lab 10: risk analysis (suitability mapping) overviewesa.snre.umich.edu/classes/gis777/lab10.pdf ·...

Lab 10: Risk Analysis (Suitability Mapping)

Overview:

In this week's lab you will identify areas within Webster Township that are most vulnerable to surface and groundwater contamination by conducting a risk analysis with raster data. You will create a contamination risk index based on three variables:

• Soil texture: based on percentage of sand in the soil, partially determines the rate of percolation of water into the groundwater.

• Elevation: variability is used as a surrogate for slope steepness, which affects the rate of lateral movement (we'll see better ways to calculate slope later).

• Nearness to water: determines how much movement is required to get the water into surface water bodies.

All of these variables are spatially continuous (i.e., fields) and determine how likely water contaminated by surface processes at a site will reach the hydrologic system.

The method you will use involves performing a weighted raster overlay that combines multiple layers and creates a composite risk score. For more information, go to Help menu and access Spatial Analysis toolbox/ Overlay toolset/“Understanding overlay analysis”.

The input datasets for this lab are the same soils, land-use, and streams feature classes as those you used in Lab 8. The source of the data is the Southeastern Michigan Council of Governments (http://www.semcog.org/MapCatalog.aspx). In addition, there is a digital elevation model (raster) from the Michigan Center for Geographic Information’s Data Library (http://www.mcgi.state.mi.us/mgdl/).

You will use these data to create grids that represent three criteria:

• An index of soil texture: you will use the soils feature class to create a grid that represents the percent sand (a measure of texture) for each cell.

• An index of elevation variability: from the elevation data you will create a grid that indexes the variation in elevation surrounding a cell.

• An index of the nearness to water body features: using the land-use and streams feature classes you will create grids that index the proximity (i.e., the inverse of distance) to important surface water features (open water, wetlands, and streams).

All of these grids will be adjusted to a standard 0-100 range of values and then an overall weighted average index will be created. You will create, manipulate, and combine raster datasets (grids) using ArcGIS Desktop's Spatial Analyst Extension.

**Important: throughout the exercise, you should view each of the datasets, look-up tables, and grids you create in ArcMap or ArcCatalog to be sure that their patterns conform with your expectations. This is a good way for you to evaluate (a) your understanding of the processes used to create the grids, and/or (b) the successful completion of each step as you proceed.

Lab 10: Risk Analysis (Suitability Mapping) GSS777 - Principles of Geographic Information Systems, Winter 2014

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Learning Objectives:

� To understand raster-based software operations for performing analysis � To understand raster overlay � To work through an example of the weighting and rating of risk analysis � To explore different ways of displaying raster data � To be able to read and create flowcharts illustrating data processing steps

To be submitted:

1. (10 pts) A write-up (up to 500 words) answering the questions throughout the lab.

2. (5 pts) A map (8.5” x 11”) of contamination vulnerability as directed within the lab.

Procedure:

0. Open ArcMap and add the layers from the geodatabase in the Lab10 data package: soils, streams, landuse and websterdem. Explore your data.

1. Activating Spatial Analysis tools: We will be using Spatial Analyst extension tools to analyze the data. First, it is required to activate the extension.

• To activate the tool, go to the Customize menu and select Extensions, and check Spatial Analyst. Close the window.

1.1. Setting your workspace:

The following steps are important to guarantee that the output files are saved in your directory. Also, you will set the extent and resolution of output files in order to ensure that all the grids you make match up with each other. Go to the Geoprocessing menu and select Environments. The Environment Settings window will open.

You are going to modify the following fields: • Workspace: For the fields “Current Workspace” and “Scratch Workspace”, select your working

directory.

Note: If you are using computers in University labs or on virtual sites, you will have to do this every time you log into a new computer. Otherwise, you will get an error message when you try to run one of the Spatial Analyst tools.


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• Processing Extent: For the option Extent, select “Same as layer landuse”. This extent will be applied to all grids created unless changed (Note: you do not have to set this for each new grid with the same extent or for analyses where it is not important to keep the extent uniform among the outputs). For the option “Snap to raster” select the digital elevation model (websterdem). This option allows you to align the output layers to match the snap raster layer.

