[1] "conf_event_id2" "access_space" "use_space" "access_res"
[5] "use_res" "benef_dist_type" "piracy" Summarize combinations
- Summarise the number of rows with a given variable or combination of variables (drivers in this case)
- I.e., the number of cases where the value of that variable equals 1
- I.e., the number of cases where the value of that variable equals 1
- Sort from the most common to least common combination of variables
- Make a new text variable which summarizes the names of the variables
- Make new data set with the new text variable and number of rows, add cumulative percentage
Most common co-occurences
| column_comb | n | percent | cumul_percent |
|---|---|---|---|
| access_space | 198 | 20.9 | 20.9 |
| access_res | 161 | 17.0 | 37.9 |
| use_space | 119 | 12.6 | 50.4 |
| access_space + access_res | 95 | 10.0 | 60.4 |
| use_res | 69 | 7.3 | 67.7 |
| access_space + access_res + use_res | 43 | 4.5 | 72.3 |
| access_res + use_res | 37 | 3.9 | 76.2 |
| access_space + access_res + use_res + benef_dist_type | 24 | 2.5 | 78.7 |
| benef_dist_type | 23 | 2.4 | 81.1 |
| access_space + use_space | 23 | 2.4 | 83.5 |
| access_res + benef_dist_type | 21 | 2.2 | 85.8 |
| access_space + use_res | 20 | 2.1 | 87.9 |
| None | 19 | 2.0 | 89.9 |
| access_space + benef_dist_type | 14 | 1.5 | 91.4 |
| use_space + benef_dist_type | 13 | 1.4 | 92.7 |
| access_space + access_res + benef_dist_type | 13 | 1.4 | 94.1 |
| use_space + use_res | 12 | 1.3 | 95.4 |
| use_space + access_res | 11 | 1.2 | 96.5 |
| access_space + use_res + benef_dist_type | 7 | 0.7 | 97.3 |
| use_res + benef_dist_type | 6 | 0.6 | 97.9 |
Plot
[1] "access_space: Access to space, including conflict over borders or boundaries (access_space)"
[2] "use_space: Use of space (use_space)"
[3] "access_res: Access to a resource (access_res)"
[4] "use_res: Use of a resource (use_res)"
[5] "benef_dist_type: Distribution of benefits (benef_dist_type)" Summarize columns but not rows
Sankey diagram, prox + type
NOTE: This plot doesn’t plot well in pdf
Including “None” group of prox
Static plot
Excluding ‘none’ group
addNA(from)
benef_dist_driv benef_dist_driv + lack_reg_prox
91 28
iuu_fish_prox lack_reg_prox
263 52
None other_prox
272 162
plan_dev res_cond_hum_prox
28 52
<NA>
0 Actors - combinations
Pick variables
[1] "conf_event_id2" "local_ngo" "natl_ngo" "intl_ngo"
[5] "loc_govt" "intl_govt" "subs_fisher" "ind_fishers"
[9] "fish_trad" "subs_living_res" "ind_living_res" "ind_non_liv"
[13] "develop" "comm_org" "worker_union" "pirates"
[17] "state_enf" "intl_multi_bod" Summarize combinations
- Summarise the number of rows with a given variable or combination of variables (drivers in this case)
- I.e., the number of cases where the value of that variable equals 1
- I.e., the number of cases where the value of that variable equals 1
- Sort from the most common to least common combination of variables
- Make a new text variable which summarizes the names of the variables
- Make new data set with the new text variable and number of rows, add cumulative percentage
Most common co-occurences
| column_comb | n | percent |
|---|---|---|
| govt + subs_fisher (not ind_fishers, state_enf) | 195 | 20.6 |
| govt + subs_fisher + ind_fishers ++ | 155 | 16.4 |
| govt + ind_fishers + state_enf ++ | 74 | 7.8 |
