Mapping Social Vulnerability and Lyme Disease in Vermont

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1 Mapping Social Vulnerability and Lyme Disease in Vermont March 30, 2017, Dr. Joseph Holler Contact Website: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Contents Introduction to the Social Vulnerability Index...2 View a map in Indiemapper...4 Experiment with cartographic design...6 Map the prevalence of disabled persons...8 Map cases of Lyme Disease Map additional social vulnerability indicators Interpret Results Optional: More advanced mapping Appendix A: Workbook in Community Social Vulnerability Analysis Appendix B: Lyme Disease Rates Appendix C: Lyme Disease and Social Vulnerability Purpose of this GIS Mapping Activity This geographic information system (GIS) mapping activity will guide you through health indicators for Vermont, based on the Social Vulnerability Index (SVI) developed by the Centers for Disease Control and Prevention (CDC). The purpose of the SVI is to rank the vulnerability of communities, defined as the likelihood of suffering harmful health outcomes and economic loss due to natural or human-caused disasters, including disease outbreaks. Although the CDC's focus was on extreme events, the same variables are relevant to the ability of patients and communities to build healthy communities and to cope with public health problems associated with long-term "creeping" hazards, like climate change. In this exercise, you will investigate the CDC data for a census tract within Vermont and compare it to a census tract you are familiar with, e.g. the community you grew up in. Then, you will design thematic maps of overall vulnerability, individual social vulnerability indicators, and Lyme disease rates in Vermont. Finally, you will interpret the patterns and relationships between indicators and consider the appropriateness of the Social Vulnerability Index for assessing vulnerability to biological natural hazards. You do not need specialized software or lab access for this activity. All you need is a web browser, and ideally a good monitor. You may want to print the instructions or view them on a second screen. Social Vulnerability Mapping - page 1

Introduction to the Social Vulnerability Index Navigate the Social Vulnerability Index Go to the Social Vulnerability Index at http://svi.cdc.

The data is aggregated by census tracts, and these are outlined in light grey.

2 Introduction to the Social Vulnerability Index Navigate the Social Vulnerability Index Go to the Social Vulnerability Index at (this may work best in Chrome browsers) Enter the zip code of Middlebury College: and submit. The interactive map will load and zoom to the zip code, outlined in blue. The data is aggregated by census tracts, and these are outlined in light grey. o The default view is the aggregate social vulnerability index, but you can also use the Theme drop-down menu to disaggregate the map by any of the four social vulnerability themes: 1) Socioeconomic, 2) Household Composition/Disability, 3) Minority Status/Language, or 4) Housing/Transportation. and then Finally, you can view all the details for a single census tract. Click on the location of Middlebury College to open the census tract information for this area (tract number ). Follow the Social Vulnerability Profile link. This query can take a minute or two to complete. Social Vulnerability Mapping - page 2

3 Social Vulnerability Index Data SVI Documentation A pop-up window will open with the full social vulnerability profile of that census tract. Each indicator has six variables, of which the most useful ones are: o The Number is the total number of indicated people, households, or housing units, or a rate or measure of centrality (e.g. per capita income or median household income). o The Percentage is the percent of the Number of indicated people, households, or housing units over the total of people, households, or housing units. o The Percentile Rank indicates the rank of this census tract in relation to all census tracts in the United States. A low rank (0) indicates low vulnerability, while a high rank (100) indicates high vulnerability. o The SVI Flag will be "yes" if the indicator is of particularly high concern for social vulnerability. Spend some time to understand the meaning of these statistics by comparing any census tract in the Middlebury, VT area with one or more census tracts you are familiar with from home. Fill out Appendix A: Workbook in Community Social Vulnerability Analysis with the statistical profile of each location (provided at the end of these instructions and as a separate Word document). Consider the following questions: o Are the statistics for communities you know well consistent with your experience and knowledge of those communities? o How do the statistics compare and contrast with communities near Middlebury, VT? o For which variables do the communities rank most poorly, looking at the Percentile Ranks? o Why do you think the CDC has used these particular indicators and how might they impact vulnerability to biological hazards, both on a day-to-day basis and in disasters and emergencies? Are the indicators consistent with theory? o Note: Ranked percentiles are always given from 0 (least vulnerable) to 100 (most vulnerable). Variables with inverse relationships have already been switched for consistency. For example, the highest per capita incomes will be ranked near the 1st percentile while the highest percentages of mobile homes will be ranked near the 100th percentile. Full documentation of the SVI methods and indicators is available at (also on Moodle) For more information about the SVI, refer to this journal article: Flanagan, B. E., E. W. Gregory, E. J. Hallisey, J. L. Heitgerd, and B. Lewis A Social Vulnerability Index for Disaster Management. Journal of Homeland Security and Emergency Management 8 (1):1 22. Social Vulnerability Mapping - page 3