• Raster Analysis: Indicate that the “Cell size” is the same as websterdem

2. Data preparation:

2.1. Soil texture:

For the purposes of this project, you will be converting the three vector layers into raster format. You will begin by creating a grid based on soil type (percentage sand) to assess how quickly water percolates through the ground at each location.

Open the Soils attribute table and examine the different fields. We will be using the SURFTEX field to determine soil type, but notice that this field contains nominal data. We need to translate these soil type names into numbers that represent percentage of sand in that given soil type. This information is in the soil_lut table. You will need to join the table of the soil feature class with the soil_lut table.

Note: For each step, you will find a flow chart of the process you will perform. When performing GIS analyses it is a good idea to document every step you take, especially those that result in a new dataset. Illustrating the steps using a flow chart can help you understand the overall process, find possible errors in the steps, keep a record of the name of the output files and help you explain to others what you did to reach your final result. On the last page you will find a flow chart of the entire process you will perform in this lab; use it as a reference as you work through the lab.


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2.1.1 Joining tables

Add the soil_lut table to the Table of Contents (TOC). Open and examine this table. Right-click on the Soils feature class in the TOC and choose Joins and Relates | Join. In the first field (Choose the field in this layer that the join will be based on) select “SURFTEX”. In the second field (Choose the table to join this layer, or load the table from the disk) select soil_lut. Finally, in the third field (Choose the field in the table to base the join on) select “SURFTEX”. In the Join Options check “Keep all records”. Click OK. Open the soils attribute table and examine the changes.

2.1.2. Rasterizing: Converting vector data to raster grids

• In ArcToolbox, select Conversion Tools | To Raster | Features to Raster. Select soils as the Input features. Select “soil_lut:PERSAND” as Field. As you already define the output of the raster resolution, the value 30 is automatically indicated (this resolution corresponds to the ‘native’ resolution of the DEM). Name the Output raster soilsGrid. Make sure the output file location is your lab folder. Indicate OK and the grid will be created. NOTE: Grid names cannot exceed 13 characters, or an error message will be generated.

3.1. Nearest to water

For the second risk factor, you would need to create a file that contains all the different water sources (e.g. streams, wetlands, lakes) in the study site. In this case, you will require the information of two different files: 1) landuse (polygon feature class), specifically water and wetland classes; and 2) streams (line feature class). You will later combine these two grids into one grid that represents all water features.

3.1.1. Rasterizing: Converting vector data to raster grids

• First rasterize the streams feature class as you did the soils dataset. Choose “STREAMS_” as the

field and a Cell size of 30. The output raster can be named streamGrid.


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Now you will create a raster of water bodies (water and wetland classes) from the landuse feature classes. Because there are multiple land-use types in the land-use feature class, it is useful to first encode the polygons of interest (those representing water or wetlands) with a “1”, and all others with a “0”. The wawet_lut table, based on a LEVEL1 land-use code, does exactly that.

• Join the landuse layer and the wawet_lut table. “LEVEL1” is the field that the join will be based on for both the layer and the table. You might get a warning message indicating the tables are not indexed; don’t worry and click OK.

• Rasterize the landuse feature class. Use “wawet_lut:WATORWET” as the Field, the same Cell size as before (30), and name it watLuGrid for the Output raster.

Be sure to examine the grids you have made (soilsGrid, streamGrid, watLuGrid) in ArcMap and verify that they make sense to you.

3.1.2. Proximity to Water: Reclassifying raster data

To create a layer that represents proximity to water bodies, you need to create a grid of distances to water sources. To do this, you will (1) reclassify the data, (2) combine the two water grids into one, and (3) run a distance operation that creates the new distance to water value for each cell.

Before you can union the two water feature grids, you need to make a slightly different streams grid. If you observe the streamsGrid file you just created, you will notice that the streams have pixel values ranging from 1 to 111 and that the pixels of areas that do not represent streams do not have a value; in other words those pixels have been assigned as “NoData” values.