| govt + ind_fishers (not subs_fishers, state_enf) | 68 | 7.2 |
| ind_fishers + state_enf (not govt) | 63 | 6.6 |
| subs_fisher + state_enf | 57 | 6.0 |
| ngo + govt | 29 | 3.1 |
| govt + subs_living_res | 28 | 3.0 |
| govt | 27 | 2.8 |
| govt + intl_multi_bod | 25 | 2.6 |
| govt + ind_non_liv | 14 | 1.5 |
| govt + subs_living_res + state_enf | 13 | 1.4 |
| ind_fishers + subs_fisher (not govt, state_enf) | 13 | 1.4 |
| govt + subs_living_res + ind_non_liv | 8 | 0.8 |
| govt + fish_trad + worker_union | 7 | 0.7 |
| govt + comm_org | 6 | 0.6 |
| govt + worker_union | 6 | 0.6 |
| subs_fisher | 6 | 0.6 |
| subs_living_res | 6 | 0.6 |
| govt + fish_trad | 5 | 0.5 |
Plot
[1] "local_ngo: Local NGO (local_ngo)"
[2] "natl_ngo: National NGO (natl_ngo)"
[3] "intl_ngo: International NGO (intl_ngo)"
[4] "loc_govt: Local or regional government (loc_govt)"
[5] "intl_govt: National government (intl_govt)"
[6] "subs_fisher: Small-scale, subsistence, or artisanal fishers (subs_fisher)"
[7] "ind_fishers: Industrial fishers (ind_fishers)"
[8] "fish_trad: Fish traders, wholesalers, and retailers (fish_trad)"
[9] "subs_living_res: Small-scale, subsistence, or recreational living resource users, except fishers (subs_living_res)"
[10] "ind_living_res: Industrial living resource users, except fishers (ind_living_res)"
[11] "ind_non_liv: Industrial non-living resource users (ind_non_liv)"
[12] "develop: Development actors (develop)"
[13] "comm_org: Community organization (comm_org)"
[14] "worker_union: Worker union or association (worker_union)"
[15] "pirates: Pirates (pirates)"
[16] "state_enf: State enforcement agents (state_enf)"
[17] "intl_multi_bod: International multilateral bodies (intl_multi_bod)" Summarize columns but not rows
Actors - simple statistics
| actor | n | percent |
|---|---|---|
| govt | 733 | 27.1 |
| subs_fisher | 457 | 16.9 |
| ind_fishers | 385 | 14.2 |
| state_enf | 351 | 13.0 |
| worker_union | 157 | 5.8 |
| ngo | 132 | 4.9 |
| subs_living_res | 125 | 4.6 |
| intl_multi_bod | 108 | 4.0 |
| ind_non_liv | 90 | 3.3 |
| fish_trad | 68 | 2.5 |
| comm_org | 62 | 2.3 |
| develop | 25 | 0.9 |
| ind_living_res | 10 | 0.4 |
Actors rel. to actors
- For each column (proximate driver), the percentage of cases of each actor
- Top row = number of cases
New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`
• `` -> `...6`
• `` -> `...7`
• `` -> `...8`
• `` -> `...9`
• `` -> `...10`
• `` -> `...11`
• `` -> `...12`
• `` -> `...13`
• `` -> `...14`# A tibble: 15 × 14
Actor All c…¹ ngo govt subs_…² ind_f…³ fish_…⁴ subs_…⁵ ind_n…⁶ develop
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Total nu… 948 132 733 457 384 68 125 90 25
2 ngo 13.9 100 14.3 9.2 7.3 7.4 24.8 18.9 56
3 govt 77.3 79.5 100 76.6 77.3 80.9 77.6 83.3 76
4 subs_fis… 48.2 31.8 47.7 100 45.8 72.1 24 37.8 12
5 ind_fish… 40.5 21.2 40.5 38.5 100 23.5 2.4 13.3 0
6 fish_trad 7.2 3.8 7.5 10.7 4.2 100 3.2 2.2 0
7 subs_liv… 13.2 23.5 13.2 6.6 0.8 5.9 100 23.3 36
8 ind_livi… 1.1 1.5 1 0.4 0.5 2.9 4.8 1.1 4
9 ind_non_… 9.5 12.9 10.2 7.4 3.1 2.9 16.8 100 4
10 develop 2.6 10.6 2.6 0.7 0 0 7.2 1.1 100
11 comm_org 6.5 15.9 7.4 5 1.3 2.9 19.2 20 44
12 worker_u… 16.6 6.1 19.9 28.4 20.3 36.8 6.4 6.7 4
13 pirates 0 0 0 0 0 0 0 0 0
14 state_enf 37 19.7 28.4 37.6 43 26.5 24.8 23.3 16
