Historical Impact Assessment (HIA) for Flood in Sudan#

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In 2023, the Sudan Red Cresent (SRCS), German Red Cross (GRC), the Red Cross Red Crescent Climate Center (RCRCCC) and the Heidelberg Institute for Geoinformation Technology (HeiGIT) worked on an Early Action Protocol (EAP) for riverine floods along the River Nile in Sudan. One of the fundamental tasks while working on an EAP is to conduct a Historical Impact Assessment (HIA), for the particular hazard. The team of HeiGIT was responsible for this particular task and this article will tell the story of how they tackled the task. Due to the outbreak of hostilities in Sudan in 2023, the team could only rely on public data from the internet and some reports and data provided by SRCS. By the end of 2023, the team had collected, 3.204 rows of data from 60 sources, covering the timeframe from 2012 to 2023.

Why is a Historical Impact Assessment (HIA) Important?#

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.

Task#

Conduct an HIA to gain a detailed understanding of the impact of past flood events on the highest spatial and temporal resolution possible.

Challenges#

What were the main problems and challenges we faced while conducting the HIA?

Challange 1: Scatert information on flood impacts

The one BIG challenge in this case was that there are no good datasets on flood impacts in Sudan. Practically, all information is scattered across a huge number of reports, maps, tables, dashboards and newspaper articles. Such documents can be found for example on RelifeWeb.

Challange 2: How to get diverse data in one easy-to-use data format

Since all information about flood impacts is contained in such a diverse range of documents and formats, it is difficult to bring the information together in one dataset.

Challange 3: Differentiating between riverine flood impacts and flash flood impacts

Riverine and flash floods often occur at the same time in the same place, so it is almost impossible to state what was the exact cause of the impact. And even when there was a single flash flood event, in reports it is often referred to as a flood. Hence, there is the real risk that impacts caused by flash floods are listed as river flood impacts.

Key concept of our methodology#

Our goal was to store all available impact information in one table or dataset. If we would use a classic weight table we would end up with a super clumsy table since there are so many different types of impacts a flood can cause. To avoid that we decided to use a long table. The advantages of a long table over a classic wider table in this context are:

Advantages

Flexibility

Long tables can accommodate a variable number of attributes, making them suitable for diverse datasets.

Scalability

They can handle large datasets efficiently, making them suitable for compiling extensive information.

Ease of Integration

Simplifies the integration of data from various sources, providing a consistent structure.

Compatibility

Many analysis tools work well with long-format data, making it easier to analyze and derive insights.

../../_images/key_value_concept.drawio.svg

Step by Step HIA Sudan#

../../_images/14022024_Sudan_HIA.drawio.svg

Step 1: Area of Interest#

The Area of Interest (AOI) for the HeiGIT team was formally limited to Khartoum, Northern, White Nile and Sennar. However, the analysis and data collection were conducted for all states of Sudan.

../../_images/Sudan_AOI_HIA.jpg

Step 2. Finding Flood Disaster Timeframes#

We want to tie single pieces of information like impacts, to knowen 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 RelifeWeb. EM-DAT is a disaster database that lists events above a certain severity. RelifeWeb is actually an information platform for humanitarian response. But it has a list of active and past disasters as well. 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 timeframes of all events.

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

  • Relief Web lists 29 events between 1988 and 2024

In total, there were 35 flood events reported between 1988 and 2021 In some cases, the start and end dates cannot precisely be identified. This is not a problem, we only want to have a rough overview about when there have been floods.

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

Step 3: Selecting datasets#

Now we search for datasets and other information for each of the flood events identified in step 2. Naturally, it will be much easier to find information on more recent events. This process can take a lot of time and sometimes needs multiple iterations.

There are three principal sources of datasets. The Sudan Red Cresent Society (SRCS), RelifeWeb and Floodlist.

Sudan Red Crescent Society (SRCS)
SRCS has documentation about flood impacts and past operations. Furthermore, SRCS was able to source some datasets from the Sudan National Council for Civil Defence (NCCD) and the Sudan Humanitarian Aid Commission (HAC).

