Potential analysis for rooftop farming in New Cairo. - A GIS-based model to calculate bed numbers and crop yield-

Transcription

1 Potential analysis for rooftop farming in New Cairo - A GIS-based model to calculate bed numbers and crop yield- Manual F model Prepared by: Lisa Kaltenbach Supervision from: Hany Abo El Wafa Prof. Dr. Stephan Pauleit Created within the master thesis of Lisa Kaltenbach (2017), M.Sc. Landscape planning and engineering ecology, TUM

2 Table of contents 1 Glossary General information Introduction Model aim and usage Model concept and scope Input data Results Transformability Technical information Employed software Model parameters Model structure RPVF toolbox Running the model Running duration Model output Credits Use limitations Appendix F final F_sub final Open the F model Spatial join operation F_sub Dissolve operation F_sub Parallel script Glossary avail_non_shadowed avail_shadowed F FC fp fp_type GeoDB ID_bldg MAX_Th Non_Sh_Unit_Yield RPVF.tbx Sh_Unit_Yield SQL struc SUM_bed_no Available non-shadowed area Available shadowed area Farming Feature Class Building footprint Building type (here: Villa, Apartment_building, Commercial, Educational, Medical) Geodatabase Building ID Maximum height of rooftop structures Non-shadowed Unit Yield Rooftop PV and Farming toolbox Shadowed Unit Yield Structured Query Language File containing above ground roof structures Sum of the bed number 2

3 2 General information 2.1 Introduction This manual is prepared to give general and technical information about the F model. A GIS-based Farming model (F model) was created to analyze the potential for rooftop farming. This F model is an extension of the Area model created within the same thesis. The F model uses the output of the Area model to select rooftop space usable for farming according to set criteria. The values for these criteria are defined by the user. A F_sub model calculates the number of crop beds that can be installed per roof and the achievable crop yield in kg/m 2 when using all space for rooftop farming. Crop bed dimension and yield unit are defined by the user. The model uses both shadowed and non-shadowed area as input data and produces two Feature Classes (FCs) for the indication of available shadowed and non-shadowed area and two geodatabase tables to represent the number of beds and the total yield per roof. 2.2 Model aim and usage The model aims to analyze rooftop space available for farming and calculate crop bed numbers and achievable yield. City planners can use the model in the city planning and development process. House owners can use the results of the model to analyze the feasibility of their rooftop for farming. The model shall ease the the integration of more sustainable solutions for food generation within urban areas. 2.3 Model concept and scope The created model is a GIS-based model. The software used to create this model is ArcGIS The model is built on the model builder environment of the software. To edit the model, the user has to use the ArcGIS model builder. A detailed description and manual of the model is given in the technical section (3). The model comprises a main part (F) for the selection according to rooftop farming criteria and a sub part (F-sub) for the bed and crop yield calculations. Figure 1 shows a graphical representation of the conceptual model. The F- model needs available rooftop area as input data. The output is stored in an output geodatabase. For the purpose of this research the model uses the output of the Area-model as input. 3

4 Figure 1: Conceptual model of the ArcGIS F model. (own illustration) a) The script of the parallel line tool is attached to the appendix Spatial scope For the purpose of this research the model calculates the farming potential for the district of Al Rehab City in New Cairo, Egypt (Figure 2). The model is transferable to other cities and lets the user select input parameters. The model is not limited to a specific area. 4

5 Figure 2: Study area of this research. (own illustration created with ArcGIS 10.4) 2.4 Input data The output of the model and its accuracy is highly dependent on the input data. The F-model uses the output of the Area model as input data. Following data was created within the framework of the Area model: Building footprint data, rooftop structures data, rooftop holes data. A detailed description including figures can be found in the Area model manual. Following input data resulting from the Area model is needed for this model: Available shadowed area (Feature Class) Available non-shadowed area (Feature Class) 5

6 2.5 Results Figure 3 shows visual examples of the results for the study area of Al Rehab City. The model generates two outputs for shadowed (FC, gdb table) and two for non-shadowed area (FC, gdb table). Figure 3 shows the parallel lines drawn in both areas to assume the best bed layout. Figure 3: Visualization of the F model results showing the parallel lines. (ArcGIS 10.4) Building footprint Roof holes Roof structures Available non-shadowed area Available shadowed area Parallel lines (distance=bed length + row spacing Figure 4 shows an excerpt of the table of attributes for the non-shadowed area taking deepwater cultivated lettuce as example. The table includes the building ID, the total bed number per building, the polygon length, the total roof area and the annual lettuce yield in kg/m 2. There is no visualization of the best bed layout except for the parallel lines (Figure 3). The model estimates the potential of the whole roof area. The user can later choose to do farming in specific parts of the roof as it is unlikely to use the whole roof for farming. 6

