Identifying Zambian Regions of Staple Food Net Consumption and Supply Geo 425: GIS Project Proposal April 1, 2009 Kim Borland, Almaz Naizghi and Steve Longabaugh I. Introduction/Problem Statement This project will identify regions in Zambia that are net producers of staple food and those that are net consumers of the same staples. By mapping the areas of supply and demand it is hoped to give insight into the flows of staple food (maize and cassava) from production to consumption areas. The value of this analysis is to inform decisions related to relief shipments in years of poor production and whether that relief is best given in kind or in cash. II. Research Objective We will look at how the pattern of supply and consumption varies between 2 survey years: 2001 and 2004. This type of analysis would be most useful if it took into consideration the supply and consumption of neighboring countries, as there are significant trade flows between neighbors, but this project will be limited in scope to only the country of Zambia. III. Required Data 1. Survey data available from the Food Security Group in the Dept of Agriculture, Food and Resource Economics at MSU. a. Per capita net purchase position maize and cassava by rural areas b. Per capita net purchase position maize and cassava by urban extents 2. Raster files of population density. Two Sources a. Global Rural-Urban Mapping Project (GRUMP) of the Center for International Earth Science Information Network, Columbia University. http://sedac.ciesin.columbia.edu/gpw/global.jsp b. LandScan Landscan project of the Oak Ridge National Laboratory in Oak Ridge TN. 3. Raster of Urban extent areas. This information is available from the Center for International Earth Science Information Network, Columbia University. http://sedac.ciesin.columbia.edu/gpw/global.jsp 4. Vector file of Zambian districts is available from the Food Security Group in the Dept of Agriculture, Food and Resource Economics at MSU.1 above. 5. All data and spatial files are in hand. IV. Software Used 1. STATA will be used to generate the survey data 2. STAT Transfer will be used to transfer the survey data from STATA data files to.dbf files that can be joined to a vector file. 3. ArcGIS will be used for the spatial component a. ArcMap for the clipping and other preprocessing b. ArcScene for 3d mapping 1
V. Preprocessing and Processing Steps 1. Assembling the data a. Download from the CIESEN website i. The GRUMP population density raster ii. The Urban Rural Exents raster b. Secure from the Landscan project the population rastor for 2008. c. Gather from the Food Security Group Project i. The 3 country vector file for Zambia, Malawi and Mozambique districts. ii. Per capita net purchase position maize and cassava by rural areas iii. Per capita net purchase position maize and cassava by urban extents 2. Preparing the files (in ArcGIS) a. From the 3 country vector file export the Zambia polygons to a new shape file so that we have a Zambia only shape file b. Clip the rastor files using the Zambia shape file as the masque i. GRUMP ii. Landscan iii. Urban Extents c. Produce the shape file of rural and urban extents polygons i. Convert the urban extent rastor (only for the country of Zambia) to a shape file (all cells with a grid value of 2 identifies an urban cell) ii. Export the urban polygons to their own shapefile iii. Overlay (union) the urban extents polygons over the Zambia districts polygons. The resulting polygons will be either rural or urban. d. Add in Survey data: i. STAT Transfer the STATA do files into dbf files ii. Join the Zambia vector file to the Zambia rural extent dbf file so that we can bring in the per captia net sales position for cassava and maize for 2002 and 2004 for each rural area iii. Join the Zambia vector file to the Zambia urban extent dbf file so the we can bring in the per captia net sales position for cassava and maize for 2002 and 2004 for each urban area iv. Convert the vector file to a grid file and then multiply by a population density raster to calculate total net position per sq KM (by year/crop combination) a. GRUMP b. LandScan 3. Mapping (in ArcScene) a. 3d map of population i. GRUMP ii. LandScan b. 3D map of maize net position in 2002 c. 3D map of maize net position in 2004 d. Calculate the national maize balance position for each year e. 3D map of cassava net position in 2002 f. 3D map of cassava net position in 2004 2
