World Food ProgrammeSouthern Africa regional Office

A Guide to Mapping Selected Food Security IndicatorsFor

Vulnerability Analysis and Mapping

Prepared by:Dr. Mahadevan Ramachandran

In Collaboration with the VAM Regional Office

March 1998Maputo, Mozambique

Table of Content

1. Use of Vegetation Index data (NDVI)............................................................................................11.1 Monthly Composite NDVI......................................................................................................11.2 Creating Window.................................................................................................................... 21.3 Difference between current month and long term average vegetation.................................21.4 Create Colour Scheme............................................................................................................ 31.5 Growing season synoptic image..............................................................................................31.6 Note on Co-ordinate System:..................................................................................................51.7 Export to Word Document as bitmap file..............................................................................61.8 Extracting vegetation information by political boundary.....................................................61.9 Steps for IDRISI – EXCEL Conversion.................................................................................8

2. Mapping Food Production Data....................................................................................................10Step 1: Prepare Your Database..................................................................................................10Step 2: Exporting Excel file to IDRISI.......................................................................................10Step 3: Importing Excel file to IDRISI......................................................................................11Step 4: Mapping Database..........................................................................................................11Step 5: Desired Classes of Food Availability.............................................................................11

3. Mapping Livestock Data at district level......................................................................................13Step 1: Prepare Your Database..................................................................................................13Step 2: Exporting Database file..................................................................................................13Step 3: Importing Database file to IDRISI................................................................................13Step 4: Mapping Database in IDRISI.........................................................................................13Step 5: Desired Intervals (Classes) of Mapped Information.....................................................14

4. Mapping point data: Price and health centre data mapping...........................................................15Step 1: Prepare Database...........................................................................................................15Step 2: Exporting Data...............................................................................................................15Step 3: Importing Database file..................................................................................................15Step 4: Mapping point data: Now you have a vector file of market locations..........................16

5. Food Systems Approach...............................................................................................................185.1 The Concept.......................................................................................................................... 185.1 Database and Layer Information requirement....................................................................185.2 Procedures to Map different layers......................................................................................18

6. Flood Risk Mapping..................................................................................................................... 246.1 General Guide....................................................................................................................... 246.2 Zambezi River Flood Risk Analysis.....................................................................................276.3 Vector/ raster transformation..............................................................................................296.3 Combining Lines and polygons in MapInfo.........................................................................296.4 Procedures for Importing Tables (data) to MapInfo...........................................................30

7. IDRISI-MAPINFO STEP BY STEP CONVERSION PROCEDURE............................................307.1 IDRISI TO MAPINFO......................................................................................................... 317.2 MapInfo to IDRISI...............................................................................................................33

8. General Guide: Area, Extract, formatting.....................................................................................348.1 Changing the legend:............................................................................................................348.2 How to put Text into an image.............................................................................................348.3 Reclass................................................................................................................................... 35

9. Notes for Access to and from GIS................................................................................................389.1 Access—IDRISI—Access......................................................................................................389.2 To MapInfo............................................................................................................................ 399.3 Notes on Plotting Point Data in GIS.....................................................................................40

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Lab/Lecture Notes:Stepwise Guide to Mapping Selected Food Security Indicators

1. Use of Vegetation Index data (NDVI)

OBJECTIVES:

Use Vegetation Index data for identifying vegetation performance. Simple difference - compare current vegetation data against long term normal for

six months of the predominant growing season in Southern Africa (Nov - Apr) Creating a composite image at the end of the season to show possible areas of

normal and below normal vegetation Extract information at administrative level (average vegetation for each

district/province) and graphing specific districts monthly vegetation difference in Excel

Dekadal (10 day) images

Original raw NDVI data will be provided to you on a dekadal (10 day) basis or monthly basis.

DISPLAY NOV191 image file (this is the first 10 days of November 1991 vegetation) Use CURSOR ENQUIRY (?) to get a sense of values

Most of our analysis will be on a monthly basis (month vegetation minus month normal)

1.1 Monthly Composite NDVI

We need to take three 10-day images for the month and make them into one image representing the whole month. Run ---

ANALYSIS/CHANGE AND TIME SERIES/NDVICOMP Maximum value Number of image - 3 Enter the names of 3 images - NOV191 NOV291 NOV391 Output image - NOV91M Title: November 1991 Maximum Value Composite Image

DISPLAY NOV91M image.

Check values across the image. Add Layer – COUNTRYH

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1.2 Creating Window

Now we will subtract the long-term normal image from the current image, which in this case is November of 1991 vegetation index. The long-term images have been created for you from 14 years of Vegetation Index data for each month.

Before we do that, let us just window out the southern African region for our analysis. The long-term average images have already been windowed out for you, and you will now window the November 1991 monthly image using the same window size as the long-term normal images. RUN ---

REFORMAT/WINDOW Input image: NOV91M Output image: SNOV91M Click on the “existing windowed image” option Existing windowed image: SNOVAV14

1.3 Difference between current month and long term average vegetation

ANALYSIS/DATABASE QUERY/OVERLAY First image: SNOV91M Second image: SNOVAV14 Output image: SNOV91DF Click on “first-second” option Title: November 1991 - November 14 year mean

DISPLAY SNOV91DF image and check values across the image.

The negative values represent the areas where vegetation was below normal for January of 1992 and the positive areas, where it was above normal. Based on earlier research, negative values of below -15 are considered to be showing much below normal vegetation. These might be the areas where crops might be doing badly. RUN -

Run ANALYSIS/DATABASE QUERY/RECLASS on SNOV91DF Output image: SNOV91DR Title: Vegetation Departures - November 1991 Assigning new values (as shown in the table)

Assign a new value of

to all values from

to just less than

comments

0 0 1 background1 -256 -15 much below

normal2 -15 0 below normal3 1 256 normal to above

normal

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1.4 Create Colour Scheme

The output image now only has three classes showing much below normal, below normal, and normal to above normal vegetation. We need to do two things with this image. One is to create a colour scheme/palette that will allow us to express the three classes intuitively. The other is to give legend names for the three classes. OPEN / RUN ---

DISPLAY/PALETTE WORKSHOP

Make the colour associated with zero, white (background), associated with 1 (which is reclassified value for old values ranging from -256 to -15) as dark red, 2 as yellow, and 3 green. Save the palette file as VIDIF3 and quit.

DOCUMENT (under FILE menu) and enter the name of the reclassified image (SNOV91DR) .

Click on Legend Categories : 0 - non-study area, 1 - much below normal, 2 - below normal, and on 3 - normal/above normal.

DISPLAY SNOV91DR and choose the palette file you created VIDIF3. Ask to display legend and title.

Add Layer Country boundary file; COUNTRYH

This image should give you an idea of likely areas of poor vegetation in November of 1991. Now we will do a similar analysis with December 1991 data. We have SDEC91M (monthly December 1991 composite image) and SDECAV14 images as initial inputs to start with. Practice creating the December vegetation departure output image.

This process would have be repeated for six months of the growing season (November 1991 to April of 1992 in our case). In actual work, it would be good to print out each monthly vegetation departure image and look at them visually across the season. For the purpose of this training, the six vegetation departure images for the 1991/92 season is given to you. On the final day of the training, provided we have time, we will do the above analysis for data from November of 1997 to February of 1998 to get an idea of the current growing season across Southern Africa.

1.5 Growing season synoptic image

The next set of steps will go into the creation of one composite image representing the overall season’s performance. There are many ways of representing the status of a season. The one we use here is meant to be an intuitive example rather than the scientifically most rigorous.

