Historical Impact Assessment (HIA) Sudan exercise

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A HIA has two purposes: First, understanding in detail what kind of problems are caused by a particular hazard, allows people to make informed decisions on the selection of early actions to counter those problems. Secondly, without a good understanding of which magnitude of flood causes significant humanitarian impact, one can not adjust trigger levels accordingly to tackle those significant events. This exercise is based on the article Historical Impact Assessment (HIA) for Flood in Sudan. In this article, there are seven steps listed for the HIA in Sudan. This exercise will start with step 3- Selecting datasets.

Aim of the exercise:

In this exercise, you will be introduced to the fundamental principles of this quantitative HIA approach. You will build a simplified HIA dataset with real-world data. Furthermore, you will visualize the produced dataset in QGIS. To analyse the data in QGIS is just one thing you can do with a HIA dataset. The wide range of other options like the production of a timeline infographic will not be covered in this exercise.

Relevant Wiki Articles

Data sources

Download the training data folder here and save it on your PC. Unzip the .zip file! The folder is called GIS_AA_HIA_Sudan_ex1 and contains the whole standard folder structure with all data in the input folder and the additional documentation in the documentation folder.

Tasks

Step 1: Area of interest ✅

This step is outlined in the article Historical Impact Assessment (HIA) for Flood in Sudan will not be the subject of this exercise.

Step 2: Finding Flood Disaster Timeframes ✅

Attention

This step is outlined in the article Historical Impact Assessment (HIA) for Flood in Sudan will not be the subject of this exercise.

We want to tie single pieces of information, like impacts, to known flood events in Sudan. Thus, we first need a comprehensive list of such events. In the case of Sudan, there are two sources: EM-DAT and ReliefWeb. EM-DAT is a disaster database that lists events above a certain severity.

Note

EM-DAT Inclusion Criteria

• At least ten deaths (including dead and missing).

• At least 100 affected (people affected, injured, or homeless).

• A call for international assistance or an emergency declaration.

ReliefWeb is actually an information platform for humanitarian response, but it also has a list of active and past disasters.

Both databases list the same events for the most part. By comparing the two datasets and only selecting unique events, we receive a list of all significant flood events in Sudan and the time frames of each event. This can easily be done in Excel

Note

EM-Date and ReliefWeb data overview In total, there were 35 flood events reported between 1988 and 2021

• EM-Dat lists 21 flood events in Sudan from 2000 to 2024

• Relief Web lists 29 events between 1988 and 2024

Flood Events List

Start Year	Start Date	End Date
1988	1988-08-06	1988-09-15
1994	1994-09-20	1988-10-21
1996	1996-08-12	1996-10-23
1998	1998-09-04	1998-10-15
2001	2001-08-06	2001-09-13
2002	2002-08-03	2002-08-03
2003	2003-07-28	2003-08-21
2005	2005-08-29	2005-09-06
2005	2005-07-14	2005-07-15
2006	2006-08-13	2006-09-26
2006	2006-08-07	2006-08-07
2007	2007-07-03	2007-10-08
2009	2009-08-16	2009-08-26
2010	2010-07-10	2010-07-15
2010	2010-07-01	2010-10-07
2012	2012-08-01	2012-08-12
2013	2013-08-01	2013-08-21
2014	2014-07-25	2014-09
2015	2015-08-08	2015-08-09
2017	2017-06-30	2017-08-14
2018	2018-06-18	2018-06-27
2018	2018-07-23	2018-07-30
2019	2019-06-08	2019-06-18
2020	2020-06	2020-09-09
2021	2021-07-20	2021-09-24

Task 3: Selecting datasets

In this step, we decide which datasets we want to include in the HIA. In this context, there are three sources we can turn to: the Sudan Red Crescent Society (SRCS), ReliefWeb, and FloodList. For this exercise, we will limit ourselves to datasets from ReliefWeb and FloodList. In the ‘Input’ folder of the exercise data, you can find the folder ‘reports_articles.’ This folder contains 10 datasets.

1. Your task now is to go through the reports and decide which one you will use in the HIA. Place the dataset you want to use in a new folder.

   As a decision support, you can use these three parameters:

   Currentness

   The assumption is that later after the event, the picture of what happens becomes clearer, thus the data report is more reliable. This means., if you have multiple similar datasets containing similar data, chose the newer one.

   Example

   In this example, the later map from the 13th of November shows more affected people for instance in Central Darfur the the map published on the 9th of November.

   SUDAN Situation Report 09 Nov 2020

   

   SUDAN Situation Report 13 Nov 2020

   

   ---

   Uniqueness

   It is important to not only use data from one source because of confidence. The assumption is that different organizations have different capacities and work in different areas; thus, they may have better information in some locations or sectors than other organizations.

2. Give the datasets you selected an ID. This is essential to be able to later pinpoint from which dataset a particular information was pulled. Change the name of the file to the new ID. Furthermore, create a list of the datasets you want to use for the HIA.

   

Use the method below to come up with the ID’s. Expected outcome:

• A folder with all datasets to be used in the HIA. The dataset’s names should be their ID.

• A list of all datasets to be used, with ID.

Task 4: Preparing Excel table Structure ✅

Attention

In this exercise, you will be provided with the ready-to-use table. You can find it „temp“ folder of the exercise data structure.

Before we can start compiling data, we need to prepare the Excel table structure we want to use. Depending on the context the table structure can look differently. In the end, you need to be able to collect information on when something happened, where it happened and what happened. Furthermore, you need to be able to identify from which dataset you take every single information. Below you can find short explanations of the different columns of the table structure.

