Atmospheric Sciences

Transcription

1 GIS Tutorial for Atmospheric Sciences J. Greg Dobson, University of North Carolina at Asheville Jennifer Boehnert, National Center for Atmospheric Research 2015 UCAR and UNC-Asheville. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported License, which permits all non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.

2 Section 1: Basic GIS Fundamentals Exercise 3 Data Symbology and Classification Use Case: Hurricane Sandy s official track and impacts GIS data can be classified into two groups: qualitative and quantitative data. Qualitative data refers to data that are grouped or mapped based on an attribute or category. An example of qualitative data is mapping world countries based on name. There are two qualitative ways to map this data one way is to symbolize all countries with the same color; and the second way is to give each country a unique color. Quantitative data, on the other hand, are data of measurements or numerical amounts. These data communicate magnitude. When mapping quantitative data, colors and symbols help to communicate patterns in the data. An example of this type of data would be to map countries based on population density. The colors used to render the county would reflect the population density magnitude. In this exercise you will learn how to symbolize point, line, and polygon features to map Hurricane Sandy s track and the storm s impact along the northeastern coast. Sub-Sections in Exercise 3: 1. Symbolizing Qualitative Data 2. Symbolizing Quantitative Data 3. Working with Layer Packages 4. Rendering Raster Datasets 2015 UCAR and UNC Asheville 3-1

3 Symbolizing Qualitative Data Mapping data using single symbol symbology In this first step you will explore different ways to symbolize data based on qualitative attributes. Open a blank ArcMap document. Click the Add Data button. Click the Connect to Folder button. Navigate to C:\Exercise3\ (or wherever you store the data for this exercise) and click OK. In the Add Data window navigate to data/basedata.gdb and add the Countries feature class. In the Table of Contents, layers can be listed in a number of ways: List by drawing order List by visibility List by source List by selection By default, the data are Listed by Drawing Order, unless you have a non-spatial datasets in ArcMap. Make sure the data are Listed by Drawing Order in the Table of Contents. The dataset is added to ArcMap as a layer with default symbology. In most cases the way the dataset is displayed by default is not the way you want it to look on your map. You will explore the options in the Layer Properties window to change the way the dataset is being displayed UCAR and UNC Asheville 3-2

4 TIP: It is possible to change the default symbology using the Advanced ArcMap Setting Utility. This utility is usually found in your C:\Program Files\ArcGIS\Utilities directory. Right-click on Countries and click on Properties This will open the Countries Layer Properties window. Click on the Symbology tab in the Layer Properties window. In the Show: box (on the left) notice that Features Single symbol is highlighted. The color patch in the middle of the window shows you that every country is symbolized in a single color. This color can easily be changed. Click on the color patch UCAR and UNC Asheville 3-3

5 In the Symbol Selector window, choose a Hollow symbol. Change the Outline Width to 2.0 Click OK to close the Symbol Selector window. Click OK again to close the Layer Properties window and to apply your changes. TIP: Click Apply to save the changes and keep the Layer Properties window open or click OK to apply the changes and to close the window. This symbology is common as a background display for country or state boundaries. From the Table of Contents there is a quicker way to change basic symbology (such as fill colors) of your layers. Click on the actual symbol patch in the Table of Contents under the layer name Countries. This will also open the Symbol Selector window UCAR and UNC Asheville 3-4

6 Click the dropdown arrow next to Fill Color on the right under the Current Symbol. Select Grey 50%. TIP: Hover your mouse over the colors to see the color name appear. Click OK to close the Symbol Selector window. Now each country is displayed in a dark grey color. Using the Single Symbol option is one way to symbolize qualitative data. Click File > Save. Save your map to C:\Exercise3\maps and call this map exercise3_<yourname>.mxd UCAR and UNC Asheville 3-5