From the steps mentioned above we have six difference images that show us each months vegetation departures in three categories (much below normal, below normal, normal to above normal). The next step of steps will basically calculate a final output image that will show the “number of months of much below normal”

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vegetation during the six months of the growing season. Higher the number of months, more likely it is that the area suffered from crop losses. To do this RUN ---

ANALYSIS/DATABASE QUERY/RECLASS Input image: SNOV91DF Output image: SNOV91MB Title: Areas of much below normal vegetation in November of

1991 Assign values as shown in the table

Assign a new value of

To all values from

to just less than

comments

1 -256 -15 much below normal

0 -15 256 Other values

The above Reclass module would give you a boolean image (an image of just zeroes and ones) showing only areas of much below normal vegetation. Re-do the same RECLASS steps for Dec 91, Jan 92, Feb 92, Mar 92, and Apr 92 using the same reclass levels given above. Now we have six boolean images that show for each month in the growing season, areas of much below normal vegetation.

Now we will run a module that will count the number of months of much below normal vegetation within the growing season for each pixel in the image. RUN ---

COUNT and specify the number of input images as 6. Enter SNOV91MB as image 1, SDEC91MB as image 2,

SJAN92MB as image 3, SFEB92MB as image 4, SMAR92MB as image 5, and SAPR92MB as image 6.

Output image: PROP92MB Title: Proportion of the growing season months that show much

below normal vegetation Count outputs an image that shows for each pixel, the proportion

of months that show much below normal. For example, if a pixel had 2 months out of 6 where it had much below normal value, the pixel value in the COUNT module output would be .33 (2/6). If a pixel had 5 out of 6 months of much below normal, then the count output would be 0.8333 (5/6). We will now reclass this output image into three classes to form a much more easy to read final output composit image for the growing season.

RECLASS on PROP92MB Output image: MBN9192 Title: Number of months of much below mean vegetation (Nov

91 - Apr 92) Assign values as in the table

Assign a new value of

to all values from

to just less than

comments

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0 0 0.01 background1 0.01 0.17 1 month much below normal vegetation

(slight to no crop damage)2 0.17 0.49 2-3 months of much below normal

vegetation (possible heavy crop loss)3 0.49 1.1 4-6 months of much below normal

vegetation (possible severe crop loss)

Palette workshop: Use the palette workshop to create a palette where the number ones gets a colour like blue, number two gets a colour like yellow, and number three red. This image along with the six monthly images would provide a very good synopsis of the growing season. Please note, that if for example, the ground level information suggests that four months (say, Dec-Mar) are the most crucial, then the same analysis (counting number of months of below normal) could be run with four images only.

1.6 Note on Co-ordinate System:

1. Now we have two outputs: one is the set of six images showing vegetation departures for each month and the other a synoptic image showing the overall nature of the growing season. We might want to add layers (like political boundaries, roads, market locations, EDP locations, etc.) on to these images to get a better idea about the areas affected.

2. Most vector layers (like roads, rivers, boundaries, points, etc.) are files that are in latitude/longitude co-ordinates. The NDVI data is processed in another co-ordinate system called the Hammer-Aitoff system. We might need to convert the vector layers into hammer-aitoff (from lat/long) or we would need to convert the final output and the six month negative departures images into latitude/longitude co-ordinates in order to use it with some of the vector layers. It is easier to convert the vector layers (political boundaries, roads, etc.) to hammer-aitoff. In the following step, we will convert one vector file. The rest of the files can be done similarly. You might also have vector layer data in Map-Info format in which case, please refer to the notes from the earlier section which covered the issue of converting these files to Idrisi vector format. To do this RUN the following:

REFORMAT/PROJECT and click on vector. Input file: COUNTRY Input reference files: This should fill automatically to lat/long Output file: COUNTRYH Output reference file: CLABSHA (stands for Clark Labs

Hammer-Aitoff) Click ok and you will have the output file (countryh) in

hammer-aitoff format, ready to be used (by doing add layer when you are viewing the NDVI departure image) with your NDVI data.

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We could also quickly practice by converting another layer called SAFROAD.

If you want to convert the NDVI output image to lat/long from Hammer-Aitoff (called Clabsha);

REFORMAT/PROJECT. Select the input image name, the input reference file (should be

automatic), and the output image name and reference file. Click on continue and take the rows/columns/min x/max x/min

y/max y as suggested by the software. If you want to know more about how to change the parameters,

please read the help system on PROJECT.

1.7 Export to Word Document as bitmap file

Once we have the output NDVI departure images and the associated vector files overlaid on them, we might want to take this image to a word document and write text around it explaining the image and what the output means. When you have the image displayed and the vector layers on it and exactly the way you would like it in the word document;

Save Composition in the composer box Take the option “screen dump map window as BMP file” Give it an output name. Now you can just go to your word document, do “insert

picture” and import this. We will test this with one of our outputs during the training

program.

1.8 Extracting vegetation information by political boundary.

1. Sometimes, you might want to extract vegetation departure values by Provinces or at least for some specific districts. Basically to do any operation that involves two images, the images have to be the same size (in terms of rows/columns and min and max values). Hence, to extract the statistics (say average vegetation) for a district, the district image has to be in raster and also the same size in rows and columns. For the sake of this exercise, we will do one district in Zambia. All of the NDVI images we have been working with are for the whole region of southern Africa. Even though the Zambia district data will only be a part of it, the rows and columns and X and Y would still have to match the whole southern Africa NDVI image. When each country office does its analysis, it can use the earlier WINDOW command to only window out its country of interest area from the region-wide or Africa-wide NDVI image.

2. From the earlier set of analysis, we have six monthly departure images (month NDVI value minus long term monthly normal) called SNOV91DF, SDEC91DF, …, SAPR91DF. We could extract the district level averages

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of the departures and take them to an MS-Excel file to graph them out. Keep in mind that departures below “-15” are usually indicative of severe vegetation loss areas. We have a vector file of Zambia districts, which we need to rasterize to the same dimensions of the NDVI southern Africa images. RUN ---

DATA ENTRY/INITIAL Output image: ZAMDISTC Image to copy parameters from: SNOV91DF (note all southern

Africa NDVI images have the same dimensions, so we can choose any

of them to copy parameters from). Say SNOVAV14 Click OK.

REFORMAT/Raster-Vector Conversion/POLYRAS Vector polygon file: ZAMDISTC Image file to be updated: ZAMDISTC Click OK.

Now we have the all the districts in Zambia in raster form ready to be used with the NDVI images. The simplest visual way to check departures is to create a group file within Idrisi listing all the six departure images and the district image. Then, when you click on a district with your cursor inquiry, you will get a box with values from all the six months. To do this RUN ---

EDIT and choose the “Image Group File” option File name: MONTHDEP Click OK. Then choose the six monthly departure images (SNOV91DF,

SDEC91DF, ….., SAPR92DF) and the ZAMDISTC image and “add” them to the group file, one by one.

DISPLAY ZAMDISTC and use the cursor inquiry option to check the NDVI monthly

departures on the box next to the image. You can actually do a screen capture (Fn-Prt Scr) on your

computer and open PAINT software (free software with Windows) and do a

EDIT/PASTE to look at the whole screen dump. Then you can actually draw an arrow from the district that you had your cursor inquiry button,

window out everything else except the image and the departures values box and save it as a bitmap file.

The way you would window out would be to use the EDIT/CUT option in PAINT and only cut out the image and the values box. Then you would open a new image (do not save the earlier image) and do another EDIT/PASTE. Now you can save this as a bitmap file and take it to your word document.

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1.9 Steps for IDRISI – EXCEL Conversion

1. The more organised way of doing this would be to calculate average departures for each district and take them to Excel. Then you could graph out the districts of your choice and put in a Word document or print out the graph directly. The steps for Idrisi-Excel conversion are as follows. RUN --

ANALYSIS/DATABASE QUERY/EXTRACT Feature definition image: ZAMDISTC Image to be processed: SNOV91DF Click on the “Average” option Output type, Values file: DNOV91DF

2. Do a similar operation for six monthly departure images. Now you will have six values file which are basically two columns (first column; district id, second column; average departures) of text showing the departures for each district.