15 intl_mul… 11.4 9.1 12.6 7.7 12.5 10.3 2.4 11.1 4
# … with 4 more variables: comm_org <dbl>, worker_union <dbl>, state_enf <dbl>,
# intl_multi_bod <dbl>, and abbreviated variable names ¹`All conflicts`,
# ²subs_fisher, ³ind_fishers, ⁴fish_trad, ⁵subs_living_res, ⁶ind_non_livActors rel. to prox drivers
- For each column (proximate driver), the percentage of cases of each actor
- Top row = number of cases
New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`
• `` -> `...6`
• `` -> `...7`
• `` -> `...8`
• `` -> `...9`
• `` -> `...10`
• `` -> `...11`
• `` -> `...12`
• `` -> `...13`# A tibble: 15 × 10
Actor All c…¹ benef…² bord_…³ inad_…⁴ lack_…⁵ res_c…⁶ ill_r…⁷ iuu_f…⁸ plan_…⁹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Tota… 948 160 20 35 100 85 26 301 64
2 ngo 13.9 7.5 0 8.6 14 34.1 11.5 3 32.8
3 govt 77.3 86.9 85 74.3 97 78.8 65.4 60.8 92.2
4 subs… 48.2 66.2 35 74.3 67 38.8 19.2 46.2 35.9
5 ind_… 40.5 48.8 15 37.1 37 20 7.7 68.1 17.2
6 fish… 7.2 13.1 0 25.7 12 4.7 19.2 4 0
7 subs… 13.2 6.2 0 17.1 6 35.3 57.7 2 17.2
8 ind_… 1.1 0.6 0 0 2 3.5 11.5 0 0
9 ind_… 9.5 15 10 2.9 1 40 7.7 0.7 45.3
10 deve… 2.6 3.1 0 0 0 14.1 7.7 0.3 25
11 comm… 6.5 8.8 15 2.9 2 23.5 15.4 1.3 26.6
12 work… 16.6 28.7 10 25.7 41 5.9 7.7 7.6 10.9
13 pira… 0 0 0 0 0 0 0 0 0
14 stat… 37 14.4 20 31.4 7 23.5 50 69.8 20.3
15 intl… 11.4 15.6 60 8.6 9 2.4 3.8 8.6 4.7
# … with abbreviated variable names ¹`All conflicts`, ²benef_dist_driv,
# ³bord_disp_prox, ⁴inad_gov_prox, ⁵lack_reg_prox, ⁶res_cond_hum_prox,
# ⁷ill_res_use_prox, ⁸iuu_fish_prox, ⁹plan_devActors rel. to conflict types (one table per driver)
Actors rel. to conflict types involving iuu_fish_prox
- For distal driver = ‘iuu_fish_prox’ only
- For each column (type of conflict), the percentage of cases of each actor
- Top row = number of cases
New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`# A tibble: 15 × 7
Actor `All conflicts` access_space use_sp…¹ acces…² use_res benef…³
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Total number 300 191 15 145 102 15
2 ngo 3 1.6 0 0 5.9 0
3 govt 60.7 61.3 93.3 53.8 79.4 80
4 subs_fisher 46.3 48.2 53.3 48.3 43.1 100
5 ind_fishers 68 71.2 73.3 70.3 78.4 73.3
6 fish_trad 4 1 0 4.8 5.9 20
7 subs_living_res 2 0.5 6.7 3.4 1 20
8 ind_living_res 0 0 0 0 0 0
9 ind_non_liv 0.7 0.5 0 0.7 0 0
10 develop 0.3 0 6.7 0 0 0
11 comm_org 1.3 1 6.7 2.1 2 0
12 worker_union 7.7 8.9 13.3 8.3 7.8 40
13 pirates 0 0 0 0 0 0
14 state_enf 69.7 75.4 26.7 70.3 45.1 13.3
15 intl_multi_bod 8.7 7.9 20 6.9 14.7 13.3
# … with abbreviated variable names ¹use_space, ²access_res, ³benef_dist_typeActors rel. to conflict types involving benef_dist_driv
- For distal driver = ‘benef_dist_driv’ only
- For each column (type of conflict), the percentage of cases of each actor
- Top row = number of cases
New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`# A tibble: 15 × 7
Actor `All conflicts` access_space use_sp…¹ acces…² use_res benef…³
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Total number 160 80 30 79 37 94
2 ngo 7.5 7.5 20 5.1 0 9.6
3 govt 86.9 86.2 96.7 86.1 94.6 89.4
4 subs_fisher 66.2 72.5 60 75.9 91.9 74.5
5 ind_fishers 48.8 58.8 30 62 78.4 53.2
6 fish_trad 13.1 10 3.3 17.7 21.6 19.1
7 subs_living_res 6.2 7.5 20 2.5 2.7 3.2
8 ind_living_res 0.6 1.2 0 0 0 0