7 Mapping data based on categories A second way to symbolize qualitative data is to color each unique feature with a unique color. You will now add a States layer to ArcMap and symbolize the data using this method of symbology. Click the Add Data button. Navigate to C:\Exercise3\data\baseData.gdb and add the States feature class. Right click on the layer States in the Table of contents and select Zoom to Layer. Right click on States and click on Properties Click on the Symbology tab. In the Show: box, notice that Features Single symbol is highlighted. Select Categories, then Unique values UCAR and UNC Asheville 3-6

8 For the Value Field select STATE_NAME. Click Add All Values. Click OK to apply the settings and close the window. Notice that each state is displayed by a unique color. This type of symbology does not show value or importance. The colors are used to differentiate the classes or groups of data. Click File > Save UCAR and UNC Asheville 3-7

9 Symbolize Quantitative Data Quantitative data can be counted or measured. When symbolizing quantitative data, you have many options on how to represent and group these measurements. In this step you will get the opportunity to explore some of the different ways to symbolize quantitative data. Symbology for quantitative data In the previous step you symbolized the layer States based on State name. Each state was rendered with a unique color but the colors did not reflect a measurement or magnitude. In this step you will symbolize the layer States based on a field which contains a measurement. Double click on the layer States in the Table of Contents to open the Layer Properties window. In the Symbology tab click Quantities option in the Show box. 1. Make sure Graduated colors is selected. 2. Change the Value under Fields to POP For the Color Ramp select the ramp that goes from light to dark green. Click Apply. (Do not close the window) UCAR and UNC Asheville 3-8

10 Move the Layer Properties window away from the map. This type of map is called a Choropleth map and it displays state population for Which states appears to have the greatest population in 2010? It may also be important to consider the size of the state compared to the population. In this case you would be mapping population density. Back in the Layer Properties window, set the Normalization field to SQMI (3rd from the bottom). This option will normalize the value field population, by the normalization field, miles squared (size of the state). Click OK. Click the Save button on the Standard Toolbar. Notice how different the map looks now. No longer are Texas and California dark green. Now the East Coast states such as New Jersey and Massachusetts are the dark green states. This example illustrates that it is important to understand what you are mapping before you start symbolizing the data. Both maps are correct but they show very different information. Color Brewer Choosing colors that convey your message and are also easy for your audience to understand is not easy. There is an Esri plug-in called Color Brewer which helps out with the color selection. Color Brewer was developed by a team of designers at Penn State University. You will now add the color brewer plug-in to ArcMap. You can download the Color Brewer plug-in from We have already downloaded the style file for you. Click the Customize menu and select Style Manager UCAR and UNC Asheville 3-9

11 Click the Styles. Button. Click Add Style to List. Navigate to C:\Exercise3\data\ColorBrewer and select ColorBrewer.style, click Open. You will use the ColorBrewer style in future exercises. To save this style to your default style list click the Set as Default List. Click OK to close the Style Reference window. Close the Style Manager. Double-click on the layer States in the Table of Contents and make sure your Symbology tab is selected. Click the Color Ramp drop-down list. Notice that you have many more color options available through the Color Brewer Plug-in. Select a color that has 5 classes going from light orange to dark red (at the bottom) and click Apply. Look at your map. In the Layer Properties window, change the number of classes from 5 to UCAR and UNC Asheville 3-10

12 Now choose a color ramp with 8 color classes. Click OK. Click File > Save. Choosing a classification method So far, you have examined quantitative data displayed as a count and normalizing data based on a spatial component such as the states size to develop a density map. In this step you are going to look at different options for classifying the data. Classifying data refers to the way in which the groups are created for your data. The way ArcMap decides how to make those groupings is called classification. Right-click on States in the Table of Contents and click on Properties Click on the Symbology tab. In the Show: box make sure Graduated Colors under Quantities is selected. The field you are mapping is still POP2010 and you are normalizing with SQMI. Click on the Classify button. The Classification Window appears UCAR and UNC Asheville 3-11