Open EXCEL and use the FILE/OPEN option Go to the directory where the files are and type in DNOV91DF.VAL. You will get a “Text Import Wizard” dialox box open automatically in

excel Click on “next” and click on “space” as your delimiter. Click on “next” and “finish” and you will now have a two column excel

file showing district ids on the left and vegetation monthly departures for November on the right.

Do a similar operation and bring in DDEC91DF.VAL, DJAN92DF.VAL, …., DAPR92DF.VAL.

You will now have six excel files open. Do a EDIT/COPY of the second column of DDEC91DF and paste it to the third column on DNOV91DF. Do January in the fourth column and so on till April. We now have six monthly departure values across the excel table for six districts. Now we can graph out whichever district we want in Zambia for NDVI departures in 1991/92 growing season.

3. Examples: Let us take the district of Choma. This has an ID of 78. If you shade the six monthly values across the id 78 and then click on graph wizard icon, then INSERT/CHART/AS NEW SHEET. Click on “next” and you will be prompted for a series of option on how you want to graph this out. Go through, choose “Column” option, “next”, then number “6”. Then click on next, look at the preview and click next again. Put in the title as “Choma District”, the X axis as “Months” and the Y axis as “NDVI departures”. Remember that departure values of under -15 are indicative of severe crop loss areas. Similar graphs for other districts of interest could be done.

4. Summary and Notes: The Choma district graph gives a very good representation of the severity of the 1991/92 drought in that area. If you do it for other years, you may not find such a convincing graph. You may see

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one or two months showing much below normal, in which case it needs to be related to local knowledge on critical crop growth periods to see if the vegetation departure would have an adverse effect on crop production or not.

The above set of analysis resulted in three major outputs: Monthly images showing vegetation departures One synoptic image to represent the status of the entire growing season District/political boundary level graphs showing vegetation departures

on a monthly basis.

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2. Mapping Food Production Data

The next set of analysis would look at the mapping of food production tables on to district level maps. The analysis would convert the food production into calories (supply calories), and divide the sum total by the population’s annual demand calories and calculate the number of months of food availability in each district.

Objectives:

1) To map out district level food availability in months based on production and population

Step 1: Prepare Your Database

Here we only show you an example of a database. Use MS-Excel to open and view the file called MOZKCAL.XLS. This file shows the production of major cereals in each district in Mozambique. These were then converted to supply calories (convert each crop into calorie equivalent and add up all the cereals production). The total of KCAL is then divided by total annual demand of population of each district (demand based on three thresholds -- 1700 calories per day, 2200 calories, and 3050 calories). Based on the demand and supply calories, we could now calculate the number of months of production available in each district. Please refer to Dr Getachew Diriba (VAM, UN-WFP, Maputo, Mozambique) for further information on creating the excel spreadsheet and the calorie conversions.

Step 2: Exporting Excel file to IDRISI

We now need to bring this database into Idrisi and link it to a map of Mozambique districts. This would allow us to map the number of months of food availability for each district.

We need to make slight alterations to the excel spreadsheet so as to make it compatible to Idrisi. We first need to convert each column heading to one line and that too within 8 characters. Then we need to make sure that there are no duplicate names (two columns with same name). Once we make the necessary changes, basically all that should remain is the first line 8-character name of the column and the data in numbers immediately following it. THEN

FILE/SAVE AS click on the save as type and choose the DBF 4 (dBASE IV) option. Call the output file MOZFOOD and click OK. Close Excel and open Idrisi for windows.

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Step 3: Importing Excel file to IDRISI

DATA ENTRY/DATABASE WORKSHOP FILE/CONVERT XBASE TO ACCESS. Select an input file: Click on MOZFOOD Output file name: MOZFDAVL Click OK and it will give a warning message that there are

fields of non-integer type, etc. Click OK to ignore that.

Step 4: Mapping Database

Now you will have the data base converted to Idrisi format. If you look at the database, you can see the district names, ids, production of each crop, population, etc.

MODIFY/ADD FIELD and choose the Integer (2 Byte) option Name of the field: IDR_ID and click OK. You now have a new field with no values in it Click on += icon and type in the following after SET idr_id = idrisi (the field from excel that contains the idrisi id’s) then do the MODIFYADD FIELD again and this time choose

the Real (4 Byte) Name of the field: FDAV1700 and click ok Then click on the += icon again and type after SET FDAV1700 = AVMO1700 (the number of months of food

availability under the 1700 calorie per day assumption). And click OK. LINK/Assign Field Values to Image Feature definition image: MOZDIST (the district image with

the same ID values as those of the districts in the data base). Name of output image: AVMO1700 Feature definition field: IDR_ID A data field from current database: FDAV1700 and click OK.

Step 5: Desired Classes of Food Availability

Now you will have an image showing number of months of food availability. We will reclassify this into lesser number of categories to understand the output better.

DATABASE QUERY/RECLASS Input file: AVMO1700 Output file: AVMO17RC Title: Number of Months of Food Availability Reclass

Assign a new value of

to all values from

to just less than

comments

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0 0 .5 background1 .5 6 Less than six months of food availability2 6 12 Between six to twelve months food

availability3 12 18 12-18 months of food availability4 18 99 greater than 18 months of food availability

The output image will now give a very good idea of surplus and deficit producing regions. It would be of interest to combine this with other forms of data such as livestock ownership, access to markets, etc. to get an idea of the vulnerability of population in deficit production areas.

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3. Mapping Livestock Data at district level

The next set of steps will go into mapping livestock ownership at district level. The steps are applicable to any variable that is available in the form of district level data (for example, WFP assessment visited areas, population under 5, average land holding, etc.)

Objective1) To map out district level data on livestock. Specifically total number of cows

per thousand families

Step 1: Prepare Your Database

This has already been done for you. Just Open Excel and Click on File/Open LIVESTOK.XLS. This gives you the table of livestock in Mozambique. We will for this exercise, only map out total number of cows per 1000 families in each district. The calculations have already been done and the column COWPTHF has the values for cows per thousand families in each district. The data as it stands now is incomplete, but we will map out the data for districts where it is available.

Step 2: Exporting Database file

FILE/SAVE AS and click on the save as type and choose the DBF 4 (dBASE IV) option. Call the output file MOZLS and click OK. Close Excel and open Idrisi for windows.

Step 3: Importing Database file to IDRISI

DATA ENTRY/DATABASE WORKSHOP FILE/CONVERT XBASE TO ACCESS. Select an input file: Click on MOZLS Output file name: MZLSTOK Click OK and it will give a warning message that there are

fields of non-integer type, etc. Click OK to ignore that.

Step 4: Mapping Database in IDRISI

Now you will have the data base converted to Idrisi format. If you look at the database, you can see the district names, ids, different livestock numbers, number of families, etc. MODIFY/ADD FIELD and choose the Integer (2 Byte) option

Name of the field: IDR_ID and click OK. You now have a new field with no values in it Click on += icon and type in the following after SET idr_id = idrisi (the field from excel that contains the idrisi id’s)

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then do the MODIFYADD FIELD again and this time choose the Real (4 Byte)

Name of the field: TOTCOWS and click ok Then click on the += icon again and type after SET TOTCOWS = COWPTHF (number of cows per thousand

families). And click OK. LINK/Assign Field Values to Image Feature definition image: MOZDIST (the district image with

same ID values as those of the districts in the data base). Name of output image: COWPTHF Feature definition field: IDR_ID A data field from current database: TOTCOWS and click OK.

Step 5: Desired Intervals (Classes) of Mapped Information

Now you will have an image showing number of months of food availability. We will reclassify this into lesser number of categories to understand the output better.

DATABASE QUERY/RECLASS Input file: COWPTHF Output file: COWPTHFR Title: Number of Cows per Thousand Families Reclass

Assign a new value of

to all values from

to just less than

comments

0 0 .5 background/no data1 .5 10 Less than 10 cows per thousand

families2 10 100 Between 10-100 cows3 100 500 Between 100-500 cows4 500 9999 Greater than 500 cows

The output image will now give an idea of the livestock ownership among the population. As you can see, the data is incomplete in a lot of districts in the North. Any data collected at district level in a country can be mapped out by a similar process as to the one we used for mapping food production and total number of cows.