7 Figure 4: Excerpt of the non_shadowed table of attributes for deepwater cultivated lettuce. (ArcGIS 10.4) The table results from running F. 2.6 Transformability This GIS model can be used to assess the potential for rooftop farming in other cities. The input files have to be changed by the user and the input parameters have to be set according to existing criteria. If needed also the tools of the model can be edited and replaced to best suit the local conditions and requests. 3 Technical information This section provides the technical information and instructions of how to run and edit the F- model. 3.1 Employed software The software that was used in this model is ArcGIS The model is built in the model builder environment of the software. The model can be run by opening it in the ArcMap catalog and can be edited in the ArcMap model builder. In case of working with an older version of ArcGIS the model has to be saved in this version to be able to use it. 3.2 Model parameters The model parameters of RPVF.tbx - model F (input, output, variables) are listed in Table 1. 7

8 Table 1: Model parameters S/N Model parameter Explanation Data Type 1 Input Input comprises three geodatabases listed below ( ) 1.1 GeoDB fp struc hole The file geodatabase where following input files are located: Building footprint, created using google earth imagery and the polygon tool provided by ArcGIS Outlined rooftop structures (above ground) using the ArcGIS polygon tool Outlined rooftop holes using the ArcGIS polygon tool 1.2 Scratch The file geodatabase generated by the shadow tool to store intermediate data 1.3 Output avail_shadowed avail_non_shadowed The file geodatabase where following output of the Area model is located: Output file from the Area model containing available shadowed area Output file from the Area model containing available non-shadowed area 2 Output Output file geodatabase created automatically (same output.gdb as in Area model) Workspace Feature Class Feature Class Feature Class Workspace Workspace Feature Class Feature Class Workspace 3 Bed_width The width of the crop beds in meters Double 4 Bed_length The length of the crop beds in meters Double 5 Row spacing The distance between bed rows in meters 6 Max_floor_no Maximum number of floors. Buildings exceeding the maximum number of floors are not considered in the rooftop farming calculation. 7 Min_roof_area Minimum available rooftop area usable for rooftop farming. Smaller areas are not considered 8 Sh_Unit_Yield The unit yield (kg/m 2 ) of the crop in the shadowed area 9 Non_Sh_Unit_Yield The unit yield (kg/m 2 ) of the crop in the non-shadowed area 10 File_Name_Ending Output file name ending for identifying results from different runs. Can be number or text. 11 Inner buffer value Value for the inner buffer of the parallel script tool; for the purpose of this research to be set 0 Double Long Double Double Double String Double 8

9 3.3 Model structure The model was built in the ArcGIS model builder environment. An overview of the model (F, F_sub) is shown in the appendix. The model consists of a main model F and a sub-model F_sub. Following sections are part of the F model: 1. Parse path to parse the input into its output path on your local drive. e.g.: C: \ToolData Figure 5 shows a graphical representation of the first model operation. Figure 5: Parse path model operation (ArcGIS model builder) 2. Multipart to Singlepart separates the multipart input features to singlepart features. Singlepart features are needed for further calculations. Figure 6 shows a graphical representation of the second step. The output files are stored in the <<Scratch>> geodatabase. Figure 6: Multipart to Singlepart model operation (ArcGIS model builder) 9

10 3. Select produces a selection of features according to the following SQL expression: Shape_Area > %Min_roof_area% AND Floors <= %Max_floor_no% This operation selects all features where the area is greater than the minimum roof area and the floor number is smaller or equal to the maximum floor number. <<Max_floor_no>> and <<Min_roof_area>> have to be set by the user. Figure 7 shows a graphical representation of the third operation. Figure 7: Select model operation (ArcGIS model builder) 4. Dissolve aggregates the selected features according to the building ID (<ID_bldg>), the building type (<Type>), the floor number (<Floor>), the building <Phase> (1-10, excl. 6), the maximum height of rooftop structures (<MAX_Th>) and the building height (<Height>). Figure 8 shows a graphical representation of the fourth operation. Figure 8: Dissolve model operation (ArcGIS model builder) 10

11 5. F_sub: Produced outputs from the model operations one to four (<<avail_shadowed_farming>>, <<avail_non_shadowed_farming>>), <<fp type>> containing the file listing the building types and the geodatabase <<GeoDB>> containing the input shapefiles are then fed into F_sub. Figure 9 shows a graphical representation of this step. The model parameters of F_sub are represented in the Figure. Figure 9: F_sub model (ArcGIS model builder) Following sections are part of the F_sub model: 1. The Parallel script 1 tool was created to draw parallel lines taking the longest polygon distance as reference. The spacing of the lines is defined by the bed length plus the row spacing. <<Inner Buffer>> has to be set as 0. The script for the parallel line tool is listed in the appendix. Figure 10 shows a graphical representation of the first operation in the F_sub model. 1 GIS Stackexchange (2016), URL: 11