Date

The date information must accommodate the flood event information from the list of flood events prepared in Step 2. And potential dates of specific impact information. The date portion of the whole Excel table would look like this:

Start Year	Start Date	End Date	Date
2018	2018-07-01	2018-08-29	2018-07-12
2019	2019-06-01	2019-06-31
2020	2020-07-20	2020-08-11

Data source

This section is simply one column with the ID of the dataset from which the particular information was taken.

source_ID
2018_OCHA_20180819.pdf
2019_floodlist_20190613.pdf
2020_PDNRA_20210531.pdf

Location

Practical all impact information refers to state, locality, town or refugee/IDP camp level. This means we need a column for each of these levels and one column to indicate the level the information is referring to. In this way, we can later filter for all information on for example locality level.

admin_level	admin_1	admin_2	admin_3	admin_camp
State	West Kordofan
Locality	South Darfur	Beliel
Camp	South Darfur	Beliel		Kalma Camp

In the Sudan HIA, most of the information has been on the state level, whereas the team found very little information on the camp level.

Tip

We highly recommend using the English names of states and localities which are compatible with P-codes. Those can be found here.

Admin 1 - States

ADM1_EN	ADM1_AR	ADM1_PCODE
Abyei PCA	إدارية أبيي	SD19
Aj Jazirah	الجزيرة	SD15
Blue Nile	النيل الأزرق	SD08
Central Darfur	وسط دارفور	SD06
East Darfur	شرق دارفور	SD05
Gedaref	القضارف	SD12
Kassala	كسلا	SD11
Khartoum	الخرطوم	SD01
North Darfur	شمال دارفور	SD02
North Kordofan	شمال كردفان	SD13
Northern	الشمالية	SD17
Red Sea	البحر الأحمر	SD10
River Nile	نهر النيل	SD16
Sennar	سنار	SD14
South Darfur	جنوب دارفور	SD03
South Kordofan	جنوب كردفان	SD07
West Darfur	غرب دارفور	SD04
West Kordofan	غرب كردفان	SD18
White Nile	النيل الأبيض	SD09