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4. Mapping point data: Price and health centre data mapping

The above two exercises (food production and cows) mapped out databases that are linked to polygons such as districts. The next two examples will deal with point data mapping. Price data though somewhat inconsistent across countries, is a very valuable source of food security information. It would be interesting to map out the points in terms of their price levels and changes if the data is available. It would also be very interesting to visually compare the price changes and the vegetation departures that we calculated before. If we find an area where the vegetation is showing poor crop conditions and the prices increase significantly, then we would need to be concerned about the food security of these areas.

Objective

1) To create a point file showing price changes in percentage across major markets in Mozambique.

Step 1: Prepare Database

We have data on 23 markets in Mozambique for which we have price data from 1991 to current on a weekly basis. We will just map out the changes in price between October of 1997 and December of 1997. Most of the manipulation of the data is in Excel or a data base and this exercise will cover the final step of mapping it out a set of points with some attribute information (in our case attribute information refers to price changes at each of those points).

Go to Excel File/Open XYZMKTS.XLS. As you can see this contains the latitude and longitude co-ordinates of the 24 markets for which we have price data on Mozambique along with their id’s and names.

Step 2: Exporting Data

We need to export this to Idrisi and map the points. Then we can add information to the points to reflect actual prices or price changes.

In Excel, delete the lat/long/id/names row. Also delete the column with the names

Then do File/Save as; File type: Text[tab delimited] and name it as XYZMKTS Exit Excel

The basic idea is to create a clean text file only with the three columns showing lat, long, and id’s. Nothing else should be there.

Step 3: Importing Database file

Go to Idrisi and Click on File/Import/Export option

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Import/Software specific options and choose the module XYZIDRIS Input ascii file with extension: XYZMKTS.TXT Output idrisi vector file: XYZMKTS Reference system: Lat/long Reference units: degrees Title: “Market Locations” and click OK.

Step 4: Mapping point data: Now you have a vector file of market locations

Analysis/Database Query/Database workshop File/Create Vector text layer Name of vector point file: XYZMKTS Name for output vector file and associated database:

PRCCHNG Click ok and you will get all the 24 vector id with 0’s next to

them for symbol The symbol number with 0’s is for specific font size, font type,

bold, etc. information. For the sake of this exercise, we will fill these two columns as follows;

IDR_ID pricchngSYM pricchngTEXT1 1 1392 1 1373 1 1194 1 111 5 1 1186 1 1007 1 No data8 1 1289 1 120

10 1 No data11 1 21712 1 16613 1 14014 1 No data15 1 18316 1 No data17 1 16618 1 16019 1 No data20 1 13321 1 16622 1 14023 1 No data24 1 233

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The figures in the 3rd column represent the December 1997 prices in these 24 markets as a percentage of September 1997 prices. After you enter the numbers, save and exit. DISPLAY MOZPROV vector file

Add Layer: XYZMKTS Add Layer: PRCCHNG

Now you will have a map showing price change percentage across the whole country.

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5. Food Systems Approach

5.1 The Concept

The food systems is a concept of identifying relatively homogeneous areas of economic activity that will result in reasonably similar coping mechanisms and exposure to risk factors. This system is essentially a form of baseline map that combines a variety of indicators (like agro-ecological zones (high lands, low and midlands, cropland use), river basin, coastal areas, arid lands, etc.) (see Diriba 1991 & 1995). The basic idea is that a drought problem within a country would have different manifestations in terms of vulnerability depending on which food systems it happens to be affecting. These systems should be used along with seasonal indicators such as vegetation loss, price changes, food production, etc.

There is an understanding though that each country’s food system needs to be thought out differently and the level of complexity (in terms of divisions within the country) needs to traded off with availability of data and analytical support.

Objective

1) To create a map of relatively homogeneous economic/physical activity zones

5.1 Database and Layer Information requirement

We will once again use the example of Mozambique to develop a first-cut food system across the whole country.

The six basic systems that will be created are as follows;1) River basin area2) Semi-arid/arid lands3) Coastal area4) Lowlands5) Midlands6) Highlands

5.2 Procedures to Map different layers

To create the above six, we need the following base layers;Map of riversMap of arid-lands based on local knowledge and NDVI long term dataMap of the coastlineElevation data

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Procedure 1: We will first look at creating the river basin areas

DISPLAY the image MAJRIVER (major rivers in Mozambique)1. DISTANCE on this image (this will take some time because of the size of

the image). Input file: MAJRIVER Output file: RIVDIST

Then Run ANALAYSIS/DATABASE QUERY Input file: RIVDIST Output file: RIVDIS10 Reclass as follows

Assign a new value of

to all values from

to just less than

comments

1 0 0.15 areas within 10-15 kms of a major river. The distance is calculated in degrees where one degree approximately works out to 100 kms

0 0.15 999 areas greater than 15 kms from river

Procedure 2: Now we will create the semi-arid land classification.

This has already been created for you based on two things; One is the local knowledge of the traditionally semi-arid areas and the other is using the long term variation in vegetation2.

DISPLAY the image ARID

Procedure 3: Combine images (rivers and aridlands):

We will now combine this with the river basins image to get two classes EDIT and

type in a new file name called ARID2 In the blank edit box, type in just 1 2 (space in between 1 and 2,

this basically will help assign an ID of 2 to all the semi-arid areas) Run ASSIGN Feature definition image: ARID Attribute values file: ARID2 Output image: ARID2

Run CROSSTAB First image: RIVBAS10 Second image: ARID2 Output cross classification image: TEMP

Run RECLASS on the TEMP input image and call the output TEMPRVAR

1 An entire rivers layer of Mozambique was taken and the major rivers were visually identified. Each rivers had a separate ID which we then reclassed to take only those ID’s that belonged to major rivers.2 Arid lands were created based on two things; One was the Variation in Vegetation values over the last 15 years and the other was the local knowledge of experts. These two were taken and using on-screen digitizing (refer earlier training notes on IDRISI on-screen digitizing) areas belonging to the arid/semi-arid lands were delineated.

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Reclass as follows

Assign a new value of

To all values from

to just less than

comments

0 0 2 background1 2 3 River basins2 3 4 Arid land1 4 5 where there is a conflict, 1:2, id 1

which is river basin dominates

Now you have an image that has an ID of 1 for river basin areas and ID of 2 for arid lands.

Procedure 4: create a coastal zone map based on the coastline and the distance from it. DISPLAY COASTLN3 image. This is the coast line of Mozambique

Run DISTANCE Input image: COASTLN Output image: TMPCDIS

The distance image calculates for both sides of the coastline.

Procedure 5: Masking

We will now need to mask out non-land areas in Mozambique and only have areas close to coast within the country. OVERLAY and choose the multiplication option

First image: TMPCDIS Second Image: MOZMASK Output image: COASDIST

Procedure 6:

We now need to run a reclass operation to assign all areas within 40 kms of the coast as belonging to the coastal food system. RECLASS with COASDIST as the input image

Call the output image: COAST3 Reclass as follows;

Assign a new value of

To all values from

to just less than

comments

3 0 0.40 areas within 40 kms of the coast. The distance is calculated in

3 The coast line image was created using a technique called edge-enhancement filter process. The easier way to do this would be take an NDVI image which has values for all of land and zero for the oceans and reclassify the oceans to have a value of 1 and everything else zeroes (use edit/assign to do this). Then run DISTANCE from this new image which would give you distance values from the oceans (because they are the only non-zero values in the image) and then you can use the reclass option to get areas only within 40 kms of the ocean.

