Transcript Slide 1

Phase Classification
Integrated Food Security
IPC Analysis: Estimating
Population in Crisis
August 2010
Kampala
1. Concepts
•
IPC Analysis provides a Situation Analysis
Phase Classification
Integrated Food Security
 Overall objective is to generate analysis on the situation that
is evidence based, linked to international standards and
informs appropriate type and level of response to
populations in crisis
IPC Analysis is not a method and does not, in itself, offer
guidance on how to estimate the number of people in crisis…
whatever method is used to estimate populations, it is necessary
to have a consistent and meaningful way to represent those
findings (IPC Technical Manual page 40)
2. Purpose
• Estimation of the number of people in each IPC Phase (3, 4 and 5)
 not all people in an area will be affected in the same way
 provide in-depth analysis and not an overall picture
Phase Classification
Integrated Food Security
• Provides a Situation Analysis not Response analysis
 population estimates for Phases 3, 4 & 5 not “number of
people in need”
 enables maintain the objectivity of the analysis
• Inform decision makers
 provide information on the depth and severity of the problem
 information for further in-depth analysis of potential response
options
There is no set way to do the population estimates and it is
necessary for countries to develop their own methods… that
allows you to estimate populations in the same way over time
and space... making the estimates in the same way each
time… in a transparent way
3. Guiding principles
• Objectivity
 estimated without judgment about possible needs or response
options
 it is a situational analysis and not response analysis
• Within a crisis phase, populations are affected differently
not all people within a crisis phase face same degree of hazard
some people may be in ‘HE’ level while other might be in ‘AFLC’
Phase Classification
Integrated Food Security
• Estimate in terms of degree or severity of the hazard
• Understanding of the differentiation between groups within the phase
 expert knowledge of population dynamics in the area
• Estimates are based on convergence of evidence not just one
evidence
• Population estimates are estimates – not exact figures
 they provide an indication of the magnitude of the hazard
IPC Key Reference Outcomes
Phase Classification
Integrated Food Security
Proximate indictors defining the Severity of the Situation
Data
Phase Classification
Integrated Food Security
• Organized population data
 disaggregated to lower unit of analysis
• administrative/livelihood zones
 develop an analysis framework
 risk populations e.g. flood prone areas
• Baseline data
 wealth ranking
 assets or poverty ranking
• Expert knowledge
 livelihood and population dynamics
 objective expert opinion
Homogeneity and Magnitude
Phase Classification
Integrated Food Security
• Degree of differentiation within groups
 in terms of access to income, food and coping
 are all the households in the poor wealth group – all at the same
level?
 is there wide variation from the better of the poor and the poorest
poor?
• Magnitude
 this is affected by the homogeneity of the households
 the more homogenous the wealth group the more likely the
shock will affect all people
Rules of logic and Evidence
Phase Classification
Integrated Food Security
• Demographics & wealth
 phase classification is systematic
 poor are affected first, then middle, then better-off
• exceptions are natural disasters
• Chronology
 analysis follows previous classification
 if situations worsens, it is expected population estimated to
increase
• Evidence
 convergence of evidence and not only one evidence
 continuity and consistency
 rational
Uganda situation
Phase Classification
Integrated Food Security
Sources of population data
• Wealth quartiles (UDHS 2006)
• Census population projections done by the bureau of
statistics
– Region
– District
– Sub counties
• Seasonal Assessment figures derived for percentage of
populations that are affected by recent hazard say drought/
dry spell