Admin 2- Localities

ADM2_EN	ADM2_AR	ADM2_PCODE	ADM1_EN	ADM1_AR	ADM1_PCODE
Abassiya	العباسية	SD07090	South Kordofan	جنوب كردفان	SD07
Abu Hamad	أبو حمد	SD16008	River Nile	نهر النيل	SD16
Abu Hujar	أبو حجار	SD14037	Sennar	سنار	SD14
Abu Jabrah	أبو جابرة	SD05140	East Darfur	شرق دارفور	SD05
Abu Jubayhah	أبو جبيهة	SD07088	South Kordofan	جنوب كردفان	SD07
Abu Karinka	أبو كارنكا	SD05155	East Darfur	شرق دارفور	SD05
Abu Kershola	أبو كرشولا	SD07104	South Kordofan	جنوب كردفان	SD07
Abu Zabad	أبو زبد	SD18028	West Kordofan	غرب كردفان	SD18
Abyei	أبيي	SD18087	West Kordofan	غرب كردفان	SD18
Abyei PCA area	إدارية أبيي	SD19001	Abyei PCA	إدارية أبيي	SD19
Ad Dabbah	الدبة	SD17019	Northern	الشمالية	SD17
Ad Dali	الدالي	SD14039	Sennar	سنار	SD14
Ad Damar	الدامر	SD16011	River Nile	نهر النيل	SD16
Ad Dinder	الدندر	SD14040	Sennar	سنار	SD14
Ad Diwaim	الدويم	SD09044	White Nile	النيل الأبيض	SD09
Ad Du’ayn	الضعين	SD05142	East Darfur	شرق دارفور	SD05
Adila	عديلة	SD05139	East Darfur	شرق دارفور	SD05
Ag Geneina	الجنينة	SD04115	West Darfur	غرب دارفور	SD04
Agig	عقيق	SD10072	Red Sea	البحر الأحمر	SD10
Aj Jabalain	الجبلين	SD09051	White Nile	النيل الأبيض	SD09
Al Buhaira	البحيرة	SD16014	River Nile	نهر النيل	SD16
Al Buram	البرام	SD07099	South Kordofan	جنوب كردفان	SD07
Al Burgaig	البرقيق	SD17016	Northern	الشمالية	SD17
Al Butanah	البطانة	SD12073	Gedaref	القضارف	SD12
Al Dibab	الدبب	SD18103	West Kordofan	غرب كردفان	SD18
Al Fao	الفاو	SD12074	Gedaref	القضارف	SD12
Al Fashaga	الفشقة	SD12075	Gedaref	القضارف	SD12
Al Fasher	الفاشر	SD02114	North Darfur	شمال دارفور	SD02
Al Firdous	الفردوس	SD05152	East Darfur	شرق دارفور	SD05
Al Galabat Al Gharbyah - Kassab	القلابات الغربية - كساب	SD12078	Gedaref	القضارف	SD12
Al Ganab	القنب	SD10069	Red Sea	البحر الأحمر	SD10
Al Gitaina	القطينة	SD09050	White Nile	النيل الأبيض	SD09
Al Golid	القولد	SD17018	Northern	الشمالية	SD17
Al Hasahisa	الحصاحيصا	SD15034	Aj Jazirah	الجزيرة	SD15
Al Idia	الأضية	SD18104	West Kordofan	غرب كردفان	SD18
Al Kamlin	الكاملين	SD15035	Aj Jazirah	الجزيرة	SD15
Al Khiwai	الخوي	SD18105	West Kordofan	غرب كردفان	SD18
Al Koma	الكومة	SD02116	North Darfur	شمال دارفور	SD02
Al Kurmuk	الكرمك	SD08106	Blue Nile	النيل الأزرق	SD08
Al Lagowa	لقاوة	SD18102	West Kordofan	غرب كردفان	SD18
Al Lait	اللعيت	SD02169	North Darfur	شمال دارفور	SD02
Al Leri	الليري	SD07105	South Kordofan	جنوب كردفان	SD07
Al Mafaza	المفازة	SD12082	Gedaref	القضارف	SD12
Al Malha	المالحة	SD02117	North Darfur	شمال دارفور	SD02
Al Manaqil	المناقل	SD15036	Aj Jazirah	الجزيرة	SD15
Al Matama	المتمة	SD16009	River Nile	نهر النيل	SD16
Al Meiram	الميرم	SD18106	West Kordofan	غرب كردفان	SD18
Al Quoz	القوز	SD07094	South Kordofan	جنوب كردفان	SD07
Al Qurashi	القرشي	SD15037	Aj Jazirah	الجزيرة	SD15
Al Qureisha	القريشة	SD12076	Gedaref	القضارف	SD12
Al Radoum	الردوم	SD03141	South Darfur	جنوب دارفور	SD03
Al Wihda	الوحدة	SD03150	South Darfur	جنوب دارفور	SD03
An Nuhud	النهود	SD18022	West Kordofan	غرب كردفان	SD18
Ar Rahad	الرهد	SD12084	Gedaref	القضارف	SD12
Ar Rahad	الرهد	SD13030	North Kordofan	شمال كردفان	SD13
Ar Rashad	الرشاد	SD07093	South Kordofan	جنوب كردفان	SD07
Ar Reif Ash Shargi	الريف الشرقي	SD07097	South Kordofan	جنوب كردفان	SD07
Ar Rusayris	الروصيرص	SD08107	Blue Nile	النيل الأزرق	SD08
As Salam - SD	السلام - ج د	SD03166	South Darfur	جنوب دارفور	SD03
As Salam - WK	السلام - غ ك	SD18086	West Kordofan	غرب كردفان	SD18
As Salam / Ar Rawat	السلام / الراوات	SD09049	White Nile	النيل الأبيض	SD09
As Serief	السريف	SD02118	North Darfur	شمال دارفور	SD02
As Suki	السوكي	SD14041	Sennar	سنار	SD14
As Sunta	السنطة	SD03156	South Darfur	جنوب دارفور	SD03
As Sunut	السنوط	SD18092	West Kordofan	غرب كردفان	SD18
Assalaya	عسلاية	SD05163	East Darfur	شرق دارفور	SD05
At Tadamon - BN	التضامن - ن ق	SD08108	Blue Nile	النيل الأزرق	SD08
At Tadamon - SK	التضامن - ج ك	SD07106	South Kordofan	جنوب كردفان	SD07
At Tawisha	الطويشة	SD02119	North Darfur	شمال دارفور	SD02
At Tina	الطينة	SD02171	North Darfur	شمال دارفور	SD02
Atbara	عطبرة	SD16012	River Nile	نهر النيل	SD16
Azum	أزوم	SD06110	Central Darfur	وسط دارفور	SD06
Babanusa	بابنوسة	SD18100	West Kordofan	غرب كردفان	SD18
Bahr Al Arab	بحر العرب	SD05160	East Darfur	شرق دارفور	SD05
Bahri	بحري	SD01003	Khartoum	الخرطوم	SD01
Bara	بارا	SD13026	North Kordofan	شمال كردفان	SD13
Barbar	بربر	SD16013	River Nile	نهر النيل	SD16
Basundah	باسندة	SD12077	Gedaref	القضارف	SD12
Baw	باو	SD08104	Blue Nile	النيل الأزرق	SD08
Beida	بيضا	SD04111	West Darfur	غرب دارفور	SD04
Beliel	بليل	SD03162	South Darfur	جنوب دارفور	SD03
Bendasi	بندسي	SD06112	Central Darfur	وسط دارفور	SD06
Buram	برام	SD03161	South Darfur	جنوب دارفور	SD03
Damso	دمسو	SD03172	South Darfur	جنوب دارفور	SD03
Dar As Salam	دار السلام	SD02113	North Darfur	شمال دارفور	SD02
Delami	دلامي	SD07107	South Kordofan	جنوب كردفان	SD07
Delgo	دلقو	SD17015	Northern	الشمالية	SD17
Dilling	الدلنج	SD07095	South Kordofan	جنوب كردفان	SD07
Dongola	دنقلا	SD17017	Northern	الشمالية	SD17
Dordieb	درديب	SD10063	Red Sea	البحر الأحمر	SD10
Ed Al Fursan	عد الفرسان	SD03143	South Darfur	جنوب دارفور	SD03
Ed Damazine	الدمازين	SD08105	Blue Nile	النيل الأزرق	SD08
Foro Baranga	فور برنقا	SD04121	West Darfur	غرب دارفور	SD04
Gala’a Al Nahal	قلع النحل	SD12079	Gedaref	القضارف	SD12