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degrees where one degree approximately works out to 100 kms

0 0.40 999 areas greater than 40 kms from coast

Procedure 7:

Run Analysis\DBQ\Overlay: 1st image: coast3, 2nd image: Mozmask, output image: coast33

Procedure 8

We assign a value of three to the areas within 40 kms of the coast so as to be able to run CROSSTAB again with the earlier image (TEMP with 1 as river basin and 2 as arid) Run CROSSTAB

First image: TEMP Second image: COAST3 Output cross classification image: TEMP1

Run RECLASS on the TEMP1 input image and call the output TEMPRAC

Reclass as follows

Assign a new value of

to all values from

to just less than

comments

0 0 2 background1 2 3 River basins2 3 4 Arid land3 4 5 Coastal zone1 5 6 where there is a conflict, 1:3, id 1

which is river basin dominates over id 3 which is coastal zone

3 6 7 where there is a conflict, 2:3, id 3 which is coastal zone dominates over id 2 which is arid lands

Now you have an image that has an ID of 1 for river basin areas, ID of 2 for arid lands and ID of 3 for coastal zones.

Procedure 9: Create Lowlands, midlands & Highlands

Reclass: We will now create a map of three elevation classes; one from 0-200 meters (lowlands), 200-1000 meters (plan alto midlands) and > 1000 meters (highlands).

We will give them id’s of 4, 5, and 6 respectively and

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DISPLAY ELEVMOZ and use the cursor inquiry option to check out the values.

Run RECLASS with ELEVMOZ as the input image Output image: ELEV45 Reclass as follows;

Assign a new value of to all values from

to just less than

comments

0 0 1 Oceans4 1 200 Lowlands5 200 1000 Plan alto midlands6 1000 9999 Plan alto highlands

Procedure 10: Combine….

Run CROSSTAB with TEMPRAC as the first image and ELEV45 as the second image. Call the output image TEMP2.

Display TEMP2 and choose to display legends along with the image. Check the different legend categories (note id 1-riverbasins, id 2-arid, id 3- coasts id 4-lowlands, id 5-midlands, and id 6-highlands).

Run EDIT/ASSIGN operation to assign new id’s to the 15 categories output by the cross tab operation to get our essential 6 classes in food systems.

Run EDIT and ask to create a file called FSYST In the blank box enter as follows; (the text within parenthesis are

just comments and not to be entered in the edit box)1 0 (background)2 1 (river basins)3 2 (arid lands)4 3 (coastal zone)5 4 (lowlands)6 1 (1:4, in which river basin dominates over lowlands)7 2 (2:4 in which arid land dominates over lowlands) 8 3 (3:4 in which coasts dominate lowlands)9 5 (midlands)10 1 (1:5 in which river basins dominate midlands)11 2 (2:5 in which arid land dominates midlands)12 3 (3:5 in which coasts dominate midlands)13 6 (highlands)14 1 (1:6 in which river basins dominate highlands)15 2 (2:6 in which arid lands dominate highlands)

The output image will have six categories indicating six different food systems within the country.

Procedure 10

Open FILE./DOCUMENT for FOODSYST image and click on legend categories option. Name the legends as follows

1 River Basins

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2 Arid Lands 3 Coastal areas 4 Lowlands 5 Midlands 6 Highlands

DISPLAY FOODSYST image with FOODSYS6 palette.

Food Systems in Mozambique

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6. Flood Risk Mapping

6.1 General Guide

In years of excess rainfall, it is important to know areas that lie within the flood zone, so that appropriate mitigation and relief strategies can be worked out. We have a map of the major rivers in Mozambique and based on elevation around the rivers, we could derive the flood risk zones.

Objective

1) To create a map of areas at risk of flooding in Mozambique

Procedure: DISPLAY the image called MAJRIVER

This is an image of major rivers across Mozambique.

DISPLAY the elevation image called ELEVMOZ

This gives you elevation values in meters across the country.

Use the Cursor Inquiry option to check values across the country

The idea behind the analysis is as follows; we need to find out all areas near to major rivers that are within a certain flooding level of the river. For example, if one part of a major river is at an elevation 300 meters and the flood level is 5 meter rise, all areas within 305 meter elevation near this part of the river would get flooded.

To do this in a GIS, we need to allocate the river elevation values to the nearby pixels and then subtract that elevation (the nearby area now has the river elevation) from the actual elevation value. If it is within, say 5 meters, then that area is under risk of floods.

We first need to create a boolean image of the rivers and then multiply that with the elevation image to get the river elevation at each pixel.

RECLASS MAJRIVER Input image: MAJRIVER Output image: RIVRBOOL Reclass as follows;

Assign a new value of

To all values ranging from

To just less than Comments

0 0 1 Background1 1 999 All rivers get an id of 1;

boolean image ANALYSIS/DATABASE QUERY/OVERLAY

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Choose the Multiply option First image: RIVRBOOL Second image: ELEVMOZ Output image: RIVRELEV

Now we have an image of actual elevation of the rivers along their course. We now will assign each pixel outside the rivers, the elevation value of the part of the river nearest to it. We need two things for the software to do this. One is a distance image from the rivers and the other is the actual river image with elevations for each pixel belonging to rivers.

Running DISTANCE to this rather large image of Mozambique is a time consuming process. We already have the output image in our directories.

DISPLAY RIVRDIST with the idris256 palette.

This shows the distance of each pixel from its nearest river.

Now we need to use this distance image to allocate all pixels in the image the elevation value of the nearest river elevation. Once again, running the module ALLOCATE4 (which allocates each pixel the nearest river elevation) is a time consuming process. We have provided that image to you.

Analysis\ Distance Operators \Distance

Feature Image: ______________ (Zambelv)Output Image: ______________ (Zambdist)Value Units: ________________ (none)Title: ______________________ (none)

Analysis\ Distance Operators \Allocate

Distance Image: _________ (Zambdist)Target Image: ___________ (Zambelv)Output Image: ___________ (Zamballo)

DISPLAY RIVRALLO image with the idris256 palette. Use the cursor inquiry option to check the values along the rivers. You can see that there is an elevation value in each pixel equal to the same elevation as the nearest river elevation.

We now have river elevations and also the actual elevation. Suppose we had a flood emergency with an expected rise in river level of 5 meters; we would expect all the pixels near the river that are less than 5 metre height as compared to the river height to be flooded.

Run OVERLAY and choose the subtract option.

4 Extended use of ALLOCATE function: allocate model can be used to calculate distance to school, health facilities, food distribution points. You could also extract total population from capture zone.

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First image: ELEVMOZ Second image: RIVRALLO Output image: DIFFELEV

Use the cursor inquiry option to check the values across this image. We would expect all areas under 5 metre difference to get flooded. We also have to satisfy another condition. Areas that are far away from rivers may not get flooded. To be really conservative, let’s take the following criteria for flooding;a) areas less than 5 metre elevation andb) within 15 kms of the river.

To satisfy the first criteria, we need to create a boolean image of DIFFELEV showing areas below 5 meters as one and everything above as zero.

RECLASS the input image DIFFELEV Output image: LOWELEV Reclass as follows;

Assign a new value of

To all values ranging from

To just less than

Comments

1 -999 6 Less than 5 meter elevation from the river elevation

0 6 999 Areas that are higher than 5 meter from river level

The above boolean image satisfies the low elevation boolean criteria (criteria a)We have a distance from rivers image. We need to create a boolean of all areas within 15 kms of the river to satisfy criteria b.

Run RECLASS on the input image RIVRDIST Call the output image CLSRIVR Reclass as follows;

Assign a new value of

To all values ranging from

To just less than

Comments

1 0 0.15 Distance is calculated in degerees and each degeree of distance corresponds to approximately 100 kms. So .15 = 15 kms

0 0.15 999 Areas greater than 15 kms from the rivers

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We now have two boolean images showing low elevations (LOWELEV) and areas close to rivers (CLSRIVR).

Run OVERLAY and choose the multiply optionFirst image: LOWELEVSecond image: CLSRIVROutput imge: FLDRISK

This output image gives us flood risk areas in Mozambique.

