Urban Spatial Scenario Design Modelling (USSDM) … · Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 3 2. Introduction This technical user guide is

Urban Spatial Scenario Design Modelling (USSDM) in Addis Ababa:

Technical User Guide

Modelling urban settlement dynamics in Addis Ababa

Revision: 2 (July 2013)

Prepared by: Hany Abo El Wafa

TUM team contributors

Prof. Dr. Stephan Pauleit

Andreas Printz

EiABC team contributors

Kumelachew Yeshitela

Alemu Nebebe Mekonnin

Created within the CLUVA-project (Climate change and Urban Vulnerability in Africa)

www.cluva.eu

Project co-funded by the European Commission within the Sixth Framework Programme

(2002-2006)

This document is designed to provide technical (GIS) information and user instructions for

modeling Urban Growth spatial dynamics in Addis Ababa. In case you have feedback or

questions, please send them to [email protected]

Technical User Guide: Urban spatial scenario design modelling for Addis Ababa

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1 Table of Contents

1. Glossary ......................................................................................................................... 2

2. Introduction .................................................................................................................... 3

3. Employed Software: ....................................................................................................... 3

4. Syntax (Model Parameters) ............................................................................................ 4

5. Model Structure .............................................................................................................. 5

6. AddisUSDM1: Folder Organization ................................................................................10

7. Running the model ........................................................................................................10

8. Running Duration ..........................................................................................................11

9. Model Output .................................................................................................................11

10. Credits .......................................................................................................................12

11. Use limitations ...........................................................................................................12

Acknowledgement:

The author wishes to express his gratitude to staff members of Office for the Revision of the

Addis Ababa Master Plan especially Mr. Abraham Workneh, urban planning institute Mr.

Waleilenn, condominium housing office Mr. Soloman, and EiABC Mr. Bisrat Kifle and Mr.

Imam Mohamood for the valuable information provided by them in their respective fields. I

am grateful for their cooperation during the period of my assignment which helped me in

completing this task through various stages.

1. Glossary

Influencing factors: Specific factors that have an influence on the transformation of cells into settlement cells based on previous urban dynamics studies and the local experts input

Transformability index: An index that is calculated using weighted overlay of several influencing factors raster files where cells could be then ranked based on this index.

Land use dynamic influencing factor: The land use dynamic influencing factor represents the probability of transformation to settlements based on geospatial change detection analysis of Addis Ababa’s development in the period 2006-2011 along with the local experts input.

Centrality influencing factor: a factor that represents the centrality of a location indicating the amount of opportunities that exist within a certain location using the distance to the nearest Addis Ababa sub-center.


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2. Introduction

This technical user guide is designed as a supplementary document to the “Background

Information” document, providing the technical information and instructions of how to run and

edit the model.

The model calculates a transformability index based on specific influencing factors (input by

user) and determines the cells that should be transformed in three iterations (2015, 2020,

2025). The number of cells being changed at each iteration and the excluded cells are

defined by the user. A raster file is produced, indicating settlement area development during

the temporal scope. Figure 1 demonstrates a graphical representation of the conceptual

model

Figure 1 Conceptual model of Addis Ababa Urban dynamics

3. Employed Software:

The software that was used in this model is ArcGIS 10.1. The model is built in the Model

builder environment of the software. Some operations in the model require the spatial analyst

extension. To run and edit a model, you use the Model builder in ArcMap. In case you work


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with an older version of ArcGIS, the model has to be saved in your version to be able to use

the model.

