The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW) Messages Distributed via Mobile Phone Apps

*Deanna D. Sellnow, Lucile M. Jones, Timothy L. Sellnow, Patric Spence, Derek R. Lane and Nigel Haarstad*

## **Abstract**

Short response time available in the event of a major earthquake poses unique challenges for earthquake early warning (EEW). Mobile phone apps may be one way to deliver such messages effectively. In this two-phase study, several hundred participants were first randomly assigned to one of eight experimental conditions. Results of phase one afforded researchers the ability to reduce the number of conditions to four. Phase two consisted of five experimental conditions. In each condition, a 10 second EEW was delivered via a phone app. The four treatment conditions were designed according to elements of the IDEA model. The control condition was based on the actual ShakeAlert EEW computer program message being used by emergency managers across the US west coast at the time. Results of this experiment revealed that EEW messages designed according to the IDEA model were more effective in producing desired learning outcomes than the ShakeAlert control message. Thus, the IDEA model may provide an effective content framework for those choosing to develop such apps for EEW.

**Keywords:** IDEA model, risk communication, crisis communication, disaster warnings, earthquake early warning, communication and technology

## **1. Introduction**

Effective earthquake early warning messages can empower target populations to take appropriate actions for self-protection and, ultimately, save lives. The communication challenges facing those who wish to design warning messages involve both content and access. Content focuses on gaining attention and providing appropriate instructions for self-protection. Access depends on sending the messages through a channel or channels and a medium or media that can be retrieved quickly and easily. A team of researchers completed a project designed to develop such content and access. The project was based on previous warning message testing research. Specifically, researchers attempted to apply the IDEA model to create brief, easily accessible earthquake early warning messages via a mobile phone app.

The IDEA model for effective instructional risk and crisis communication is an acronym that stands for internalization, distribution, explanation, and action [1]. According to the IDEA model, such messages ought to include appeals to internalization (e.g., proximity, personal relevance, impact, timeliness), be distributed over multiple channels deemed appropriate based on crisis type and target audience(s), and offer cogent explanations about what is happening. These explanations should be offered by credible sources and the scientific information provided in them be both accurate and translated intelligibly for the target population(s). These messages also must include specific action steps receivers are to take (or not take) for self-protection [2–7]. The following paragraphs describe the message design and testing project processes, results, and conclusions based on the timeline under which it unfolded.

### **2. The IDEA model message design and testing experiment**

The design and testing process occurred in two phases. Thus, this section first describes the study design process followed by the results of the two-phase experiment. It closes with a discussion of the results as they may inform the design of effective EEW messages delivered via phone apps.

#### **2.1 Designing the study**

To launch the project, a multidisciplinary group comprised of seismologists, instructional risk and crisis communication scientists, graphic artists, and emergency managers from the US west coast states met in Pasadena, California to participate in a 3-day design storm focused on earthquake early warning messaging. This design storm was essentially a synergistic brainstorming session to formulate an ecologically valid plan based on a broad cross-section of expertise represented in crisis communication and earthquake science that would inform earthquake early warning message design.

Ultimately, the group agreed that message content **distributed** via a phone app would likely be a predominant interface for US west coast residents. Thus, message content (both visual and aural) would be designed for a phone app. The group also agreed that the content would need to be developed based on social science crisis communication best practices research.

Message content would address **internalization** components as follows. To test proximity, some conditions will include a map and others will not. To test timeliness, the conditions will include a countdown to when the strong shaking is expected to occur. Timeliness would also be tested by providing no more than 10 seconds for the entire message. Personal relevance would be addressed by focusing on "very strong shaking" (i.e., "7" or higher Intensity level shaking).

Message content would address **explanation** components as follows. To address source credibility, Dr. Lucy Jones' voice was recorded and applied as she is a known and credible earthquake expert among many throughout the US west coast states. The accurate science provided by seismologists was translated into simple, easily comprehended language. Also, intensity level was selected rather than magnitude because intensity is directly related to very strong shaking that can harm individuals that do not enact the appropriate actions for self-protection. Finally, some conditions used a verbal message–very strong sharking—and others a numerical message—"7"—to signal the kind of shaking to occur. This allowed researchers to test for potential lack of understanding regarding what "7" might mean. Existing research suggest that less numerate people—those that lack the ability to process mathematical concepts—tend to trust verbal risk information that they can

**13**

*2.2.1 Intensity*

intensity (χ<sup>2</sup>

*The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW)…*

Message content would address **action** components by a visual graphic accompanied by a verbal message—Drop! Cover! Hold on!—reinforced orally by a speaker saying "Drop, take cover, hold on." All experts in the design storm agreed that all conditions testing high intensity earthquake early warning message should include this specific action statement in some way. Thus, all treatment conditions included

The graphic artists created eight visual representations of a smart phone app screen, which the instructional risk and crisis team would test during the fall semester. The team would create an online survey to collect responses to the various versions and measure their effectiveness based on affective, cognitive, and behavioral learning outcomes. A snowball sample of participants would be invited via Lucy Jones' Facebook page and the Shakeout website. The survey collected quantita-

The results for phase one of the project were collected using a survey distributed via an invitation on Lucy Jones' Facebook page and the Shakeout website. Both outlets targeted users across the US west coast. Of the 469 surveys entered, 198 were completed in entirety and, thus, usable for the analysis. The usable data resulted in 22–28 responses per condition for the eight varieties tested. The sample was comprised of 106 (56%) females and 92 (44%) males. A majority of the participants (80%) were Caucasians from Southern California (n = 153). Notably, 37% (n = 66)

tive and qualitative data and employed a mixed methods analysis.

of the participants reported earning incomes over \$100,000 annually.

of visual images, and perceived helpfulness of aural components.

vocal instructions to "drop, take cover, and hold on."

Eight message conditions were manipulated in ways that indicated the location with or without a map, intensity in numerical or non-numerical form, and countdown in graphic or numerical form. All eight conditions used the same aural warning alert sound and voice command. All eight conditions offered actionable

Survey responses were examined using both quantitative and qualitative methods. Statistically significant quantitative results and dominant qualitative themes emerging in open-ended responses are reported here. The quantitative analysis produced five sets of meaningful results. The subsequent qualitative examination of open-ended responses provided additional insight to inform message refinement. In total, 87 participants (44%) offered open-ended responses to the prompt, "Please provide any additional feedback you believe would be helpful concerning the quality of the app." These comments ranged from 3 to 328 words (M = 50) Exemplars from these responses are reported with the corresponding quantitative results. These sets of results focus on intensity, location, countdown, perceived helpfulness

A chi-square test revealed that participants were more likely to recall earthquake intensity level correctly when the message included a numerical representation of

recalled the numeral "7" than the phrase "strong shaking." Qualitative responses indicated confusion, however, regarding what the number "7" actually means. Some noted that "the number was important," but many also claimed that "many people

= 78.049, df = 7, p < 0.0001). In other words, participants more accurately

comprehend more than numeric information that may be unintelligible to them and, consequently, make poorer decisions based on numerical data than highly numerate people [8]. Thus, it seemed critical to test intensity comprehension based

*DOI: http://dx.doi.org/10.5772/intechopen.85557*

on numeric versus verbal reporting.

**2.2 Message testing: phase one**

this message.

*The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW)… DOI: http://dx.doi.org/10.5772/intechopen.85557*

comprehend more than numeric information that may be unintelligible to them and, consequently, make poorer decisions based on numerical data than highly numerate people [8]. Thus, it seemed critical to test intensity comprehension based on numeric versus verbal reporting.

Message content would address **action** components by a visual graphic accompanied by a verbal message—Drop! Cover! Hold on!—reinforced orally by a speaker saying "Drop, take cover, hold on." All experts in the design storm agreed that all conditions testing high intensity earthquake early warning message should include this specific action statement in some way. Thus, all treatment conditions included this message.

The graphic artists created eight visual representations of a smart phone app screen, which the instructional risk and crisis team would test during the fall semester. The team would create an online survey to collect responses to the various versions and measure their effectiveness based on affective, cognitive, and behavioral learning outcomes. A snowball sample of participants would be invited via Lucy Jones' Facebook page and the Shakeout website. The survey collected quantitative and qualitative data and employed a mixed methods analysis.

#### **2.2 Message testing: phase one**

*Earthquakes - Impact, Community Vulnerability and Resilience*

and conclusions based on the timeline under which it unfolded.

effective EEW messages delivered via phone apps.

**2.1 Designing the study**

warning message design.

communication best practices research.

