Urban Readiness Guideline: Interfacing of Urban
Vulnerability, Impact of Climate Change & COVID-19 Scenario

Presented byDr-ing. Khurshid Zabin Hossain Taufique
Urban Development Directorate (UDD)
Ministry of Housing and Public Works
Government of People’s Republic of Bangladesh
82, Segunbagicha, Dhaka-1000

Aim & Objectives
Aim of this study is to understand the comparative urban status of Bangladesh.
There are 3 objective for this study,-to assess the Ecological Suitability of urban unit.
-to assess the infrastructure-service suitability of urban unit.
-to interact ecology and urban infrastructure-service suitability forunderstanding the existing urban scenario (Urban Readiness)- to test this interactive matrix for explaining the scenario on ground.
According to BBS 2011 there were 484 upazila with 506 urban centers in Bangladesh. These urban centers included
present 12 city corporation, 316 paurashava & 178 Upazila headquarters. To address the geographical overlapping andreduces the complexity this research redefine these upazila as growth center with different level of urban center. characterof all 484 upazila/ growth center is quantified based on 18 ecological character and 11 existing infrastructure-services
from urban planning point of view.

Urban Readiness Workflow
DiagramThere are 11 major steps in total work flow.
Step 01 : Data Sourcing (Pre processing)
Step 02 : Data Formatting (Pre Processing)
Step 03 : Data Translation (Pre Processing)
Step 04 : Infrastructure-Service Suitability
Step 05 : Ecological-Sensitivity
Step 06 : Ecological-Suitability
Step 07 : Determination of Aggregation method(Iso-Quant Curve)
Step 08 : Urban Readiness
Step 08 : Urban Readiness Matrix
Step 10 : SUG
Step 11 : Policy Formulation

1. Sourcing Secondary Data
2. Evaluating, Shorting and Formatting Data
3. Geo-referencing and Spatial Translation of Data
4. Infrastructure-service
Suitability
4.1. Identification of
Infrastructure-service
variable

5. Ecological Sensitivity
5.1. Identification of
Ecological variable
5.2. Determine evaluation
criteria for each variable

4.2. Determine evaluation
criteria for each variable

4.3. Determine weighted
factor for each variable
4.4. Determination of
aggregation method

4.5. Infrastructureservice Suitability

Urban
Readiness&Spatial Urban
Group (SUG)5.3. Sensitivity
Calculation for eachvariable

Urban
Readiness
Guideline
FlowChart
6. Ecological Suitability
6.1. Suitability Calculationfor each variable

5.4. Determination ofweighted factor for eachvariable

6.2. Determination
weighted factor for eachvariable

5.5. Determination ofaggregation method

6.3. Determination ofaggregation method

5.6. Ecological
Sensitivity

6.4. Ecological
Suitability

7. Determination ofaggregation method
8. Urban Readiness9. Urban Readiness
Matrix

10. Spatial Urban Group (SUG)(SUG)

11. Policy Formulations for different SUGs on the basis ofsuitability

Ecological Sensitivity Variables (18 nos.)
This study consider 18 Ecological phenomenon as
Ecological sensitivity indicative variable.
(8)

From non-farm economic point of view, efficiency or
productivity of an urban economic system isaffected by this 18 ecological phenomenon in
Bangladesh.

(8)

Urban centers in a growth center (upazila) is ecologicallymore sensitive then other if it has higher concentrationof these ecological phenomenon(2)Urban Ecological Sensitivity(Example: River Flood)As
examplesensitivity
calculation based on variable
04 :River Flood is presented inthis slide.
Different severity level atdifferent part of a growthcenter is assigned withsensitivity score presented
below.
Growthcenter
sensitivity was calculated byarea percentage and scaled in 0to 1.
Sensitivity score assigned for
different intensity level is
Not flood prone
Low River Flooding
Moderate River Flooding
Severe River Flooding

1234Urban Ecological
Sensitivity (Example:River Flood)
For each growth centersuitability
score
isassigned according tooppositeofcorresponding sensitivity
score
aspresented
below. Growth centersuitability was calculatedby scaling the score in 0to 1.
Suitability score assigned
for different intensity
level
Not flood prone
Low River Flooding
ModerateRiver Flooding
Severe River Flooding

4321

Urban Ecological Sensitivity (18 Nos.)
A growth center (upazila) obtain a sensitivity score
between 0 and 1. It is converted in to 0 to 100 scales
for easy visualization. growth center (upazila) withhigher sensitivity score is more sensitive from urban
point of view.

