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

Transcription

1 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) Project co-funded by the European Commission within the Sixth Framework Programme ( ) 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 hanywafa@mytum.de

2 1 Table of Contents 1. Glossary Introduction Employed Software: Syntax (Model Parameters) Model Structure AddisUSDM1: Folder Organization Running the model Running Duration Model Output Credits 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 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. Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 2

3 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 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 Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 3

4 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 ( ) Workspace A raster file that has the values of slope. Reclassification based on the following: 1.1 slope 8-20 deg >20 deg deg Raster 3-8 deg Dataset 1.2 Roadprox2 1.3 Centrality 1.4 transform 1.5 neighborhood Euclidean distance operation is applied to the road infrastructure of Addis Ababa and is then reclassified according to: m m m m >4000 m Euclidean distance operation is applied to the sub centers of Addis Ababa and is then reclassified according to: m m m m >4000 m 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 Vegetable Farm Plantation Mixed forest Riverine Grassland BARE LAND 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 Raster Dataset Raster Dataset Raster Dataset Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 4

5 value of 0) 1.6 excluded_area 1.7 residvalue1 2 output 3 Final 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 In this Raster file, Settlement cells have the value of 1 Output file geodatabase. Please select an empty Geodatabase on your local Drive Final file geodatabase. Please select an empty Geodatabase on your local Drive The number of cells that will be transformed into settlements in the first period The number of cells that will be transformed into settlements in the second period The number of cells that will be transformed into settlements in the third period Raster Dataset Raster Dataset Workspace Workspace SQL Expression SQL Expression 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 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 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. Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 5

6 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. Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 6

7 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 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>>. Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 7

8 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 Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 8

9 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 ( ), 3 for the settlements transformed in the second iteration ( ), and 4 for the settlements transformed in the third iteration ( ). 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) Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 9

10 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 Figure 10: AddisUSDM1 Parent Folder - 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. Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 10

11 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. Figure 11: Model Parameters input window 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 Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 11

12 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. Technical User Guide: Urban spatial scenario design modelling for Addis Ababa 12

13 foc4b4nor slopereclass rdproxrecls2 transf1 foc4b4nor m2 P Weighted Sum Raster to Point sumptsort1 (2) Calculate Field (2) 2015 Select resselec1 Merge exclud1 (4) exclud2 (3) Calculate Field resselec1 (2) wtsum1 exclud1 (3) wtsumpt1 Add Field sumptsort1 (3) sumptsort1 (4) mergres1 mergrast1 (2) exclud22 Select (2) nonreselec1 P wtsumpot1 Sort (2) sumptsort1 Add Field (2) Expressio n (2) Feature to Raster Calculate Field (3) nonreselec1 (2) (17) residv1 (10) step2 (7) (9) focstatsnorm2 slopereclass (2) rdproxrecls2 (2) transf1 (4) residv02 residv12 (8) focstsnonres Weighted Sum (2) P (13) step3 Focal Statistics focsts2 wtsum2 Raster to Point (2) sumptsort1 (7) Calculate Field (6) 2020 Select (3) resselec2 Merge (2) (4) wtsumpot2 wtsumpt2 Add Field (4) sumptsort1 (8) sumptsort1 (5) Calculate Field (4) resselec1 (4) Select (4) nonreselec2 mergres2 mergrast2 (5) exclud3 (6) exclud33 Sort (3) sumptsort2 Add Field (3) P Expressio n (4) Feature to Raster (2) Calculate Field (5) nonreselec1 (4) (12) focstatsnorm3 transf1 (2) slopereclass (3) rdproxrecls2 (3) (14) residv03 (16) Weighted Sum (3) P Raster to Point (3) sumptsort1 (9) Calculate Field (9) 2025 Select (5) resselec3 residv13 (15) focstsnonres3 wtsum3 Merge (3) Calculate Field (7) resselec1 (3) Focal Statistics (2) focsts3 (11) wtsumpot3 wtsumpt3 Add Field (6) sumptsort1 (10) sumptsort1 (6) mergres3 mergrast3 P Select (6) nonreselec3 Sort (4) sumptsort3 Add Field (5) Expressio n (6) Calculate Field (8) nonreselec1 (3) Feature to Raster (3) Final