6.2 Zambezi River Flood Risk Analysis

The objective to analyse flood risk on Zamezi and Shire rivers for 1998 season

Working director: d:\Moz-GIS3\Zambezi

Basic Layers: Zambezi/ Mozriver/ Elevmoz

We need to separate Zambezi river (id 18) and Shire river (id 3) from the rest of rivers in order to run flood risk analysis for the two rivers. We need to run Edit/Assign to perform this function.

Data entry\ Edit Filename: Zambezi and then hit OK IDRISI Text editor window pops up, type

18=13=2, save and exit

Data entry\assign Feature definition image: Mozriver Attribute value file: Zambezi

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Output file: Zambezi Analysis \DB Querry\overlay

First image: Zambezi Second image: elevmoz use MULTIPLY option Output image: Zambelv

Analysis\distance operators\distance

Feature image: Zambelv Output image: Zambdist value units: ----- Title: --------

Analysis\distance operators\Allocate

Distance image: Zambdist Target image: Zambelv Output image: Zamballo

Analysis/DBQ/Overlay

First image: Zambelv Second image Zamball (use subtract option) Output image: Zambdiff

Analysis\DBQ\Reclass (reclassing Zambezi River Elevation)

Analysis/DBQ/Reclass Input image = Zambelv Output image = Zamblow1 -999= 4 (assumption 2, 3, 4 meter water rise)0 4 999

Analysis\DBQ\Reclass (reclassing Distance from Zambezi River)

Assumption distance from Zambezi river within 10 km from the river

Input image: Zambdist Output image: Zambcls

1 0 0.100 0.10 999

Calculating Flood Risk areas around Zambezi River: Analysis/DBQ/overlay

First image: Zamblow 2nd image: Zambcls {use multiply option} Output image: Zambfld

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Composition file: Zambfld, palette: Zambclr

6.3 Vector/ raster transformation

Exporting layers from MapInfo to IDRISI In Maputo Info, go to TABLE\EXPORT\SAVE AS:

File name MozriverSave as: MIF file

Ensure that vector objects are lines and polylines (in the case of receives).

In IDRISI

File\Import\Export\Software Format MIFIDRIS\MIF to IDRISI

Input file name: Output vector file Output reference System: (Lat long) Output reference unit: (degrees)

Rasterizing the vector image Reformat\Raster/vector conversion

PointrasLineras choose appropriate formatPolyras

Now we have Mozriver (vector) convert to Mozriver (Raster) images

6.3 Combining Lines and polygons in MapInfo

Combining Objects/Lines/Polygons

Activity 1 (e.g.): Combining different sections of Zambezi River into singleriver layer

Go to MapInfo Open a table (image that consists objects to be combined Go to MAP\Layer control:

Turn the layer " editable" Ensure that the objects to be combine are of the same type (lines, points,

polygons) Select objects to be combined

Click on the object(s) to be combined Go to Object\combine\ -- four options --- hit ok.

That creates a single object

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6.4 Procedures for Importing Tables (data) to MapInfo

Prepare your data in a designated software (excel, access, dbf, etc). This example concentrates on the use of Excel. After you entered the data:

Go to File Save as

File name File type: (Text (Tab delimited *.txt)

Close the file Go to MapInfo

File Open Table Specify location of the file (directory); Specify file type (Delimited ASCII) Specify the file name

Open an existing MapInfo layer (and table) to which current data be mapped to (updated with).

Then Go to TableUpdate coloumn

Table to update: existing MapInfo Table (e.g. District) Coloumn to update: Add New Temporary Coloumn Get Value from: the newly imported table (e.g. EMOP) Calculate: Value Of: :Choose the coloumn from which to update data,

e.g.colomn 5 Then GO to JOIN

Where: District (existing map layer) Matches: Coloumn that consists the matching ID

Then you need to do other intermediate procedure before you map the data

Go to File Save copy as : Take the District layer as NEWFILE. This will ensure that

you keep the original layer from the currently updated layer. This will allow you to open the new Table (image). Then you could do with Thematic Map or any other facility available.

7. IDRISI-MAPINFO STEP BY STEP CONVERSION PROCEDURE

This document describes the steps in converting GIS data from IDRISI to MapInfo and back.

In both conversion directions, keep in mind the big picture:

you can only convert IDRISI vector files into MapInfo (therefore, if you have raster data you want to convert, you must vectorise it first, and

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unlike raster images where the attribute data is in the same file as the geographic data (the .img file), vector coverages have the geograpic data stored in a separate file from the attribute data (assuming there is indeed a data set associated with the vector coverage). The vector geographic file in IDRISI has a .vec extension, and the associated database has a .mdb extension (this database can be viewed in IDIRSI by using DATABASE WORKSHOP. The point is this: we must independently import and export the geographic and database files.

In the following example, geographic and database files can be identified by their extensions. Any pulls down menus are written in CAPITOL LETTERS and will be followed by hyphens for any submenus. Example:

FILE—IMPORT/EXPORT—

EXPORT--SOFTWARE SPECIFIC FORMATS—MIFIDRIS.

The example data can be found in the IDRISI exercise data set, and will include the Ethiopia data set on Awrajas.

7.1 IDRISI TO MAPINFO

Open both the awrajas.vec coverage and the ethiopia.mdb database using DISPLAY LAUNCHER and DATABASE WORKSHOP respectively.

Let’s first export awrajas.vec (the geographic data file) into the Mapinfo import format

FILE—IMPORT/EXPORT—EXPORT--SOFTWARE SPECIFIC FORMATS—MIFIDRIS

Specify that you want to go from idrisi to MIF select your input vector file (awrajas.vec) type output file name (e.g. awraj) specify the Mapinfo version number you are working with (e.g. 41) (note:

if the version number has a decimal (e.g. 4.1) you must write this without the decimal point)

type a field name for the ID column in the soon to be created mapinfo file specify the field width (the # of characters) for this field (this should be at

least as long as the longest number in the vector ids specify the number of decimal places in the vector ids (if it is only an

integer number, you can specify 0. click OK, and IDRISI will begin exporting the .vec file into a file type that

Mapinfo can understand, and thus we will use from within Mapinfo to import it. This file type is called the Mapinfo Interchange Format, and has a .mif extension.

Now lets export the IDRISI database file into a file type that Mapinfo will understand.

With DATABASE WORKSHOP open and the Ethiopia.mdb database on the screen, go to FILE—IMPORT/EXPORT EXTERNAL ASCII FILE.

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specify that you want to export current database to ASCII and click ok in the second dialogue box, specify a name for the output file (e.g. ethiop) specify a code number for null characters (places on the database that do

not have data (0 is the default) specify the text formatting type to be comma deliminated and click ok. now we have created an ASCII text file (ethiop.txt) that Mapinfo can read.

Now open Mapinfo, and lets first import the geographic file (awraj.mif) go to TABLE—IMPORT

specify the director and name of the awraj.mif file, and save as file type Mapinfo (this will create a file with a .TAB extension). Call our file awraj.tab, and click ok.

now we can display awraj.tab go to FILE—OPEN TABLE and specify that you want to open awraj.tab

click ok, and the awrajas vector file will appear on the screen. You might have to change the zoom factor in order to see the whole coverage.

Now let’s import the database file ethiop.txt go to FILE—OPEN TABLE and specify that you want to open a file of type

external ASCII. specify that it is comma deliminated and accept the other defaults Mapinfo will display the newly created ethiop.tab database Now we need to link theses two files: the geographic awraj.tab, and the

associated database ethiop.tab go to QUERY—SQL SELECT

leave the selected columns as is (with a * to designate all columns) put you cursor in the from tables box, and then click on the tables down

arrow to select first the geographic file awraj and then also add in there the database file ethiop. It should look like: awraj, ethiop

now put your cursor n the where condition box and click on the tables down arrow. Select the awraj.awrajas. Then type an = sign. Then go back to the columns down arrow and select the ethiop._col1.

now enter a new name in the “into Table Named” box. Lets call this awreth.