Galabat Ash-Shargiah	القلابات الشرقية	SD12083	Gedaref	القضارف	SD12
Gebrat Al Sheikh	جبرة الشيخ	SD13027	North Kordofan	شمال كردفان	SD13
Geisan	قيسان	SD08109	Blue Nile	النيل الأزرق	SD08
Gereida	قريضة	SD03153	South Darfur	جنوب دارفور	SD03
Ghadeer	غدير	SD07108	South Kordofan	جنوب كردفان	SD07
Gharb Bara	غرب بارا	SD13029	North Kordofan	شمال كردفان	SD13
Gharb Jabal Marrah	غرب جبل مرة	SD06131	Central Darfur	وسط دارفور	SD06
Ghubaish	غبيش	SD18021	West Kordofan	غرب كردفان	SD18
Guli	قلي	SD09052	White Nile	النيل الأبيض	SD09
Habila - SK	هبيلة - ج ك	SD07103	South Kordofan	جنوب كردفان	SD07
Habila - WD	هبيلة - غ د	SD04122	West Darfur	غرب دارفور	SD04
Hala’ib	حلايب	SD10066	Red Sea	البحر الأحمر	SD10
Halfa	حلفا	SD17014	Northern	الشمالية	SD17
Halfa Aj Jadeedah	حلفا الجديدة	SD11052	Kassala	كسلا	SD11
Haya	هيا	SD10070	Red Sea	البحر الأحمر	SD10
Heiban	هيبان	SD07096	South Kordofan	جنوب كردفان	SD07
Janub Al Jazirah	جنوب الجزيرة	SD15031	Aj Jazirah	الجزيرة	SD15
Jebel Awlia	جبل أولياء	SD01001	Khartoum	الخرطوم	SD01
Jebel Moon	جبل مون	SD04123	West Darfur	غرب دارفور	SD04
Jubayt Elma’aadin	جبيت المعادن	SD10067	Red Sea	البحر الأحمر	SD10
Kadugli	كادقلي	SD07098	South Kordofan	جنوب كردفان	SD07
Karrari	كرري	SD01005	Khartoum	الخرطوم	SD01
Kas	كاس	SD03144	South Darfur	جنوب دارفور	SD03
Kateila	كتيلا	SD03159	South Darfur	جنوب دارفور	SD03
Kebkabiya	كبكابية	SD02124	North Darfur	شمال دارفور	SD02
Keilak	كيلك	SD18085	West Kordofan	غرب كردفان	SD18
Kelemando	كلمندو	SD02126	North Darfur	شمال دارفور	SD02
Kereneik	كرينك	SD04125	West Darfur	غرب دارفور	SD04
Kernoi	كرنوي	SD02168	North Darfur	شمال دارفور	SD02
Khartoum	الخرطوم	SD01007	Khartoum	الخرطوم	SD01
Kosti	كوستي	SD09047	White Nile	النيل الأبيض	SD09
Kubum	كبم	SD03157	South Darfur	جنوب دارفور	SD03
Kulbus	كلبس	SD04127	West Darfur	غرب دارفور	SD04
Kutum	كتم	SD02128	North Darfur	شمال دارفور	SD02
Madeinat Al Gedaref	مدينة القضارف	SD12080	Gedaref	القضارف	SD12
Madeinat Kassala	مدينة كسلا	SD11053	Kassala	كسلا	SD11
Medani Al Kubra	مدني الكبري	SD15030	Aj Jazirah	الجزيرة	SD15
Melit	مليط	SD02129	North Darfur	شمال دارفور	SD02
Mershing	مرشنج	SD03145	South Darfur	جنوب دارفور	SD03
Merwoe	مروي	SD17020	Northern	الشمالية	SD17
Mukjar	مكجر	SD06130	Central Darfur	وسط دارفور	SD06
Nitega	نتيقة	SD03151	South Darfur	جنوب دارفور	SD03
Nyala Janoub	نيالا جنوب	SD03167	South Darfur	جنوب دارفور	SD03
Nyala Shimal	نيالا شمال	SD03164	South Darfur	جنوب دارفور	SD03
Port Sudan	بورتسودان	SD10064	Red Sea	البحر الأحمر	SD10
Rabak	ربك	SD09046	White Nile	النيل الأبيض	SD09
Rehaid Albirdi	رهيد البردي	SD03158	South Darfur	جنوب دارفور	SD03
Reifi Aroma	ريفى أروما	SD11055	Kassala	كسلا	SD11
Reifi Gharb Kassala	ريفى غرب كسلا	SD11054	Kassala	كسلا	SD11
Reifi Hamashkureib	ريفى همش كوريب	SD11058	Kassala	كسلا	SD11
Reifi Kassla	ريفى كسلا	SD11056	Kassala	كسلا	SD11
Reifi Khashm Elgirba	ريفى خشم القربة	SD11060	Kassala	كسلا	SD11
Reifi Nahr Atbara	ريفى نهر عطبرة	SD11062	Kassala	كسلا	SD11
Reifi Shamal Ad Delta	ريفى شمال الدلتا	SD11057	Kassala	كسلا	SD11
Reifi Telkok	ريفى تلكوك	SD11059	Kassala	كسلا	SD11
Reifi Wad Elhilaiw	ريفى ود الحليو	SD11061	Kassala	كسلا	SD11
Saraf Omra	سرف عمرة	SD02133	North Darfur	شمال دارفور	SD02
Sawakin	سواكن	SD10068	Red Sea	البحر الأحمر	SD10
Sennar	سنار	SD14038	Sennar	سنار	SD14
Shamal Jabal Marrah	شمال جبل مرة	SD06132	Central Darfur	وسط دارفور	SD06
Sharg Aj Jabal	شرق الجبل	SD03147	South Darfur	جنوب دارفور	SD03
Sharg Al Jazirah	شرق الجزيرة	SD15033	Aj Jazirah	الجزيرة	SD15
Sharg An Neel	شرق النيل	SD01004	Khartoum	الخرطوم	SD01
Sharg Sennar	شرق سنار	SD14042	Sennar	سنار	SD14
Shattaya	شطاية	SD03154	South Darfur	جنوب دارفور	SD03
Sheikan	شيكان	SD13024	North Kordofan	شمال كردفان	SD13
Shendi	شندي	SD16010	River Nile	نهر النيل	SD16
Shia’ria	شعيرية	SD05148	East Darfur	شرق دارفور	SD05
Sinja	سنجة	SD14043	Sennar	سنار	SD14
Sinkat	سنكات	SD10071	Red Sea	البحر الأحمر	SD10
Sirba	سربا	SD04134	West Darfur	غرب دارفور	SD04
Soudari	سودري	SD13025	North Kordofan	شمال كردفان	SD13
Talawdi	تلودي	SD07089	South Kordofan	جنوب كردفان	SD07
Tawila	طويلة	SD02170	North Darfur	شمال دارفور	SD02
Tawkar	طوكر	SD10065	Red Sea	البحر الأحمر	SD10
Tendalti	تندلتي	SD09048	White Nile	النيل الأبيض	SD09
Tulus	تلس	SD03149	South Darfur	جنوب دارفور	SD03
Um Algura	أم القري	SD15032	Aj Jazirah	الجزيرة	SD15
Um Bada	أمبدة	SD01002	Khartoum	الخرطوم	SD01
Um Baru	أم برو	SD02120	North Darfur	شمال دارفور	SD02
Um Dafoug	أم دافوق	SD03146	South Darfur	جنوب دارفور	SD03
Um Dam Haj Ahmed	أم دم حاج أحمد	SD13028	North Kordofan	شمال كردفان	SD13
Um Dukhun	أم دخن	SD06135	Central Darfur	وسط دارفور	SD06
Um Durein	أم دورين	SD07091	South Kordofan	جنوب كردفان	SD07
Um Durman	أم درمان	SD01006	Khartoum	الخرطوم	SD01
Um Kadadah	أم كدادة	SD02136	North Darfur	شمال دارفور	SD02
Um Rawaba	أم روابة	SD13023	North Kordofan	شمال كردفان	SD13
Um Rimta	أم رمتة	SD09045	White Nile	النيل الأبيض	SD09
Wad Al Mahi	ود الماحي	SD08110	Blue Nile	النيل الأزرق	SD08
Wad Bandah	ود بندة	SD18029	West Kordofan	غرب كردفان	SD18
Wadi Salih	وادي صالح	SD06137	Central Darfur	وسط دارفور	SD06
Wasat Al Gedaref	وسط القضارف	SD12081	Gedaref	القضارف	SD12
Wasat Jabal Marrah	وسط جبل مرة	SD06139	Central Darfur	وسط دارفور	SD06
Yassin	يس	SD05165	East Darfur	شرق دارفور	SD05
Zalingi	زالنجى	SD06138	Central Darfur	وسط دارفور	SD06