12 Figure 10: Parallel script tool operation (ArcGIS model builder) 2. Select operation to select all features according to following SQL expression: Shape_Length >= %Bed width% The <<bed width>> has to be set by the user. The selection then makes sure all lines smaller than the bed width are excluded as they are of irrelevance (no crop bed can be installed). Figure 11 shows a graphical representation of the second operation in the F_sub. Figure 11: Select operation (model builder) 12

13 3. Add field and calculate field: A new field with the name <Bed_no> is added and calculated according to following SQL expression: math.trunc(!shape_length! / %Bed width% (Expression type is python_9.3.) The calculation produces a column to list the number of beds that can be installed per line. Figure 12 shows a graphical representation of the third operation in F_sub. Figure 12: Add Field, Calculate Field operation (ArcGIS model builder) 4. Spatial join joins the attributes of <<fp_no_6>> (model parameter fp) to the attributes of <<avail_shadowed_select (4)>> and <<avail_non_shadowed_select (4)>> with the join operation one-to-many and the math option contains. Figure 10 shows a graphical representation of the fourth operation in F_sub. Figure 13: Spatial Join (ArcGIS model builder) A screenshot of the spatial join tool settings is shown in the appendix under 6.4, Figure

14 5. Dissolve produces the <Bed_no> dissolved according to the <ID_bldg>. The field <ID_bldg> is selected as dissolve field. A statistical field is added to summarize the number of beds. The output lists the <SUM_Bed_no> according to the <ID_bldg>. Figure 14 shows a graphical representation of the fifth operation of F_sub. Figure 14: Dissolve operation (ArcGIS model builder) A screenshot of the dissolve tool settings is shown in the appendix under 11.5, Figure Add field and calculate field: A new field type <float> with the name <Unit_Yield> is added and calculated according to following SQL expression: %Sh unit yield%* [SUM_Bed_no]*(%Bed width%*%bed length%) The box <field is nullable> is checked and the expression type <VB> is selected. <<Sh_Unit_Yield>> and <<Non_Sh_Unit_Yield>> are both defined as double variables (model parameters) and are to be set by the user. Figure 15 shows a graphical representation of the sixth operation of F_sub. Figure 15: Add Field, Calculate Field (ArcGIS model builder) Outputs from F_sub are fed back into F, then: 14

15 Table to Table is the final operation in the F model and takes the results from the F_sub <SUM_bed_no>, <Sh_Unit_Yield>, <Non_Sh_Unit_Yield> as input and converts them to a geodatabase output table. There is no visualization of the final results. One table for the shadowed area and one table or the non-shadowed area is generated and saved to the <Output> geodatabase. Figure 16 shows a graphical representation of the finale model operation. 3.4 RPVF toolbox Figure 16: Table to Table (ArcGIS model builder) It is recommended to copy the whole RPVF toolbox (RPVF.tbx) on your drive. The toolbox contains all necessary models and sub models including the following: Area and Area_Sub Bldg_Type Choose_Multiple_Field_Values (to select building types upon running the model) Choose_Multiple_Field_Values script tool F and F_Sub Parallel script tool PV and PV_Sub In case you need the input data for Al Rehab city, you copy the whole folder (Geo_DB) containing all needed input files. The toolbox also includes the model for PV calculation (PV, PV_sub) as well as the model for the building type calculation according to the input data and the Choose_Multiple_Field_Values model. Further details on the PV model are given in the PV model manual prepared by Jingsui Huang (2016). Figure 17 shows the RPVF toolbox in ArcCatalog. 15

16 Figure 17: RPVF toolbox (ArcCatalog) 3.5 Running the model In ArcMap please do the following: Connect the folder where the RPVF toolbox is saved (or the whole Geo_DB folder) to ArcMap by creating a new folder connection Open ArcCatalog in ArcMap, open the folder, open the RPVF toolbox Open the F model by double-clicking it Select the input data, set the model parameters and choose a file name ending 16

17 Figure 18: Model parameters input window (ArcMap) A detailed description on the fields is given in the appendix under 9.1. To run the model, click Ok after setting the parameters. 3.6 Running duration The duration depends on the processing speed of the computer used and will take approximately 5 minutes. The main Area model has to be run first to create the needed input files. The duration of the Area model is approximately 1 hour and 15 minutes for the processing of 3176 buildings and 1.34 km 2 of roof area. Due to little running duration, the F model can easily be run for various bed dimensions and crop produces. 3.7 Model output The final output of the F model are two geodatabase tables <<sha_bed_yield_%file Name Ending%>> and <<non_sha_bed_yield_%file Name Ending%>>. The tables represent the total number of crop beds per building and the achievable yield for selected crop types. 17