ReliefWeb
ReliefWeb is the largest database for humanitarian reports and bulletins. The datasets are very conveniently organized based on specific flood events. This makes it easy to match information with an event.

Flood List
Floodlis is a news portal all about floods. The articles are ordered chronologically.

Of course, much information from the different sources is redundant. A significant part of the work is to sift through the datasets and identify unique information.

Datasets#

We use the term data set to refer to all types of publications from which we can obtain data. In the table below we list typical datasets used in the Sudan HIA. Remember, we can pull all kinds of information out of a dataset. It does not have to be quantitative or in table format or anything like that. It can also be info directly from texts or graphics.

Typical Publishing Organisation (Examples)

Type

Example

Typical Features

IFRC

Emergency Plan of Action

Emergency Plan of Action (EPoA) Sudan: Floods 2018

Tables; Graphics; Maps; Info directly from texts

IFRC

Flood Interagency Assessment Reports

Not public

Tables; Graphics; Maps; Info directly from texts

IFRC

Government Datasets

Not public

Tables in many different forms

UN OCHA

Humanitarian Bulletins

Sudan Humanitarian Bulletin Issue 18/ 8 October – 4 November 2018

Tables; Graphics; Maps; Info directly from texts

UN OCHA

(Flood) Situation Reports

SUDAN Situation Report 13 Nov 2020

Tables; Graphics; Maps; Info directly from texts

UN OCHA

Humanitarian Snapshots

Sudan: Humanitarian Snapshot - September 2021 (as of 18 October 2021)

Tables; Graphics; Maps; Info directly from texts

UN OCHA

Dashboards

Sudan Floods: People & areas affected 25 October 2022

Data in Excel or CSV data format

World Bank

Large Reports

Sudan Rapid Post Disaster Needs and Recovery Assessment (Rapid PDNRA)

Tables; Graphics; Maps; Info directly from texts

Floodlist

Newspaper Articles

Sudan – North Darfur Town Devastated by Rains, Flash Floods

Info directly from texts

Data selection parameters#

In general, we can use any kind of dataset e.g. reports, maps, bulletins, tables…. However, we do not need to check all datasets available for one event. The selection of datasets is based on two principal 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.

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

SUDAN Situation Report 09 Nov 2020
../../_images/Map_affected_pop_20201109.png
SUDAN Situation Report 13 Nov 2020
../../_images/Map_affected_pop_20201113.png

Uniqueness It is important to not only use data from one source becaus of confience. The assumption is that different organisations have different capacities and work in different areas, thus they may have better information in some locations of sectors than other organisations.

Dataset ID#

When selecting datasets it is important to save the datasets on your computer. To be able to later retrace which piece of information was taken from which datasets, we have to give the datasets unique identification codes. In the Sudan project, we used the simple structure year of the flood the document is referring to (for example 2019), the publishing organisation (for example IFRC) and the publishing date (for example 20191003).

../../_images/Sudan_HIA_ID.drawio.svg

Step 3: Outcome#

At the end of the selection process, you should have multiple relevant datasets with IDs for every event, covering all relevant areas and sectors.

../../_images/Sudan_HIA_dataset_typ_overview.drawio.svg
All datasets with ID used by HeiGIT