Urban Ecological Suitability (18 Nos.)
All growth centers obtain a suitability score between 0and 1. It is converted in to 0 to 100 scales for easy
visualization.
growth center (upazila) with higher suitability score ismore suitable from urban point of view.

Urban Infrastructure - service suitability variables (11 Nos.)
This research consider 11 infrastructure-service variable to assess infrastructure-service
suitability of urban centers in all growth center from economic point of view
capital indicative
variable19. Education Health and Religious Services
20. Agriculture and Emergency Services
21. Rural Economy related Infrastructure
22.Water based Communication Services
23. Rail communication Services
24. Road Connectivity
25. Airport Proximity
26. Seaport Proximity
27. Capital Proximity

entrepreneurship indicative
variablelabour indicative
variable

28. Administration level
29. Urban Population

Locationadvantage

Social Serviceindicator

ConnectivityindicatorUrban Infrastructure and Services Suitability (Example: Education, Health & Religious inf.)
As example for variable no 19:
Education, Health & Religious
services, this study assumes
growth center with higher levelof these service infrastructure
has better access to the services.
Suitability score for growthcenter with different feature isgiven below.
CollegeHospitalUniversity
Vocational InstituteCommunity Clinic
Family Welfare CentreHigh School
Madrasa
Mosque/Eidgah/Church
Primary School

1.89
1.89
3.09
1.29
0.69
0.69
1.29
0.69
0.69
0.69

Urban Infrastructure and Services Suitability( 11 Nos.)
A growth center obtain a suitability score between 0 and 1. It
is converted as 0 to 100 scales for easy visualization.
Urban centers in growth center with higher
infrastructure-service has higher level of preparedness
against the impact of climate change.

Urban Readiness

Ecology

Urban readiness of an urban system is afunction of its infrastructure-service
suitability and ecological sensitivity. Bestpossible way to represent this function
is “Iso-Quant Curve.” Urban centers ina growth center can have same urban
readinessscore
withdifferent
combination of infrastructure-service &ecological condition.

Infrastructure-service

Urban Readiness (partial view)

Ecological Suitability

Urban readiness score is a quantitative indication of urban centers sensitivity due to ecological phenomena
including climate change considering the arrangement of infrastructure- service.

Infrastructure-service Suitability

Urban Readiness (Graph & Statistics)
To understand the urban readiness of urban centers in growth center this guideline symbolized them into equal class interval of 5.

Urban Readiness (Graph & Statistics)

Urban Readiness (Absolute View in Map)
-No growth center has urban readiness score above
80.
-Capital (Dhaka) has highest readiness score which isonly growth center between 75.00-79.99-There is no growth center with readiness score
between 70.00-50.00-Khan Jahan Ali (KCC) has 2nd highest readiness score
which is only growth center between 45.00-49.99-Monpura (Bhola) has lowest readiness score which isonly growth center between 05.00-09.99-All other 481 growth center have readiness score
between 40.00-10.00

Khanjahan
Ali (KCC)DNCC & DSCC

CCC

MonpuraUrban Readiness (Comparative view in map)
To understand the Spatial pattern of Urban Readiness this guidelineclassified 483 growth center excluding Dhaka in to 5 classes. Urban
centers with lower readiness score will be directly affected by theimpact of climate change then other. But population displacement due toclimate change in these area will affect the high score urban centers
more harshly. This study can not estimate the magnitude of climatechange impact, but it shows the relative impact at upazila level based on
the available data. This study sagest that different policy guideline isnecessary for different area based on their ecological setup as well as
infrastructure-service status.
Readiness Chart no 01(a): Growth Center Readiness Statistics