4. Syntax (Model Parameters)

AddisUSDM1 (input, output, final, 2015, 2020, 2025) Cell size of all raster files is 50. Model

Parameters are shown in table 1

Table 1 Model Parameters

S/N Model Parameter Explanation Data Type

1 Input Input file geodatabase that contains the seven files listed below (1.1-1.7)

Workspace

1.1 slope

A raster file that has the values of slope. Reclassification based on the following: 0-3 deg---------100 3-8 deg----------75 8-20 deg--------50 >20 deg---------25

Raster Dataset

1.2 Roadprox2

Euclidean distance operation is applied to the road infrastructure of Addis Ababa and is then reclassified according to: 0-500 m ------------100 500-1000 m ---------80 1000-2000 m --------60 2000-4000 m --------40 >4000 m --------------20

Raster Dataset

1.3 Centrality

Euclidean distance operation is applied to the sub centers of Addis Ababa and is then reclassified according to: 0-500 m ------------100 500-1000 m ---------80 1000-2000 m --------60 2000-4000 m --------40 >4000 m --------------20

Raster Dataset

1.4 transform

Based on the calibration of 2006 and 2011 timesteps, Experts opinion and after normalization. This Raster file is reclassified according to the following: 1.1 Field crops-------------70 1.2 Vegetable Farm------14 2.1 Plantation--------------14 2.2 Mixed forest-----------14 2.3 Riverine-----------------14 2.4 Grassland---------------70 11. BARE LAND-----------100

Raster Dataset

1.5 neighborhood

This Raster file is created using Focal Statistics process for a raster file where all settlement cells have the value of 1 and using the sum (4*4 Algorithm). The file is then normalized and the value 16 (settlement cells) are replaced with a

Raster Dataset


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value of 0)

1.6 excluded_area

A raster file that has the value of 0 for the cells that are excluded (will not be transformed to settlements). The active cells have the value of 1. Exclusion criterion is based on the scenario

Raster Dataset

1.7 residvalue1 In this Raster file, Settlement cells have the value of 1

Raster Dataset

2 output Output file geodatabase. Please select an empty Geodatabase on your local Drive

Workspace

3 Final Final file geodatabase. Please select an empty Geodatabase on your local Drive

Workspace

4 2015 The number of cells that will be transformed into settlements in the first period

SQL Expression

5 2020 The number of cells that will be transformed into settlements in the second period

SQL Expression

6 2020 The number of cells that will be transformed into settlements in the third period

SQL Expression

5. Model Structure

The model was built in ARCGIS model builder environment. A general overview of the whole

model with its three iterations is shown in Appendix A1. The model consists of the following

sections:

1. A Weighted sum operation with input of five different raster files whose values are the

influencing factor scores that were calculated based on specific spatial indicators (check

background information document). This step produces a raster file that has the

transformability index values for the first iteration (2015). The equation used in calculating

the transformability is shown in figure 2

Figure 3 shows a graphical representation of the first stage model operations where the 5

files are input to the weighted sum operation and <<wtsum1>> output file is produced.

Transformability index = Sf+Rp+Cy+Tf+Nh

Where SF is the slope influencing factor RP is the road proximity influencing factor Cy is the Centrality Score TF is the land use dynamic score NH is the neighborhood score

Figure 2: Weighted overlay equation


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Figure 3 Weighted Sum model operation (ArcGIS Modelbuilder)

2. Excluding the cells that are not assumed to transform to settlements in the next

temporal scope of the model (15-20 years). Figure 4 shows a graphical representation of

the second stage model operations where wtsum1 is multiplied by excluded areas file to

exclude the areas from being processed by the model and producing <<wtsumpot1>>

Figure 4 Exclusion of specific Cells (ArcGIS Modelbuilder)

3. Identifying the highest ranking cells with the highest Transformability index: In

this step the raster data is converted to vector data (point) with the grid-value added as

an attribute to be then sorted descendingly. Figure 5 shows a graphical representation of

the third stage model operations where <<wtsumpot1>> is first converted to points

producing <<wtsumpt1>> and then sorted descendingly producing <<sumptsort1>>. A

field is added to the <<sumptsort>> and calculated with the ranking of each feature.


