Measuring Quality of Life in Communities Surrounding Airports (2020)

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34 Analyzing and Communicating Results of Assessment This chapter presents instructions for basic analysis and presentation of the qualitative and quantitative data that are gathered as part of a QOL assessment (Step 5 of the process). Because each assessment will lead to unique results, the suggestions for data presentation below are necessarily general. However, to illustrate how data analysis and visualization may work, the research team informally gathered qualitative data responses from a small sample of 32 volun­ teers living within the service area of a large international airport and quantitative data from publicly available data sets covering the same spatial area. The individuals participating in this example assessment are employed by the research team contractor firms and are not represen­ tative of the diversity of the population in the study area. Thus, the results of this assessment are not meaningful as a full scale QOL assessment on a representative population surrounding an airport. Rather, these results are used merely to illustrate how one could use the following approaches to analyze survey responses and to communicate meaningful information about QOL in communities surrounding an airport. Additional details are provided in Appendix G: Examples of Data Visualizations. 5.1 Visualizing Data in a Quadrant Plot After compiling participants’ responses and indicator scores in a spreadsheet, many options are available for visualizing the data and presenting them for interpretation, analysis, and decision­support purposes. The Quality of Life Assessment Methodology was designed to allow for simple visualization of the final QOL data in the form of quadrant graphs that display indicators along two axes: QOL score and importance score (Figure 5). Such graphs can display indicators for an individual category (e.g., transportation, social relationships, or environment); exclusively qualitative or quantitative indicators; or all indicators across the assessment categories and indicator types, among other analysis options. Figure 5 uses data from the environmental category of the assessment framework. In this plot, the research team presents average results calculated for each qualitative indicator (represented by the diamond icons), based on responses from a small sample of 32 individuals living near the airport and results for the quantitative indicators (represented by the circle icons), based on publicly available data sets. For both types of indicators, the research team plots average QOL and importance scores as reported from the 32 participants. For an airport or other entity interested in understanding what issues drive QOL determina­ tion in a community, it is useful to plot an average of QOL scores based on the results for a representative group of individuals in the community. Additionally, indicators for one indi­ vidual can be plotted in a quadrant plot. Averaging QOL scores for individuals participating in the assessment, and averaging corresponding importance scores for assessment indicators, C H A P T E R 5

Analyzing and Communicating Results of Assessment 35 can be somewhat challenging. Appendix F: Sample Quality of Life Assessment Introduction PowerPoint, found at www.trb.org by searching for “ACRP Research Report 221,” discusses some of the choices that can be made to ensure that the quadrant plot is easy to interpret and that important issues do not get diluted just because a portion of the population is not concerned about them. For the purposes of Figure 5, a simple average was used to obtain a single QOL score for each of the qualitative indicators and an importance score for each of the qualitative and quantitative indicators. These values were then rounded to the nearest whole number to determine which quadrant each indicator fell within. For example, 32 participants in the example assessment responded to the question for qualitative Indicator EN8 on light pollution (i.e., how much are you bothered by light pollution from streetlights, cars, buildings, billboards, etc.?) by choosing one of the following responses: (1) extremely, (2) somewhat, (3) very little, and (4) not at all. These results were translated into QOL scores ranging from 1 to 4, and the average of the QOL scores for the 32 respondents was calculated at a value of 2.6. This QOL score was then rounded to the nearest whole number (i.e., a value of 3). A similar process was followed for the importance score, which was estimated and rounded to a value of 2. Therefore, Indicator EN8 has a QOL score of 3 and an importance score of 2 and falls within the “low priority” quadrant. Regardless of how the results are averaged, as long as the quadrant plots display indicators for some categories across more than one quadrant (three or four quadrants being the ideal), the visualization is likely to be helpful in setting priorities and stimulating ongoing discussions about what brings about or serves as an obstacle to the pursuit of high QOL for community residents. The visualizations allow straightforward interpretations of what the qualitative and quantitative indicators mean for a local community’s QOL and what steps an airport may take to improve QOL in the surrounding community or to lessen their impact on QOL. 5.2 Interpreting a Quadrant Plot As shown in Figure 5, importance scores are presented on the vertical y-axis beginning with a score equal to 1, and QOL scores are presented on the horizontal x-axis beginning with a QOL score of 4. This organization results in a quadrant providing quick visualization and simple interpretation of the various aspects of QOL represented by the qualitative and quantitative indicators. Each quadrant is defined by a unique combination of QOL and importance scores (1 through 4) and categorized based on priority into the following groups: • Low priority = high QOL (3 or 4) and low importance (1 or 2). • Small problems that can add up = QOL and importance both low (1 or 2). • Monitor for changes = QOL and importance both high (3 or 4). • Problems contributing to a low QOL = low QOL (1 or 2) and high importance (3 or 4). Identification numbers are included for each qualitative and quantitative indicator in the visualizations to allow the reader to determine exactly what aspects of QOL are being shown within each quadrant. As noted above, airports and their stakeholders are interested in analysis and interpretation of indicator results that represent a compilation, aggregation, or average QOL score for the indicator in question. When all indicators are plotted on the quadrant plot for the communi- ties in question, it becomes clear what the critical problems are (those indicators in the upper right quadrant of the plot). In this way, the quadrant plot can be used to facilitate the interpretation of QOL assessment results and, more importantly, assist airports in identifying QOL issues in the surrounding communities that should be addressed or monitored for changes.