– % of the population expecting harvest of <50% of their
normal harvest/ previous season harvest
– Reports of most affected sub-counties
Phase Classification
Integrated Food Security
Scenario 1
Scenarios
Process
Assumptions
1. If 1 overall
phase
classification is
has been
assigned for a
population
Apply
Even
the wealth
rankings to the rural
population or area
classified
Check with popn
estimation in
previous analysis
if an area is classified in one phase
there are parts of the population that belong
to different phases
The lowest quartile (poorest) are the most
affected by food insecurity therefore belong to
the worst phase
middle quartiles (to the middle phases)
Upper quartile usually in the upper phases
e.g 1/2
Classification done for rural popn bse urban
popns are likely to skew classification- able to
purchase &use a variety of food sources
Population and wealth rankings
Selected population indicators by district
C
1
al
tr
en
C
2
al
tr
en
E
t
en
tC
as
o
es
Phase Classification
l
ra
T
E
n
o
lg
L
g
an
% Rural
Total Pop
(1,000)
100.0
8.5
10.6
2.5
4.5
2.2
7.7
0.0
91.5
89.4
97.5
95.5
97.8
92.3
1480.2
50.8
816.2
441.9
231.5
202.3
1158.2
Kayunga
Kiboga
Luwero
Mubende
Mukono
Nakasongola
6.7
5.2
12.2
7.3
17.2
5.1
93.3
94.8
87.8
92.7
82.8
94.9
Bugiri
Busia
Iganga
Jinja
Kamuli
4.1
16.3
5.6
22.1
1.6
Mayuge
Kampala
Kalangala
Masaka
Mpigi
Rakai
Sembabule
Wakiso
Total Population
Urban Pop
1,480,200.00
50,800.00
816,200.00
441,900.00
231,500.00
202,300.00
1,158,200.00
1,480,200.00
4,318.00
86,517.20
11,047.50
10,417.50
4,450.60
89,181.40
330.8
293.3
396.5
525.3
929.2
143.6
330,800.00
293,300.00
396,500.00
525,300.00
929,200.00
143,600.00
22,163.60
15,251.60
48,373.00
38,346.90
159,822.40
7,323.60
95.9
83.7
94.4
77.9
98.4
543.9
265.4
661.4
451.0
670.0
543,900.00
265,400.00
661,400.00
451,000.00
670,000.00
22,299.90
43,260.20
37,038.40
99,671.00
10,720.00
2.7
97.3
399.4
399,400.00
10,783.80
1.8
2.0
2.3
4.5
11.3
98.2
98.0
97.7
95.5
88.7
168.1
150.3
345.5
471.7
499.8
3,025.80
3,006.00
7,946.50
21,226.50
56,477.40
4.6
9.9
4.0
6.5
95.4
90.1
96.0
93.5
182.3
392.9
328.8
440.0
168,100.00
150,300.00
345,500.00
471,700.00
499,800.00
1,635,400.00
182,300.00
392,900.00
328,800.00
440,000.00
Apac
Lira
1.5
10.9
98.5
89.1
507.2
626.5
507,200.00
626,500.00
7,608.00
68,288.50
Adjuman
Arua
Moyo
Nebbi
Yumbe
9.8
8.8
6.2
14.4
6.1
90.2
91.2
93.8
85.6
93.9
292.1
491.5
303.8
509.2
398.1
292,100.00
491,500.00
303,800.00
509,200.00
398,100.00
28,625.80
43,252.00
18,835.60
73,324.80
24,284.10
Gulu
Kitgum
Pader
Amuru
25.1
14.8
2.7
25.1
74.9
85.2
97.3
74.9
353.5
357.0
436.0
208.3
353,500.00
357,000.00
436,000.00
208,300.00
88,728.50
52,836.00
11,772.00
52,283.30
Kotido
Moroto
Nakapiripiti
Abim
Kaabong
6.9
3.9
1.1
6.9
6.9
93.1
96.1
98.9
93.1
93.1
179.3
265.3
217.5
54.1
301.2
12,371.70
10,346.70
2,392.50
3,732.90
20,782.80
Bundibugyo
6.6
93.4
282.1
179,300.00
265,300.00
217,500.00
54,100.00
301,200.00
1,017,400.00
282,100.00
Kaberemaido
Katakwi
Kumi
Pallisa
Soroti
Kapchorwa
Mbale
Sironko
Tororo
8,385.80
38,897.10
13,152.00
28,600.00
o
t
es
W
Integrated Food Security
Wealth quartiles
% Urban
District
N
ile
A
li
o
ch
K
am
ar
a
oj
18,618.60
Rural Pop
46,482.00
729,682.80
430,852.50
221,082.50
197,849.40
1,069,018.60
2,694,967.80
308,636.40
278,048.40
348,127.00
486,953.10
769,377.60
136,276.40
2,327,418.90
521,600.10
222,139.80
624,361.60
351,329.00
659,280.00
388,616.20
2,767,326.70
165,074.20
147,294.00
337,553.50
450,473.50
443,322.60
1,543,717.80
173,914.20
354,002.90
315,648.00
411,400.00
1,254,965.10
499,592.00
558,211.50
1,057,803.50
263,474.20
448,248.00
284,964.40
435,875.20
373,815.90
1,806,377.70
264,771.50
304,164.00
424,228.00
156,016.70
1,149,180.20
166,928.30
254,953.30
215,107.50
50,367.10
280,417.20
967,773.40
263,481.40
Lowest
second
middle
fourth
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.11
0.11
0.11
0.11
0.11
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.21
0.21
0.21
0.21
0.21
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.29
0.29
0.29
0.29
0.29
0.11
0.11
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.582