g. Calculate the national cassava balance position for each year h. Convert the net positions to calories and combine the two staples into a net staple food caloric position for each year i. Calculate the national food staple balance position for each year VI. Descriptions of processing operations in ArcGIS (not in any particular order) How to cookie cutter a raster 1. In ArcGIS load in the raster file first (GRUMP or LandScan for example) and then the vector file (Zambia) 2. ArcToolbox: Spatial Analyst Tools: Extraction: Extract by Mask a. Input raster: the raster that you want to cookie cut b. Input raster or feature mask data: the vector file to use as cookie cutter c. Output raster: new name How to create a new shapefile from an exisiting shapefile. 1. load in the original vector file 2. select the polygons that you want in the new shapefile 3. right click on the vector file to export: Data: Export Data a. in the export data window: i. select 1. all features 2. this layer s source data ii. give the file a new name How to make a 3 d image of a raster: 1. This is all done in ArcScene 2. In layer properties a. Give a good color ramp b. In base heights select the raster file c. In Scene properties: play around with the extent, begin with the automatic one and then adjust as needed How to add STATA data file to a shape file. 1. STAT TRANSFER to convert STATA file to dbf file. a. (z_dist_net_positions.dta to z_dist_net_positions.dbf) 2. Join with file that has the spatial characteristic needed a. Zambia.shp b. Join on the dist variable 3. Once they are joined, export to a new shapefile: a. Right click on shape file: Data: Export i. Zambia_2.shp b. Delete unnecessary fields i. ArcToolBox: Data Management Tools: Fields: Delete Field 3
How to multiply the values in a population raster by attribute values in a vector file (polygons representing rural and urban extent). 1. convert the variables in the vector file to raster files a. Spatial Analyst: Convert : Features to raster b. Input features: zambia.shp c. Field: mfpcnp04 d. Ouput raster: z-mfcpnpo4 e. Repeat for each field: maize/cassava, 04/01, district 2. multiply the two rasters together a. arctoolbox: spatial analyst tools: math: times b. input raster: mfcpnp04 c. input raster: grump rastor d. output raster: z_mfcpnp04_f How to extract raster data to shapefile polygons. 1. Spatial Analyst Toolbar: Zonal Statistics 2. Zone data set: Zambia.shp 3. Zone Field: DISTRICT_1 4. Value raster: zam_grump 5. Check all three boxes 6. Output Table: Del1 7. Then select the shapefile that the new table was attached to and Data: Export as a new shapefile. You will likely want to delete unnecessary field and rename those that you want to keep (you ll have to create a new field, fill it with information from the old field and then delete the new field. We have two shapefiles that we want to overlay. The Zambia district shapefile and the newly created urban extent shapefile. We want to merge them into one shape file. How to do it? 1. ArcToolbox: Analysis tools: Overlay: Union a. Input features: i. urban_poly (on the top) ii. Zambia_poly (second on the list) b. Output feature class: RuralUrban_poly c. Delete unnecessary fields 2. May need dissolve some of the adjacent urban polyons that have been divided by district boundaries a. Create a new field dissolve and fill it with unique values b. Select those contiguous urban polygons that should be one polygon and assign a unique number to the lot (in dissolve ) c. ArcToobbox: Data Manaagement Tools: Generalizations: Dissolve i. Input features: rural_urban_polygons_3 ii. Output feature class: rural_urban_polygons_4 4
VII. Timeline and Responsibilities iii. Dissolve field: dissolve iv. Statistics field 1. gridcode: first 2. COUNTRY_1: first 3. PROVENCE_1: first 4. PROV: first 5. DISTRICT: first 6. DIST: first 7. DISSOLVE: first Deliverable Date Item Lead Person March 27 Assemble all Data Steve April 1 Submit Proposal Steve April 3 Preparing the files Kim April 10 Mapping Almaz April 15 Web Page Completed Kim April 17 Presentation Completed Almaz VIII. Flow Diagram See next page. 5