18 4 Credits Author: Lisa Kaltenbach, Date: December 2016 Technische Universität München. The input data was compiled by the Al Rehab City GIS online portal and own digitalization using google earth imagery. The data was prepared in cooperation with Jingsui Huang. 5 Use limitations There are no access and use limitations for this model. The model was created within the framework of the master thesis The potential of rooftop farming and solar power in New Cairo, Egypt A local perspective and GIS-based suitability model: the case study of Al Rehab City, Technische Universität München, M.Sc. Environmental planning and engineering ecology, Lisa Kaltenbach,

19 6 Appendix 6.1 F final

20 6.2 F_sub final

21 6.3 Open the F model Field 1 GeoDB: Input geodatabase containing all files needed as input data (here: Rehab.gdb); change folder by clicking on the folder connection symbol, then browse to correspondent folder and select it Field 2 Scratch: Scratch geodatabse being created to save all intermediate data; if you wish you can relocate the path by clicking on the folder connection symbol and browse for wished storage location Field 3 Output: Output geodatabase being created to save all outputs; if you wish you can relocate the path by clicking on the folder connection symbol and browse for new storage location Field 4 Bed width: Enter the crop bed width in meters Field 5 Bed length: Enter the crop bed length Field 6 Row spacing: Enter the row space between crop beds in meters Field 7 Max_floor_no: Enter the maximum floor number Field 8 Min_roof_area: Enter the minimum available rooftop area in meters for rooftop farming Field 9 Sh_Unit_Yield: Enter the unit crop yield in kg/m 2 in the shadowed area Field 10 Non_Sh_Unit_Yield: Enter the unit crop yield in kg/m 2 in the non-shadowed area Field 11 File Name Ending: Enter the file name (output will have selected name as file ending) 6.4 Spatial join operation F_sub Figure 19: Spatial join operation (ArcMap)

22 6.5 Dissolve operation F_sub Figure 20: Dissolve operation (ArcMap) 6.6 Parallel script # import libraries import arcpy # set input/output parameters polyfc = arcpy.getparameterastext(0) outparallel = arcpy.getparameterastext(1) linespacing = arcpy.getparameterastext(2) buffdist = arcpy.getparameterastext(3) # input polygons # output parallel lines # line spacing # inner buffer distance # parse numbers from parameters linespacenum = float(linespacing.split(' ')[0]) buffnum = float(buffdist.split(' ')[0]) # establish spatial reference desc = arcpy.describe(polyfc) SR = desc.spatialreference # set overwrite environment arcpy.env.overwriteoutput = True arcpy.env.outputcoordinatesystem = SR parallels = [] # loop through each input shape for row in arcpy.da.searchcursor(polyfc, ["SHAPE@"], spatial_reference=sr): # create inner buffer polybuff = row[0].buffer(buffnum * -1) # create hull rectangle to establish a rotated area of interest coordsplit = row[0].hullrectangle.split(' ') 22

23 # collect corner coordinates coordlist = arcpy.array([arcpy.point(coordsplit[0],coordsplit[1]),arcpy.point(coordsplit[2],coordsplit[ 3]),arcpy.Point(coordSplit[4],coordSplit[5]),arcpy.Point(coordSplit[6],coordSplit[7]),arcpy.Point(coordSplit[0],coordSplit[1])]) # create lines from hull rectangle currentlines = [] for pointnum in range(0,4): arcpy.array([coordlist.getobject(pointnum),coordlist.getobject(pointnum+1)]) hullrecline = arcpy.polyline(arcpy.array([coordlist.getobject(pointnum),coordlist.getobject(pointnum+1)]) ) currentlines.append(hullrecline) # compare first and second line to determine if first line is short or long firstlong = 0 if currentlines[0].length > currentlines[1].length: firstlong = 1 # calculate number of points needed along short axis numpoints = int(math.floor(currentlines[firstlong].length/linespacenum)) # create and join points to create parallel lines for point in range(1,numpoints+1): shortpoint1 = currentlines[firstlong].positionalongline(linespacenum*point) shortpoint2 = currentlines[firstlong + 2].positionAlongLine(currentLines[firstLong + 2].length - (linespacenum*point)) parallel = arcpy.polyline(arcpy.array([shortpoint1.centroid,shortpoint2.centroid]), SR) # intersect parallel lines with buffer parallelbuff = parallel.intersect(polybuff,2) parallels.append(parallelbuff) # write geometries to disk arcpy.copyfeatures_management(parallels, outparallel) # add to map mxd = arcpy.mapping.mapdocument("current") dataframe = arcpy.mapping.listdataframes(mxd, "*")[0] addlayer = arcpy.mapping.layer(outparallel) arcpy.mapping.addlayer(dataframe, addlayer) del row 23