0.582
0.582
0.23
0.23
0.23
0.23
0.23
0.23
0.69
0.69
0.69
0.19
0.19
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.28
0.246
0.246
0.246
0.40
0.40
0.40
0.40
0.40
0.40
0.22
0.22
0.22
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.069
0.069
0.069
0.14
0.14
0.14
0.14
0.14
0.14
0.05
0.05
0.05
0.29
0.29
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.058
0.058
0.058
0.12
0.12
0.12
0.12
0.12
0.12
0.02
0.02
0.02
0.69
0.760
0.760
0.760
0.760
0.760
0.760
0.12
0.22
0.073
0.073
0.073
0.073
0.073
0.073
0.21
0.05
0.034
0.034
0.034
0.034
0.034
0.034
0.30
0.02
0.088
0.088
0.088
0.088
0.088
0.088
0.27
Phase Classification
Integrated Food Security
Scenario 2
Scenario Process
Assumptions
2.Affected
areas/ subcounties could
be identified
through
assessments
We
Usually
assessment are done by
administrative zones
Affected sub-counties are isolated
through assessments
Establish numbers of households
affected by drought/dry spell/
hazard in that administrative zone
% of affected households of total
hhd in admin unit
Multiply by the average
household size to get affected
population per sub-county affected
Total affected popn =to sum of all
affected popn for all affected subcounties
Check with popn estimation in
previous analysis
set some categories:
<50% of normal harvest- worst
hit/most affected
50-75% of a normal harvestfair to Normal harvest
>75% of a normal harvest- good
harvest
For
most areas that are reliant
on crop production and income
derived from crop sales and
casual labour opportunities
Worst hit sub counties
District
Sub-county
Kalapata
Loyoro
Kaabong
Kaabong
Sidok
Katile
Panyangara
Phase Classification
Integrated Food Security
Kapedo
Kotido
Kotido T/C
Nakapelimolu
Rengen
Nakapiririt
Lorengedwat
Lolachat
Rupa
Moroto
Nadunget
Katikekile, Lopeei
Abim
Nyakwaye
KARAMOJA PRODUCTION ZONES
Ka th ile
Ka re nga
Kaabong
Ka lapa ta
Ka ped o
Kaabong
Sid ok
Lole lia
Ka ch eri
Loyoro
Ren ge n
Ko tid o
Ale re k
Naka pelimo ru
Pa nyang ara
Rup a
Ab im
Lop ei
Phase Classification
Integrated Food Security
Abim
Kotido
Nyakwae
Mo ru lem
Ngo leriet
Moroto
Ka tike kile
Lokop o
Nad un get
Ma tany
Lorengedwat
Lotome
District boundary
Subcounty boundary
Agriculture
Agro-Pastoral
Pastoral
Loroo
Nab ilatuk
Iriiri
Nakapiripirit
Amudat
Ka ko mon gole
Lola ch at
Mo ru ita
Nam alu
Ka rita
Phase Classification
Integrated Food Security
scenario 3
Scenario
Process
3. We want to
include the Livelihood
aspect but lack
livelihood information
but
AEZ information is
available
Assessment
information shows
that one livelihood
group is more
affected than others
in a particular sub
county
Get
Assumptio
ns
AEZ information or map
AEZ usually
concede with live
Overlay Affected subhoods
counties maps over the AEZ
Population estimations are
made based on which subcounties are covered by a
particular AEZ/LZ group that is
affected
Summation of populations in
most affected LZ/ sub-counties
gives the affected population
Check with popn estimation
in previous analysis
Somalia example
Estimating proportions of overall population in
given phase:
District
Livelihood Zone
Phase Classification
Integrated Food Security
Total number of people in AFLC in District 1= (D1
* X1 *X2 *X3)
Where:
D1 = is the district population (from UNDP)
X1 =is the percent of Population in that LZ in that
district (established by FSAU)
X2 = is the percent of the poor wealth group (or
other analytical unit) in that LZ (from baselines)
X3 = is the percent of poor wealth group in AFLC
in LZ1 (from the analysis & evidence)
Belet Weyne
Agro pastoral
Belet Weyne
Hawd Pastoral
Belet Weyne
Belet Weyne
District
UNDP
2005
Populatio
n
135,580
Riverine
S. Inland Pastoral
Livelihood Zone
%
population
in LZ
(establishe
d by
FSNAU)
Total LZ
population
affected
(calculated)
56%
75,328
22%
30,126
11%
15,063
11%
15,063
% population breakdown by livelihood zone
and wealth group
(FSNAU baseline assessments)
Very poor
Poor
Middle
Better off
Belet Weyne
Agro pastoral
0%
35%
55%
10%
Belet Weyne
Hawd Pastoral
0%
45%
35%
20%
Belet Weyne
Riverine
3%
32%
55%
10%
Belet Weyne
S. Inland Pastoral
2.5%
22.5%
45%
30%