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Figure 5 Ranking the Transformability Index (ArcGIS Modelbuilder)

4. Changing the highest ranking non settlement non excluded cells to settlement

cells based on population demand:

The population demand is input by the user as the number of settlement cells required in

each iteration based on the different scenarios being modeled. The model selects the

highest ranking cells that are needed to meet the population demand and changes their

state to settlement cells. The vector data is now converted back to raster data producing

a raster file with value 1 for settlement cells and value 0 for non-settlement cells. Figure

6 shows a graphical representation of the fourth stage model operations where a field is

added to <<sumptsort1>> and expression of 2015 and 20152 is input by the user which

is the number of required settlement cells in the first iteration. Two parallel select

operations are performed to <<sumptsort1>> one for the cells that should not be

transformed to settlements producing <<nonresselect1>> and another for the cells that

should be transformed to settlements producing <<resselect1>>. A field is added then to

both files and calculated with the value of 0 for non settlement cells and 1 for the

settlement cells. <<nonresselect1>> and <<resselect1>> are then merge producing

<<mergres1>> which will be then converted to raster using the feature to raster

operation producing <<mergrast1>>.


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Figure 6 Transformation to settlement cells (ArcGIS Modelbuilder)

5. Update excluded cells for next iteration: the excluded cells raster file is updated by

adding the new settlement areas that were transformed in the iteration to the excluded

cells. Figure 7 shows a graphical representation of the fifth stage model operations

where <<mergrast1>> is added to the <<excluded_area>> file using a raster calculator

operation producing <<exclud22>>.

Figure 7 Update exclusion areas (ArcGIS Modelbuilder)

6. Generate new neighborhood influencing factor raster file based on new settlements

for next iteration (2020): Neighborhood influencing factor is influenced by the number of

settlement cells around each cell. Therefore it is important to update neighborhood

raster file after each iteration since there are new settlement areas. Figure 8 shows a

graphical representation of the sixth stage model operations where <<mergrast1>> is

added to the <<residvalue1>> file using 2 raster calculator operations producing

<<residv12>> and <<residv02>> and using a focal statistics operation, the

neighborhood values are calculated producing <<focsts2>> whose values are


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normalized using raster calculator operation producing <<focstatsnorm2>>.

Figure 8 Update of Neighborhood score (ArcGIS Modelbuilder)

7. Repeat step 1-4 for iteration 2 (2020) and 5-6 for iteration 3 (2025)

8. Repeat steps 1-4 for iteration 3 (2025)

9. Calculate a raster file that includes settlement cells in iterations 1, 2, 3: at the end

the model creates a raster file that has the value 0 for non-settlement cells, 1 for the

settlement cells in 2011, 2 for the settlements transformed in the first iteration (2011-

2015), 3 for the settlements transformed in the second iteration (2015-2020), and 4 for

the settlements transformed in the third iteration (2020-2025). This is regarded as the

final output of the model. Figure 9 shows a graphical representation of the final stage

model operations where <<mergrast1>> and <<mergrast2>> are added to

<<residvalue1>> with a factor of 2 and 3 respectively producing <<step2>> which is

then added to <<mergrast3>> with a factor of 4 producing the final output of the model

<<step3>>

Figure 9 Creating future settlement output (ArcGIS Modelbuilder)


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6. AddisUSDM1: Folder Organization

It is recommended, to copy the whole AddisUSDM1 folder on your drive, so that the

connection to other files and folders are maintained. AddisUSDM1 contains the following

files:

- Input (File Geodatabase)

- Output (File Geodatabase)

- Final (File Geodatabase)

- AddisUSDM (Toolbox containing the model)

7. Running the model

In Arccatalog, please do the following:

- Copy the folder AddisUSDM1 into a local drive as shown in figure 10

- Open the AddisUSDM1 toolbox

- Open the model AddisUSDM1 by right clicking on it, then click on Open

- Set the model parameters by browsing to the respective geo-databases in the

AddisUSDM1 folder, adding the values for the settlement area demand for each

iteration. The model parameters input window shown in figure 11 will open.

Field 1: “Output” geodatabase to store intermediate data.