**2. The IDEA model message design and testing experiment**

The IDEA model for effective instructional risk and crisis communication is an acronym that stands for internalization, distribution, explanation, and action [1]. According to the IDEA model, such messages ought to include appeals to internalization (e.g., proximity, personal relevance, impact, timeliness), be distributed over multiple channels deemed appropriate based on crisis type and target audience(s), and offer cogent explanations about what is happening. These explanations should be offered by credible sources and the scientific information provided in them be both accurate and translated intelligibly for the target population(s). These messages also must include specific action steps receivers are to take (or not take) for self-protection [2–7]. The following paragraphs describe the message design and testing project processes, results,

The design and testing process occurred in two phases. Thus, this section first describes the study design process followed by the results of the two-phase experiment. It closes with a discussion of the results as they may inform the design of

To launch the project, a multidisciplinary group comprised of seismologists, instructional risk and crisis communication scientists, graphic artists, and emergency managers from the US west coast states met in Pasadena, California to participate in a 3-day design storm focused on earthquake early warning messaging. This design storm was essentially a synergistic brainstorming session to formulate an ecologically valid plan based on a broad cross-section of expertise represented in crisis communication and earthquake science that would inform earthquake early

Ultimately, the group agreed that message content **distributed** via a phone app would likely be a predominant interface for US west coast residents. Thus, message content (both visual and aural) would be designed for a phone app. The group also agreed that the content would need to be developed based on social science crisis

Message content would address **internalization** components as follows. To test proximity, some conditions will include a map and others will not. To test timeliness, the conditions will include a countdown to when the strong shaking is expected to occur. Timeliness would also be tested by providing no more than 10 seconds for the entire message. Personal relevance would be addressed by focusing

Message content would address **explanation** components as follows. To address source credibility, Dr. Lucy Jones' voice was recorded and applied as she is a known and credible earthquake expert among many throughout the US west coast states. The accurate science provided by seismologists was translated into simple, easily comprehended language. Also, intensity level was selected rather than magnitude because intensity is directly related to very strong shaking that can harm individuals that do not enact the appropriate actions for self-protection. Finally, some conditions used a verbal message–very strong sharking—and others a numerical message—"7"—to signal the kind of shaking to occur. This allowed researchers to test for potential lack of understanding regarding what "7" might mean. Existing research suggest that less numerate people—those that lack the ability to process mathematical concepts—tend to trust verbal risk information that they can

on "very strong shaking" (i.e., "7" or higher Intensity level shaking).

**12**

The results for phase one of the project were collected using a survey distributed via an invitation on Lucy Jones' Facebook page and the Shakeout website. Both outlets targeted users across the US west coast. Of the 469 surveys entered, 198 were completed in entirety and, thus, usable for the analysis. The usable data resulted in 22–28 responses per condition for the eight varieties tested. The sample was comprised of 106 (56%) females and 92 (44%) males. A majority of the participants (80%) were Caucasians from Southern California (n = 153). Notably, 37% (n = 66) of the participants reported earning incomes over \$100,000 annually.

Eight message conditions were manipulated in ways that indicated the location with or without a map, intensity in numerical or non-numerical form, and countdown in graphic or numerical form. All eight conditions used the same aural warning alert sound and voice command. All eight conditions offered actionable vocal instructions to "drop, take cover, and hold on."

Survey responses were examined using both quantitative and qualitative methods. Statistically significant quantitative results and dominant qualitative themes emerging in open-ended responses are reported here. The quantitative analysis produced five sets of meaningful results. The subsequent qualitative examination of open-ended responses provided additional insight to inform message refinement. In total, 87 participants (44%) offered open-ended responses to the prompt, "Please provide any additional feedback you believe would be helpful concerning the quality of the app." These comments ranged from 3 to 328 words (M = 50) Exemplars from these responses are reported with the corresponding quantitative results. These sets of results focus on intensity, location, countdown, perceived helpfulness of visual images, and perceived helpfulness of aural components.

#### *2.2.1 Intensity*

A chi-square test revealed that participants were more likely to recall earthquake intensity level correctly when the message included a numerical representation of intensity (χ<sup>2</sup> = 78.049, df = 7, p < 0.0001). In other words, participants more accurately recalled the numeral "7" than the phrase "strong shaking." Qualitative responses indicated confusion, however, regarding what the number "7" actually means. Some noted that "the number was important," but many also claimed that "many people

don't known what INTENSITY means." One respondent wrote, for example, "I assume the 7 means 7 out of 10?" Thus, although respondents could recall seeing the number "7," many did not know what it meant. Therefore, if a numerical representation is present in the warning message, the message must also somehow indicate its meaning and/or prior instruction may be necessary for it to be truly meaningful/effective.

#### *2.2.2 Location*

Four of the eight conditions tested included a map indicating the epicenter of the earthquake in relation to well-known California cities and highways and four did not. Participants viewing a message without a map were more likely to recall the earthquake's location incorrectly or not at all than those viewing a message that included a map (χ<sup>2</sup> = 43.831, df = 7, p < 0.0001). Qualitative analysis of open-ended responses confirmed the value of the map. Participants viewing the map reported, for example, that "the map showing the general area of the quake was important" and "the map helped me realize where the earthquake was occurring." Moreover, some commented about the size of the map saying, "the map was far too small to be useful." Those viewing a message without a map made queries such as: "Did I miss the location?" Based on these results, then, the prototype messages should include a map and the map should be large enough to see easily via a smart phone app.

#### *2.2.3 Countdown*

Four conditions provided countdowns represented numerically and four offered countdowns represented in graphic form. The quantitative analysis revealed no statistically significant differences in terms of message recall or effectiveness. The qualitative analysis of the open-ended responses provided insight as to why. When participants viewed the numerical countdown, some reported that the static image was confusing because it did not actually count down from 6 to 5, 4, 3, 2, and 1. Moreover, participants that viewed conditions that conveyed both the countdown and the intensity in numerical form were confused about the meaning of each one. When participants viewed the graphic countdown, they also indicated confusion due to the fact that it was static and did not actually move as the number of seconds to impact declined. Many commented as this participant did: "I'm not sure what the pie graph was supposed to represent." Regardless of the version participants viewed, many suggested using a countdown clock (stopwatch image or digital clock face: 06, 05, 04 that actually ticked down with each second). As one participant noted, "a countdown clock would underscore the importance of acting quickly." Clearly, additional message refinement using an active countdown was warranted.

#### *2.2.4 Perceived helpfulness*

Perceived helpfulness was measured using a Likert-type scale ranging from 1 to 5, where 1 was least helpful and 5 was most helpful. In addition, open-ended responses were collected and analyzed for recurring themes. Perceived helpfulness results were analyzed and reported for visual images and aural components.

An Analysis of Variance test revealed significant differences among conditions. Regarding visuals, messages that did not include maps and numerical intensity indicators were perceived as least helpful (F(7,188) = 7.789, p < 0.0001). These results support previous findings regarding location and intensity indicators.

An Analysis of Variance revealed no significant differences in the perceived helpfulness of the app based on the alert sound or the speaker's voice. Since this phase one experiment did not examine different alert message sounds or speaker

**15**

*The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW)…*

voices, this result is not surprising. The qualitative analysis did reveal several dominant themes, however, regarding aural components that may inform future message design and testing. Regarding the alert sound, for example, participants responded it "was too light and high pitched" and "should also vibrate the phone." Some also argued "it should be the same as other national emergency radio announcements." Others contended that it should be the same as the sound used in Japanese earthquake warning messages. Regarding the speaker's voice, some claimed that recognizing the voice of Lucy Jones provided a sense of credibility. In other words, the voice "has meaning because I recognize it is Dr. Lucy Jones I find her voice compelling and reassuring." Thus, this qualitative analysis suggests that the familiar voice of a noted expert may be most important for fostering trustworthiness. These preliminary findings support the meta-analysis of hundreds of communication studies drawing similar conclusions that there are, in fact, negligible differences in perceived credibility and effectiveness based on sex and perceived gender [9].

The results of phase one of this research project revealed several findings:

• Participants were significantly more likely to recall the location of the earthquake when the app included a map. They also perceived the apps that included

• Participants were significantly more likely to recall the intensity level of the earthquake when a numerical indicator was included. However, a qualitative analysis of open-ended responses revealed a great deal of confusion about

• No significant differences were found among apps that used numerical versus graphical countdown imaging. The qualitative analysis of open-ended responses revealed confusion because neither countdown approach actually counted down by seconds from 6, to 5, 4, 3, 2, and 1. Participants indicated a desire to see the seconds dropping via an image that represents a digital clock-

• This phase one pilot project did not test different alert sounds or voice commands statistically as the project was already comprised of eight conditions. However, a qualitative analysis of open-ended responses revealed that participants believed the alert sound should be familiar (e.g., similar to the one used in the US for other warning messages or similar to the one being used already

Based on these results and input from risk and crisis communication specialists, seismologists, and emergency manager practitioners, the research team moved into phase two of the project. More specifically, the researchers used this information to refine the prototype IDEA model messages down from eight to four conditions and created a control message based on the existing ShakeAlert warning message computer program used by emergency managers throughout the US west coast states at the time.