Urban Readiness
Index

GrowthCenter

Total Population
2011

(%)

UrbanPopulation 2011(%)

Area(SqKm)(%)

Score: 75.93

187,47,042

5.75

87,47,042

20.55

305.840.21

Score:
37.13 –49.99

14

1,92,72,647

12.67

1,58,10,543

37.14

3,125.53

2.14

Score:
27.97 - 37.12

79

2,79,19,901

18.36

58,00,506

13.63

23107.14

15.84

Score:
22.47 - 27.96

142

4,17,22,231

27.44

61,33,154

14.41

38,276.19

26.24

Score:
17.33 -22.46

168

4,05,18,807

26.65

47,03,360

11.05

49,712.40

34.08

Score:
9.45 - 17.32

80

1,38,88,148

9.13

13,71,661

3.22

31,326.05

21.48

Total

484

15,20,68,776

100

4,25,66,266

100

1,45,853.15

100Urban Readiness Guideline: Interfacing of Urban Vulnerability, Impact of Climate Change & COVID-19 Scenario

Spatial Urban Group (SUG)In contrast 8.10%
urban population
lives in 26.19%
urban land in SUG
08.

Urban Population: 27,39,776 (08.10%)
Population: 2,77,43,116 (19.35%)
Nos. of Growth center: 123 (25.41%)
Area 38,120.19 sqkm (26.19%)

SUG 07

Urban Population: 55,00,613 (16.26%)
Population: 3,56,46,987 (24.87%)
Nos. of Growth center: 120 (24.79%)
Area 33,644.48 sqkm (23.12%)

Ecological suitability score 74.41

SUG 06
Urban Population: 53,12,328 (15.70%)
Population: 60,51,178 (04.22%)
Nos. of Growth center: 06 (01.23%)
Area 1,072.23 sqkm (0.74%)

Source: BBS 2011

Urban Population: 87,47,042 (25.86%)
Population: 87,47,042 (06.10%)
Nos. of Growth center: 01 (0.21)
Area 305.84 sqkm (0.21%)

SUG 01

SUG 02

Inf.-service suitability score 50.00

SUG 08

Area 1,45,547.31 sqkm

Urban Population: 16,24,924 (1.45 %)Population: 39,81,635 (02.78%)
Nos. of Growth center: 07 (01.45%)
Area 1,270.50 sqkm (0.87%)

SUG 03
Inf.-service suitability score 30.75

Statistical data shows
25.86% urban
population lives in
0.21% urban land in
SUG 01.

Nos. of Growth center: 484

Urban Population: 66,33,907 (19.61%)
Population: 3,63,64,821 (25.37%)
Nos. of Growth center: 112 (23.14%)
Area 33,500.33 sqkm (23.02%)

Urban Population: 32,60,634 (09.64%)
Population: 2,47,86,955 (17.29%)
Nos. of Growth center: 115 (23.76%)
Area 37,623.73 sqkm (25.85%)

All growth center SUG 04
have been groupedin 8 Quadrant
based on theirposition in the 2D
Plot.

Population: 14,33,21,734

Inf.-service suitability score 80.00

Urban Population: 3,38,19,224

SUG 05
No Growth Center
in SUG 05

Policy requirement for different SUGs toaddress the impact of climate change.
To understand the Spatial pattern of SUGs thisguideline symbolized 484 growth center in to 8
different color of SUG.
Different SUG show different policy requirement fromgrowth centers in different SUGs to address the impact ofclimate change.
SUG 04

SUG 03

SUG 02

SUG 01

Urban Population: 32,60,634 (09.64%)
Population: 2,47,86,955 (17.29%)
Nos. of Growth center: 115 (23.76%)
Area 37,623.73 sqkm (25.85%)