Impact information

The actual impact information consists of two parts. One part is always the impact type. This explain what happened. For example, people were affected by the flood, the cholera broke out or schools got damaged.

The other part is either the impact quantity or the impact quality. It can not be both! The impact quantity describes simply how many of something. How many people have been affected? How many schools got damaged?

The impact quality is used if something cannot be described with numbers but with “Yes” or “No”. For example, there was a disease outbreak of cholear. Cholera is not a number. Yes there was cholera outbreak. Or a locality was affected -> Yes the locality was affected.

Hence we need three columns to describe impacts: impact_typ, impact_quality and impact_quantity.

impact_typ	impact_quality	impact_quantity
houses_damaged_totaly	2500
deaths	6
disease_cholera		yes

It makes sense to list some of the basic impact types we are interested in or which are very commonly reported. Such as affected people or deaths. The list of impact types can be extended on the fly. It is however important to stay consistent. The HeiGIT team used 75 different impact types. You can find the whole list below.

Impact types used in Sudan HIA

Impact Type	Description
Affected
Agricultural_land_affected
Agriculturalsectors_fedan
Agriculture_Affected
Agriculture_Bananplantation_damged_totally
Agriculture_crops_damaged
Agriculture_Livestock_crops_damaged
Deaths
Diseases
Economic
Evacuation
Event
Eviromental_sanitation
Flooding
Foodprice_increas
Healt_center_affected
Health_damaged_partially
Health_damaged_totally
HH_Affected
HH_displaced
Houses_damaged_partially
Houses_damaged_totally
Infrastructure_damaged_partially
Infrastructure_electricity_damaged_partially
Injuries
Institutions_damaged_partially
Institutions_damaged_totally
Livestock_Cattle
Livestock_Deaths
Livestock_Goats
Livestock_Poultry
Livestock_Sheep
Mosques_damaged_partally
Mosques_damaged_totally
Mosquitos
Others
Pop_affected
Pop_displaced
Problem_Health_access
Problem_Water_access
Protection_Issus
Public_facilities_damaged_partially
Public_facilities_damaged_totally
Public_facilities_damged_partially
Public_facilities_damged_totally
Public_facilities_schools_affected
Sanitation_partially
School_dropout
Schools_affeacted
Schools_damaged_partially
Schools_damaged_totally
Schools_Primary_damaged_partially
Schools_Primary_damaged_totally
Schools_Secondary_damaged_partially
Schools_Universities_damaged_partially
Shelter
Shops_damaged_partally
Shops_damaged_totally
Villages_affacted
WASH_damaged_partally
WASH_damaged_totally
WASH_drinking_river
WASH_home_latrines_damaged_partially
WASH_latriens_damaged_totally
WASH_latrines_affected
WASH_latrines_damaged_partially
WASH_latrines_damaged_partially
WASH_Latrines_damaged_totally
WASH_latrines_damaged_totaly
WASH_open_defecation
WASH_sewage_damaged_totally
WASH_Water_station_affected
WASH_Water_station_source_damaged_partially
WASH_Water_station_source_damaged_totally
WASH_watersource_contaminated

Task 5: Data compiling

Finally, we can start to compile the data. Remember we are using the key + value concept to create a long table. Every piece of information gets one row!

Tipps for data compiling

Compiling the data in EXCEL is a time-intensive and repeatable task. Here are some tips to speed up the process:

Excel copy function:

Try to use the copy function of Excel as much as possible. When taking info from a map, the information in the columns, year, start_date, end_date, date, Source_ID, and admin_level usually stay the same. So you can just copy and paste this information. In most cases, you should only write the name of location, Impact_type, Impact_quality and Impact_quantity.

Use ChatGPT:

If you encounter tables that do not fit into your table structure use ChatGPT to turn them into a long table format. For example, look at the image in the table below.

You can copy the content which produces this text:

DREF Final Report Sudan: Floods 31 May 2013

```md
Affected 
State
Locality Houses damage Institutions Sanitation Death 
of 
Animals
Damage 
to 
property 
crops, 
livestock
Wounded
of Death
No of people 
affected
completely
partially
Completely
partially
Completely
partially
Sinnar Abohojar/Senja 3,651 4,819 0 0 0 0 0 1 0 1 34,562
Kasalla Algrba/Wad 
helio
4,875 2,047 165 44 641 17 0 0 0 35 28,245
White Nile Alrgig/Tandlti/A
lsalam
1,800 1,348 0 0 0 0 0 865 7 7 12,845
Gadarif Aalgalabat/Alma
faza/Alfaw
406 591 0 0 0 0 0 0 0 0 4,068
Khartoum Khartoum 931 1,963 0 0 20 0 0 0 0 0 17,364
Algeziara Um Algora 521 110 0 4 0 0 0 0 0 0 3,786
West Darfur Benddsi/Wadi 
Salih
1,600 576 0 0 572 0 0 4 0 4 13,056
Blue Nile Aldamazain/Ba
w
381 2,785 0 0 0 0 0 1 0 1 18,996
Northern Elbrgig/Aldaba 66 302 0 7 0 60 115 0 0 0 2,208
South 
Darfur
Kubom 1,000 1,000 0 0 0 0 0 0 0 0 12,000
River Nile Albawga/Shandi
/Aldamir
54 31 0 0 43 40 0 0 0 0 510
North 
Kordofan0
Shikan/Um 
Roaba
370 2,511 0 0 669 0 0 0 0 0 17,286
South 
Kordofan
Aldebb 600 533 0 4 0 0 0 0 0 6,798
Total 16,225 18,616 167 59 1,945 117 115 865 7 49 177,724
```