Based on the results of phase one message testing and focused feedback from crisis and risk communication subject matter experts, seismologists, and

*DOI: http://dx.doi.org/10.5772/intechopen.85557*

*2.2.5 Phase one summary and next steps*

a map to be most helpful.

what this number means.

face or stopwatch-type image.

**2.3 Message testing: phase two**

in Japan for earthquake warning messages).

## *The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW)… DOI: http://dx.doi.org/10.5772/intechopen.85557*

voices, this result is not surprising. The qualitative analysis did reveal several dominant themes, however, regarding aural components that may inform future message design and testing. Regarding the alert sound, for example, participants responded it "was too light and high pitched" and "should also vibrate the phone." Some also argued "it should be the same as other national emergency radio announcements." Others contended that it should be the same as the sound used in Japanese earthquake warning messages. Regarding the speaker's voice, some claimed that recognizing the voice of Lucy Jones provided a sense of credibility. In other words, the voice "has meaning because I recognize it is Dr. Lucy Jones I find her voice compelling and reassuring." Thus, this qualitative analysis suggests that the familiar voice of a noted expert may be most important for fostering trustworthiness. These preliminary findings support the meta-analysis of hundreds of communication studies drawing similar conclusions that there are, in fact, negligible differences in perceived credibility and effectiveness based on sex and perceived gender [9].

## *2.2.5 Phase one summary and next steps*

*Earthquakes - Impact, Community Vulnerability and Resilience*

*2.2.2 Location*

included a map (χ<sup>2</sup>

*2.2.3 Countdown*

*2.2.4 Perceived helpfulness*

don't known what INTENSITY means." One respondent wrote, for example, "I assume the 7 means 7 out of 10?" Thus, although respondents could recall seeing the number "7," many did not know what it meant. Therefore, if a numerical representation is present in the warning message, the message must also somehow indicate its meaning and/or prior instruction may be necessary for it to be truly meaningful/effective.

Four of the eight conditions tested included a map indicating the epicenter of the earthquake in relation to well-known California cities and highways and four did not. Participants viewing a message without a map were more likely to recall the earthquake's location incorrectly or not at all than those viewing a message that

responses confirmed the value of the map. Participants viewing the map reported, for example, that "the map showing the general area of the quake was important" and "the map helped me realize where the earthquake was occurring." Moreover, some commented about the size of the map saying, "the map was far too small to be useful." Those viewing a message without a map made queries such as: "Did I miss the location?" Based on these results, then, the prototype messages should include a

map and the map should be large enough to see easily via a smart phone app.

countdowns represented in graphic form. The quantitative analysis revealed no statistically significant differences in terms of message recall or effectiveness. The qualitative analysis of the open-ended responses provided insight as to why. When participants viewed the numerical countdown, some reported that the static image was confusing because it did not actually count down from 6 to 5, 4, 3, 2, and 1. Moreover, participants that viewed conditions that conveyed both the countdown and the intensity in numerical form were confused about the meaning of each one. When participants viewed the graphic countdown, they also indicated confusion due to the fact that it was static and did not actually move as the number of seconds to impact declined. Many commented as this participant did: "I'm not sure what the pie graph was supposed to represent." Regardless of the version participants viewed, many suggested using a countdown clock (stopwatch image or digital clock face: 06, 05, 04 that actually ticked down with each second). As one participant noted, "a countdown clock would underscore the importance of acting quickly." Clearly,

additional message refinement using an active countdown was warranted.

support previous findings regarding location and intensity indicators.

Perceived helpfulness was measured using a Likert-type scale ranging from 1 to 5, where 1 was least helpful and 5 was most helpful. In addition, open-ended responses were collected and analyzed for recurring themes. Perceived helpfulness results were analyzed and reported for visual images and aural components.

An Analysis of Variance test revealed significant differences among conditions. Regarding visuals, messages that did not include maps and numerical intensity indicators were perceived as least helpful (F(7,188) = 7.789, p < 0.0001). These results

An Analysis of Variance revealed no significant differences in the perceived helpfulness of the app based on the alert sound or the speaker's voice. Since this phase one experiment did not examine different alert message sounds or speaker

Four conditions provided countdowns represented numerically and four offered

= 43.831, df = 7, p < 0.0001). Qualitative analysis of open-ended

**14**

The results of phase one of this research project revealed several findings:


Based on these results and input from risk and crisis communication specialists, seismologists, and emergency manager practitioners, the research team moved into phase two of the project. More specifically, the researchers used this information to refine the prototype IDEA model messages down from eight to four conditions and created a control message based on the existing ShakeAlert warning message computer program used by emergency managers throughout the US west coast states at the time.

## **2.3 Message testing: phase two**

Based on the results of phase one message testing and focused feedback from crisis and risk communication subject matter experts, seismologists, and practitioners, the original eight conditions were reduced to four. These four treatment conditions were manipulated as follows:

1.Japanese alert sound with numerical intensity display


The map either rotated with the numerical intensity display or with the verbal intensity display. All other elements were the same across the four conditions (map, countdown, action steps).

The demographics for the sample (N = 294) for phase two was 62.5% female and 37.5% male, 88% Caucasian, and age (M = 47.5; SD = 14.04). Regarding socio-economic status, 52% of the sample reported an annual income of \$70,000 or more and 32% currently live in southern California. Of the 294 respondents, 133 provided comments in response to the prompt: "Please provide any additional feedback you believe would be helpful concerning the quality of the app." Key findings from this round of message testing focus on perceived quality of the app overall, as well as intensity (verbal/ numerical), location (map), and behavioral intentions (drop/take cover/hold on).

#### *2.3.1 Perceived quality of the app*

A series of stepwise regression analyses were conducted to examine the research question about perceived quality of the app. The single item asking about the quality of the app used a five-point Likert type response scale (1 = very effective to 5 = not effective). Overall, 75% of the participants across conditions rated the app as "effective" or "very effective" and only 2% rated the app as "not effective." On the first block, demographic variables were entered in order to account for any variance attributable to respondent characteristics. These included sex, age, race/ethnicity, and income. The second predictor block included these variables, as well as experimental condition. The examination focused on significant models and predictors, as well as potential improvements based on the addition of experimental condition.

The results for the first predictor block indicate a significant model, F(4, 223) = 6.775, p < 0.001. R2 = 0.108. Of the demographic variables only sex β = −249 p < 0.000, and age β = −175 p < 0.01 were predictive of ratings of app quality. When experimental condition was added to the predictor block a significant model was also produced, F(5, 222) = 4.32, p < 0.001, R2 = 0.112. However, the change in variance accounted for was not significant ΛR2 = 0.004. Of the variables in the predictor block, only sex β = −245 p < 0.000, and age β = −176 p < 0.01 were predictive of ratings of app quality.

A t-test was conducted for the variables of sex and overall quality across conditions. Women (M = 1.73 SD = 0.81) were more likely than men (M = 2.14, SD = 1.04) to rate the app as being of high quality t(2) = 3.592, p < .001. Sex differences in perceptions of app quality were then broken down by each condition. Differences were found for condition 2, where women (M = 1.61, SD = 1.12) reported higher perceptions of app quality than men (M = 2.30. SD = .74) t(2) = 2.696, p < 0.01, and condition 5 where women (M = 1.70, SD = 0.65) reported higher perceptions of quality than men (M = 2.19, SD = 1.01) t(2) = 2.190, p < 0.05.

Perhaps most important here is that participants in all treatment conditions rated the quality of the app as high. Since all treatment conditions used similar content

**17**

*2.3.3 Location*

*The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW)…*

based on the IDEA model (i.e., alert sound, oral and visual countdown, intensity level, map, actionable instructions), it seems the appropriate content is being included. Moreover, a thematic analysis of the open-ended responses revealed that those viewing the control (ShakeAlert) condition were "overwhelmed by the visuals" and wanted to see and hear directions to "duck, cover, and hold on." These themes suggest that (a) too much information, although accurate, can defeat the purpose of the warning and (b) specific action steps need to be included. In addition to perceived quality of the app, the researchers sought to learn more regarding numerical versus verbal intensity displays, the effect of the map in location cognition (proximity), and

Key findings from this round of message testing regarding intensity are as follows. First, there were no significant differences among conditions regarding intensity. However, an exploration of descriptive statistics shed additional light on this issue. When asked "how important is it to know the kind of shaking," 76–87% reported it as very important across all conditions. Moreover, 77–85% of the respondents across conditions answered correctly (i.e., 10 seconds or less) when

Important findings emerged when asked what kind of shaking would occur. It is encouraging to note that 77–93% of the respondents reported correctly that very strong shaking was going to occur. The researchers placed a screen shot before entering the survey that summarized the meaning of the numerical intensity numbers. When respondents that viewed the verbal intensity display were asked about the numerical intensity level (8), only 15 and 22.4% recalled the correct number. Of the respondents that viewed the numerical intensity display, 69 and 80% recalled the correct number. Of the respondents that viewed the control (ShakeAlert) message, only 35.5% recalled the correct number. This low percentage may be impacted by the amount of detailed information being displayed in the control message. So much information may be difficult to process in 10 seconds or

Subsequently, when asked how well they understand the meaning of intensity level numbers, 48.4 and 38.8% of those viewing the verbal display marked "very well." Respondents that viewed the numerical intensity display reported knowledge comprehension of "very well" at 56.7 and 56.5%. Those viewing the control (ShakeAlert) message reported knowing the meaning very well at 45.9%. These results suggest the verbal intensity display is more meaningful than the numerical display. These results also suggest that displaying both (as in the control ShakeAlert message) appears to be

All conditions included a map identifying where the shaking was going to occur. There were no significant differences among the conditions regarding the importance of the map or for accurate location identification. Across conditions, 74–92% reported a map as "important" or "very important." A somewhat troubling finding, however, was that when asked where the shaking was going to occur, only 33–55% answered correctly (Los Angeles area) across conditions. When the researchers drilled down to include only participants currently living in southern California, the results improved slightly among the four treatment conditions (64–74% correct). However, only 29% of the respondents that viewed the control (ShakeAlert) message answered correctly. Moreover, when asked how helpful the visual images

too much information to process accurately in a short amount of time.

behavioral intentions to take appropriate self-protective actions.