4 View a map in Indiemapper Why make your own map? Launch Indiemapper and load the SVI map The online SVI map only allows you to visualize aggregated data by percentile rank, despite the fact that a wealth of good data has been used to compose the overall index and its four components. The SVI data has been prepared for thematic mapping in the free on-line application, indiemapper. Indiemapper is an easy-to-use web-based cartography application capable of helping novice users design surprisingly decent (but not perfect) maps in a short amount of time. Indimapper will allow you to visualize more than one variable at a time, e.g. rates of Lyme disease and an indicator of social vulnerability. Advanced users can export maps from Indiemapper (as layered SVG files) and refine them in Adobe Illustrator. First, download the SVI data package for indiemapper from Moodle: VTSVI.imp For now, leave the file in your downloads folder without opening it. Second, maximize your web browser to the full height of your monitor. o Note: Please keep your web browser set to the same size/extent on your screen while using indiemapper to avoid distorting the layout of your map. Next, go to the indiemapper application: o LAUNCH the application. o A "Your Data" window opens automatically after launching. o On the right side, go to Browse for IMP. o Select the VTSVI.imp file that you just downloaded above and Load it. Social Vulnerability Mapping - page 4

5 Check your Results and Export a Map The default map should look like this, and represents the overall Vulnerability Index. You may need to click and drag map items in order to arrange them as seen below You may not like the formatting of my title (frankly, it's terrible) and wisely change it. Please export an image of this social vulnerability map (for printing or importing into Word or Powerpoint). o o o Use the export button at the top right I suggest using the JPEG file format to make it easiest to use the map in any software program. First Generate File and then Export to your Computer and Save. To save a working copy of the data and your current layout (for later use only in indiemapper), use the save button: Social Vulnerability Mapping - page 5

Experiment with cartographic design Cartographic Design Change the Visibility and Order of Layers This section will guide you through changing the visibility and order, classification, and symbology

6 Experiment with cartographic design Cartographic Design Change the Visibility and Order of Layers This section will guide you through changing the visibility and order, classification, and symbology of data layers to improve the aesthetics of your map. This section is optional, so if you're short on time or have no interest in cartographic design, skip to the Map the prevalence of disabled persons section on page 8. The Layers panel, shown at right, allows you to control which layers are visible and the order in which they are drawn. These are aesthetic suggestions: o Check or uncheck the boxes to make layers seen or invisible. o I suggest unchecking the Graticule layer to remove the latitude and longitude lines from the map. o Click and drag layers under the Data Sources folder to bring layers to the foreground or background of the map. o If you want to label urban areas, you may check the TOWN labels layer under urban.shp. o If you want to remove water or the red outlines of urban areas, uncheck the water.shp layer or the Areas layer under urban.shp. Social Vulnerability Mapping - page 6

Change the style of a choropleth layer Once you have data in Indiemapper, you have the power to alter the symbols, classifications, and colors used to represent your data.