Go to ChatGPT and use a prompt like „Can you turn this information taken from a Table into a long table format?“ + the table as text

Probably you will have to ask ChatGPT to give you the whole table. Something like: “Super good! Can you give me a table on the other states as well? “

Use Gemini AI:

Google Gemini Can also handle images. For example, it can turn this graph into a table which is much easier to handle than copying everything by hand.

The resulting table can easily be adjusted in Excel.

State	Affected People	Deaths
AL GEZIRA	23,280	26
BLUE NILE	1,930	-
E. DARFUR	150	-
GEDAREF	11,355	13
KASSALA	55,880	6
KHARTOUM	4,305	2
N. DARFUR	7,367	-
N. KORDOFAN	5,310	4
NORTHERN	3,210	-
RED SEA	520	3
RIVER NILE	N/A	-
SENNAR	16,980	-
S. DARFUR	38,575	17
S. KORDOFAN	10,812	7
W. DARFUR	285	13
W. KORDOFAN	14,340	6
WHITE NILE	10,160	1

1. Find a relevant information in a dataset

2. Date:

   1. Year: This information is mandatory! Check to which year the information is revering.

   2. Start_Date & End_Date: If the information on the timeframe of the flood is available, place the start and end date in these columns. This information should be listed in the Flood Events List.

   3. Date: If exact information of the date of the impact is available, place it here.

3. Source_ID: Write the ID in the Dataset_ID column.

4. Location:

   1. admin_level: Check t which admin level the information is revering to e.g. state, locality, town or camp.

   2. admin_1: This information is mandatory! To which state is the information revering?

   3. admin_2: If avilebel. To which locality is the information revering?

   4. admin_3: If avilebel. To which town is the information revering?

   5. admin_camp: If avilebel. To which camp is the information revering?

Attention

Use standardised location names ! It is extremely important to use standardised location names for every piece of information. This will limit the effort we need to put into data cleaning later on.

5. Impact_type: Select the fitting impact type from the Impact types list or create a new one.

6. Impact_quantity: If quantitative information, place the number here.

7. Impact quality: If qualitative information, place „Yes“ or „No“ here.

Below you can find some examples of pieces of information from different datasets placed in the table structure.

Emergency Plan of Action (EPoA) Sudan: Floods 2018

This is a small extract from Emergency Plan of Action (EPoA) Sudan: Floods 2018 page 4.

Data extracted from dataset

Year	Start_Date	End_Date	Date	source_ID	admin_level	admin_1	admin_2	admin_3	admin_camp	Impact type	Impact_quantity	Impact_quality
2018	23/07/2018	30/07/2018	23/07/2018	2018_IFRC_20180813	Locality	West Kordofan	Elnohoud			houses damaged_totaly	2500
2018	23/07/2018	30/07/2018	23/07/2018	2018_IFRC_20180813	Locality	West Kordofan	Einhoud			houses_damaged_partially	1500
2018	23/07/2018	30/07/2018	23/07/2018	2018_IFRC_20180813	State	West Kordofan				deaths	6
2018	23/07/2018	30/07/2018	23/07/2018	2018_IFRC_20180813	State	West Kordofan				missing people	3
2018	23/07/2018	30/07/2018	23/07/2018	2018_IFRC_20180813	State	West Kordofan				Injured	49
2018	23/07/2018	30/07/2018	23/07/2018	2018_IFRC_20180813	State	West Kordofan				livestock deaths	121
2018	23/07/2018	30/07/2018	23/07/2018	2018_IFRC_20180813	State	West Kordofan				water infrastructure_damage	3

SUDAN FLOOD RESPONSE HUMANITARIAN PARTNERS UPDATE BY STATE #3 (as of 19 Oct, 2020)

The map on the first page shows affecte population per state. This information can be extracted.

Data extracted from dataset

Year	Start_Date	End_Date	Date	source_ID	admin_level	admin_1	admin_2	admin_3	admin_camp	Impact type	Impact_quantity	Impact quality
2020	07/2020	09/2020	2020 OCHA 20201025	State	Northern					pop_affected	125660
2020	07/2020	09/2020	2020 OCHA 20201025	State	River Nile					pop_affected	33225
…	…	…	…	…	…	…	…	…	…	…	…

Humanitarian Bulletin Sudan Issue 35 | 22 - 28 August 2016

Data extracted from dataset

Year	Start_Date	End_Date	Date	source_ID	admin_level	admin_1	admin_2	admin_3	admin_camp	Impact type	Impact_quantity	Impact quality
2016	06/2016	09/2016	2016 OCHA 20160828	State	Kassala					affected		yes
2016	06/2016	09/2016	2016 OCHA 20160828	State	South Darfur					affected		yes
2016	06/2016	09/2016	2016 OCHA 20160828	State	Al Gezira					affected		yes
2016	06/2016	09/2016	2016 OCHA 20160828	State	Sennar					affected		yes
…	…	…	…	…	…	…	…	…	…	…	…	…

Sudan Floods Countinue (FloodList)

The quote below is just one bit of relevant information from the flood list article.