In this case, the "NoData" value was assigned to locations with no data when converting from vector features to raster. “Every cell location in a raster has a value assigned to it. When information is unavailable for a cell location, the location will be assigned NoData. NoData and 0 are not the same— 0 is a valid value.”1 The problem with keeping “NoData” values in grid analysis is that any pixel identified as “NoData” in a grid will be assigned as “NoData” for that specific cell location when being combined with other grid features. So you have the risk of losing information in your output analysis grids, i.e. these “NoData” areas will be left blank in your output raster.

You will use the reclassify operation to assign a value to the “NoData” cells:

• In ArcToolbox select Spatial Analysis Tools | Reclass | Reclassify. In the Input raster field select

1 ESRI, 2008. ArcGIS Desktop Help: NoData and how it affects analysis.


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streamGrid. The Reclass field is VALUE. Select Classify. A window similar to the one used to classify symbology value data in Lab 2 will open. This is the Classification window. In this case, the classification method is irrelevant as we only need to group all the values into one class. Indicate 1 in the number of classes and select OK.

• Back in the Reclassify window, under the section “Reclassification” you will see two columns, one called “Old values” and the other, “New values.” Also, you will see two rows, the first row has the range from 1-111 in the “Old Value” column and a “1” in the “New Value”. The second row contains the Nodata values. Type the value 0 under the “New Values” column.

• Define the output raster as streamGrid2 in your working directory and press OK. This will create the new reclassified grid.

3.1.3. Proximity to Water: Combining two grids

In ArcToolbox select, Spatial Analyst Tools | Map Algebra | Raster Calculator. You will overlay both layers to create a new grid with the information from streamGrid2 and watLuGrid. The operator you are going to use is UNION; employing the BOOLEAN operator “OR.”

Launch the Raster Calculator. It looks like a regular calculator, however, as you can see, all the available raster data in the TOC are listed under the Layers section. Functions available include: Arithmetic, Trigonometric, Logarithmic, and Power and Boolean operators. To introduce an expression on the raster calculator, select the raster that you are going to use on the Layer list and double click on it, then click on the function you like to use. For our lab, introduce the following expression:


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streamGrid2 OR watLuGrid

However, notice that on the calculator the function OR will be represented with the “|” symbol, so the expression on the raster calculator would look like this:

“streamGrid2” | “watLuGrid”

Name the output layers wetGrid. Click OK.

3.1.4. Proximity to Water: Calculating distance in rasters

The Euclicidian Distance operation calculates Euclidean distance from a cell with a value to each NoData cell of the grid. For this reason, you need to create a grid in which cells that do not represent water features are NoData instead of zero.

• Reclassify the wetGrid layer so that all zeroes are given a “NoData” value and all cells representing wet areas are“1.” Call the new output grid wetGrid2.

• In ArcToolbox, select Spatial Analyst Tools | Distance | Euclidean Distance. In the Euclidean Distance dialog box, select wetGrid2, as the input file. Call the Output distance raster distWetGrid. Leave the other fields with the default values. Indicate OK. Inspect your new distWetGrid layer.

If you do not remember how Euclidian Distance is calculated, consult the lecture notes from the class “Distance and Buffer.”

The summary of the steps employed to create distances to water is summarized in this flow chart:


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3.2. Elevation variable

3.2.1. Employing Neighborhood Focal Statistics to modify raster data

The final variable to be included in the risk assessment is variation in elevation, which will serve as a proxy for the steepness of an area. We will calculate this variable using a digital elevation model (DEM) and assigning each cell a value that corresponds to the range in elevation among its neighbors.

• In ArcToolbox select, Spatial Analyst Tools | Neighborhood | Focal Statistics.

• Use the following specifications (as shown in the image to the right).

o Input raster: websterdem o Output raster: demVar. o Neighborhood: Rectangle o Neighborhood Settings: H = 3 & W = 3 o Units: Cell o Statistic type: Range

• Examine the new grid in ArcMap.

The summary of the preceding steps is illustrated in the following flow chart:


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You should understand what the Focal Statistics operation does. Check the Help menu if you need clarification.

Question 1 (2 points): Explain the difference between "NoData" and "0" values in a raster grid and how each is used in calculating raster distance grids.