Urban Population: 66,33,907 (19.61%)
Population: 3,63,64,821 (25.37%)
Nos. of Growth center: 112 (23.14%)
Area 33,500.33 sqkm (23.02%)144

Urban Population: 16,24,924 (1.45 %)Population: 39,81,635 (02.78%)
Nos. of Growth center: 07 (01.45%)
Area 1,270.50 sqkm (0.87%)

Urban Population: 87,47,042 (25.86%)
Population: 87,47,042 (06.10%)
Nos. of Growth center: 01 (0.21)
Area 305.84 sqkm (0.21%)

Different SUG show different policy requirement
Urban Population: 27,39,776 (08.10%)
Population: 2,77,43,116 (19.35%)
Nos. of Growth center: 123 (25.41%)
Area 38,120.19 sqkm (26.19%)

Urban Population: 55,00,613 (16.26%)
Population: 3,56,46,987 (24.87%)
Nos. of Growth center: 120 (24.79%)
Area 33,644.48 sqkm (23.12%)

Urban Population: 53,12,328 (15.70%)
Population: 60,51,178 (04.22%)
Nos. of Growth center: 06 (01.23%)
Area 1,072.23 sqkm (0.74%)

SUG 08SUG 07

SUG 06

No Growth Center in SUG 05

SUG 05

Urban Readiness Score and Night Illumination in Bangladesh 2022
Out come of urban readiness score is verified with illumination intensity index of upazila

Light Index 2022
UrbanReadiness(Absolute Viewin Map)

Urban Readiness Score and Employment Distribution in Bangladesh 2011
Out come of urban readiness score is overlaid with the number of non-farm employment and indirectly estimated GDP (FY 2011-12)

Distribution ofNon-FarmEmployment

Indirect estimation of GrowthCenters Contribution innational GDP based on totalemployment

UrbanReadiness(Absolute Viewin Map)

Impact of Covid-19, Bangladesh&Urban Readiness Guideline

COVID-19 positive cases from 50th to100th day in Bangladesh on
Geographic space-First COVID 19 positive case identified in 8th March 2020.
-Since then Directorate General of Health Services (DGHS) publish daily statistics ofidentification and causality report up to district level.
-Data shows Dhaka and districts surrounding the capital is more expose to the pandemic.

COVID-19 positive cases from 50th to100th day in Bangladesh on Geographic space-As case study urban readiness data set are overlaid on
pandemic data set to assess its real world implication.
-Data inputted against 64 district among 484 growth center.
All analysis were confined at district level considering dataavailability.
-The graph shows most of the significant picks corresponds withreadiness picks.

Table shows data of 10 districts with highest COVID-19 Positive Cases on 100th day from first identification
Rank

District

12345678910

Dhaka
Chattogram
Naraynganj
ComillaMunshiganj
Cox-Bazar
Gazipur
Noakhali
Mymanshing
Faridpur

Total cases

Total cases (%)

New case

New case (%)

Slope(avg. increase /day)

Positive/10,000

26,369
3,680
3,011
1,854
1,558
1,411
1,278
1,057
878
734

49.91
6.97
5.70
3.51
2.95
2.67
2.42
2.00
1.66
1.39

438
88
41
169
60
71
830
2339

28.11
5.65
2.6
10.85
3.85
4.56
0.51
1.93
1.48
2.50

452.63
70.17
46.26
27.84
24.19
23.24
20.34
19.39
13.17
9.56

22.07
4.52
10.21
3.44
10.77
6.16
3.75
3.40
1.78
4.21

Density(population/SqKM)

28,60019,344
12,994
3,453
2,317
2,049
3,974
1,571
1,557
1,233

Readiness score

75.97
46.72
38.04
37.85
18.09
22.51
31.23
21.17
43.60
32.72

3 sate of district cluster based on COVID-19 positive cases increase by day (Slope) vs Urban Readiness on 100th day
Three graph below represents the 3 district cluster separately.
All 3 cluster show positive co-relationship between pandemic and Urban readiness with different magnitude.
This case study shows that 40 out of 64 cases (62.50%), urban readiness can explain urban property or humanurban activity of growth center in the form of pandemic.
Cluster 1: Higher Co-relationship(R squire =0.80)
9 District (14.06%)