Data extracted from dataset

Year	Start_Date	End_Date	Date	source_ID	admin_level	admin_1	admin_2	admin_3	admin_camp	Impact type	Impact_quantity	Impact quality
2013	01/08/2013	21/08/21	11/08/2013	2013 Floodlist 201308	Camp	South Darfur	Bellel		Kalma Camp	deaths	14
2014	01/08/2014	21/08/22	11/08/2014	2014 Floodlist 201308	Camp	South Darfur	Bellel		Kalma Camp	houses_damaged_totaly	874

Task 6: HIA Data Cleaning ✅

Attention

In this exercise, you can skip this step by using the provided data in the folder task_7

When creating such a dataset there will be errors like misspellings of names or wrong numbers. To not create faulty analysis products, we need to make sure that our data is consistent. Especially location names are vital in this context since we use these columns to join the data later on a geodata set in QGIS. If for example state names are spelled wrong, this data will be lost. To make sure our data is consistent, we need to clean it. Although this can be done using many different programs like Excel, R or Python, we recommend using OpenRefine.

You can do this step task by cleaning the data you compiled in the previous steps or the data from the folder task_6. If you want to use the data from the „task_6“ folder, your principal task is to clean the file „Sudan_Impact_uncleaned“. To check your results, you can compare your cleaned data with the file „Imapct_Sudan_cleaned“.

1. Year and Columns:

   • Check if all values are within the expected range of years.

   • Ensure that there are no missing or invalid year values becaus we need a year information for all data

   • OpenRefine Step: Use the “Text facet” or “Numeric facet” to explore the distribution of values in the Year column. Use the “Text filter” or “Numeric filter” to filter out any rows with invalid or missing year values.

2. Date Columns (Start_Date, End_Date, Date):

   • Check if all dates are in the correct date format.

   • Ensure that there are no inconsistent date values.

   • OpenRefine Step: Use the “Edit cells” > “Common transforms” > “To date” option to convert date columns to a standard date format. Use the “Text facet” to identify and correct any inconsistencies in formatting or missing dates.

3. source_ID Column:

   • Check that there is only one way of spelling for every individual source_ID!

   • Check if all source IDs follow a consistent naming convention.

   • Ensure that there are no missing source IDs.

   • OpenRefine Step: Use the “Text facet” to explore the distribution of values in each admin level column. Click on Cluster and set Method to Key collisionor Nearest neighbor. Adjust wrong names by checking Merge and adjust the New cell value and click on Merge selected & re-cluster

4. Admin Columns (admin_level, admin_1, admin_2, admin_3, admin_camp):

   • Check if all administrative units are correctly categorized.

   • Ensure that there are no misspelled or inconsistent administrative unit names.

   • OpenRefine Step: Use the “Text facet” to explore the distribution of values in each admin level column. Click on Cluster and set Method to Key collision or Nearest neighbor. Consolidate the of states and loclities that they are consisten with the list in the location chapter.Adjust wrong names by checking Merge and adjust the New cell value and click on Merge selected & re-cluster

5. Impact Type Column:

   • Check if all impact types are correctly categorized and named.

   • Ensure that there are no duplicate or missing impact types.

   • OpenRefine Step: Use the “Text facet” to explore the distribution of values in each admin level column. Click on Cluster and set Method to Key collision or Nearest neighbor. Adjust wrong names by checking Merge and adjust the New cell value and click on Merge selected & re-cluster

6. Impact Quantity Columns:

   • Check if all numerical values are within expected ranges.

   • Ensure that there are no missing or invalid numerical values.

   • OpenRefine Step: Use the “Numeric facet” to explore the distribution of values in each numerical column. Use the “Edit cells” or “Transform” option to handle missing or invalid values, and standardize formatting.

7. Impact Quality Columns:

   • Check that the same impacts are spelt consistently. For example illness names.

   • OpenRefine Step: Use the “Text facet” to explore the distribution of values in each admin level column. Click on Cluster and set Method to Key collision or Nearest neighbor. Adjust wrong names by checking Merge and adjust the New cell value and click on Merge selected & re-cluster

8. Delete redundant data

   • It might be the case that you have for one flood event, multiple information on for example affected populations for the same location. This could later be a problem when you want to analyse the data. Probably, the reddened information is from two different sources. So you can delete the information of the older source and only keep the one from the up-to-date source as possible.

Task 7: Joining P-Code columns ✅

Attention

In this exercise, you can skip this step by using the provided data in the folder task_8

Usually, it is easier in QGIS to work with data having P-code columns in contrast to state or district names. There is just less potential for errors. Once you are done with data compiling and cleaning, it is recommended to join p-code columns to your data. Below you can find a method to do that in Excel.

Join P-code columns in Excel

1. Create a new empty Excel file and name it „Suadn_impact_p_code“.

2. Open the new Excel file and click on the Data tab. Click on Get Data -> From Workbook -> select your cleaned impact data file.

3. The „Navigator“ Window will open. Select the relevant Excel sheet. Click on the drop-down menu Load -> Select Load to. The „Import Data“ Window will open. Here select Only Create Connection

4. Repeat steps 2 and 3 for the file „sdn_adminboundaries_tabulardata.xlsx“. Select the sheet „ADM1“

5. Once you loaded both files you should see the „Queries & Connection “panel on the right-hand side of your Excel. The panel should show the impact sheet and the ADM1 sheet

6. Now, click on the Data tab -> Get Data -> Combine Queries -> Merge. The window „Merge“ should open.

7. In the „Merge“ window, select the „admin_1“ column for the impact dataset and the „ADM1_EN“ and „ADM1_PCODE“ for the ADM1 table.

8. Under „Join Kind“ select Left Outer (all from first, matching from secound). This will take all rows from the impact dataset and the matching data from the ADM1 table. The information below should show a green check and „The selection has matched [the number of your rows] out of the first [Number of your rows] rows. If not, some of your state names are not consistent with the ones in the ADM1 file. You would need to do more data cleaning. Click Ok

9. The new window „Merge 1 – Power Query Edito“ will open. Click on the icon next to ADM1. Select only the columns you want to keep. We recommend to only keep „ADM1_EN“ and „ADM1_PCODE“.

   1. Uncheck „Use original column names as prefix“

   2. Click OK.

10. The table preview should now show your whole impact table with the columns keep „ADM1_EN“ and „ADM1_PCODE“ on the right.