*DOI: http://dx.doi.org/10.5772/intechopen.85557*

asked when the shaking would begin.

less and, thus, may result in misunderstanding.

*2.3.2 Intensity*

#### *The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW)… DOI: http://dx.doi.org/10.5772/intechopen.85557*

based on the IDEA model (i.e., alert sound, oral and visual countdown, intensity level, map, actionable instructions), it seems the appropriate content is being included. Moreover, a thematic analysis of the open-ended responses revealed that those viewing the control (ShakeAlert) condition were "overwhelmed by the visuals" and wanted to see and hear directions to "duck, cover, and hold on." These themes suggest that (a) too much information, although accurate, can defeat the purpose of the warning and (b) specific action steps need to be included. In addition to perceived quality of the app, the researchers sought to learn more regarding numerical versus verbal intensity displays, the effect of the map in location cognition (proximity), and behavioral intentions to take appropriate self-protective actions.

### *2.3.2 Intensity*

*Earthquakes - Impact, Community Vulnerability and Resilience*

1.Japanese alert sound with numerical intensity display

2.US alert sound with numerical intensity display

3.Japanese alert sound with verbal intensity display

4.US alert sound with verbal intensity display

countdown, action steps).

*2.3.1 Perceived quality of the app*

F(4, 223) = 6.775, p < 0.001. R2

predictive of ratings of app quality.

ment conditions were manipulated as follows:

practitioners, the original eight conditions were reduced to four. These four treat-

The map either rotated with the numerical intensity display or with the verbal intensity display. All other elements were the same across the four conditions (map,

The demographics for the sample (N = 294) for phase two was 62.5% female and 37.5% male, 88% Caucasian, and age (M = 47.5; SD = 14.04). Regarding socio-economic status, 52% of the sample reported an annual income of \$70,000 or more and 32% currently live in southern California. Of the 294 respondents, 133 provided comments in response to the prompt: "Please provide any additional feedback you believe would be helpful concerning the quality of the app." Key findings from this round of message testing focus on perceived quality of the app overall, as well as intensity (verbal/ numerical), location (map), and behavioral intentions (drop/take cover/hold on).

A series of stepwise regression analyses were conducted to examine the research

= 0.108. Of the demographic variables only sex

= 0.112. However, the

= 0.004. Of the variables

question about perceived quality of the app. The single item asking about the quality of the app used a five-point Likert type response scale (1 = very effective to 5 = not effective). Overall, 75% of the participants across conditions rated the app as "effective" or "very effective" and only 2% rated the app as "not effective." On the first block, demographic variables were entered in order to account for any variance attributable to respondent characteristics. These included sex, age, race/ethnicity, and income. The second predictor block included these variables, as well as experimental condition. The examination focused on significant models and predictors, as well as potential improvements based on the addition of experimental condition. The results for the first predictor block indicate a significant model,

β = −249 p < 0.000, and age β = −175 p < 0.01 were predictive of ratings of app quality. When experimental condition was added to the predictor block a significant

in the predictor block, only sex β = −245 p < 0.000, and age β = −176 p < 0.01 were

A t-test was conducted for the variables of sex and overall quality across conditions. Women (M = 1.73 SD = 0.81) were more likely than men (M = 2.14, SD = 1.04) to rate the app as being of high quality t(2) = 3.592, p < .001. Sex differences in perceptions of app quality were then broken down by each condition. Differences were found for condition 2, where women (M = 1.61, SD = 1.12) reported higher perceptions of app quality than men (M = 2.30. SD = .74) t(2) = 2.696, p < 0.01, and condition 5 where women (M = 1.70, SD = 0.65) reported higher perceptions of

Perhaps most important here is that participants in all treatment conditions rated the quality of the app as high. Since all treatment conditions used similar content

model was also produced, F(5, 222) = 4.32, p < 0.001, R2

change in variance accounted for was not significant ΛR2

quality than men (M = 2.19, SD = 1.01) t(2) = 2.190, p < 0.05.

**16**

Key findings from this round of message testing regarding intensity are as follows. First, there were no significant differences among conditions regarding intensity. However, an exploration of descriptive statistics shed additional light on this issue. When asked "how important is it to know the kind of shaking," 76–87% reported it as very important across all conditions. Moreover, 77–85% of the respondents across conditions answered correctly (i.e., 10 seconds or less) when asked when the shaking would begin.

Important findings emerged when asked what kind of shaking would occur. It is encouraging to note that 77–93% of the respondents reported correctly that very strong shaking was going to occur. The researchers placed a screen shot before entering the survey that summarized the meaning of the numerical intensity numbers. When respondents that viewed the verbal intensity display were asked about the numerical intensity level (8), only 15 and 22.4% recalled the correct number. Of the respondents that viewed the numerical intensity display, 69 and 80% recalled the correct number. Of the respondents that viewed the control (ShakeAlert) message, only 35.5% recalled the correct number. This low percentage may be impacted by the amount of detailed information being displayed in the control message. So much information may be difficult to process in 10 seconds or less and, thus, may result in misunderstanding.

Subsequently, when asked how well they understand the meaning of intensity level numbers, 48.4 and 38.8% of those viewing the verbal display marked "very well." Respondents that viewed the numerical intensity display reported knowledge comprehension of "very well" at 56.7 and 56.5%. Those viewing the control (ShakeAlert) message reported knowing the meaning very well at 45.9%. These results suggest the verbal intensity display is more meaningful than the numerical display. These results also suggest that displaying both (as in the control ShakeAlert message) appears to be too much information to process accurately in a short amount of time.

#### *2.3.3 Location*

All conditions included a map identifying where the shaking was going to occur. There were no significant differences among the conditions regarding the importance of the map or for accurate location identification. Across conditions, 74–92% reported a map as "important" or "very important." A somewhat troubling finding, however, was that when asked where the shaking was going to occur, only 33–55% answered correctly (Los Angeles area) across conditions. When the researchers drilled down to include only participants currently living in southern California, the results improved slightly among the four treatment conditions (64–74% correct). However, only 29% of the respondents that viewed the control (ShakeAlert) message answered correctly. Moreover, when asked how helpful the visual images

were in conveying information about location, only 27.9–50% said "very helpful" across conditions. However, in all four treatment conditions, respondents reported more preference for the visual images (M = 1.90, SD = 1.82) than those in the control condition (M = 2.26, SD = 1.30) t(2) = −2.106 = p < 0.05. Moreover, a thematic analysis of the open-ended comments revealed a desire for a simple map that merely showing a familiar city with a bullseye target or location flag would be more helpful than one showing both the epicenter and location where shaking will occur. Taken together, these results suggest that a simple map highlighting the location may be more effective than a detailed one showing lots of information.

#### *2.3.4 Behavioral intentions*

A series of stepwise regression analyses were conducted to examine the research question regarding behavioral intentions. The composite measures were used to assess perceptions of behavioral intentions. The measure for behavioral intentions used nine items with a response scale of 1 = "Very Unlikely" to 5 = "Very Likely." On the first block, demographic variables were entered in order to account for any variance attributable to respondent characteristics. These included sex, age, race/ethnicity, and income. The second block added experimental condition to these possible predictor variables. The analyses focused on significant models and predictors, as well as possible improvement to the model based on the addition of experimental condition.

The results for the first predictor block did not indicate a significant model, F(4, 227) = 0.989, p = n.s. R2 = 0.017. None of the demographic variables were predictive of behavioral intentions. When experimental condition was added to the predictor block the model did not improve, F(5, 229) = 0.788, p = n.s., R2 = 0.017. None of the variables in the predictor block were predictive of behavioral intentions.