9 ind_non_liv 15 10 56.7 0 0 13.8
10 develop 3.1 3.8 16.7 0 0 3.2
11 comm_org 8.8 10 33.3 2.5 5.4 6.4
12 worker_union 28.7 30 20 34.2 37.8 34
13 pirates 0 0 0 0 0 0
14 state_enf 14.4 15 6.7 15.2 13.5 11.7
15 intl_multi_bod 15.6 17.5 0 16.5 8.1 13.8
# … with abbreviated variable names ¹use_space, ²access_res, ³benef_dist_typeActors rel. to conflict types involving lack_reg_prox
- For distal driver = ‘lack_reg_prox’ only
- For each column (type of conflict), the percentage of cases of each actor
- Top row = number of cases
New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`# A tibble: 15 × 7
Actor `All conflicts` access_space use_sp…¹ acces…² use_res benef…³
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Total number 100 63 5 53 20 23
2 ngo 14 6.3 20 20.8 15 0
3 govt 97 98.4 100 96.2 100 91.3
4 subs_fisher 67 76.2 20 66 75 69.6
5 ind_fishers 37 42.9 0 49.1 50 34.8
6 fish_trad 12 9.5 0 13.2 10 26.1
7 subs_living_res 6 3.2 40 7.5 0 17.4
8 ind_living_res 2 1.6 0 0 0 4.3
9 ind_non_liv 1 1.6 0 0 0 0
10 develop 0 0 0 0 0 0
11 comm_org 2 3.2 20 3.8 0 0
12 worker_union 41 42.9 0 45.3 35 60.9
13 pirates 0 0 0 0 0 0
14 state_enf 7 11.1 0 1.9 0 0
15 intl_multi_bod 9 9.5 20 11.3 15 0
# … with abbreviated variable names ¹use_space, ²access_res, ³benef_dist_typeActors rel. to conflict types involving lack_cond_hum_prox
- For distal driver = ‘res_cond_hum_prox’ only
- For each column (type of conflict), the percentage of cases of each actor
- Top row = number of cases
New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`# A tibble: 15 × 7
Actor `All conflicts` access_space use_sp…¹ acces…² use_res benef…³
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Total number 85 20 59 19 21 7
2 ngo 34.1 65 39 15.8 19 71.4
3 govt 78.8 90 76.3 84.2 71.4 100
4 subs_fisher 38.8 40 27.1 73.7 61.9 42.9
5 ind_fishers 20 25 11.9 31.6 47.6 28.6
6 fish_trad 4.7 0 3.4 15.8 0 14.3
7 subs_living_res 35.3 25 35.6 21.1 33.3 14.3
8 ind_living_res 3.5 5 1.7 5.3 14.3 14.3
9 ind_non_liv 40 30 49.2 0 9.5 14.3
10 develop 14.1 35 20.3 0 4.8 57.1
11 comm_org 23.5 30 28.8 10.5 14.3 42.9
12 worker_union 5.9 5 5.1 21.1 9.5 0
13 pirates 0 0 0 0 0 0
14 state_enf 23.5 30 22 26.3 33.3 14.3
15 intl_multi_bod 2.4 0 0 10.5 4.8 0
# … with abbreviated variable names ¹use_space, ²access_res, ³benef_dist_typeIllegal activity
Pick variables
[1] "conf_event_id2" "iuu_fish" "ill_spa_acc" "ill_spa_use"
[5] "ill_res_acc" "ill_res_use" "gear_destr" "infrast_destr"
[9] "ill_mar_crime" "ill_none" Summarize combinations
- Summarise the number of rows with a given variable or combination of variables (drivers in this case)
- I.e., the number of cases where the value of that variable equals 1
- I.e., the number of cases where the value of that variable equals 1
- Sort from the most common to least common combination of variables
- Make a new text variable which summarizes the names of the variables
- Make new data set with the new text variable and number of rows, add cumulative percentage
Most common co-occurences
| column_comb | n | percent |
|---|---|---|
| ill_none | 470 | 49.6 |
| iuu_fish | 333 | 35.1 |
| ill_spa_use | 45 | 4.7 |
| ill_res_use | 19 | 2.0 |
| ill_res_acc | 19 | 2.0 |
| ill_spa_acc | 14 | 1.5 |
| iuu_fish + gear_destr | 9 | 0.9 |