Mapinfo will join these two databases and the associated geographic file into a single file with the name awreth.tab. The database is automatically displayed on the screen.

To display the geographic file, go to WINDOW—NEW MAP WINDOW and specify the map table to be awreth.

As a last step we need to save our joined table go to FILE—SAVE COPY AS and specify the table to be awreth and click save

as. Now accept the default information (“awreth.tab” and Mapinfo file type) and click save

Now we have successfully imported and linked files into Mapinfo.

7.2 MapInfo to IDRISI

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From MapInfo, we are going to have to separately export a .tab file as a geographic file (.mif) and as a database file (.dbf) for import into IDRISI.

In Mapinfo, open any .tab file (for this example we will use awreth.tab) Let’s first export the geographic data

go to TABLE—EXPORT and specify that you want to export the awreth.tab into the .mif format. Put this file in the c:\exercise directory

Now let’s export the database file go to TABLE—EXPORT and specify the you want to export awreth into

the file type of dbase DBF. Again put this in the c:\exercise directory Now open IDRISI and lets first import the geographic file awreth.mif

go to FILE—IMPORT/EXPORT—IMPORT—SOFTWARE SPECIFIC FORMATS—MIFIDRIS

specify mif to idrisi; the input file is awreth; the output file as awrvect; the output reference systems as lat/long (this came from looking in the table maintenance of Mapinfo); the output reference units as degrees; and the output unit distance as 1 (this is pretty much always the case); specify “regions” as the feature to import (if the map had points or lines we could have selected them as such); click ok

now we can go to DISPLAY LAUNCHER and view our awrvect.vec file in IDRISI vector format.

Now let’s import the database file awreth.dbf go to DATABASE WORKSHOP—FILE—CONVERT XBASE TO ACCESS

specify that you want to convert awreth.dbf and lets call the output awrdata. This will create a file called awrdata.mdb

Now you have both a .vec and a .mdb in IDRIS format. You can now link these files as you would normally do in IDRISI.

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8. General Guide: Area, Extract, formatting

I was trying to modify legend of a map, i.e. to put the legend in Portuguese. In order not to tamper with the original map I saved the composition under different name. Then I started modifying the component through the normal procedure. I saved the composition file (this is foodsys file). Then I exited so that I could save changes to the legend. I hit “yes” but then the reply was “access denied”. It sounds brutal cia. I went through this no less than 100 times with exaggeration (I mean at least 20 times). What went wrong? Please help

8.1 Changing the legend:

Go to FILE/FILE MAINTANENCE Click on an image, (e.g FOOSYST) file and then click on the COPY

button. Call the copy file, say, FSYSPOR (Food system in Portuguese) Hit CANCEL

Then go to FILE/DOCUMENT and put in the name of the image file FSYSPOR Change the TITLE and then hit the legend categories button on the right

bottom. The type in the Portuguese names for the 1,2,3,4 and 5 according to the same order in

our earlier English image. Leave O blank. Say OK and OK again and get out.

Now display the FSYSPOR image with the same legend as the FOODSYST (the legend file is called foodsyst).

Just go to properties and double click on the legend box and pick out a similar named legend.

Add vector file and add the MOZDIST with WHTPOLY symbol file.

Add vector file again and add MOZPROV (or is it PROVINCE) with PROV as the symbol file

Then follow the steps I have outlined below to get the Portuguese text stuff (like we did for the flood risk zone you have on the wall) on the image also.

8.2 How to put Text into an image

Objective: to add source, explanation about the map:

Go to DATA ENTRY\EDIT and click on “Other file in data directory” (last but one option).

Give the file name, say FSYST.TXT then you will get a blank box where you can type in ‘text image’, e.g. Portuguese or English or whatever you want. Type the stuff in.

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Then hit enter twice or thrice and put WFP, VAM Mozambique there (the source stuff you wanted). This way you will have the text explaining the image and also the source stuff in the same file.

Then go to the FSYSTPOR image you are looking at. Go to PROPERTIES in the composer.

Click on MODIFY MAP COMPONENTS. Click on TEXT FRAME. TEXT FRAME visible and then enter the name of the text file you just

created (FSYST.txt). Choose the font and the size and the colour you want for the text

characters. Click on OK and then OK. You have your text on the screen now. You can click on it, resize it and also move it about freely across the

image box.

8.3 Reclass

We managed running RECLASS (Analysis\DBQ\Reclass). Through the RECLASS exercise, we discovered the notion that the flood risk assumption of the elevation classes may be too high. We wanted to work with the following classes:0-20, 20-50, 50-100 meters. The classes as per the above seems very reasonable on the ground. We wanted to know how we could redo the flood risk map.

Two things we need for this. One is to be close to rivers, the other is to be within the heights we want. I think there is already an image there showing areas close to river. I think you can use RIVBASIN (major river basins), or NEARRIVR or CLSRIVR (I am guessing the names here).

You need to take MOZELEV and then run re-class on it as follows:

Assign a new value of for values ranging from to just less than 0 -9999 01 0 202 20 503 50 1000 100 9999

Call the output image what you want (NEWFLDR).

In this image, value 1 would be first level flood risk, value 2would be second level Flood risk and value 3 would be third level flood risk.

Assign a new value of to all values ranging from to just less than 0 0 1

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1 1 20 2 99

Call the output image NFLDRZ1 (New flood risk zone 1). Do similar things so that only areas that show up with 2 (fld risk zone 2) and areas that show up with 3 form two separate images.

Now you have three boolean (0,1) images for each of the three flood risk zones.

Now run EXTRACT with MOZDIST as the feature definition image and NFLDRZ1 as the image to be processed. Choose the TOTAL option and to save it as value file DISTFLD1. Analysis\DBQ\Extract

Feature definition image: Mozdist Image to be processed: NFLDR1, etc) --- choose “total” option Output type (Value or tabular output):

Do the same for flood zone 2 and 3 (NFLDRZ2 and NFLDRZ3).

Now you have three values file. Use EDIT to look at the value file, say DISTFLD1. The numbers in the left are the Idrisi ID for the district, and the numbers on the right are the areas under the flood risk zone 1. The other two values file will have the same meaning with the left column as district id’s and the right as the flood risk zone area.

Then go into EXCEL. Run \File \open and ask to open DISTFLD1.VAL. This will give you the option of importing a text file, \Ask for delimited as the option and space as the field separator. This will bring in the idrisi values file as two columns into excel. Do the same for flood risk 2 and 3 values files.

Now you will have three excel files which you can copy and paste onto one file. You have a database of flood risk zones in each district.

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Monthly Analysis of NDVI Images (Regional)

1. One would normally receive monthly NDVI images from Rome (Sa+3 letter month + year + M.img), e.g. SAFEB99M.IMG. Rename this image to read as

S+3 letter month + year + M.img.

Note that S is standard pre-fix for all south Africa images;

2. Run OVERLAY procedure on IDRISI as follows

1st image S+3 letter month + year + M.img2nd image S+3 letter month + av14.imgOutput image S+3 letter month + year + df.imgCLICK on first – second option.

3. Run Reclass procedure as follows:

Type of file to reclass use Image optionClassification types use file mode optionInput file S+3 letter month + year + df.imgOutput file S+3 letter month + year + R.imgReclass file name NDVICLR

4. Legend categories: Go to File \document \ file name \ legend categories input the following

1. Much Below Normal2. Below Normal3. Normal to Above Normal

5. Title: Go to Properties \ modify map composition \ title and type the following

Southern Africa: Mean NDVI Image for --- Month ---- Year Use Times New Roman 12, Bold, Italic and underlined

6. Adding Country layer to the image:Vector file to display CountryhSymbol file user defined (thkblak)

9. Notes for Access to and from GIS

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9.1 Access—IDRISI—Access

A. The IDRISI database workshop uses Microsoft Access directly. Thus one can move back and forth from IDRISI to Access without any import or export procedures.

