1. MSc Thesis - Presentation
Modelling informal settlement growth
in Dar es Salaam, Tanzania
By Fikreselassie K. Abebe
Thesis Assessment Board:
Chair: Prof. Dr. Ir. M.F.A.M. van Maarseveen
External examiner: MSc. Ms. Olena Dubovyk
Supervisors: Dr. J. Flacke, (1st)
Dr. R.V. Sliuzas, (2nd)
March 08, 2011, Enschede, The Netherlands

2. Research aim Research problem
To model informal settlement growth by coupling the potentials of GIS with logistic regression modelling techniques.
Research problem
Probable drivers of ISG in Dar es Salaam over time is not well explored.
Driving forces responsible for expansion and densification of IS have not been thoroughly investigated.
Future probable areas of expansion and densification of IS is not known.
Urban planners and policy makers are in need of approaches and tools to understand the nature of ISG.
Modelling informal settlement growth in Dar es Salaam, Tanzania
5-Dec-18

3. Research objectives Main objective:
To investigate key driving forces of informal settlement growth (ISG) in Dar es Salaam by coupling GIS with logistic regression model.
Sub-objectives (SO):
SO₁: To build conceptual model of informal settlement growth (ISG) in Dar es Salaam
SO₂: To build a logistic regression (LR) model of ISG in Dar es Salaam
SO₃: To determine future ISG pattern in Dar es Salaam
Modelling informal settlement growth in Dar es Salaam, Tanzania
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4. Conceptual framework (adapted from Cheng, 2003)
Modelling informal settlement growth in Dar es Salaam, Tanzania
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5. Concept of ISG – cont’d
Modelling informal settlement growth in Dar es Salaam, Tanzania
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6. Research design and methodology
Modelling informal settlement growth in Dar es Salaam, Tanzania
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7. Research design and methodology (cont’d)
Modelling informal settlement growth in Dar es Salaam, Tanzania
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8. Study area – spatial extent of the research
Source: Hill & Lindner (2010)
Modelling informal settlement growth in Dar es Salaam, Tanzania
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9. ISG in Dar es Salaam
Informal settlement growth, expansion, in Dar es Salaam,
Modelling informal settlement growth in Dar es Salaam, Tanzania
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10. ISG in Dar es Salaam – cont’d
Informal settlement growth, expansion, in Dar es Salaam,
Modelling informal settlement growth in Dar es Salaam, Tanzania
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11. Compiling of variables – Driving forces of ISG
Site specific characteristics
e.g., population density, slope, migration, ...etc
Proximity characteristics
e.g., distance to major road, distance to CBD, ...etc
Neighbourhood characteristics
e.g., proportion of urban land, proportion of developable land,...etc
Modelling informal settlement growth in Dar es Salaam, Tanzania
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12. Professional opinions on drivers of ISG
Modelling informal settlement growth in Dar es Salaam, Tanzania
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13. Conceptual model of ISG
Modelling informal settlement growth in Dar es Salaam, Tanzania
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14. IS expansion – key drivers and probable areas
Key driving forces of IS expansion in DSM
Dist. to Minor roads (-ve)
Dist. to existing IS (-ve)
Prop. of IS in surrounding area (+ve)
Prop. of undeveloped land (+ve)
Dist. to other-urban (-ve)
Population density (-ve)
Drivers that shift roles
env. hazard, distances to major roads, satellite centres and major rivers
Modelling informal settlement growth in Dar es Salaam, Tanzania
5-Dec-18

15. IS densification – key drivers and trends
Key driving forces of IS densification
in DSM
Low to medium density
Population density (+ve)
Distances to minor rivers (+ve), other-urban land use (+ve), CBD (-ve), major rivers (-ve), and major roads (-ve)
‘Other’ to low density
Population density (-ve)
Distances to other-urban land use (-ve) and minor roads (-ve)
‘Other’ to medium density
Distances to existing IS (-ve), river valleys (-ve) and major rivers (+ve)
‘Other’ to high density
Distances to minor roads (+ve), existing IS (-ve) and CBD (-ve)
Modelling informal settlement growth in Dar es Salaam, Tanzania
5-Dec-18

16. Model evaluation and validation
Evaluation – PCP (Percentage of correct predictions)
76.53%, expansion
93.43%, densification
Validation – Kappa
78%, expansion
Modelling informal settlement growth in Dar es Salaam, Tanzania
5-Dec-18

17. Model output v reality of ISG
Data input
may not consider defining elements in ISG, e.g. Political will, legal and policy impediments, but those included in the model are representative of the reality
Detecting significant driving forces
method employed may incorporate a number of assumptions, but it delivers plausible drivers
Setting future predictions
may suffer from the very method employed at some areas, but overall result shows substantial prediction power
Modelling informal settlement growth in Dar es Salaam, Tanzania
5-Dec-18

18. Implication of proposed model for actual urban planning
Benefits
reveal the key drivers of IS expansion and densification
inform the most likely areas of future expansion and density status
help in spatial decision making
Limitations
Absence of a modelling environment addressing different sampling schemes, various statistical analysis and evaluation of built model.
Modelling informal settlement growth in Dar es Salaam, Tanzania
5-Dec-18