36 Measuring Quality of Life in Communities Surrounding Airports Qualitative and quantitative indicators with high importance weights and high QOL scores show the areas where communities are doing well. For example, if a qualitative or quantitative indicator ranked as highly important is also identified as demonstrating high QOL, the airport may consider the community as thriving with respect to that data point or topic (“monitor for changes”). This means that the community has an inherently high QOL or has already taken steps to increase QOL. The term “monitor for changes” indicates that even though QOL is currently high for the indicator, it would be prudent to remain aware of how that indica- tor may be affected by future airport decisions because the indicator was ranked as very important to overall QOL. Conversely, indicators with high importance scores and low QOL scores demonstrate opportunities for improvement in the community. The problems may need to be addressed by individuals or local organizations within the community, health care professionals, city or local governments, or others. In some cases, the airport may be able to help address the problem over time. Regardless, it will be helpful for the airport to be aware of issues contributing to lower QOL in the community. Quadrant plots can be used to identify indicators—and associated issues—that represent problems for community stakeholders. Overall QOL for an individual and aggregated community-level QOL is dependent on many factors. The Quality of Life Assessment Methodology presented in this guidebook is intended to reflect these factors in the 100 indicators that cover various aspects of overall QOL over six categories. Results for an individual could show that many indicators of high importance (i.e., importance score of 3 or 4) reflect a low QOL score (i.e., QOL score of 1 or 2), which could point to an overall low QOL for that individual. Similarly, if many indicators of high impor- tance receive a high QOL score for an individual, it may be that the individual has a very high QOL. The same is true when considering aggregated averaged QOL scores for the entire commu- nity or study area. This information can be easily communicated via quadrant plots. 5.3 Examples of Quadrant Plots The research team created an example data set by gathering information from a small sample of 32 individuals residing near a large international airport. Participants were asked to respond to the questions developed for each of the qualitative indicators in an online survey, as well as to rank the importance of each qualitative and quantitative indicator to their overall QOL or the QOL of their community (in the case of indicators focused on larger community issues or vulnerable populations). Additional demographic information was collected to better under- stand and characterize the sample of participants (e.g., age, gender identity, race or ethnicity, marital status, education, income, current housing situation, and proximity to the nearest major airport). Collection of this demographic information is optional but may be useful as an airport analyzes assessment results and seeks to understand patterns in the data. The research team concurrently gathered publicly available data at the scale of the community being assessed— or the region, if finer scale data were not available—for each of the quantitative indicators, following the approach described in Section 4.1 and the detailed instructions provided in Appendix B. For all qualitative and quantitative indicators, the research team assigned an importance score of 1 through 4 to each of the participants’ responses. For example, for one indicator a response of “not at all” equals an importance score of 1, a response of “a little” equals an importance score of 2, a response of “somewhat” equals an importance score of 3, and a response of “extremely” equals an importance score of 4. For the quantitative indicators and as described in Section 4.1, the research team gathered available data for each indicator, reviewed the indicator’s data thresholds (e.g., separating data values that would give the indicator a score in the low QOL range from data values that would give the indicator a score in the moderate