B. One useful application is to extract data from a map into the IDRISI database (such as rainfall, NDVI, numbers of health clinics, etc.), and then use that database in Access.

1. Example of extracting NDVI averages into a table within IDRISI and then opening the table in Access.

a. Within IDRISI, have MJAN91MM.IMG, EPA2.VEC, AGPROD.MDB

b. Open all files within IDRISIc. We want to extract the average NDVI value per EPA

(extension planning area) and put that information into a table which can be read by Access

d. The extraction process requires 2 images: the feature definition image (an image of EPA boundaries) and the image to be processed (the NDVI image). We have the NDVI image, but only have the EPA boundaries in vector format. Therefore, need to convert the vector EPA map into raster.

e. Go to REFORMAT-RASTER/VECTOR CONVERSION-POLYRAS

f. Put in EPA2 for the vector to be converted, and EPA2 as the new raster image; click ok

g. When asked if you want to initialize a new EPA2 image click yes.

h. Copy the parameters (mix/max X and Y, rows and columns) from the NDVI image

i. Now you have a raster image of EPA boundariesj. Within DATABASE WORKSHOP open database called

AGPROD.MDB. Add field called AVGNDVI and select real as the datatype.

k. On the pull down menu, choose LINK-EXTRACT STATISTICAL SUMMARY FROM IMAGE. Enter EPA2 as the feature definition image and MJAN91MM as the image to be processed. IDR_ID is the linking field and AVGNDVI is the field to accept the extracted data values. For the summary statistic, select average. Click OK

l. Now you have the average NDVI value per EPA. Save database as NAGPROD.

m. You can now open this database in Access (if the database is in an older version of Access, you can convert it if you want).

9.2 To MapInfo

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Let’s first export epa.vec (the geographic data file) into the Mapinfo import format

FILE—IMPORT/EXPORT—EXPORT--SOFTWARE SPECIFIC FORMATS—MIFIDRIS

Specify that you want to go from idrisi to MIF select your input vector file (epa.vec) type output file name (e.g. epa) specify the Mapinfo version number you are working with (e.g. 41) (note:

if the version number has a decimal (e.g. 4.1) you must write this without the decimal point)

type a field name for the ID column in the soon to be created mapinfo file (e.g. “ID”).

specify the field width (the # of characters) for this field (this should be at least as long as the longest number in the vector ids

specify the number of decimal places in the vector ids (if it is only an integer number, you can specify 0.

click OK, and IDRISI will begin exporting the .vec file into a file type that Mapinfo can understand, and thus we will use from within Mapinfo to import it. This file type is called the Mapinfo Interchange Format, and has a .mif extension.

Now open Mapinfo, and lets first import the geographic file (epa.mif) go to TABLE—IMPORT specify the director and name of the epa.mif file, and save as file type Mapinfo

(this will create a file with a .TAB extension). Call our file awraj.tab, and click ok.

now we can display epa.tab go to FILE—OPEN TABLE and specify that you want to open epa.tab click ok, and the epa vector file will appear on the screen. You might have to

change the zoom factor in order to see the whole coverage.

The table associated with epa.vec was called agprod.mdb. MapInfo can import this table directly. Go to FILE--OPEN TABLE. For file type, specify Microsoft Access Database, and select Agprod.mdb to be opened.

Now we need to link theses two files in MapInfo: the geographic epa.tab, and the associated database ethiop.tab

go to QUERY—SQL SELECT leave the selected columns as is (with a * to designate all columns) put you cursor in the from tables box, and then click on the tables down arrow to

select first the geographic file epa and then also add in there the database file agprod. It should look like: epa, agprod

now put your cursor n the where condition box and click on the columns down arrow. Select the epa.id. Then type an = sign. Then go back to the columns down arrow and select the agprod.idr_id.

now enter a new name in the “into Table Named” box. Mapinfo will join these two databases and the associated geographic file into a

single file with the name you specify. This file will have a .tab extension. The database is automatically displayed on the screen. To display the geographic file,

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go to WINDOW—NEW MAP WINDOW and specify the map table to be your new map.

As a last step we need to save our joined table go to FILE—SAVE COPY AS and specify the table to be savedawreth and click

save as.

9.3 Notes on Plotting Point Data in GIS

These notes discuss two techniques for entering point data into a GIS layer. This is process is distinct from merely drawing points or lines on a map in that drawn objects are not georeferenced. The advantages of having georeferenced objects are that you can link the point map to a table of data and you can conduct spatial analysis with the georeferenced points.

Within IDRISI, there are two ways to enter point data:

1) From Tabular Data Using a module called XYZIDRIS

You can import any point data that is in the form of X Y Z. The X is the latitude, the Y is the longitude, and the Z is the attribute (or feature identifier)5. Typically this data can come from recordings of a global positioning system (GPS). XYZIDRIS expects an ASCII text file of points that are in X,Y,Z format. The X, Y and Z values for each point should be on a separate line with a carriage return and line feed at the end, and should be separated by spaces. This file can be from a spreadsheet/database (e.g. Excel or Access), or you can create a text file in IDRISI using the EDIT module.

XYZIDRIS is located in File<Import/Export<Import<Software Specific Formats<XYZIDRIS. XYZIDRIS first requires the name of the input ASCII file (with extension) and a name for the output vector file. Then you must specify the output parameters. Click on the down arrow next to the input boxes for reference system and reference units for a list of choices or type them in manually. Then set your unit distance. The default is set to 1. Finally, enter a title. When finished you can view the points by displaying the vector file.

2) On-screen Digitising

With the cursor on the map, the location of the cursor is continuously displayed in the lower status bar of IDRISI. If you know the location of a point of interest, or if you just want to estimate the location (such as over a known city), you can use the on-screen digitizing module in IDRISI to point with your mouse and click to add new points (you can as well create lines and polygons).

In order to digitize on screen, the map window you wish to digitize must have focus. Clicking on the DIGITIZE button (shaped like a cross in a circle) on the toolbar will bring up the dialog box. You must first enter a name for the vector file to be created. Then you must specify the type of feature to be digitized

5 This example is for the lat/long reference system. If you were using the UTM system, you would use the appropriate units, such as meters.

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(point, line or polygon). You are also required to enter the feature ID (identifier) to be used for the first feature that will be digitized. You may enter any positive integer value here. In addition, if the vector layer type is a point layer, you will need to enter the step value (meaning, after each consecutive point digitized, should IDRISI automatically increase the feature ID value). Finally, give the vector file a title. Press OK and you may begin digitizing.

To digitize, use the left mouse button to identify points that define the position, course or boundary of a feature. You will notice it being formed as you digitize. To finish the feature (or the current sequence of points in the case of point features), click the right mouse button.

Note very importantly that many of the interactive screen options will become inactive (and will thus appear grey on the toolbar) while you are digitizing. To restore them, you must terminate the feature you are working upon by clicking the right mouse button while it is on the map window concerned.

To delete an entire feature, you can click on the DELETE button to the immediate right of the DIGITIZE button). If you are currently digitizing a feature, this will cause it to be erased from the screen and be deleted from the vector layer data file. If you are not currently digitizing a feature, but have not yet closed the vector layer data file (see below), clicking this button will have the effect of deleting the most recently digitized feature from that file.

Once you have finished a feature by clicking the right mouse button, you can continue to add further features to the same vector layer data file. Make sure that the map window concerned has focus and then click on the DIGITIZE button on the toolbar. This dialog box requires that you specify the feature ID of the new feature, and step value if it is a point layer. You can then start digitizing again once you press OK.

As you digitize features, they will each in turn be added to the vector layer data file. In order to close this file, and stop the process of adding features to it, simply click on the SAVE button (it looks like an arrow bent downwards). If you do this, the next time you click on the DIGITIZE button, you will need to enter the name of a new vector layer in which to save the data.

With either of these techniques, the spatial map created can be linked to a database that has other attributes you want to associate with the point file. You would use the feature ID value as the linking value to the database.

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