Field 2: “Input” geodatabase that has all input files.

Figure 10: AddisUSDM1 Parent Folder


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Field 3: “Final” geodatabase to store final results.

Field 4-6: required settlement cells for each iteration (2015, 2020, 2025)

- In Environment, set the “processing extent” to any of the influencing factor files and the

“workspace” both current and scratch to <<output.gdb>>

To run the model, click OK.

More details on the input, output and final geodatabase files are found in Appendix 2

8. Running Duration

The duration depends on the processing speed of the computer used. The running time

ranges between 14 and 30 minutes.

9. Model Output

The output of the model is a raster file <<Step3>> which has the values of 1, 2, 3, 4

representing settlement cells before starting the modeling (2011), in iteration 1 (2015),

iteration 2 (2020) and iteration 3 (2025) respectively. Figure 12 shows an example of the

model output

Figure 11: Model Parameters input window


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Figure 12: Model Output file

10. Credits

Author: Hany Abo El Wafa Date: January 2013 Technische Universität München The input

data was compiled for the project CLUVA 2012 in collaboration with EIABC, urban planning

institute and the university of Manchester.

11. Use limitations

There are no access and use limitations for this model.

foc4b4norfoc4b4norfoc4b4norfoc4b4norm2m2m2m2transf1transf1transf1transf1

Weighted SumWeighted SumWeighted SumWeighted Sum

wtsum1wtsum1wtsum1wtsum1

Feature to RasterFeature to RasterFeature to RasterFeature to Raster

mergrast1mergrast1mergrast1mergrast1

Raster CalculatorRaster CalculatorRaster CalculatorRaster Calculator wtsumpot1wtsumpot1wtsumpot1wtsumpot1

Raster CalculatorRaster CalculatorRaster CalculatorRaster Calculator(2)(2)(2)(2)

exclud2exclud2exclud2exclud2Raster CalculatorRaster CalculatorRaster CalculatorRaster Calculator

(3)(3)(3)(3)

exclud22exclud22exclud22exclud22

SelectSelectSelectSelect resselec1resselec1resselec1resselec1

Select (2)Select (2)Select (2)Select (2) nonreselec1nonreselec1nonreselec1nonreselec1

Add Field (2)Add Field (2)Add Field (2)Add Field (2)

sumptsort1 (4)sumptsort1 (4)sumptsort1 (4)sumptsort1 (4)

Calculate FieldCalculate FieldCalculate FieldCalculate Field resselec1 (2)resselec1 (2)resselec1 (2)resselec1 (2)

Calculate Field (3)Calculate Field (3)Calculate Field (3)Calculate Field (3)nonreselec1nonreselec1nonreselec1nonreselec1

(2)(2)(2)(2)

MergeMergeMergeMerge

mergres1mergres1mergres1mergres1

Raster to PointRaster to PointRaster to PointRaster to Point

wtsumpt1wtsumpt1wtsumpt1wtsumpt1

Sort (2)Sort (2)Sort (2)Sort (2) sumptsort1sumptsort1sumptsort1sumptsort1

Add FieldAdd FieldAdd FieldAdd Field

sumptsort1 (2)sumptsort1 (2)sumptsort1 (2)sumptsort1 (2) Calculate Field (2)Calculate Field (2)Calculate Field (2)Calculate Field (2)


2015201520152015

PPPP

ExpressioExpressioExpressioExpression (2)n (2)n (2)n (2)

PPPP

transf1 (4)transf1 (4)transf1 (4)transf1 (4)

Weighted Sum (2)Weighted Sum (2)Weighted Sum (2)Weighted Sum (2)


Feature to RasterFeature to RasterFeature to RasterFeature to Raster(2)(2)(2)(2)



wtsumpot2wtsumpot2wtsumpot2wtsumpot2





Select (3)Select (3)Select (3)Select (3) resselec2resselec2resselec2resselec2




Calculate Field (4)Calculate Field (4)Calculate Field (4)Calculate Field (4) resselec1 (4)resselec1 (4)resselec1 (4)resselec1 (4)