11. Click on Close & Load.

The Result should be the Excel file „Suadn_impact_p_code“. The file should contain all info from the original impact table and the two new columns „ADM1_EN“ and „ADM1_PCODE“.

Task 8: Data export using Pivot-Table in Excel ✅

Impact Quantity for one year on state level:

1. Open the Excel dataset.

2. Turn the data in a table by clicking on Insert -> Table-> check My table has headers

3. Also under the Insert-Tab click on Pivot Table. Make sure your table range is correct. Check New Worksheet. Click OK.

4. Setup the pivot table by placing the columns as follows:

• Filter: Start_year

• Columns: Impact_Type

• Rows: admin_1 or admin_1_PCODE (If you want to use this table in QGIS, you should use admin_1_PCODE Instead of admin_1)

• Values: Impact_quantity

5. To see the sum of the different impacts click on Impact_quantity under Values -> Value Field Settings -> select Sum.

6. Directly above the pivot table, you should see the option to filter by year. Select the year you are interested in. For the following example the year 2020 was used.

Now you can just copy the whole table, and place it in a new worksheet. Make sure to only paste the values. Save this output as a CSV-file, this will make the import of the subset into QGIS easier. Now we can use this table to join it with an existing geodataset in QGIS.

Flood events in Sudanese states for all the recorded years

In this section we want to analyse and visualise all recorded flooding events for all the Sudanese state. With our filter for the Impact-Type we derive information for the years 2003 until 2021.

1. In the first step we will create a new pivot table and store it in a new sheet. Here we will specify the following:

• Columns: Impact_Type

• Rows: admin_1_PCODE

• Values: Count of impact_quality

2. Now we want to filter the columns labels to the following impact types:

• Event

• Flash-flood

• Flood

• Flooding

We also want to include the Event and Flash-floods as these types can also be associated with flooding events as a result of heavy rainfall, seasonal rainfall, etc. We also want to make sure that we rename the column containing the sum of the flooding events to Sum flood events.

3. Now we export this excel sheet as a CSV file and import it into QGIS. We will open the Data Source Manager and select the Delimited Text section. Here we need to do the following specifications (Fig. 85).

Task 9: Data visualization and analysis in QGIS

1. Open QGIS and create a new projeckt.

2. Import the file “sdn_adm_cbs_nic_ssa_20200831 — sdn_admbnda_adm1_cbs_nic_ssa_20200831” from the input file in QGIS.

Visualise impact quantity data for one year on state level in QGIS:

1. Import the csv fille “Sudan_admin1_Flood_Impact_quantity_2020.CSV” from the input folder of task 8 in QGIS by opening the Data Source Manager and select the Delimited Text section.

   1. Here you can input your CSV-file and depending on the File Format you need to define Costum delimiters or you can just select CSV. In thix case go with semicolon ;. Always check the Sample Data output at the bottom to see if the import is working as expected.

   2. Record and Fields Options: Specify that your first record is a header.

   3. Geometry Definition: Select No geometry.

   4. Click Add

Outcome: You should now have your “Sudan_admin1_Flood_Impact_quantity_2020” file as table in QGIS.

Fig. 82 Import of the CSV data impact quantity 2020 on state level

2. To turn the impact data into a geodataset we will join it with the — layer based on th p-codes. We will use the tool Join attributes by field value.

   1. Input Layer: “sdn_adm_cbs_nic_ssa_20200831 — sdn_admbnda_adm1_cbs_nic_ssa_20200831”

   2. Table field: “admin1Pcode”

   3. Input Layer 2: “Sudan_admin1_Flood_Impact_quantity_2020”

   4. Table field 2: “admin1_pcode”

   5. Join Type : “Take attributes of the first matching features only (one-to-one)

   6. Name the new file “Sudan_admin1_impact_quantity” and save it in your “result” folder

   7. Click Run

Outcome: You should now have the layer „Sudan_admin1_impact_quantity“ in your QGIS. The layer should have all columns from Sudan_admin1_Flood_Impact_quantity_2020 and sdn_adm_cbs_nic_ssa_20200831 — sdn_admbnda_adm1_cbs_nic_ssa_20200831.

Fig. 83 Join the table information onto the geodata

3. Now we can start creating maps or spatial analysis with our impact data. Let’s create a map that shows destroyed houses in 2020. Since absolute destroyed houses are natural numbers, we use the option Graduated (Wiki Video).

   1. Right-click on the layer “Sudan_admin1_impact_quantity” in the Layer Panel -> Properties. A new window will open up with a vertical tab section on the left. Navigate to the Symbology tab.

   2. On the top you find a dropdown menue. Open it and choose Graduated.

   3. Under Value select “Houses_damaged_totally”.

   4. Color ramp: Select a white-to-red color ramp.

   5. Mode: Equal Count (Quantile)

   6. Classes: 5

   7. Click Classify

   8. Click Apply

Fig. 84 Example map of destroyed houses in Sudan 2020

Flood events in Sudanese states for all the recorded years

In this section we want to analyse and visualise all recorded flooding events for all the Sudanese state. With our filter for the Impact-Type we derive information for the years 2003 until 2021.

1. Now we impoer the file “Floods_Sudan_all_years” from the input folder. We will open the Data Source Manager and select the Delimited Text section. Here we need to do the following specifications (Fig. 85).

Fig. 85 Import of the CSV data

2. The next step follows the same logic as step 2 in the previous example. We will use geodata that contains information about the states of Sudan and join them with the imported CSV data. We will use the Join attributes by field value tool and select the ADM1_PCODE as the table field used for the join. An example is shown in Fig. 86.

Fig. 86 Join the table information onto the geodata

3. Now we can visualise our results using Graduated and selecting the corresponding column of the attribute table Sum flood events. Select an appropriate color scheme and start creating your map. Your final product could look like Fig. 87.

Fig. 87 Example map

Note

Two states have NULL values and will not appear when styling the data. The original admin1 Sudan dataset can be used as a mask with the correct styling to include them in our map. This way, we can display the state boundaries without adding any new content.