4. Stretching raster data values

At this point you have three grids: soilsGrid, demVar, and distWetGrid (you can now remove any other grids or feature classes from ArcMap). These grids represent indices of soil texture, elevation variability, and distance to water features, respectively. To combine them and create a weighted-average index that represents site risk based upon these three criteria, you need to stretch the values of the grids to a range between 0 and 100 so that all three criteria have the same relative measurement scale. You can do this using the Raster Calculator but first you would need to determine the Maximum Value and Minimum Value of each of the current grids.

• There are different ways to determine a grid’s minimum and maximum values. One of them is from the TOC right-click on the soilsGrid grid and select Properties, and check the Source tab. Scroll down and check the field Statistics. Write down the MIN and MAX values.

• Repeat this procedure for demVar, and distWetGrid. Use the following table to record the max and min values from the other grids:

Dataset Minimum value

(GRIDMin) Maximum value

(GRIDMax) soilsGrid 5 80 demvar distWetGrid

3.3. Creating a common relative scale measurement among layers

• Construct the following expression in the Raster Calculator to stretch the soilsGrid grid from a range of 5-80 to a range of 0-100. The conceptual format of the equation takes the following form:

(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑐𝑐𝑐𝑐𝑠𝑠𝑠𝑠 𝑣𝑣𝑣𝑣𝑠𝑠𝑣𝑣𝑐𝑐 − 𝑔𝑔𝑔𝑔𝑠𝑠𝑔𝑔 𝑚𝑚𝑠𝑠𝑚𝑚𝑠𝑠𝑚𝑚𝑣𝑣𝑚𝑚) × 100𝑔𝑔𝑔𝑔𝑠𝑠𝑔𝑔 𝑚𝑚𝑣𝑣𝑚𝑚𝑠𝑠𝑚𝑚𝑣𝑣𝑚𝑚 − 𝑔𝑔𝑔𝑔𝑠𝑠𝑔𝑔 𝑚𝑚𝑠𝑠𝑚𝑚𝑠𝑠𝑚𝑚𝑣𝑣𝑚𝑚


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• To avoid errors, use the raster calculator keyboard

• Introduce the following expression to stretch soilsGrid:

(("soilsGrid" - 5) * 100) / (80 - 5)

• Call the output field soil_stre (Output raster field). Click OK. Examine the output file.

• Go through the same process for demVar with the values you recorded:

(("demVar" – min) * 100) / (max – min)

• Call the output DEM_stre (Output raster field). Click OK. Examine the output file.

The nearness of water body variable implies that the closer a cell is to a water body, the higher the risk value it should be. If you view the distance grid "distWetGrid", you will notice that the values increase with increasing distance from the water features. This is the inverse of the required measurement scale, so it will be necessary to invert the distance grid values.

• To make a layer that represents inverse distance, first stretch the image from 0-100.

(("distWetGrid" - min) * 100) / (max-min)

• Name output file Wet_stre. Examine your output. Verify that the lowest values occur CLOSEST to the water.

• To invert the values of the Wet_stre grid, use the raster calculator and subtract all values in the raster from 100.

(100 - "Wet_stre")

• Name the output file NearWetGrid. Examine your output. Verify that the higher values are closest to the water bodies.

Be sure to examine your output in ArcMap/ArcCatalog to make sure that the output grid values make sense. The stretch procedure is described in the following flow chart.


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4. Creating the overall risk index (weighted average)

Your last step is to create a new grid by combining the soil_stre, DEM_stre, and NearWetGrid grids. You will combine them using a weighted average. In short, you should perform a summation, with each grid weighted (i.e., multiplied by some fraction to represent the relative importance of the layer).

• In Raster Calculator, build the following expression to create a map depicting the following weights:

(0.4 * "soil_stre" + 0.2 * "DEM_stre" + 0.4 * "NearWetGrid")

• Call the output file risk_index

• In ArcMap, compare the grid you have just made with the grids that were used to create it to confirm that it is what you would expect.


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• Select one different set of weights for the three factors (remember: the weights across the three factors must sum to 1). Use Raster Calculator to calculate a new index grid based on this alternative set of weights.