13 The default data setting in ArcMap classifies data using the Natural Breaks (Jenks) method. Click the Natural Breaks (Jenks) dropdown to view the other options for classifying your data. Select Equal Interval. Change the number of classes to 5. Click OK to close the Classification window, and click Apply. Move the Layer Properties window to the side so you can see your map. TIP: Equal Intervals divide the range of data into equally spaced ranges. Notice the whole map is yellow using this classification method UCAR and UNC Asheville 3-12

14 This table shows state-level population density symbolized using the different classification methods. Equal Interval Natural Breaks Quantiles Standard Deviation In the Layer Properties window, click the Classify button, to open the Classification window. Take a look at the Histogram and the blue lines representing the class breaks. Notice that all the data except one data point (an outlier) fall within the first class break. Because that outlier has such a high population density, using Equal Interval is not a good option for this dataset. The outlier for our data is the District of Columbia. Equal Interval is best used when your classification steps are nearly equal in size and if your dataset has a rectangular shape in the histogram. Even without the District of Columbia our histogram is not a rectangle and therefore is not a good case for using Equal Interval. Choose the Quantile classification method. Notice how the class breaks are now dividing the data. Most of the break points are towards the left of the Histogram, where most of the data lie. TIP: Quantiles create classes that contain an equal number of features UCAR and UNC Asheville 3-13

15 Click OK in the Classification window and Apply in the Layer Properties window. Move the Layer Properties window to the side so you can view the map. Quantile classification is great for ordinal data. Ordinal data is data that has an arbitrary numerical scale where the exact numerical quantity of a particular value has no significance beyond its ability to establish a ranking. Quantile classification will group an equal number of features in each group. In this case, Florida, New York, New Jersey, and many more states are lumped into the class with the District of Columbia, even though the District of Columbia has a much higher population density. This is not an ideal classification method for our data. Click the Classify button, to open the Classification window. Choose the Standard Deviation classification method. Notice how the class breaks are defined using this classification method. TIP: Standard deviation classes the data based on how much a feature value varies from the mean. Standard deviation can be used when the data are normally distributed, when the histogram forms a bell-shaped curve. When using Standard Deviation classification method, one can choose to divide data by a 1, ½, or ¼ standard deviation. Select ½ Std Dev for the Interval Size. Is your dataset normally distributed in the histogram? Does it form a bell curve? Click OK in the Classification window and Apply in the Layer Properties window. The data are not normally distributed and standard deviation is not an appropriate classification method for the data. Click the Classify button, to open the Classification window. Choose the Natural Breaks (Jenks) classification method. Notice how the class breaks are defined with this classification method. TIP: Natural Breaks create classes based on natural groupings of data values (Jenks method) UCAR and UNC Asheville 3-14

16 Set the number of classes to 7. TIP: Usually map viewers have difficulty discerning between more than 7 or 8 classification groupings. Click OK in the Classification window and OK in the Layer Properties window. Right click on the layer States in the Table of Contents and select Label Features. Click the Zoom In tool and draw a box around District of Columbia. The District of Columbia is the outlier with the highest population density. Notice that it is symbolized with the darkest shading. The Natural Breaks classification is considered visually logical. This method minimizes value differences between data within a class and maximizes value differences between classes. For this case, data natural breaks may be one of the better classification methods to use. Below is a table with various other classifications and a description of each method: Natural Breaks Quantile Classes that are based on natural groupings of data values (Jenks method). Classes contain an equal number of features. Good default Ordinal data. Equal interval Classes are equally spaced. Rectangular-shaped histogram. Standard deviation Classes are based on how much a feature s attribute value varies from the mean. Normal distribution of data. Bell-shaped curve. Click File > Save UCAR and UNC Asheville 3-15