(4)(4)(4)(4)

Merge (2)Merge (2)Merge (2)Merge (2)


Raster to Point (2)Raster to Point (2)Raster to Point (2)Raster to Point (2)


Sort (3)Sort (3)Sort (3)Sort (3)sumptsort2sumptsort2sumptsort2sumptsort2




2020202020202020

PPPP


PPPP

residv1residv1residv1residv1



Focal StatisticsFocal StatisticsFocal StatisticsFocal Statisticsfocsts2focsts2focsts2focsts2

Raster CalculatorRaster CalculatorRaster CalculatorRaster Calculator(8)(8)(8)(8) focstsnonresfocstsnonresfocstsnonresfocstsnonres

Raster CalculatorRaster CalculatorRaster CalculatorRaster Calculator(9)(9)(9)(9) focstatsnorm2focstatsnorm2focstatsnorm2focstatsnorm2


step2step2step2step2

transf1 (2)transf1 (2)transf1 (2)transf1 (2)

Weighted Sum (3)Weighted Sum (3)Weighted Sum (3)Weighted Sum (3)


Feature to RasterFeature to RasterFeature to RasterFeature to Raster(3)(3)(3)(3)



wtsumpot3wtsumpot3wtsumpot3wtsumpot3

Select (5)Select (5)Select (5)Select (5) resselec3resselec3resselec3resselec3




Calculate Field (7)Calculate Field (7)Calculate Field (7)Calculate Field (7) resselec1 (3)resselec1 (3)resselec1 (3)resselec1 (3)


(3)(3)(3)(3)

Merge (3)Merge (3)Merge (3)Merge (3)


Raster to Point (3)Raster to Point (3)Raster to Point (3)Raster to Point (3)


Sort (4)Sort (4)Sort (4)Sort (4) sumptsort3sumptsort3sumptsort3sumptsort3



sumptsort1sumptsort1sumptsort1sumptsort1(10)(10)(10)(10)

2025202520252025

PPPP


PPPP



Focal StatisticsFocal StatisticsFocal StatisticsFocal Statistics(2)(2)(2)(2) focsts3focsts3focsts3focsts3

Raster CalculatorRaster CalculatorRaster CalculatorRaster Calculator(15)(15)(15)(15) focstsnonres3focstsnonres3focstsnonres3focstsnonres3


focstatsnorm3focstatsnorm3focstatsnorm3focstatsnorm3Raster CalculatorRaster CalculatorRaster CalculatorRaster Calculator(12)(12)(12)(12)



step3step3step3step3

rdproxrecls2rdproxrecls2rdproxrecls2rdproxrecls2slopereclassslopereclassslopereclassslopereclass

rdproxrecls2rdproxrecls2rdproxrecls2rdproxrecls2(2)(2)(2)(2)

slopereclassslopereclassslopereclassslopereclass(2)(2)(2)(2)

slopereclassslopereclassslopereclassslopereclass(3)(3)(3)(3)

exclud1 (3)exclud1 (3)exclud1 (3)exclud1 (3)

exclud1 (4)exclud1 (4)exclud1 (4)exclud1 (4)



rdproxrecls2rdproxrecls2rdproxrecls2rdproxrecls2(3)(3)(3)(3)

Final

Appendix A2

The input file geodatabase on the local drive should contain all impact factors which are

represented by the following input files*:

Slope

A raster file that has the values of slope. Reclassification or normalization is

recommended to standardize the weighting sum operation. Name: <<slope>>

Road proximity

A raster file that has the values of distance to the nearest road infrastructure.