| iuu_fish + ill_mar_crime | 5 | 0.5 |
| None | 4 | 0.4 |
| ill_spa_acc + ill_spa_use | 4 | 0.4 |
| iuu_fish + infrast_destr | 3 | 0.3 |
| gear_destr | 2 | 0.2 |
| ill_res_acc + infrast_destr | 2 | 0.2 |
| iuu_fish + ill_none | 2 | 0.2 |
| iuu_fish + ill_res_use | 2 | 0.2 |
| iuu_fish + ill_spa_use | 2 | 0.2 |
| iuu_fish + ill_spa_acc | 2 | 0.2 |
| ill_mar_crime | 1 | 0.1 |
| infrast_destr | 1 | 0.1 |
| ill_res_acc + ill_res_use | 1 | 0.1 |
Plot
[1] "iuu_fish: IUU fishing (iuu_fish)"
[2] "ill_spa_acc: Illegal access to a space, excluding IUU fishing (ill_spa_acc)"
[3] "ill_spa_use: Illegal use of a space, excluding IUU fishing (ill_spa_use)"
[4] "ill_res_acc: Illegal access to a resource, excluding IUU fishing (ill_res_acc)"
[5] "ill_res_use: Illegal use of a resource, excluding IUU fishing (ill_res_use)" Summarize columns but not rows
column_comb
gear_destr ill_mar_crime ill_none ill_res_acc ill_res_use
12 6 474 21 19
ill_spa_acc ill_spa_use infrast_destr iuu_fish
14 52 6 344 Sankey diagram driver - type - illegality
Including “None” group of prox
Excluding ‘None’ group
Resolution
Pick variables
N Y
911 37 ev_res
ev_stalemate N Y
N 618 293
Y 37 0, , ev_unres = N
ev_res
ev_stalemate N Y
N 2 292
Y 20 0
, , ev_unres = Y
ev_res
ev_stalemate N Y
N 616 1
Y 17 0 ev_unres_inab_cons
ev_unres -999 0 1
N 50 264 0
Y 304 8 322 ev_unres_inab_cons
ev_unres -999 0 1
N 50 264 0
Y 304 8 322 unres_inab_gov
ev_unres -999 0 1
N 50 264 0
Y 430 12 192 ev_unres_soc_facts
ev_unres -999 0 1
N 50 264 0
Y 583 15 36 [1] "conf_event_id2" "ev_res_conf_med" "ev_res_inc_gov"
[4] "ev_leg_court_res" "ransom" "ev_unres_inab_cons"
[7] "unres_inab_gov" "ev_unres_soc_facts" "stalemate_other"
[10] "resolved_other" "unresolved_other" Summarize combinations
- Summarise the number of rows with a given variable or combination of variables (drivers in this case)
- I.e., the number of cases where the value of that variable equals 1
- I.e., the number of cases where the value of that variable equals 1
- Sort from the most common to least common combination of variables
- Make a new text variable which summarizes the names of the variables
- Make new data set with the new text variable and number of rows, add cumulative percentage
Most common co-occurences
| column_comb | n | percent |
|---|---|---|
| ev_unres_inab_cons | 268 | 28.3 |
| ev_res_inc_gov | 194 | 20.5 |
| unresolved_other | 157 | 16.6 |
| unres_inab_gov | 127 | 13.4 |
| ev_unres_inab_cons + unres_inab_gov | 37 | 3.9 |
| ev_leg_court_res | 37 | 3.9 |
| stalemate_other | 28 | 3.0 |
| resolved_other | 21 | 2.2 |
| ev_res_inc_gov + ev_leg_court_res | 19 | 2.0 |
| unres_inab_gov + ev_unres_soc_facts | 15 | 1.6 |
| ev_unres_inab_cons + unres_inab_gov + ev_unres_soc_facts | 13 | 1.4 |
| ev_res_conf_med | 12 | 1.3 |
| ev_res_conf_med + ev_leg_court_res | 6 | 0.6 |
| ev_unres_soc_facts | 4 | 0.4 |
| ev_unres_inab_cons + ev_unres_soc_facts | 4 | 0.4 |
| ev_res_conf_med + ev_res_inc_gov | 4 | 0.4 |
| None | 2 | 0.2 |
Plot
[1] "ev_res_inc_gov: Increased governance or enforcement (ev_res_inc_gov)"
[2] "ev_leg_court_res: Legal or court resolution (ev_leg_court_res)"
[3] "ev_unres_inab_cons: Inability to reach consensus (ev_unres_inab_cons)"
[4] "unres_inab_gov: Inability to increase or change governance or enforcement (unres_inab_gov)"