17 Symbolizing vector data using graduated symbols and graduated colors On October 29 th, 2012, Hurricane Sandy made landfall near Atlantic City, NJ as a Category 1 hurricane. Sandy made landfall in the most densely populated state in the middle of the most densely populated region. It was the deadliest and most destructive hurricane of the 2012 Atlantic hurricane season. You will now add the hurricane track for Sandy from the National Hurricane Center. This is a forecast run 2 days before landfall. ( Click the Add Data button. Navigate to C:\Exercise3\data\ Hurricanes.gdb. and add the following layers o Sandy_cone_021, o Sandy_line_021, and o Sandy_pt_021 If a message appears about a coordinate system, click Close. All the layers are now selected. Click once on the layer Sandy_cone_021. When adding multiple layers to ArcMap at once all layers that are added become highlighted. Before symbolizing data or zooming to a layer you need to de-highlight all the layers first. Right-click on Sandy_cone_021 and select Zoom to Layer. The points represent the official National Hurricane Center forecast locations of the center of a tropical cyclone. The cone represents the probable track of the center of a tropical cyclone. The official track forecast is intended to aid in the visualization. The forecast was run October 27 th, two days before Sandy hit landfall in New Jersey. Left-click once on the symbol patch under the Sandy_cone_021 layer in the Table of contents. Again, this is a quick way to access the Symbol Selector window. Change the color to Yellow and click OK. Double-click on the layer Sandy_cone_021 to open the Layer Properties window. Click the Display tab. Change the Transparent value to 40%. Click OK to apply your changes and to close the window UCAR and UNC Asheville 3-16

18 Transparency provides a way to see multiple polygon layers at the same time by seeing through them. Hurricane Sandy made landfall in New Jersey with winds of around 80 mph (Category 1) hurricane. You will now symbolize the model winds speed of Hurricane Sandy. Double-click on Sandy_pt_021 to open the layer properties window. Click the Symbology tab. Choose Quantities > Graduated Symbols. Graduated Symbols change their size according to the value of the attribute. Change the Fields Value to MAXWIND. Set the Symbol Size from: to be 10 to: 30. Leave all other options to the default and click OK. Using graduated symbols is another way of expressing the difference in magnitude of your data. Bigger symbols show greater magnitudes than smaller symbols UCAR and UNC Asheville 3-17

19 Now you will add the official Hurricane Sandy track and compare how well this model forecast did two days before landfall. Click the Add Data button Navigate to C:\Exercise3\data\ Hurricanes.gdb. and add the following layers o HurricaneSandyTrack HurricaneSandyTrack is the official track and Sandy_line_021 is the model simulation. Double-click on the layer HurricaneSandyTrack to open the Layer Properties window. In the Symbology tab click on Quantities > Graduated Colors. Change the Fields Value to wmo_wind. This field is the highest 1-minute average wind associated with a tropical cyclone at a particular point in time (mph). Click the Classify button. Change the number of Classes to 6. Set the Method of Classification to Manual UCAR and UNC Asheville 3-18

20 Starting at the bottom, change the Break Values to match the table below for the hurricane categories. The break points will be 73, 95, 110,129, 156, and 300. TIP: These are the break values for the different hurricane categories. Click OK. The table below shows the wind speeds for the hurricane categories. CATEGORY WIND SPEED BREAK VALUES Tropical Storm < 64 knots < 73 mph knots mph knots mph knots mph knots mph 5 >137 knots > 157 mph UCAR and UNC Asheville 3-19

21 Left-click on the Label bar and select Format Labels In the Number Format window change the Number of decimal places to 0. Click OK. Choose a PURPLE color ramp with 6 classes. Right-click on one of the values under Color Ramp and select Properties for All Symbols. Change the Width to 3. Click OK twice to apply changes and close the windows. From this map we can see that Hurricane Sandy made landfall in New Jersey as a Category 1 hurricane and then weakened. The official track falls within the cone of uncertainty from the forecast. The forecast also shows the storm strengthening at sea and then weakening as it approaches land. The real damage was caused by the huge storm surge the hurricane triggered along the coast. Now you will add a layer that shows the storm surge UCAR and UNC Asheville 3-20