Reclassification or normalization is recommended to standardize the weighting sum

operation. Name: <<roadprox2>>

Centrality

A raster file that has the values of distance to the nearest subcenter. Reclassification or

normalization is recommended to standardize the weighting sum operation. Name:

<<centrality>>

Transformability

A raster file that has the values of an index that differentiates the probability of a certain

UMT/ Landcover to transform to settlements. The UMT/landcover that has higher

probability to transform to settlements has a higher transformability value. This factor

could be based on the multi temporal analysis or local experts opinion. Normalization or

Reclassification is recommended to standardize the weighting sum operation. Name:

<<transform>>

Neighborhood

A raster file that has the values representing the existence of settlement cells in the

surrounding of the cell and the magnitude of this existence (surrounded by 100%

settlements or 10% settlements). This factor could be created using focal statistics

process. Normalization or Reclassification is recommended to standardize the weighting

sum operation. This value should be updated after each iteration. Name:

<<neighborhood>>

Excluded Areas

A raster file that has the value of 0 for the cells that are excluded (will not be transformed

to settlements) while the cells that could be transformed have value of 1. Exclusion

criterion should be based on the scope and objective of the scenario.

<<excluded_area>>

residvalue1

A raster file that has the value of 0 for the cells that are non settlements and settlements

cells have the value of 1. <<residvalue1>>

Background information: Urban growth scenario modelling for Addis Ababa

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The <<Output geodatabase>>: a file geodatabase (*.gdb) where the intermediate output of

the model will be stored.

The <<Final geodatabase>>: a file geodatabase (*.gdb) where the final output of the model

will be stored.

The Population Demand

The model is population growth driven and the projected population growth should be input

to the model in terms of required settlement cells. The required settlement cells should be

input by the user in the model parameters 2015, 2020, 2025. These three values change

based on the objective of the scenario modeling and depends on several factors (natural

population growth, migration rate, relocation of settlements, population density…etc)


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Appendix A3

1.1 Data preparation

Transformability

Urban Morphology Types (UMTs) maps are used as input data for these factors. The UMTs

were selected when compared to land cover or land use maps as they combine both urban

form and function (CLUVA D2.7). UMT maps give detailed information that could be under

represented in other administrative or other maps. This is particularly useful in having a

robust model that consider different activities done by people in a land cover as UMTs

“integrate spatial units linking human activities and natural processes” (Gill et al., 2008: 211).

Urban Morphology Maps for the years 2006 and 2011 were provided by Ethiopian institute of

architecture and building construction. The UMT maps were prepared as part of the task 2.7

in CLUVA project. The transformability impact factor represents the geospatial change detection

analysis of Addis Ababa’s development in the period 2006-2011 along with the local experts input

through an experts’ questionnaire that was conducted during a CLUVA workshop. The impact factor

is calibrated from the 2006 to 2011 dataset where the UMTs that were transformed to residential

UMTs in the period from 2006-2011 are considered. The area statistics are used to identify the

transformation pattern of particular UMT’s to settlements. The pattern is then represented in a

transformability score that is attributed to the different UMT’s. Figure 1A shows the cell count of

2006 urban morphology types that were transformed to settlements in 2011.


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The highest cell count that was changed to settlement areas in this period was found to be

the field crops followed by the bare land. Other UMT’s field has a high cell count score.

When investigated, it turned out to be mainly due to the change of UMT 5.1 major roads,

offices, education and religious facilities. The reason behind this is believed to be due to the

shortcoming g in the preparation of 2006 map and also due to the interaction in between the

different urban UMT’s that happened in this period. The grassland UMT area is found to be

low (rank 6) although it is regarded as highly dynamic UMT based on experts’ opinion. More

investigation on the grassland UMT has shown that the total area of grass land is relatively

small (around 500 ha) as compared with Field Crops (14500 ha). Comparing UMT area

transformed in 2011 to the total area of this particular UMT in 2006, Grassland came in the

third position with around 11 % of its total area transformed to settlements as shown in figure

4.