The fact that no significant model stood out as a better predictor for behavioral intentions combined with the descriptive statistics suggest that the including the IDEA model components as we did in each condition may be effective for earthquake early warning messages delivered via a smart phone app. Although the means reported are encouraging, the fact that the pretest self-efficacy (M = 4.44) also may point to a respondent pool comprised of members of a disaster sub-culture that is already pre-disposed to taking appropriate actions for self-protection.

### **3. Conclusions**

Several promising conclusions may be drawn from these two rounds of message design and testing. First, a phone APP can be designed in ways that employ the IDEA elements of effective instructional risk and crisis messages for earthquake early warnings in 10 seconds or less. Second, the elements of the IDEA model do appear to positively influence affective (perceived value/importance), cognitive (comprehension), and behavioral (efficacy and intention) learning outcomes.

Also based on these message testing results, however, more honing of some particulars are still warranted. For example, with regard to internalization, the design of the map (proximity) needs to be simplified to ensure accurate comprehension of location. Regarding explanation, it appears that verbal intensity displays are more effective than numerical displays unless a comprehensive educational campaign could be conducted to teach users what the different numbers mean.

The sample for both rounds of message testing was not representative of the entire population in southern California. Additional message testing targeting more representative demographic diversity and marginalized populations is warranted in order to be certain about ultimately launching the most effective warning app.

**19**

**Author details**

Derek R. Lane3

Deanna D. Sellnow1

provided the original work is properly cited.

\*, Lucile M. Jones2

\*Address all correspondence to: deanna.sellnow@ucf.edu

2 Lucy Jones Center for Science and Society, Los Angeles, CA, USA

and Nigel Haarstad4

1 University of Central Florida, Orlando, FL, USA

3 University of Kentucky, Lexington, KY, USA

4 New Knowledge, Austin, TX, USA

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Timothy L. Sellnow1

, Patric Spence1

,

*The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW)…*

*DOI: http://dx.doi.org/10.5772/intechopen.85557*

*The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW)… DOI: http://dx.doi.org/10.5772/intechopen.85557*

## **Author details**

*Earthquakes - Impact, Community Vulnerability and Resilience*

more effective than a detailed one showing lots of information.

*2.3.4 Behavioral intentions*

F(4, 227) = 0.989, p = n.s. R2

**3. Conclusions**

were in conveying information about location, only 27.9–50% said "very helpful" across conditions. However, in all four treatment conditions, respondents reported more preference for the visual images (M = 1.90, SD = 1.82) than those in the control condition (M = 2.26, SD = 1.30) t(2) = −2.106 = p < 0.05. Moreover, a thematic analysis of the open-ended comments revealed a desire for a simple map that merely showing a familiar city with a bullseye target or location flag would be more helpful than one showing both the epicenter and location where shaking will occur. Taken together, these results suggest that a simple map highlighting the location may be

A series of stepwise regression analyses were conducted to examine the research question regarding behavioral intentions. The composite measures were used to assess perceptions of behavioral intentions. The measure for behavioral intentions used nine items with a response scale of 1 = "Very Unlikely" to 5 = "Very Likely." On the first block, demographic variables were entered in order to account for any variance attributable to respondent characteristics. These included sex, age, race/ethnicity, and income. The second block added experimental condition to these possible predictor variables. The analyses focused on significant models and predictors, as well as possible improvement to the model based on the addition of experimental condition. The results for the first predictor block did not indicate a significant model,

predictive of behavioral intentions. When experimental condition was added to the

None of the variables in the predictor block were predictive of behavioral intentions. The fact that no significant model stood out as a better predictor for behavioral intentions combined with the descriptive statistics suggest that the including the IDEA model components as we did in each condition may be effective for earthquake early warning messages delivered via a smart phone app. Although the means reported are encouraging, the fact that the pretest self-efficacy (M = 4.44) also may point to a respondent pool comprised of members of a disaster sub-culture that is

Several promising conclusions may be drawn from these two rounds of message

Also based on these message testing results, however, more honing of some particulars are still warranted. For example, with regard to internalization, the design of the map (proximity) needs to be simplified to ensure accurate comprehension of location. Regarding explanation, it appears that verbal intensity displays are more effective than numerical displays unless a comprehensive educational campaign

The sample for both rounds of message testing was not representative of the entire population in southern California. Additional message testing targeting more representative demographic diversity and marginalized populations is warranted in order to be certain about ultimately launching the most effective warning app.

design and testing. First, a phone APP can be designed in ways that employ the IDEA elements of effective instructional risk and crisis messages for earthquake early warnings in 10 seconds or less. Second, the elements of the IDEA model do appear to positively influence affective (perceived value/importance), cognitive (comprehension), and behavioral (efficacy and intention) learning outcomes.

predictor block the model did not improve, F(5, 229) = 0.788, p = n.s., R2

already pre-disposed to taking appropriate actions for self-protection.

could be conducted to teach users what the different numbers mean.

= 0.017. None of the demographic variables were

= 0.017.

**18**

Deanna D. Sellnow1 \*, Lucile M. Jones2 , Timothy L. Sellnow1 , Patric Spence1 , Derek R. Lane3 and Nigel Haarstad4

1 University of Central Florida, Orlando, FL, USA

2 Lucy Jones Center for Science and Society, Los Angeles, CA, USA

3 University of Kentucky, Lexington, KY, USA

4 New Knowledge, Austin, TX, USA

\*Address all correspondence to: deanna.sellnow@ucf.edu

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Sellnow D, Sellnow T. Risk communication: Instructional principles. In: Thompson T, editor. Encyclopedia of Health Communication. Vol. 17. Thousand Oaks: Sage; 2014. pp. 1181-1184. DOI: 10.4135/9781483346427.n463

[2] Sellnow D, Sellnow T. The IDEA model of effective instructional risk and crisis communication by emergency managers and other key spokespersons. Journal of Emergency Management. DOI: 10.5055/jem.2017.0000. In press

[3] Sellnow D, Lane D, Sellnow T, Littlefield R. The IDEA model as a best practice for effective instructional risk and crisis communication. Communication Studies. 2017;**68**:552-567. DOI: 10.1080/10510974.2017.1375535

[4] Sellnow D, Johannson B, Sellnow T, Lane D. Toward a global understanding of the effects of the IDEA model for designing instructional risk and crisis messages: A food contamination experiment in Sweden. Journal of Contingencies & Crisis Management. 2018. (Online early view). DOI: 10.1111/1468-5973.12234

[5] Sellnow T, Sellnow D, Lane D, Littlefield R. The value of instructional communication in crisis situations: Restoring order to chaos. Risk Analysis. 2012;**32**:633-643. DOI: 10.1111/j.1539-6924.2022.01634.x

[6] Sellnow-Richmond D, George A, Sellnow D. An IDEA model analysis of instructional risk communication messages in the time of Ebola. Journal of International Crisis and Risk Communication Research. 2018;**1**: 135-159. DOI: 10.30658/jicrcr.1.1.7

[7] Sellnow T, Parker J, Sellnow D, Littlefield R, Helsel E, Getchell M, et al. Improving biosecurity through instructional crisis communication:

Lessons learned from the PEDv outbreak. Journal of Applied Communications. 2017;**101**. DOI: 10.4148/1051-0834.1298

[8] Peters E, Vastfjall D, Slovic P, Mertz C, Mazzocco K, Dickert S. Numeracy and decision making. Psychological Science. 2006;**17**:407-413. DOI: 10.1111/j.1467-9280.2006.01720.x

[9] Canary D, Hause K. Is there any reason to research sex differences in communication? Communication Quarterly. 1993;**41**:129-144. DOI: 10.1080/01463379309369874

**21**

**Chapter 3**

**Abstract**

**1. Introduction**

2018 killing hundreds of people [8].

and that nothing can be done about it.

Knowledge and Perception on

*Daniel Velázquez-Martínez, Vladimir Avalos-Bravo*

*Tatiana Gouzeva, Diego Padilla-Pérez,* 

the better may be their preparedness to earthquakes.