22 Turn off the following layers: Countries, Sandy_pt_021, Sandy_line_021, and Sandy_cone_021. Save you map. Working with Layer Packages Working with a layer package You are now going to add a Layer Package to your map. A Layer Package contains the data as well as how that data should be symbolized. Click Windows > Catalog. In the Catalog window that appears to your right navigate to C:\Exercise3\data directory. Click on the file stormsurge.lpk and drag it into your Table of contents, just above the States layer. If a warning appears about Coordinate systems click Close. This warning is telling you that the datum (shape of the earth) upon which the dataset was based is a different shape of the earth than the map document. Refer to Exercise 5 to learn more about datums and projections. A layer package is a helpful way to share data and symbology. This package contains the data as well as symbology. Remember that a layer file only contains the symbology and pointers to the data. Zoom into New Jersey using the Zoom In tool on the Tools toolbar. The storm surge dataset displays the areas that experienced storm surge caused by Hurricane Sandy. Symbolizing using proportional symbols Proportional symbol is a symbology that represents features based on a field value as a series of graduated symbol sizes. Values are not classified but are drawn based on field magnitude relative to other values. Click the Add Data button and the layer NJCities located in your basedata.gdb Double-click on NJCities to open the Layer Properties window. In the Symbology tab click Quantities > Proportional Symbols UCAR and UNC Asheville 3-21

23 For the fields Value select Population. Click the Min Value point to open the Symbol Selector. In the Symbol Selector change the size to Change the color to Solar Yellow. Click OK to apply your changes and close the Symbol Selector window. Click the Display tab and change the Transparent value to 50%. Click OK to close the Layer Properties window. Proportional symbols represent the data as a series of graduated symbol sizes. The dataset is not classified, but sized according to its value based on the range of the rest of the data. Notice how many cities and towns are within regions of storm surge.. Click the Bookmarks menu and select Create Bookmark. Name your Bookmark New Jersey and click OK. Remember from Exercise 1 that a Bookmark is a way to save spatial extent. It will make it easy to return to this extent later in the Exercise. Your map displays cities and towns in the state of New Jersey based on population size, the hurricane track, and the storm surge. Notice the large yellow area along the New Jersey/ 2015 UCAR and UNC Asheville 3-22

24 New York border. This indicates this area has many towns and cities. This region also had a strong storm surge. Zoom into the region along the New Jersey / New York border. The proportional points scale along with your zoom factor. In the next step you will look at the storm impacts at the county level for the state of New Jersey. Click File > Save. Importing symbology using a layer file So far you have mapped model hurricane tracks for Hurricane Sandy, the official track, population density by state, cities, and towns in New Jersey, and the storm surge from Hurricane Sandy. The storm-surge data you added to ArcMap show the magnitude and extent of the storm surge as a result from the hurricane. In this step you will take a look at impacts from the storm surge according to FEMA s assessment. You are about to add a feature class to your map that was developed by FEMA. It represents a composite of surge, wind, precipitation, and snow impacts used to assess impacts for each County. Click the Add Data button and add the Impacts layer from the Hurricanes.gdb file geodatabase. The feature class displays the counties for the region. Right-click on the Impacts layer in the Table of Contents and select Open Attribute Table. More information about this layer can be found at Hurricane%20Sandy%20Products%20README% pdf. The field ImpactRnk is relative impact based on surge, wind, precipitation, and snow. You will use this field now to symbolize the data. One way to apply symbology to data is by importing that symbology from a layer file (.lyr). A layer file includes all feature class display properties for symbology and labeling. Close the Table UCAR and UNC Asheville 3-23