Figure 1A 2006 UMT'S transformed to settlements (Source: UMT Maps (CLUVA2.7)


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It was planned to have a separate dynamicity impact factor to be added to the model which

represent the dynamicity of UMTs and the probability of them being transformed to

residential UMTs based on the local experts opinions. However to avoid redundancy and

since the opinion of experts matched with the transformability impact factor results, the

transformability impact factor was merged with the dynamicity factor which includes both the

calibration from 2006-2011 and the expert’s questionnaire results.

The values are then normalized where maximum value is 100 (Bare land) and the minimum

value is 0 (other UMTs). Table 1A shows the final Transformability impact factor values.

Table 1A Transformability Impact factor (source: UMT map CLUVA2.7)

UMT 1.1 Field crops

1.2 Vegetable Farm

2.1 Plantation

2.2 Mixed forest

2.3 Riverine

2.4 Grassland

11 Bareland

Other UMTs

Transforma-bility Score

70 14 14 14 14 70 100 0

The values are added to the attributes of 2011 UMT map and a raster file is generated with

grid-values of the transformability impact factor. The transformability impact factor raster map

is shown in figure 3A.

Figure 2A Transformed UMT cells in 2011 / Total UMT Area 2006. Source: UMT Maps (CLUVA2.7)


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1.1.1.1 Nature based Factor:

Slope

Elevation data that was used is a contour 5m map provided by the EIABC team. Using the

Topo to raster tool, a raster file whose values were the elevation values for Addis Ababa was

produces. Using the slope tool in the spatial analyst toolbox, the slope raster file is

generated in degrees. A reclassify operation is then performed to the slope raster file based

on the classification shown in table 2A

Table 2A Slope values classification (source: CLUVA 2012)

Slope 0-3% 3-8% 8-20% >20%

Transformability Score 100 75 50 25

The processing steps for the slope impact factor is shown in figure 3A

Addis Ababa: Urban growth Spatial DynamicsHany A. Abo El Wafa

Projected coordinate system: Adindan UTM zone 37

Chair for Strategic Landscape Planning and ManagementTechnische Universität MünchenData Source: CLUVA D2.7

Addis Ababa: Transformability Score

1:240,000¯0 8 164km

Urban Morphology Types 2011

Other UMT's

1.1 Field Crops

1.2 Vegetable Farm

11 Bareland

2.1 Plantation

2.2 Mixed Forest

2.3 Riverrine

2.5 Grassland

Transformability Score

0

14

70

100

Figure 3A Transformability impact factor


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Figure 3A Slope Impact Factor Processing steps

1.1.1.2 Location based Factors

1.1.1.2.1 Road proximity impact factor

The road network of Addis Ababa was provided by the Eiabc team. The road network

was classified based on importance into three classes. The classes are the ring road

which was recently constructed as an orbital road around the periphery of the central

business district, major roads which have varying widths ranging from 30m to

60mand minor streets and roads which include all streets and roads that have lower

hierarchy than the other two classes. The road network of Addis Ababa is shown in

figure 4A



Chair for Strategic Landscape Planning and ManagementTechnische Universität München

Data Source: CLUVA D2.7

Addis Ababa: Slope Impact factor

Step 2Slope (degree)

High : 30,46

Low : 0,0025

Step 3Slope Score

25

50

75

100

1:300,000 ¯ 0 10 205km

Step 1Elevation

High : 3022,11

Low : 2046


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Figure 5A Distance to Addis Ababa Road network

Figure 4A Road Network of Addis Ababa

Using Euclidean distance tool in the spatial analyst toolbox, a proximity analysis

was applied which generates a raster file whose grid-values equal to the distance to

the nearest road. Figure 5A shows the distance to different road classes in Addis

Ababa.


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A reclassification was then made based on the classification scheme shown in table

3A.

Table 3A Road proximity value classification (source: CLUVA 2012)

Figure 6A shows the proximity score values to the ring road, major roads and minor

roads and streets.

Figure 6A Proximity to road network score

The varying importance of different road classes is represented in assigning different

weights to the proximity score values to different types of roads. Proximity to ring

road, major roads and minor roads and streets classes were 40%, 35% and 25%

respectively.