Analyzing and Communicating Results of Assessment 37 QOL range), and then assigned QOL scores of 1 through 4, based on the data value for the community. The averages of importance scores and QOL scores were calculated, as described in Chapter 4. Discussions of example quadrant plots from this data set are found in Section 5.3.1 and Section 5.3.2 for environmental indicators and transportation indicators, respectively. Details on the sample population and additional example quadrant plots are provided in Appendix G. 5.3.1 Example Quadrant Plot for Environmental Indicators The sample quadrant plot in Figure 5 presents results for all the indicators within the envi- ronmental category. While the data shown on this plot are not representative of the community surrounding the airport, the data can be used to illustrate how one would interpret such a plot. Most of the qualitative and quantitative indicator data for this category plot to the “monitor for changes” quadrant (i.e., high QOL and high importance). These are indicators that participants found to be highly important to their overall QOL and that were sufficiently addressed with respect to QOL in their community. For example, qualitative Indicators EN4 (local aesthetics) and EN7 (convenience to amenities) fall within this quadrant. The sample data suggest that while participants consider these indicators to be important to their overall QOL, both are sufficiently addressed in the local community. More specifically, these results indicate that participants, on average, find their community to be very attractive and feel that they have easy access to local amenities. There may be some outliers hidden by the averaging process. That is, it may be that some portion (perhaps 10 percent) of assessment participants scored these indicators in the low QOL range. The averaging of responses from 32 participants—or many hundreds, if conducting a full-scale community assessment—can mask information about the distribution of responses, some of which may be valuable for airports to be aware of. To address this issue, it would be valuable for airports to graph the distribution of responses for each indicator. Such graphs could be made accessible within a quadrant plot, such that when a user clicks on an icon for an indicator, a small graph of the data distribution for that indicator pops up. In Figure 5, quantitative Indicators EN12 (outdoor air quality) and EN14 (amount of pro- tected area) fall within the “problems contributing to a low QOL” quadrant (low QOL and high importance). These indicators represent QOL components that the participants found to be highly important but for which publicly available data suggest a low QOL. For Indicator EN12, the median air quality index for the area mapped to the range of values developed to represent a QOL score of 1. For Indicator EN14, the percentage of land in the city that is under at least some degree of legal protection from development mapped to a QOL score of 2. The indicators that appear in the “low priority” quadrant include EN8 (light pollution), EN9 (satisfaction with the environmental stewardship of the nearest airport), and EN10 (intensity of aircraft noise annoyance). Participants found the components reflected by these indicators to be of low importance and to have a high QOL. Not all of the QOL indicators in Figure 5 are directly affected by airports, but it may be worthwhile for airports to consider these factors when planning activities or events or—more broadly—when evaluating the effect of airport operations on the surrounding community. For example, the local airport may wish to remain mindful of any issues reflected by indicators within the “monitor for changes” quadrant (e.g., local aesthetics and quality of parks and natural spaces) and should consider whether there are ways to work with the local community to help address issues appearing in the “problems contributing to a low QOL” quadrant (e.g., amount of protected area). These efforts would help ensure that any future planned activities or projects at the airport do not negatively affect QOL for the surrounding community.

38 Measuring Quality of Life in Communities Surrounding Airports 5.3.2 Example Quadrant Plot for Transportation Indicators Figure 6 is an example quadrant plot from the sample population for transportation-related indicators. In this plot, there are a fair number of indicators within the “problems contributing to a low QOL” quadrant, including: • Traffic congestion (qualitative Indicator T1 and quantitative Indicator T9), • Transportation system redundancy (qualitative Indicator T4), • Maintenance of transportation infrastructure (qualitative Indicator T5), and • Access to transportation by vulnerable populations (qualitative Indicator T7). Even though data from this sample of the population suggest that satisfaction with the nearest airport (Indicator T8) is a low priority, the airport will benefit from greater awareness of the issues—as previously listed—that were identified by their local community members (if this were a full assessment of that community). Airports may wish to consider these issues in planning decisions and when working with and communicating with the local community on many topics of interest. Figure 6. Sample quadrant plot for transportation indicators. T1T2 T3 T4 T5 T6 T7 T8 T9T11 T12 1 2 3 4 1234 Im po rt an ce S co re QOL Score Quality of Life Indicators Low priority Qualitative Indicator Quantitative Indicator Monitor for changes Problems contributing to a low QOL Small problems that can add up T10

Analyzing and Communicating Results of Assessment 39 5.4 Communicating Results The goal of a QOL assessment is not for airports to calculate an overall QOL score for comparison to other airports, but rather to stimulate increased communication and under- standing within an airport (among different departments, which may have unique perspectives on how they affect and are affected by the community outside the airport), with community leaders and stakeholders, and with the community outside of the airport as a whole. QOL assess- ment results can serve as a useful tool in facilitating that understanding via presentations and publications that can be made available to those beyond the initial group engaged with develop- ment or ongoing meetings about the assessment. Further, assessment results can help airports target planning, community outreach, or environmental efforts and focus on preserving or increasing QOL in those QOL areas considered to be most important by community members. The quadrant plots previously described offer a tool for airports to display indicators of QOL in the surrounding community—including which issues are high priority—to airport departments not involved in the assessment process, to committees where community members are included, and to airport management. QOL assessment results may have very practical consequences with regard to what airport decisions need to be vetted with community members, and assessment results may serve as a starting point for discussing issues that either the airport or community leaders believe warrant further discussion. In some cases, showcasing an under- standing that the airport is aware of community priorities may go a long way toward making discussions with community members more productive than they would be otherwise. Though airports may not have a direct effect on all the indicators included in the QOL assessment, indicators appearing in the “problems contributing to a low QOL” quadrant should be consid- ered when making decisions concerning operations, planning, project development, and so on. As noted, the tool considers QOL across multiple categories. This allows for understanding the breadth of QOL across a community, relative QOL among the categories, and to assess progress over time as the tool is used iteratively. As the Quality of Life Assessment Methodology framework is applied iteratively, applying the indicators necessitates interaction with and between airport stakeholders. These interactions provide additional learning and coordination opportu- nities, and these interactions can be used to further refine the QOL assessments and prioritize activities in response to the assessments’ findings.