25 Double-click on Impacts in the Table of Contents to open the Layer Properties window. In the Symbology tab click the Import button. Click the Browse button and navigate to C:\Exercise3\data\ and select Impacts.lyr. Click Add. Click Ok. Make sure the Value Field is set to ImpactRnk and click OK. You just applied the symbology saved in a layer file to your feature class. Click OK to close the Layer Properties window. Make sure your Table of contents is displaying layers based on drawing order. Move Impacts below stormsurge in the Table of contents. Right-click on Impacts and select Zoom to Layer. Turn off the NJCities layer in the Table of contents UCAR and UNC Asheville 3-24

26 Surge is the primary driver of the severe impacts as a result of Hurricane Sandy and the relative impact assessment is summarized as follows: Very High (Purple): Greater Than 10,000 of County Population Exposed to Surge High (Red): ,000 of County Population Exposed to Surge, or Modeled Wind Damages > $100M, or High Precipitation (>8 ) Moderate (Yellow): of County Population Exposed to Surge, or Modeled Wind Damages $10 - $100M, or Medium Precipitation (4 to 8 ) Low (Green): No Surge Impacts, or Modeled Wind Damages < $10M, or Low Precipitation (<4 ) Creating a Layer Package in ArcMap Within ArcMap you can copy a layer from a data frame and paste it into the same data frame or another data frame. The symbology and all properties from the layer will be copied as well. In this step you will copy the layer Impact to change the symbology of the layer based on a different attribute. Right-click on the layer Impacts and select Copy. Right-click on the Data Frame Layers and select Paste Layer(s). TIP: Copying and pasting a layer within the Table of Contents is a quick and easy way to add another instance of the same layer to ArcMap. Often you will want to symbolize the same layer in multiple ways. Double-click on the newly pasted Impacts layer to open the Layer Properties window. In the Symbology tab click Quantities > Graduated colors. Change the field you are symbolizing to E_Pop10 (population affected by storm). Change the Normalization field to Pop10 (total population in 2010) UCAR and UNC Asheville 3-25

27 Choose any color ramp you wish. Click Apply. Click on the General tab and enter the following for the Description. This dataset was downloaded from FEMA and displays the percent of the population impacted by Hurricane Sandy. Change the name of the layer to Percent of Population Affected. Click OK. Drag the Percent of Population Affected layer underneath the stormsurge layer. Now save this new layer as a Layer Package. Remember from Step 7, you brought in a Layer Package to display. Layer Packages are nice way to share data and symbology with others. Right-click on the Layer Percent of Population Affected and select Create Layer Package. In the Layer Package window click the option to Save package to file. Click the Browse button and navigate to C:\Exercise3\data. Keep the default name and click Save UCAR and UNC Asheville 3-26

28 Click the Item Description option to the right. For the Summary, type in the following; Percent of population affected by Hurricane Sandy For Tags type in Hurricane Sandy; impacts. Click the Analyze option at the top. Click the Share option. Once the process is complete click OK UCAR and UNC Asheville 3-27

29 Click the Catalog tab on the right hand side of the ArcMap application. In the Catalog window in ArcMap, navigate to C:\Exercise3\data. The file Percent of Populations Affected.lpk can be ed or shared with others. It contains the data as well as the layer file information such as how the data are symbolized. Click File > Save. Rendering Raster Datasets Display raster data In this final step you will add a gridded elevation dataset to your map. A gridded (raster) dataset is cell-based information. Gridded data are primarily used for continuous surfaces such as elevation, temperature, etc. Click the Bookmarks menu and select New Jersey bookmark. The data and maps have shown you areas where the highest percent of the population who were affected by the storm, and the counties that incurred the highest Impact Risk to the storm. Storm surge and flooding is the biggest contributor to the impacts and damage caused by Hurricane Sandy. Add the elevation file from the basedata.gdb file geodatabase. ArcMap will always add raster data to the bottom of the drawing order in the Table of Contents. Turn off the layers Percent of Population Affected, States and Impacts. Double-click on elevation in the Table of Contents to open the Layer Properties window. Notice that the Layer Properties window is a bit different for raster data than it is for vector data. Click the Symbology tab UCAR and UNC Asheville 3-28