**Keywords:** risk perception, earthquake, Mexico City, students, education

Communities have been exposed to disasters and extreme events. For example, earthquakes have caused destruction worldwide in recent years [1–7]. Some of them have triggered tsunamis with catastrophic consequences, such as in the cases of Indonesia in 2004 [1] and Japan in 2011 [3]. More recently, it is believed that a tsunami was triggered by a landslide caused by a volcanic eruption in Indonesia in

Preparedness to natural disasters is essential to mitigate the impact of earthquakes. Research has shown that, for example, culture belief has an influence on people's reactions to risk [9–12]. Moreover, some studies have shown that in some cultures people shows a fatalistic attitude to earthquake disasters as acts of God [12]

On the other hand, some scholars argue that emergency preparedness and reduction of vulnerability, among others, are strongly influenced by both past disaster experience and risk perception [13]. Moreover, it is thought that risk

*and José Lourdes Félix-Hernández*

Seismic Risk of Students in Mexico

This paper presents some of the results of a cross-sectional study conducted in Mexico City in 2015–2016. The approach has been the application of a questionnaire to a sample size of *n* = 1489. Six high schools participated in the study that are located within the seismic zones of the city. Some of the results and conclusions are given below: (a) 95% of the students have experienced an earthquake and 71% considered that earthquakes cannot be predicted; however, 29% did not know this fact; (b) 82.2% of students were all aware of the likelihood of an earthquake occurrence sometime in the future. (c) One of the key conclusions is associated with the need to educate the residents of the capital city on a more realistic scale of the size of an earthquake; this could be the "Modified Mercalli Intensity Scale" or similar. (d) More generally, the residents of the city should be educated with urgency on these basic concepts. The more effective is the communication on risks and consequences,

City Before the 2017 Earthquakes

## **Chapter 3**

## Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes

*Tatiana Gouzeva, Diego Padilla-Pérez, Daniel Velázquez-Martínez, Vladimir Avalos-Bravo and José Lourdes Félix-Hernández*

## **Abstract**

This paper presents some of the results of a cross-sectional study conducted in Mexico City in 2015–2016. The approach has been the application of a questionnaire to a sample size of *n* = 1489. Six high schools participated in the study that are located within the seismic zones of the city. Some of the results and conclusions are given below: (a) 95% of the students have experienced an earthquake and 71% considered that earthquakes cannot be predicted; however, 29% did not know this fact; (b) 82.2% of students were all aware of the likelihood of an earthquake occurrence sometime in the future. (c) One of the key conclusions is associated with the need to educate the residents of the capital city on a more realistic scale of the size of an earthquake; this could be the "Modified Mercalli Intensity Scale" or similar. (d) More generally, the residents of the city should be educated with urgency on these basic concepts. The more effective is the communication on risks and consequences, the better may be their preparedness to earthquakes.

**Keywords:** risk perception, earthquake, Mexico City, students, education

## **1. Introduction**

Communities have been exposed to disasters and extreme events. For example, earthquakes have caused destruction worldwide in recent years [1–7]. Some of them have triggered tsunamis with catastrophic consequences, such as in the cases of Indonesia in 2004 [1] and Japan in 2011 [3]. More recently, it is believed that a tsunami was triggered by a landslide caused by a volcanic eruption in Indonesia in 2018 killing hundreds of people [8].

Preparedness to natural disasters is essential to mitigate the impact of earthquakes. Research has shown that, for example, culture belief has an influence on people's reactions to risk [9–12]. Moreover, some studies have shown that in some cultures people shows a fatalistic attitude to earthquake disasters as acts of God [12] and that nothing can be done about it.

On the other hand, some scholars argue that emergency preparedness and reduction of vulnerability, among others, are strongly influenced by both past disaster experience and risk perception [13]. Moreover, it is thought that risk

**20**

*Earthquakes - Impact, Community Vulnerability and Resilience*

Lessons learned from the PEDv outbreak. Journal of Applied Communications. 2017;**101**. DOI:

[8] Peters E, Vastfjall D, Slovic P, Mertz C, Mazzocco K, Dickert S. Numeracy and decision making. Psychological Science. 2006;**17**:407-413. DOI: 10.1111/j.1467-9280.2006.01720.x

[9] Canary D, Hause K. Is there any reason to research sex differences in communication? Communication Quarterly. 1993;**41**:129-144. DOI: 10.1080/01463379309369874

10.4148/1051-0834.1298

**References**

[1] Sellnow D, Sellnow T. Risk communication: Instructional principles. In: Thompson T, editor. Encyclopedia of Health Communication. Vol. 17. Thousand Oaks: Sage; 2014. pp. 1181-1184. DOI:

10.4135/9781483346427.n463

[2] Sellnow D, Sellnow T. The IDEA model of effective instructional risk and crisis communication by emergency managers and other key spokespersons. Journal of Emergency Management. DOI: 10.5055/jem.2017.0000. In press

[3] Sellnow D, Lane D, Sellnow T, Littlefield R. The IDEA model as a best practice for effective instructional

risk and crisis communication.

Communication Studies. 2017;**68**:552-567. DOI: 10.1080/10510974.2017.1375535

[4] Sellnow D, Johannson B, Sellnow T, Lane D. Toward a global understanding of the effects of the IDEA model for designing instructional risk and crisis messages: A food contamination experiment in Sweden. Journal of Contingencies & Crisis Management. 2018. (Online early view). DOI: 10.1111/1468-5973.12234

[5] Sellnow T, Sellnow D, Lane D, Littlefield R. The value of instructional communication in crisis situations: Restoring order to chaos. Risk Analysis. 2012;**32**:633-643. DOI: 10.1111/j.1539-6924.2022.01634.x

[6] Sellnow-Richmond D, George A, Sellnow D. An IDEA model analysis of instructional risk communication messages in the time of Ebola. Journal of International Crisis and Risk Communication Research. 2018;**1**: 135-159. DOI: 10.30658/jicrcr.1.1.7

[7] Sellnow T, Parker J, Sellnow D, Littlefield R, Helsel E, Getchell M, et al. Improving biosecurity through instructional crisis communication:

perception is a function of communication [13, 14]. That is, risk communication plays an important role between those in charge of making decisions, for example, on preparedness, disaster knowledge, and people exposed to seismic risk [13, 14].

This chapter presents some preliminary results of seismic risk perception and other natural hazards (e.g., volcanic eruption and floods). It also presents some results on the knowledge on some basic concepts related to the earthquake prediction, among others. The approach has been the application of a questionnaire to a sample size of *n* = 1489; the study has been conducted in 2015–2016. The results are discussed in the context of past earthquakes (i.e., those that occurred before the study was conducted) and the 2017 earthquakes that hit the capital city.

## **2. Research methods**

A cross-sectional study was conducted in 2015–2016 in Mexico City; the sample size considered in the analysis was *n* = 1489. Six high schools decided to participate in the study, and all of them were located within the critical areas of the city, i.e., the seismic zones defined as "zones I, II, and III." A questionnaire was designed to capture the data on several issues related to seismic risk perception, other natural hazard perceptions, and knowledge on actions to take during the occurrence of an earthquake, among others (however, the results of the few questions are reported here). The questionnaire was pretested before the final application to the sample; it took about 25 min to complete. The questionnaires were applied from December 2015 to March 2016.

The analysis of the collected data was done by conducting frequency analysis, and the relationship among the variables was performed by conducting cross tabulations. Overall, a basic descriptive statistical analysis of the variables considered in the analysis is presented here; some of the results associated with an inferential analysis are presented elsewhere.

The results related to the following questions are presented in the next section:


#### **3. Results and discussion**

#### **3.1 Experience and earthquake prediction**

As mentioned in the previous section, the questionnaire was applied to a particular kind of population, i.e., students from six high schools in the capital city. The demographic characteristics were the following: the range of age were from 14 to 19 years old; the highest percentage of them was for the case of students with 16 years

**23**

**Figure 1.**

*magnitude of an earthquake.*

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes*

When considering the experience of the participants in relation to earthquakes,

For example, according to the "National Seismological Service" (SSN) statistics, from 1 January to 31 December 2014 (a year before the study), there were about 7588 earthquakes [5], that is, an average of 632 earthquakes per month. It also should be emphasized that there are seven strong earthquakes of magnitudes > 6.0. Moreover, earthquakes on the range of magnitudes 3.0–3.9 (i.e., 6343 events) were those that occurred most frequently in 2014. This was followed by those in the range

When asked the following question, "can an earthquake be predicted?", the possible responses to the question were the following: "Yes," "No," and "I do not know" (**Figure 1b**). The results showed that about 71% of students responded "No," which may be regarded as the right answer. Interestingly, 29% of the participants did not know this fact (i.e., 9.4% "Yes"; 19.5% "I do not know"). Effectively, earthquakes still cannot be predicted. There has been a vast amount of studies published in the

Also, we were interested to know how the participants perceived the seismic risk threat at that time of the study. The following question was included in the questionnaire, "how likely an earthquake will occur in the future?" The possible answers to the question were the following: "Not likely," "Somewhat likely," and

Overall, students were aware of the likelihood of an earthquake occurrence sometime in the future when the study was conducted. That is, 82.2% (1212/1475) responded "Very likely" and 15.2% (227/1475) "Somewhat likely," and 2.4% (36/1475) considered "Not likely" for the occurrence of an earthquake.

The relationship between seismic risk perception and those variables related to gender, age, and schools is presented in **Table 1** and **Figures 2**-**4**. For example,

*Experience and the magnitude of an earthquake: (a) experience on earthquakes and (b) scale of the* 

"Very likely." The results are shown in **Table 1** and **Figures 2**-**4**.

old (34.1%) and the lowest was represented by students from 14 years old (3.1%). Regarding the gender of the participants, 48.6% were women and 51.5% men.