7 Change the style of a choropleth layer Once you have data in Indiemapper, you have the power to alter the symbols, classifications, and colors used to represent your data. Select the RPL_THEMES layer and examine the style: choropleth menu beneath the Layers tab. o A choropleth map displays numerical values by filling geographic regions with color or patterns. E.g. this map displays social vulnerability percentiles in five classes with a color gradient ranging from yellow to purple. This menu displays a histogram of the frequency of geographic regions (in this case, census tracts) on the map. Try altering the number of classes and the classification method. Try changing the color scheme. For best results, avoid colors similar to the water or urban area layers. Use the help menus for more information and best practices advice in cartography. After making changes to the map aesthetics, you may want to export a new map image and save your work. Social Vulnerability Mapping - page 7

Map the prevalence of disabled persons Add a new data layer to the map to display the percentage of disabled persons Hover the mouse over the VTSVI.shp layer in the Layers panel.

o If the menu is hidden beyond the bottom of your screen, try collapsing most of the Layers panel folders before adding a new layer Use the following settings to create a map of disabled

8 Map the prevalence of disabled persons Add a new data layer to the map to display the percentage of disabled persons Hover the mouse over the VTSVI.shp layer in the Layers panel. Click the + button appearing to the left to create a new thematic layer A Choose a new layer to add menu appears, giving you all of the mapping options for the VTSVI.shp dataset. o If the menu is hidden beyond the bottom of your screen, try collapsing most of the Layers panel folders before adding a new layer Use the following settings to create a map of disabled persons: o Choose the type of layer: one attribute o Choose an attribute: EP_DISABL (not EPL_DISABL) o Choose a map type: Choropleth o Standardize your data: No o Okay Social Vulnerability Mapping - page 8

Notice the default classification method is Optimal Breaks. The data has a fairly normal distribution, and Optimal Breaks is placing a few high outlier tracts in a class of their own.

9 Customize the disabled persons map layer A style: choropleth panel (shown at right) appears after you create the layer. o If the panel has not appeared, select the EP_DISABL layer and expand the style: choropleth menu at the bottom of the panel menus by clicking on it. Notice the default classification method is Optimal Breaks. The data has a fairly normal distribution, and Optimal Breaks is placing a few high outlier tracts in a class of their own. Try changing the method to Quantile. This will place an equal number of tracts in each class. Now try Equal Interval to assign equal data ranges to each class. Which method do you think best represents the geographic pattern of disability prevalence? Set the layer to use the method of your preference. This is a subjective, aesthetic decision with no one correct answer. Explore the Disability and SVI Data If you click once on any census tract on the disability map, a pop-up window will display the data for that census tract. E.g. clicking on Middlebury College's census tract reveals a 11.1 percentage of disabled persons. Expand the Other attributes menu to view all the social vulnerability data associated with a census tract. Click the "x" to close the attribute information pop-up. Social Vulnerability Mapping - page 9

Update the map elements to match your new data theme Double-click the title to change "Social Vulnerability" to "Disabled Persons" Double-click the "National Percentile of Social Vulnerability by

In the Layers panel under the Layout Objects and Legends folder, turn off visibility of the RPL_THEMES legend. Turn on visibility of the EP_DISABL legend.

10 Update the map elements to match your new data theme Double-click the title to change "Social Vulnerability" to "Disabled Persons" Double-click the "National Percentile of Social Vulnerability by Census Tract" note and edit the text to explain the EP_DISABL variable. In the Layers panel under the Data Sources folder, switch off the visibility of the RPL_THEMES layer. In the Layers panel under the Layout Objects and Legends folder, turn off visibility of the RPL_THEMES legend. Turn on visibility of the EP_DISABL legend. Highlight the EP_DISABL layer and expand the style: legend panel (see its heading at the bottom of the panels). Uncheck the options for a BACKGROUND and BORDER Click and drag the EP_DISABL legend to an appropriate position on the map. Double-click the map credits in the lower-left corner to attribute this map as your own work. Social Vulnerability Mapping - page 10

Check your Results and Save Your map should look something like the one below, depending on the classification method and color scheme you chose.