2012

2013

2014

2016

2017

2012_IFRC_20120531.pdf

2013_Floodlist_20130816.pdf

2014_Floodlist_20140801.pdf

2016_IFRC_20160902.pdf

2017_floodlist_20170825.pdf

2012_IFRC_20120910.pdf

2013_Floodlist_20130827.pdf

2014_Floodlist_20140804.pdf

2016_OCHA_20160828.pdf

2017_floodlist_20170829.pdf

2013_IFRC_20131010.pdf

2014_Floodlist_20140814.pdf

2017_floodlist_20170905.pdf

2013_OCHA_20130915.pdf

2014_Floodlist_20140920.pdf

2017_floodlist_20170906.pdf

2013_OCHA_20130919.pdf

2014_IFRC_20140925.pdf

2017_HAC_20170919.pdf

2013_OCHA_20130930.pdf

2014_OCHA_20140909.pdf

2013_OCHA_HAC_20130902.pdf

2014_OCHA_20140914.pdf

2014_RelifeWeb_20140915.pdf

2014_RelifeWeb_20141009.pdf

2014_SRCS_20140903.pdf

2018

2019

2020

2021

2022

Extra

2018_floodlist_20180625.pdf

2019_ECHO_20190906.pdf

2020_OCHA_20201025.pdf

2021_OCHA_20211011.xlsx

2022_OCHA_20221025.xlsx

HAC_1_affected.pdf

2018_floodlist_20181105.pdf

2019_floodlist_20190613.pdf

2020_PDNRA_20210531.pdf

2021_OCHA_20211018.pdf

Interagency_mission2022.pdf

2018_floodlist_DG_ECHO_20180824.pdf

2019_floodlist_20190816.pdf

NCCD_1_Impact.xlsx

2018_IFRC_20180813.pdf

2019_floodlist_20190821.pdf

2018_OCHA_20180819.pdf

2019_floodlist_20190901.pdf

2018_OCHA_20181104.pdf

2019_floodlist_20191008.pdf

2019_OCHA_20191003.pdf

Step 4: Preparing Excel table Structure#

Before we can start compiling data, we need to prepare the table structure we will use. The table needs to accommodate four components. Date, data source, location,and impact information.

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.

Admin_level

Number of informations

State

1,979

Locality

596

Town/ Village

565

IDP & Refugee Camp

63

Tipp: 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 taypes 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

Step 4: Outcome#

Now we have your final table structure. We can put ALL information from the selected datasets we deem relevant into this table structure thus creating a consistent historical impact dataset.

Start Year

Start Date

End Date

Date

source_ID

admin_level

admin_1

admin_2

admin_3

admin_camp

impact_typ

impact_quality

impact_quantity

2018

2018-07-01

2018-08-29

2018-07-12

2018_OCHA_20180819.pdf

State

West Kordofan

disease_outbreak

Cholera

2019

2019-06-01

2019-06-31

2019_floodlist_20190613.pdf

Locality

South Darfur

Beliel

deaths

6

2020

2020-07-20

2020-08-11

2020_PDNRA_20210531.pdf

Camp

South Darfur

Beliel

Kalma Camp

houses_damaged_totaly

2500

Step 5: Data compiling#

During the data compilation, we simply identify the relevant information in the dataset and transfer it into the table. Below you can find some examples of the process.

Emergency Plan of Action (EPoA) Sudan: Floods 2018

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

../../_images/2018_IFRC_20180813_snap_shot.png
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.

../../_images/UN_OCH_MAP_2021.png
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

../../_images/2016_OCHA_20160828_snapshot.png
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.

../../_images/2013_Floodlist_20130816_snapshot.png
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

../../_images/20240216_Sudan_HIA_data_compiling.drawio.png

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
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.

../../_images/Sudan_flood_impact_table_2016.png

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

Step 6: HIA Data Cleaning#

When creating such a dataset there will be errors like misspellings of names or wrong numbers. To address that you will need to clean the dataset before actually using it. To clean the dataset you can use Excel or specialized tools like OpenRefein.

Independently on which tool you will use, here are some important points you have to check when cleaning the data:

  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.

Step 7: Adding P-Codes to the table#

Create a new empty Excel file and name it Sudan_impact_p_code.

Open the new Excel file and click on the Data tab. Click on Data -> Get Data -> From File -> From Excel Workbook and select your cleaned impact data file from the previous section HIA Data Cleaning. We will enrich the information of this table by adding the P-Codes.

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

Repeat the previous two steps for the file sdn_adminboundaries_tabulardata.xlsx. Select the sheet ADM1. The data can be downloaded from HDX containing the Subnational Administrative Boundaries of Sudan. Here we can find information about the different administrative levels of Sudan beginning with 0 (country), 1 (state), and 2 (district). Make sure to download the xlsx file.

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

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

In the Merge window, select the admin_1 column for the impact dataset and the ADM1_EN for the ADM1 table. Click on these columns to mark them green.