14 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>>

15 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, 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) Background information: Urban growth scenario modelling for Addis Ababa 2

16 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 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 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 Background information: Urban growth scenario modelling for Addis Ababa 3

17 Figure 1A 2006 UMT'S transformed to settlements (Source: UMT Maps (CLUVA2.7) 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. Background information: Urban growth scenario modelling for Addis Ababa 4

18 Figure 2A Transformed UMT cells in 2011 / Total UMT Area Source: UMT Maps (CLUVA2.7) 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 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) Field 2.1 UMT Vegetabl Mixed crops Plantation e Farm forest Transformability Score 2.3 Riverine 2.4 Grassland 11 Bareland 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. Other UMTs Background information: Urban growth scenario modelling for Addis Ababa 5

19 Transformability Score 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 Addis Ababa: Transformability Score Addis Ababa: Urban growth Spatial Dynamics Hany A. Abo El Wafa Projected coordinate system: Adindan UTM zone Data Source: CLUVA D km 1:240,000 Chair for Strategic Landscape Planning and Management Technische Universität München Figure 3A Transformability impact factor 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 The processing steps for the slope impact factor is shown in figure 3A Background information: Urban growth scenario modelling for Addis Ababa 6

20 Step 1 Elevation High : 3022,11 Low : 2046 Step 2 Slope (degree) High : 30,46 Low : 0,0025 Step 3 Slope Score Addis Ababa: Slope Impact factor Projected coordinate system: Adindan UTM zone :300,000 km Data Source: CLUVA D2.7 Addis Ababa: Urban growth Spatial Dynamics Hany A. Abo El Wafa Chair for Strategic Landscape Planning and Management Technische Universität München Figure 3A Slope Impact Factor Processing steps Location based Factors 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 Background information: Urban growth scenario modelling for Addis Ababa 7

21 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. Figure 5A Distance to Addis Ababa Road network Background information: Urban growth scenario modelling for Addis Ababa 8

22 A reclassification was then made based on the classification scheme shown in table 3A. Table 3A Road proximity value classification (source: CLUVA 2012) Distance Value Figure 6A shows the proximity score values to the ring road, major roads and minor roads and streets. Ring Road Proximity score Major Roads Proximity Score Minor Roads and streets Proximity score Addis Ababa: Proximity Score Projected coordinate system: Adindan UTM zone : km Data Source: CLUVA 2012 Addis Ababa: Urban growth Spatial Dynamics Hany A. Abo El Wafa Chair for Strategic Landscape Planning and Management Technische Universität München 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. Background information: Urban growth scenario modelling for Addis Ababa 9

23 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) Distance Value Step1: Sub Centers Road_network Step 2: Sub centers Distance (m) High : Low : 0 Step 3: Centrality Score Addis Ababa: Centrality Impact Factor Projected coordinate system: Adindan UTM zone :300,000 km Data Source: CLUVA 2012 Addis Ababa: Urban growth Spatial Dynamics Hany A. Abo El Wafa Chair for Strategic Landscape Planning and Management Technische Universität München Figure 7A Centrality Impact factor processing steps 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 Background information: Urban growth scenario modelling for Addis Ababa 10

24 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 (: 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 Value Step1: Settlements 0 Non-settlements 1 Settlements Step 2: Focal Statistics Value Step 3: Neighborhood score Value Addis Ababa: Neighborhood Impact Factor Projected coordinate system: Adindan UTM zone :300,000 km Data Source: CLUVA 2012 Addis Ababa: Urban growth Spatial Dynamics Hany A. Abo El Wafa Chair for Strategic Landscape Planning and Management Technische Universität München Figure 8A Neighborhood impact factor processing steps 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 Background information: Urban growth scenario modelling for Addis Ababa 11

25 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) 0 Excluded 1 non excluded 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 Airport_zone Addis Ababa: Exclusion Projected coordinate system: Adindan UTM zone km 1:240,000 Data Source: CLUVA D2.7, Land use map Urbn Planning Institute AA Council Addis Ababa: Urban growth Spatial Dynamics Hany A. Abo El Wafa Chair for Strategic Landscape Planning and Management Technische Universität München Figure 9A Exclusion Areas in 2011 (first iteration) Background information: Urban growth scenario modelling for Addis Ababa 12