11 Check your Results and Save Your map should look something like the one below, depending on the classification method and color scheme you chose. At this point, export a JPEG of your map to save an image of it. o See the "Export a Map" section above to remember instructions for this. You may also want to save the map and its current layout as an indiemapper file for later use. Social Vulnerability Mapping - page 11

12 Map cases of Lyme Disease Make a map of Lyme Disease rates in Vermont I prepared the vtlyme.shp layer for you to contain Lyme Disease cases and all the social vulnerability index data at the level of counties in Vermont. o Most health data in the U.S. is reported at the county level, in part to protect the privacy of individuals living with rare diseases. Fortunately, the SVI data is also available at the county level. Therefore, I joined 2015 Lyme Disease data from the CDC to the County version of the SVI data for you in vtlyme.shp Hover your mouse over vtlyme.shp and use the + button to add a thematic layer. Lyme Disease is currently stored as a total number, rather than a rate, percentage, or density. It's unfair to represent total numbers as a choropleth map because of the variable sizes and populations of counties. Therefore, the following instructions will show you how to standardize the data by dividing by the total population, thereby calculating a rate of disease cases per total population. Use the following settings to create a map of Lyme Disease cases: o Choose the type of layer: one attribute o Choose an attribute: LymeCases o Choose a map type: Choropleth o Standardize your data: yes o By: E_TOTPOP (this is the total population) o Okay The legend for this is hideous: there is no way to truncate the high precision of the percentages. Therefore, let's leave the legend off and label the counties with the total number of Lyme cases. Hover your mouse over vtlyme.shp and use the + button to add a map labels layer. o o o o o Choose the type of layer: map labels Choose an attribute: LymeCases Standardize your data: no Label your data: auto placement Okay Now the color of the map gives a sense of the rate of Lime Disease cases, and the numbers on the map tell you the total number of cases. See Appendix B for my map. Alternatively, you could map the total number of Lyme Disease cases using proportional symbols or dot density. You should not standardize the totals for either of these map types: they are designed to represent total cases. The advantage to these two map types is that they can be overlaid on top of social vulnerability indicators. o Just don't take the exact location of the dots too seriously: they are randomly placed within each county! o See Appendix C for an example of a dot density map of Lyme Disease overlaid on the Social Vulnerability Index. Social Vulnerability Mapping - page 12

13 Map additional social vulnerability indicators Repeat the process used for the disability map to map a theme of your own interest. Deleting Mistakes Finalize the Map Use the SVI 2014 Documentation to choose and interpret a new variable to represent a theme of interest to you. o The documentation is available at o A graphic overview of available variables is found on page 2 of the SVI 2014 Documentation, followed by detailed explanations of each variable. The most straightforward variables to map are percentages or percentile rankings. In the SVI data, those variables have the following prefixes: o EPL_ indicates a percentile of all U.S. Census tracts, and should be mapped with the choropleth type. I also suggest an equal interval method to represent the data in percentiles from 0 to 20, 20 to 40, etc. o EP_ indicates a percentage, and should be mapped with the choropleth type. The following list includes root labels for the more interesting variables: o Below Poverty: POV o Unemployed: UNEMP o Per Capita Income: PCI o No High School Diploma: NOHSDP o Aged 65 or Older: AGE65 o Aged 17 or Younger: AGE17 o Older than Age 5 with a Disability: DISABL o Single-Parent Households: SNGPNT o Minority: MINRTY o Speak English "Less than Well": LIMENG o Multi-Unit Structures: MUNIT o Mobile Homes: MOBILE o Crowding: CROWD o No Vehicle: NOVEH o Group Quarters: GROUPQ o Uninsured Population: UNINSUR You may want to delete mistakes along the way! If so, go to the Layers panel and click on the unwanted layer under the Data Sources folder and click the Delete button at the bottom of the Layers panel. Do not delete the VTSVI.shp or vtlyme.shp layers, or you will have to reload the map. Remember to adjust the layout of the map to match your new variable, including the title, legend, and explanatory text annotation. Export the map as a JPEG image and Save an indiemapper file with your work. Social Vulnerability Mapping - page 13