Under Join Kind select Left Outer (all from first, matching from second). 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 matches [number of rows] of [number of rows] rows from the first table. If not, some of your state names are not consistent with the ones in the ADM1 file. In this case, you would need to do more data cleaning and make sure that naming conventions are met. Click Ok.

The new window Merge1 Power Query Editor will open. Click on the icon next to ADM1 (column header). Select only the columns you want to keep. We recommend to only keep ADM1_EN and most importantly ADM1_PCODE. Uncheck Use original column names as prefix and click OK.

The table preview should now show your entire impact table with the columns ADM1_EN and ADM1_PCODE on the right end of the table. Click on Close & Load.

The result should be in the Excel file Sudan_impact_p_code. The file should contain all the information from the original impact table and the two new columns ADM1_EN and ADM1_PCODE with this important information for later operations.

Video: How to combine Workbooks using Excel

Step 8: Data export from Excel to QGIS#

Now we have our impact dataset cleaned and ready to use. Since the dataset is a long table we cannot use it as a normal table in Excel. To get the data in a format so we can use it in QGIS or create a graph out of it, we need to pivot it. Depending on your work with impact quantity or quality, there are small differences. Here is how you do it:

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

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

  2. 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.

Step 9: Data analysis in QGIS#

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

  1. Import the previously created CSV file into QGIS. Open the Data Source Manager and select the Delimited Text section. 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. Always check the Sample Data output at the bottom to see if the import is working as expected. You propably will also need to check the Record and Fields Options and specify if your first record is a header or already data. Lastly, it is important to specify the Geometry Definition, were you can just select No geometry. An example will be shown in Fig. 267.

Note

To ensure easy data import into QGIS, export the filtered long table data as a CSV file.

../../_images/en_HIA_csv_import.PNG

Fig. 258 Import of the CSV data#

  1. For this example we will use geodata that contains information about the states of Sudan. Make sure that your geodata has the admin_1_PCODE column that will be used for joining the table data with the geodata. We will use the tool Join attributes by field value. And select the corresponding columns. This is shown in Fig. 268 below.

../../_images/en_HIA_join.PNG

Fig. 259 Join the table information onto the geodata#

The information can now be visualized on a spatial scale and maps can be created to transport important information. An example could be to visualize the total affected population for the year of 2020 on state level. But we can also visualize more specific impact types such as damaged schools or damaged sanitation. This is how such a map could look like.

../../_images/en_HIA_map_houses.png

Fig. 260 Example map#

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

  1. 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.

  1. 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. 270).

Note

Always check the Sample Data window to see what your data looks like. These specifications may not always be applicable to your data, and you may need to make minor adjustments.

../../_images/en_HIA_import_csv_floods.png

Fig. 261 Import of the CSV data#

  1. 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. 271.

../../_images/en_HIA_join_floods.png

Fig. 262 Join the table information onto the geodata#

  1. 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. 272.

../../_images/en_HIA_map_floods.png

Fig. 263 Example map#

We can expand this analysis by also including the affected population an calculate the average number of affected population per flood event on state level.

  1. We continue with creating a new pivot table. We will do the following specifications:

  • Columns: Impact_Type

  • Rows: admin_1_PCODE

  • Values: Sum of Impact_quantity

Again we will apply a filter to the column labels and select the impact type Pop_affected. Also make sure to rename the Grand total to Sum affected pop. Export the subset as a CSV file.

  1. The next steps in QGIS will follow the same logic as before. Import the data and join the table onto your output from the previous task. This will look the following Fig. 264.

../../_images/en_HIA_join_aff_pop.png

Fig. 264 Join the table information onto the geodata with the flood information.#

  1. Now we will calculate the the average number of affected population per flood event on state level. To do so, we need to activate the editing mode for our geodata clicking on this symbol . In the next step we will open the Field calculator . Here we will calculate the sum of the affected population divided by the number of flood events for each state. Your calculation will look like Fig. 265.

../../_images/en_HIA_field_calculator.png

Fig. 265 Calculate the average number of affected population per flood event on state level.#

  1. Visualise the results and create a 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.

../../_images/en_HIA_map_affected_pop.png

Fig. 266 Example map#