Cluster 2: Positive Co-relationship(R squire =0.731)
40 District (62.50%)

Cluster 3: Lower Co-relationship(R squire =0.356)
15 District (23.44%)

All 3 district cluster shown on geographicspaceCOVID-19 positive cases from 50th
to100th day in BangladeshImpact of flood in August 2020, Bangladesh&Urban Readiness Guideline

Flood in August 2020, Bangladesh
On 3rd august of 2020, FFWC published a flood map of 53,565.04 square kilometer inundated area including rivers and other water bodies.
340 growth centers among 484 growth center are affected.
This Inundated area is divided by 1km x 1km grid to incorporated the inundation level and flood related sensitivity information.

Flood in August 2020, Bangladesh

Comparative Flooding Depth Sensitivity Index Observation
The map represent the cross-table observation ingeographical space. There is no major dissimilarities
among the flooding depth sensitive area of urban
readiness study and inundated area on august 2020
flooding.
Summery Table of Evaluation% of Area

Observation

16.55

Perfectly Identical

36.22

Nearly Identical

32.21

Moderately Identical

12.60

Near Different

2.41

Completely Different% of Area

Evaluation

84.98

Identical

15.02

Different

Summery observation in the table shows there is
84.98% area that shows identical character specified in
the flood sensitivity index of urban readiness study.

Impact of climate change&Urban Readiness GuidelineImpact of sea level rise in Bangladesh
The map represent the area with altitude below 5m, 3m and 1.5m
from Mean Sea Level (MSL). There are 13 district and 73 growthcenter in total that are mostly sensitive due to sea level rise.

Nos ofGroundgrowthheightcenter
3.01m5.00m
1.51m3.00m
1.50m
andbelow

212
153
73

Area
SqKM

% of% of Total
Total
area inpopulation
country populationin country

62,250.042.77 6,48,52,183
347,325.5
32.52 3,94,34,003
129,050.7
19.96 1,65,35,435
717.7415.98
11.54

Impact of sea level rise on soil salinity of costal area in Bangladesh
Sea level rise will also
impact the soil salinity
problem in costal area.
Intensity of salinity will
increase and new area will
face the problem.
% ofNos% ofArea ofTotal
ofarea in Total
Groundpopulat
Growthgrowt
countr populatio
Centerheight
ion innhy
countr(SqKM)
centery
3.01m6,48,52,1
212
62,250.03
42.77
17.74
5.00m
83
1.51m3,94,34,0
153 47,325.51 32.52
15.98
3.00m
03
1.50m
1,65,35,4
73 29,050.77 19.96
11.54
and35
below
Salinity
84
2,52,33,2
17.61
affected (Partia 32,225.30 22.1474l)

Impact of rainfall anomaly (due to CC?) & flash flood in Bangladesh
The map represent thehydrological catchment
area (watershed) of Haor
in Bangladesh. In 2022
1,72,919.18 cubic meterof precipitation occurredin these area between
march 23rd and October
24th and caused sever
flash flood. Any anomaly
in the rainfall can cause
seriateprobleminagriculture sector in thisarea. On the other hand
it is a large amount ofclean water that cansolve the dry season
water crises in thecountry.

Daily Precipitation in Haor
Catchment Area in 2022
Catchment area 41,427.27 Sqkm
Amount of daily precipitation measured as cubic meter
Total1,72,919.18 cubic meter between March 23rd and October 24th
Data Source: GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1degree;
https://disc.gsfc.nasa.gov/datacollection/GPM_3IMERGDL_06.html

1st Wave
23rd March, 2022

2nd Wave23th June 2022

Flood in North-Eastern Part of Bangladesh from March 23rd, 2022 & October 24th2022

Thank you