Data Source: CLUVA 2012

Addis Ababa: Proximity Score

Major RoadsProximity Score

20

40

60

80

100

Minor Roads and streetsProximity score

20

40

60

80

100

1:300.000 ¯ 0 10 205km

Ring Road Proximity score

20

40

60

80

100

Distance 0-500 500-1000 1000-2000 2000-4000 4000-5700

Value 100 80 60 40 20


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1.1.1.2.2 Centrality impact factor

The sub-centers data of Addis Ababa was provided by the local African partner

university team. Using Euclidean distance tool in the spatial analyst toolbox, a

proximity analysis was applied which generates a raster file whose grid-values equal

to the distance to the nearest Sub-center. A reclassification was then made based on

the classification scheme shown in table 4A. Figure 7A shows the processing steps

of the centrality impact factor.

Table 4A Centrality distance value classification (source: CLUVA 2012)

Figure 7A Centrality Impact factor processing steps

1.1.1.3 Neighborhood based Factor

Neighborhood based factor raster file was generated by adding an attribute to

UMT2011 dataset with the value of 1 for the settlement UMTs and 0 for all other

values. The file is then converted using the tool polygon to raster using this





Addis Ababa: Centrality Impact Factor

Step 2:Sub centers

Distance (m)High : 10811.7

Low : 0

Step 3:Centrality Score

20

40

60

80

100

1:300,000 ¯ 0 10 205km

Step1: Sub Centers

1

2

3

Road_network

Distance 0-500 500-1000 1000-2000 2000-4000 4000-5700

Value 100 80 60 40 20


11

attribute. A focal statistics operation was then used on the raster file using 4*4

rectangular neighborhood setting with a sum statistic type. Residential cells (carrying

the value of 16) were removed(multiplied to zeron). The neighborhood output raster

file weas normalized (Raster Calculator: value*100/maximum value) where maximum

value is 100 (focal statistics value is 15) and minimum value is 0 ( focal statistics

value is 0). The final impact factor values are shown in table 5A and the

neighborhood impact factor processing stages are shown in figure 8A.

Table 5A Focal statistics value classification (UMT map CLUVA 2.7)

Focal

Statistics 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Value 0 7 13 20 27 33 40 47 53 60 67 73 80 87 93 100

1.1.2 Exclusion Areas

Within the scope of this research, the urban morphology types that are probable to

transform in the next 15 years are Agriculture 1, Vegetation 2, and bare land 11. An





Addis Ababa: Neighborhood Impact Factor

1:300,000 ¯ 0 10 205km

Step1: Settlements

0 Non-settlements

1 Settlements

Step 3:Neighborhood score

Value

0

6

13

20

26

33

40

46

53

60

66

73

80

86

93

100

Step 2:Focal Statistics

Value

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Figure 8A Neighborhood impact factor processing steps


12

Figure 9A Exclusion Areas in 2011 (first iteration)

exclusion raster file was produced that have the values of 0 for all the excluded

UMT’s and the value of 1 for all the non-excluded UMT’s. Due to the aviation

regulation and the criticality of the area, the airport area is also considered as an

excluded area. The airport exclusion area was based on the land use map of Addis

Ababa provided by the urban planning institute of Addis Ababa City Council. Figure

9A shows the exclusion areas for Addis Ababa in 2011 (to be used in the first

iteration: 2015)




Data Source: CLUVA D2.7, Land use mapUrbn Planning Institute AA Council

Addis Ababa: Exclusion

1:240,000¯0 8 164km

0 Excluded

1 non excludedOther UMT's

1.1 Field Crops

1.2 Vegetable Farm

11 Bareland

2.1 Plantation

2.2 Mixed Forest

2.3 Riverrine

2.5 Grassland

Urban Morphology Types 2011

Airport_zone

Documents

Urban Spatial Scenario Design Modelling (USSDM) … · Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 3 2. Introduction This technical user guide is