30 Notice that by default the data is symbolized using a Stretched method. Using a Stretch will increase the visual contrast of your data. There are different stretches you can apply. By default, ArcMap uses Percent Clip. You can often assume that the majority of your data values fall between an upper and lower limit. The Percent Clip will apply a linear stretch between the percent clip minimum and percent clip maximum pixel values. By default, these min and max values are 0.5. In the Stretch section change the min and max values to 10 and click Apply. Notice you can now see a bit more variation in the low-lying regions along the coast and the higher elevation areas are darker. This method clipped out the bottom 10% and top 10% of the data and then applied the stretch to the rest of the data. Change the color ramp to a color scheme ranging from yellow to brown and click Apply. Click the Histogram button. This histogram tells you that the majority of the elevation data points fall towards the lower elevations and very few are at the higher elevations. In the histogram, notice aren t many data points past the value 596. Click OK to close the Histogram for elevation window. Make sure the Stretch Type is Percent Clip. Change the min value to 0 and change the max value to UCAR and UNC Asheville 3-29

31 Click Apply. By changing the max value to 10, this stretch bundles the top 10% of the data values into the top end of the stretch. This is a good option for our purposes since most of the data values are in the lower elevation regions. Change the Stretch Type to Minimum-Maximum and click Apply. Notice that the window changes a bit and you now have a check box between the values. Click the checkbox between the values. Change the upper value to 596 and click Apply. Using this stretch method will group all values above 596 into the top end of the stretch. Based on this dataset, this may be an acceptable way to symbolize the data. Change the Stretch Type to Standard Deviation and click Apply. With this stretch option you can specify the n value for the number of standard deviations to be used. This method emphasizes how features vary from the mean. Similar to the vector classification method this method is best when used on normally distributed data. Another way to symbolize the raster data is by using a classification method similar to what you did with vector data. In the Symbology tab click the Classified option on the Show: box. Click the Classify button UCAR and UNC Asheville 3-30

32 Notice that you have the same options for classifying raster data as you did with vector. For this dataset you will use the Natural Breaks classification method. Click OK to close the Classification window and click Apply to apply the changes. Change the color ramp back to a yellow to brown ramp if necessary, and click Apply. This method of symbolizing data is good when you want to group values into a set number of classes. This method is also a good way to symbolize thresholds in the data. You will now map all areas located at 2 meters or below sea level. In the Layer Properties window click the Classify button. Change the method to Natural Breaks. Change the number of classes to 2. Under Break Values on the right, change the first break value to UCAR and UNC Asheville 3-31

33 Click OK. Double-click on the color symbol patch and change the color of the first group to Dark Amethyst (Purple) UCAR and UNC Asheville 3-32

34 Double-click on the color symbol patch and change the color of the second group to No Color. Click OK to close the Layer Properties window. Turn on the States layer. Open the Layer Properties window and click the Symbology tab. Click Features > Single Symbol. Set the Symbol patch to Hollow fill and click OK You should have only the layer stormsurge, States, and elevation turned on. The area in dark purple is where elevation is 2 meters or less. Notice how this area corresponds to the storm surge region in blue. Double-click on elevation in the Table of Contents to open the Layer Properties window. In the Symbology tab click Classified under the Show: box. Click the Classify button. Set the first Break Values to 5. Click OK. Change the color of the first break to Purple and the second to No Color and click OK. Turn on the NJCities layer in the Table of Contents. Notice how many cities are located at or below 5 meters of elevation above sea level. Double-click on elevation in the Table of contents to open the Layer Properties window. In the Symbology tab click the Classification symbology type. Click the Classify button. Set the first Break Values to 10. Click OK. Change the color of the first break to Purple and click OK UCAR and UNC Asheville 3-33

35 Notice that by adjusting the elevation threshold the number of cities at or below 5 meters was drastically reduced. Click File > Save to save your map. Close ArcMap UCAR and UNC Asheville 3-34