**Figure 1a** shows the results. It can be seen that 95% of the students have experienced an earthquake; only 5% did not. This is consistent with the fact that the frequency of earthquake occurrence before the study was conducted in 2015 was

*DOI: http://dx.doi.org/10.5772/intechopen.85556*

of 4.0–4.9, with a total of 955 events [5].

literature on this very topic [15–19].

**3.2 Seismic risk perception**

unusually high.

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes DOI: http://dx.doi.org/10.5772/intechopen.85556*

old (34.1%) and the lowest was represented by students from 14 years old (3.1%). Regarding the gender of the participants, 48.6% were women and 51.5% men.

When considering the experience of the participants in relation to earthquakes, **Figure 1a** shows the results. It can be seen that 95% of the students have experienced an earthquake; only 5% did not. This is consistent with the fact that the frequency of earthquake occurrence before the study was conducted in 2015 was unusually high.

For example, according to the "National Seismological Service" (SSN) statistics, from 1 January to 31 December 2014 (a year before the study), there were about 7588 earthquakes [5], that is, an average of 632 earthquakes per month. It also should be emphasized that there are seven strong earthquakes of magnitudes > 6.0. Moreover, earthquakes on the range of magnitudes 3.0–3.9 (i.e., 6343 events) were those that occurred most frequently in 2014. This was followed by those in the range of 4.0–4.9, with a total of 955 events [5].

When asked the following question, "can an earthquake be predicted?", the possible responses to the question were the following: "Yes," "No," and "I do not know" (**Figure 1b**). The results showed that about 71% of students responded "No," which may be regarded as the right answer. Interestingly, 29% of the participants did not know this fact (i.e., 9.4% "Yes"; 19.5% "I do not know"). Effectively, earthquakes still cannot be predicted. There has been a vast amount of studies published in the literature on this very topic [15–19].

#### **3.2 Seismic risk perception**

*Earthquakes - Impact, Community Vulnerability and Resilience*

**2. Research methods**

2015 to March 2016.

you?"

**3. Results and discussion**

analysis are presented elsewhere.

a."Have you experienced an earthquake?"

c. "Can an earthquake be predicted?"

**3.1 Experience and earthquake prediction**

perception is a function of communication [13, 14]. That is, risk communication plays an important role between those in charge of making decisions, for example, on preparedness, disaster knowledge, and people exposed to seismic risk [13, 14]. This chapter presents some preliminary results of seismic risk perception and other natural hazards (e.g., volcanic eruption and floods). It also presents some results on the knowledge on some basic concepts related to the earthquake prediction, among others. The approach has been the application of a questionnaire to a sample size of *n* = 1489; the study has been conducted in 2015–2016. The results are discussed in the context of past earthquakes (i.e., those that occurred before the

study was conducted) and the 2017 earthquakes that hit the capital city.

A cross-sectional study was conducted in 2015–2016 in Mexico City; the sample size considered in the analysis was *n* = 1489. Six high schools decided to participate in the study, and all of them were located within the critical areas of the city, i.e., the seismic zones defined as "zones I, II, and III." A questionnaire was designed to capture the data on several issues related to seismic risk perception, other natural hazard perceptions, and knowledge on actions to take during the occurrence of an earthquake, among others (however, the results of the few questions are reported here). The questionnaire was pretested before the final application to the sample; it took about 25 min to complete. The questionnaires were applied from December

The analysis of the collected data was done by conducting frequency analysis, and the relationship among the variables was performed by conducting cross tabulations. Overall, a basic descriptive statistical analysis of the variables considered in the analysis is presented here; some of the results associated with an inferential

The results related to the following questions are presented in the next section:

e. "If any of these events happen in the future, how likely is it that it will affect

As mentioned in the previous section, the questionnaire was applied to a particular kind of population, i.e., students from six high schools in the capital city. The demographic characteristics were the following: the range of age were from 14 to 19 years old; the highest percentage of them was for the case of students with 16 years

b."How does the magnitude of an earthquake being measured?"

d."How likely an earthquake will occur in the future?"

**22**

Also, we were interested to know how the participants perceived the seismic risk threat at that time of the study. The following question was included in the questionnaire, "how likely an earthquake will occur in the future?" The possible answers to the question were the following: "Not likely," "Somewhat likely," and "Very likely." The results are shown in **Table 1** and **Figures 2**-**4**.

Overall, students were aware of the likelihood of an earthquake occurrence sometime in the future when the study was conducted. That is, 82.2% (1212/1475) responded "Very likely" and 15.2% (227/1475) "Somewhat likely," and 2.4% (36/1475) considered "Not likely" for the occurrence of an earthquake.

The relationship between seismic risk perception and those variables related to gender, age, and schools is presented in **Table 1** and **Figures 2**-**4**. For example,

#### **Figure 1.**

*Experience and the magnitude of an earthquake: (a) experience on earthquakes and (b) scale of the magnitude of an earthquake.*


#### *Earthquakes - Impact, Community Vulnerability and Resilience*

**Table 1.**

*Seismic risk perception.*

#### **Figure 2.**

*Seismic risk perception and age of the participants.*

when considering the age of the participants, interestingly, participants whose age was 16 years old were the ones that score the highest percentage (29.2%, 430/1473), which was followed by students from 17 (22.3%, 329/1473), 15 (16.3%, 240/1473), and 18 years old (136/1473). On the other hand, only 2.5% of the participants were unaware of the threat of seismic risk at the time of the study. Overall, it may be argued that about 70% of the participants are aware of the seismic risk (**Figure 2**).

**Figure 3** shows the results of the relationship of the variables related to earthquake risk perception and the variables related to the gender of the students.

**25**

respectively.

**Figure 4.**

**Figure 3.**

by school 4 (17.8%, 262/1473).

**3.3 Seismic risk vs. other natural hazards**

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes*

From the figure, it can be seen that women were more aware of the seismic risk (i.e., "Very likely") than men, with 43.7% (644/1473) and 38.42% (566/1473),

*Seismic risk and the schools located in different seismic risk zones of the city.*

Finally, when considering the schools (**Figure 4**), the one which is located within the zone of highest seismic risk (i.e., school 3) scores the highest percentage of the likelihood of an earthquake occurrence (20.6%, 303/1473); this was followed

As in many places in the world, Mexico City may be affected with a number of natural hazards, and among them are earthquakes, floods, and volcanic eruptions (e.g., the "Popocatépetl" volcano, which is located at about 72 km from the capital city). In order to assess the awareness of the participants of the study on these hazards, the following question was included in the questionnaire: "If any of these

events happen in the future, how likely is it that it will affect you?"

*DOI: http://dx.doi.org/10.5772/intechopen.85556*

*Seismic risk perception and the gender of the participants.*

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes DOI: http://dx.doi.org/10.5772/intechopen.85556*

#### **Figure 4.**

*Earthquakes - Impact, Community Vulnerability and Resilience*

**"Not likely", N (%) "Somewhat likely", N (%) "Very likely", N (%)**

 0 (0.0) 12 (26.1) 34 (73.9) 17 (5.2) 67 (20.7) 240 (74.1) 9 (1.8) 65 (12.9) 430 (85.1) 5 (1.3) 62 (15.6) 329 (82.9) 4 (2.6) 15 (9.7) 136 (87.7) 1 (2.1) 6 (12.5) 41 (85.4)

Women 10 (1.3) 107 (14.1) 644 (84.2) Men 26 (3.6) 120 (16.8) 566 (79.3)

School 1 2 (1.1) 20 (11.4) 154 (87.5) School 2 2 (1.0) 16 (7.8) 186 (91.2) School 3 5 (1.4) 37 (10.7) 303 (87.3) School 4 8 (2.7) 29 (9.7) 262 (87.6) School 5 13 (4.9) 111 (41.7) 142 (153.4) School 6 6 (3.3) 14 (7.7) 163 (89.1)

when considering the age of the participants, interestingly, participants whose age was 16 years old were the ones that score the highest percentage (29.2%, 430/1473), which was followed by students from 17 (22.3%, 329/1473), 15 (16.3%, 240/1473), and 18 years old (136/1473). On the other hand, only 2.5% of the participants were unaware of the threat of seismic risk at the time of the study. Overall, it may be argued that about 70% of the participants are aware of the seismic risk (**Figure 2**). **Figure 3** shows the results of the relationship of the variables related to earthquake risk perception and the variables related to the gender of the students.

**24**

**Figure 2.**

**Age**

**Gender**

**Schools**

**Table 1.**

*Seismic risk perception.*

*Seismic risk perception and age of the participants.*

*Seismic risk and the schools located in different seismic risk zones of the city.*

From the figure, it can be seen that women were more aware of the seismic risk (i.e., "Very likely") than men, with 43.7% (644/1473) and 38.42% (566/1473), respectively.