19. Model predictions in comparison (LRM & CA)
IS area at 2012
Substantial agreement (Kappa = 0.74)
New IS cluster around existing IS
IS area at 2022
Moderate agreement (Kappa = 0.56)
Compact IS dev’t by LRM
Leapfrog IS dev’t by CA
Modelling informal settlement growth in Dar es Salaam, Tanzania
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20. Conclusions Experts opinion is indispensable in understanding ISG
Hierarchical investigation of ISG gives enriched understanding
Consistency and accuracy of data are concerns in modelling
Spatio-temporal scale of analysis has a significant role in LRM
Site specific, proximity and neighbourhood characteristics differently affects IS expansion and densification
ISG – concomitant act of expansion and densification
LRM in different time slices would unveil drivers responsible for sustained ISG, and change of roles in drivers, if any
Hybrid urban growth models can better inform policy makers
Modelling informal settlement growth in Dar es Salaam, Tanzania
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21. Recommendations
To incorporate much more site specific drivers for the same scale
To conduct LRM at varied spatial resolution and explore its influence on parameter estimation
To consider mutual effects of interaction between drivers in LRM
To do pro-expert LRM and compare it with all inclusive model
Carry out LRM with enlarged spatial and temporal scale
To use key driving forces revealed by LRM as an input in other modelling approach
To build LRM with rigorous statistical tests and procedures
To accomplish an integrated ISG model addressing key drivers of IS expansion and densification
Modelling informal settlement growth in Dar es Salaam, Tanzania
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22. Thank You All! On behalf of the UPM class 2009-2011
Modelling informal settlement growth in Dar es Salaam, Tanzania
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23. 1992-2002 expansion model output
Variable (rank)
Coefficient
Standard error
z-value
t-test (p)
Odds ratio
X1 (6)
0.0193*
0.0961
X2 (17)
0.0725**
0.967
X3 (10)
0.0000*
0.2414
X4 (15)
0.4674
X5 (1)
0.0017
X6 (2)
0.0037
X7 (8)
0.1213
X8 (11)
0.2511
X9 (14)
2.4243
X11 (4)
0.0049
X13 (16)
0.0001*
0.7617
X14 (9)
0.1879
X15 (7)
9.7616
X16 (13)
3.0294
X17 (12)
3.8256
X19 (5)
59.872
X20 (3)
Constant -
Modelling informal settlement growth in Dar es Salaam, Tanzania
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24. 1982-2002 expansion model output
Variable (rank)
Coefficient
Standard error
z-value
t-test (p)
Odds ratio
X1 (6)
0.0000*
0.0225
X2 (16)
1.1167
X3 (13)
0.4787
X4 (15)
1.3855
X5 (1)
0.001
X6 (5)
0.0106
X7 (8)
0.2257
X8 (10)
0.2813
X9 (14)
0.5353
X11 (2)
-5.471
0.0042
X13 (12)
2.5908
X14 (9)
0.2301
X15(not sig.)
0.4166
0.9476
X16 (7)
9.4269
X17 (11)
3.0763
X19 (3)
X20 (4)
Constant -
Modelling informal settlement growth in Dar es Salaam, Tanzania
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25. 1992-1998 densification model, low to medium
Variable (rank)
Coefficient
Standard error
z-value
t-test (p)
Odds ratio
X1 (1)
0.0022*
E+21
X2 (17)
0.0159*
X3 (15)
0.0705**
X4 (6)
0.0000*
X5 (not sig.)
0.8232
X6 (3)
X7 (4)
0.0000
X8 (11)
X9 (10)
X10 (13)
0.0039*
X11 (not sig.)
0.1709
E+15
X12 (8)
X13 (5)
X14 (2)
X15 (14)
0.0747**
X16 (7)
X18 (9)
X19 (12)
X20 (16)
0.0676**
Constant
5.9287 -
Modelling informal settlement growth in Dar es Salaam, Tanzania
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26. 1992-1998 densification model, ‘other’ to low
Variable (rank)
Coefficient
Standard error
z-value
t-test (p)
Odds ratio
X1 (1)
0.0000*
0.0000
X2 (not sig.)
0.5339
X3 (13)
X4 (10)
4.6926
X5 (3)
X6 (2)
X7 (7)
0.0018
X8 (8)
X9 (not sig.)
0.1709
X10 (6)
X11 (5)
X12 (not sig.)
0.1317
X13 (15)
0.0133*
X14 (9)
X15 (12)
X16 (16)
0.0126*
X18 (14)
X19 (11)
X20 (4)
Constant -
Modelling informal settlement growth in Dar es Salaam, Tanzania
5-Dec-18

27. 1992-1998 densification model, ‘other’ to medium
Variable (rank)
Coefficient
Standard error
z-value
t-test (p)
Odds ratio
X1 (not sig.)
0.2106
X2 (18)
0.0000*
X3 (13)
0.0022*
X4 (5)
X5 (11)
0.0005*
X6 (16)
0.0728**
X7 (7)
X8 (4)
X9 (9)
X10 (15)
0.0251*
X11 (1)
0.0000
X12 (12)
X13 (3)
X14 (14)
0.0135*
X15 (2)
X16 (8)
X18 (17)
0.0425*
X19 (10)
X20 (6)
Constant -
Modelling informal settlement growth in Dar es Salaam, Tanzania
5-Dec-18

28. 1992-1998 densification model, ‘other’ to high
Variable (rank)
Coefficient
Standard error
z-value
t-test (p)
Odds ratio
X1 (not sig.)
0.1834
X2 (15)
0.0000*
X3 (not sig.)
0.4414
X4 (not sig.)
0.6819
X5 (1)
E+12
X6 (9)
0.0017*
X7 (3)
X8 (14)
0.0974**
X9 (8)
X10 (11)
0.0001*
X11 (2)
0.0000
X12 (12)
0.0483*
X13 (6)
X14 (13)
0.0010*
X15 (4)
X16 (not sig.)
0.1149
X18 (10)
X19 (7)
X20 (5)
Constant -
Modelling informal settlement growth in Dar es Salaam, Tanzania
5-Dec-18