14 Interpret Results Compare multiple variables and look for relationships Open or print the JPEG images of the disabled persons map and the single-variable map you just created and view them side-by-side. Consider the following questions: o Are there strong spatial patterns in the percentage of disabled persons and in the map you just greated? o Do the patterns in the two maps generally correlate with each other, where high values on one map correlate with high values on the other map? o Are the patterns inversely correlated, where high values on one map almost always correlate with low values on the other map, and vis a versa? o Are there interesting exceptions to the patterns? Optional: More advanced mapping Mapping variables other than percentages or percentiles More advanced users may also use variables with these prefixes: o E_ indicates an estimate, and this is a total number. Only map a total number with the proportional symbol or dot density types. A total number must be standardized in order to map it with the choropleth type. o RPL_ indicate the percentile rank of the overall vulnerability indicator or one of its four themes. It should only be mapped with the choropleth type. Advice for map type decisions: o If you chose a percentage, mean, median, or percentile variable, map them with the choropleth map type. o Totals should be mapped as proportional symbols or dot density, or standardized for choropleth maps. Advice for standardization: o If you are mapping percentages or percentile ranks, you do not need to standardize your Data. o If you are mapping a total number as a choropleth map, you need to standardize. This entails dividing by a second variable in order to calculate a percentage or density. For example: o To map a percentage of total population, standardize by E_TOTPOP o To map a percentage of households, standardize by E_HH o To map a percentage of housing units, standardize by E_HU o To map a density by area, standardize by AREA_SQMI o For more discussion on standardization, see the help menu or the Health Geography Primer for RI at Social Vulnerability Mapping - page 14

15 Mapping multiple attributes in indiemapper Other useful interactive maps for community health analysis It is possible to view two variables on a map simultaneously. One option is to layer a choropleth map (of density, percent, or percent rank) below, and map a proportional symbol or dot density map (of a total number) above. For example, you could visualize total people living in group quarters (colleges, prisons, and military barracks) with the E_GROUPQ variable and a dot density map. Another option unique to indiemapper is a multivariate map. I suggest choosing two percentile rank variables for the attributes, since the CDC guarantees they'll both be measured on the same scale with the same directionality. I also suggest a bivariate choropleth map as the map type. I get the best results with a Sequential color scheme, since this will assign dull colors to the least vulnerable areas and increasingly intense colors to the most vulnerable areas according to the two variables. For more geographic data exploration related to community health analysis, try out the following interactive maps on your own: o The Environmental Protection Agency developed an Environmental Justice screening tool, rich with data on both social and demographic variables and environmental risks. o The American Medical Association recently updated the AMA Health Workforce Mapper, which can display point locations of health providers and choropleth rates of providers per population at the county level. Social Vulnerability Mapping - page 15

16 Appendix A: Workbook in Community Social Vulnerability Analysis Geographic Location Middlebury College Area a Reference Location State County Town or City ZIP Code Census Tract Social Vulnerability Indicators Percent, Mean, or Median Percentile Rank Percent, Mean, or Median Percentile Rank Poverty Unemployment Income Education Elderly Children and Youth Single Parent Households Minority Race/Ethnicity Non-English Speaking High-density housing (>= 10 units per building) Mobile homes Crowded housing (more people than rooms) No vehicle ownership Group quarters (e.g. prisons, nursing homes, dormitories, or barracks) a Choose one census tract near Middlebury College. Choose a second census tract for a Reference Location for comparison elsewhere in Vermont or in a place you are familiar with. Social Vulnerability Mapping - page 16

17 Appendix B: Lyme Disease Rates Social Vulnerability Mapping - page 17

18 Appendix C: Lyme Disease and Social Vulnerability Note: I noticed that I misspelled "represents" in the annotated text here. Be careful: Indiemapper doesn't spell check! Social Vulnerability Mapping - page 18

Module 10 Summative Assessment

Module 10 Summative Assessment Activity In this activity you will use the three dimensions of vulnerability that you learned about in this module exposure, sensitivity, and adaptive capacity to assess