Question 2 (3 points): Write out the equation (similar to the one written above) used for your alternative set of weights. How did this affect your risk analysis? Explain what you were trying to achieve conceptually and if you feel the resulting grid accurately represents what you were trying to achieve.

5. Displaying raster data

In ArcMap, there are different ways of displaying raster grids in your layouts, including Stretched or Classified. You should classify your raster data into categories (i.e., low, medium, and high risk) for purposes of display.

• To do this, right-click on your raster risk file (TOC) and select Properties | Symbology. Select “Classified” and click Yes if asked to create a histogram. The Classified option will let you classify your image by different classification schemes (Review Lab 2 for more info). Note that one’s choices of classification is arbitrary, so the legend should include information on what “low” “med” and “high” mean numerically.

NOTE: If the colors are displayed as Stretched and the color-ramp goes the opposite direction as you would like it to, double-click on the color ramp in the TOC and a pop-up window will appear giving you the option of inverting the color ramp. If the data are classified, right-click on one of the color patches in the Layer Properties Symbology tab and select “Flip colors”


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Map to Submit (5 points): Create a complete map that best conveys the range of risk to surface and groundwater contamination across Webster Township based upon the original weighted average of the three criteria. Designate three classes of distinction: high, moderate, and low. Within your map include a text box that explains what the risk index is based upon (i.e. describe soil_stre, DEM_stre, and NearWetGrid), and the relative weights given to each.

Question 3 (10 points):

Option 1. Think back to the analysis you proposed in your first lab and the layers used in your Lab 6 report. Using the spatial data layers you described there (or any other layers you would ideally like to have), construct a hypothetical series of processing steps (using raster or vector analysis tools, or both) which you could apply to those layers to address a component of your proposed research question. This will be useful if you eventually plan to analyze your own data in the upcoming Lab 12.

1) Succinctly (no more than 1-2 sentences), state the goal of these processing steps in terms of the analysis* question they will allow you to address. (*simply combining or simplifying data is not enough. You must be addressing an analysis question. If you are unsure, ask your GSI).

2) Draw a flowchart similar to the one on the last page of this lab. You could use any of the analytical tools we have learned in Labs 8, 9 and 10 such as select, intersect, clip, union, erase, dissolve, reclassify, buffer, join and raster calculations as appropriate to your data processing goals. Use succinct text under each step or footnotes to explain key parameters (e.g. what will be reclassified, what equation will be used in raster calculator, etc). Include a total of between 5 and 10 processing operations. If you exceed this limit, you will only be graded for the first 10 operations! If 10 operations are not sufficient to generate your original final data product, limit yourself to a subset of your analysis. Note: You do not actually have to execute this plan – but it should be plausible!

Option 2. If you prefer to consider a predefined scenario, answer the following question instead of the above. You will describe a series of processing steps which you could apply to data layers (using raster or vector analysis tools, or both) that you would consider important to address the problem.

The problem: You are the GIS analyst for EnviroWaste consulting company. The company is creating a solid waste management plan for the city of Kalamazoo (MI, including the establishment of a new landfill. Your task is to identify suitable sites for the new landfill using the following criteria:

a. It cannot be located near open water (rivers, lakes, or wetlands).

b. It cannot be located near urban and rural residential areas.

c. It cannot be established within protected areas or forest land.

d. It has to be accessible (nearby roads or train tracks)

e. It should avoid permeable soil types (prefer soil with low %sand)

Your task:

1. Identify data layers that you consider representative of the criteria cited above


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2. Describe the data (indicate the original format and attributes required for the analysis).

3. Draw a flowchart similar to the one on the last page of this lab. You could use any of the analytical tools we have learned in Labs 8, 9 and 10 such as select, intersect, clip, union, erase, dissolve, reclassify, buffer, join and raster calculations, as appropriate to your data processing goals. Use succinct text under each step or footnotes to explain key parameters (e.g. what will be reclassified, what equation will be used in raster calculator, etc). Note: You do not actually have to execute this plan – but it should be plausible!

-- End of Lab 10 (flow chart on next page)

lab 10: risk analysis (suitability mapping) overviewesa.snre.umich.edu/classes/gis777/lab10.pdf ·...

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