Finally, when considering the schools (**Figure 4**), the one which is located within the zone of highest seismic risk (i.e., school 3) scores the highest percentage of the likelihood of an earthquake occurrence (20.6%, 303/1473); this was followed by school 4 (17.8%, 262/1473).

#### **3.3 Seismic risk vs. other natural hazards**

As in many places in the world, Mexico City may be affected with a number of natural hazards, and among them are earthquakes, floods, and volcanic eruptions (e.g., the "Popocatépetl" volcano, which is located at about 72 km from the capital city). In order to assess the awareness of the participants of the study on these hazards, the following question was included in the questionnaire: "If any of these events happen in the future, how likely is it that it will affect you?"

The results showed that earthquakes were regarded as an event "Very likely" to occur in the future at the time of the study, that is, "Very likely" (82%, 1210/1475), "Somewhat likely" (15.5%, 228/1475), and "Not likely" (2.5%, 37/1475). Then it was followed by floods ("Very likely" (27%, 398/1475); "Somewhat likely" (53.9%, 795/1475); "Not likely" (19.1%, 282/1475)). Finally, volcanic eruption came third ("Very likely" (18%, 265/1474); "Somewhat likely" (43.4%, 639/1474); "Not likely" (38.7%, 570/1474)).

**Table 2** shows, on the other hand, the results of the relationship between the participants' perception on other natural hazards and the gender of the participants.

Overall, it can be argued that the participants of the study were aware of the likelihood of the occurrence of these events. Effectively, seismic events were at the top of this likelihood, given that earthquakes occurred very often prior to the study (i.e., in 2014 as shown in Section 3.1).

Regarding floods, usually the capital city is heavily affected with heavy rains (and in particular during the raining season). For example, apart from the households and avenues being flooded, the public transport system is also affected and consequently the urban mobility. In particular, the Metro transport system, for example, during the raining season, the metro lines (some of them) are usually flooded. For example, in relation to this problem a politician said this:

*"The strongest crisis due to rain that occurred in the Metro last year began in May, when due to the saturation in the drainage network, the water flooded in from outside to the corridors of Cuatro Caminos, Pantheons, and Tacuba stations. (Metro) Line 2 reaching up to 15 cm in height. This week in Mexico City we have already started with the first heavy rains of the year, and due to the climate change, that we are experiencing, it is difficult to know exactly when storms can occur, so the Metro must be prepared not to suffer damages, guarantee the service and protect (the) users." [20]*

Finally, the participants' perception on the occurrence of a volcanic eruption was "Not likely" (38.7%, 570/1474). Interestingly, there was an eruption in 2017 and 2018. That is, at about 17:54 in 2017, the Popocatépetl volcano exhaled a "fumarole" of about 4 km [21]; similarly, in 2018, the volcanic registered an explosion at about 18:58 [22]. It is important to mention that every time an eruption occurs, usually the areas where the participants came from are unaffected by these eruptions.


**27**

**Figure 5.**

*Responses on the magnitude of an earthquake.*

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes*

Regarding the question "how the magnitude of an earthquake is measured?", the possible responses to the question were the following: "Mercalli," "Richter," and "I do not know" (the results are presented in **Figure 5**). As expected, most of the participants considered "Richter" (i.e., 96%). Only 3% did not know, whereas only

However, the above raises the question as to whether the high percentage of the participants (96%) really knows the meaning of the Richter scale. Two key concepts regarding the measure of the size of an earthquake are magnitude and intensity [23]. The seismic energy released during an earthquake occurrence is measured by the magnitude, and it is commonly measured in a Richter scale. However, "Richter magnitude" (it is believed that it was named after Charles Richter who proposed the measure), may be regarded a measure only appropriate for earthquakes originating in California, USA [23]. Given this confusion, the moment magnitudes have become

But what is the intensity of an earthquake? How is it measured? It is thought that intensity measures the consequences of an earthquake; that is, it is based on the observations made by people (as opposed to the Richter scale which employs instrumental measurements), and, consequently, it varies from place to place. The intensity is measured by what is known as the "Modified Mercalli Intensity Scale"

What can we say about this in the context of the earthquakes that occurred in Mexico City in 2017? Was the Richter scale meaningful to the participants of the study? Was it meaningful to the residents of the capital city? The answer may be probably no. In fact, following the 2017 earthquakes, there was a debate on the mass media on this very issue. That is, it was a confusion among the residents of the city regarding the "intensity" felt by them during the two earthquakes that occurred on 7 and 19 September 2017. That is, for the earthquake on September 7 (magnitude of 8.1), with epicenter on the Pacific coast, the shaking was felt not that strong as the one on September 19 (with a magnitude of 7.1). Moreover, the consequences of the

Given the results presented above, it may be argued that participants considered seismic risk as their top threat when compared to floods and volcanic eruptions. Effectively, the two powerful earthquakes in 2017 that hit the capital city demon-

*DOI: http://dx.doi.org/10.5772/intechopen.85556*

strated that their worries came true.

a universally appropriate scale (Mw) [23].

**3.4 Richter vs. Mercalli scales**

1% considered "Mercalli."

(MMIS) [24].

#### **Table 2.**

*Seismic risk perception on other natural hazards.*

#### *Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes DOI: http://dx.doi.org/10.5772/intechopen.85556*

Given the results presented above, it may be argued that participants considered seismic risk as their top threat when compared to floods and volcanic eruptions. Effectively, the two powerful earthquakes in 2017 that hit the capital city demonstrated that their worries came true.

## **3.4 Richter vs. Mercalli scales**

*Earthquakes - Impact, Community Vulnerability and Resilience*

(38.7%, 570/1474)).

*users." [20]*

**Earthquake**

**Floods**

**Volcanic eruption**

*Seismic risk perception on other natural hazards.*

(i.e., in 2014 as shown in Section 3.1).

participants.

The results showed that earthquakes were regarded as an event "Very likely" to occur in the future at the time of the study, that is, "Very likely" (82%, 1210/1475), "Somewhat likely" (15.5%, 228/1475), and "Not likely" (2.5%, 37/1475). Then it was followed by floods ("Very likely" (27%, 398/1475); "Somewhat likely" (53.9%, 795/1475); "Not likely" (19.1%, 282/1475)). Finally, volcanic eruption came third ("Very likely" (18%, 265/1474); "Somewhat likely" (43.4%, 639/1474); "Not likely"

**Table 2** shows, on the other hand, the results of the relationship between the participants' perception on other natural hazards and the gender of the

Overall, it can be argued that the participants of the study were aware of the likelihood of the occurrence of these events. Effectively, seismic events were at the top of this likelihood, given that earthquakes occurred very often prior to the study

Regarding floods, usually the capital city is heavily affected with heavy rains (and in particular during the raining season). For example, apart from the households and avenues being flooded, the public transport system is also affected and consequently the urban mobility. In particular, the Metro transport system, for example, during the raining season, the metro lines (some of them) are usually

*"The strongest crisis due to rain that occurred in the Metro last year began in May, when due to the saturation in the drainage network, the water flooded in from outside to the corridors of Cuatro Caminos, Pantheons, and Tacuba stations. (Metro) Line 2 reaching up to 15 cm in height. This week in Mexico City we have already started with the first heavy rains of the year, and due to the climate change, that we are experiencing, it is difficult to know exactly when storms can occur, so the Metro must be prepared not to suffer damages, guarantee the service and protect (the)* 

Finally, the participants' perception on the occurrence of a volcanic eruption was "Not likely" (38.7%, 570/1474). Interestingly, there was an eruption in 2017 and 2018. That is, at about 17:54 in 2017, the Popocatépetl volcano exhaled a "fumarole" of about 4 km [21]; similarly, in 2018, the volcanic registered an explosion at about 18:58 [22]. It is important to mention that every time an eruption occurs, usually the

Women 10 (1.3) 107 (14.1) 644 (84.6) Men 26 (3.6) 120 (16.8) 566 (79.3)

Women 298 (39.2) 335 (44.1) 127 (16.7) Men 272 (38.1) 304 (42.6) 138 (19.3)

Women 162 (22.7) 120 (15.8) 235 (30.9) Men 120 (15.8) 389 (54.5) 162 (22.7)

**"Not likely", N (%) "Somewhat likely", N (%) "Very likely", N (%)**

areas where the participants came from are unaffected by these eruptions.

flooded. For example, in relation to this problem a politician said this:

**26**

**Table 2.**

Regarding the question "how the magnitude of an earthquake is measured?", the possible responses to the question were the following: "Mercalli," "Richter," and "I do not know" (the results are presented in **Figure 5**). As expected, most of the participants considered "Richter" (i.e., 96%). Only 3% did not know, whereas only 1% considered "Mercalli."

However, the above raises the question as to whether the high percentage of the participants (96%) really knows the meaning of the Richter scale. Two key concepts regarding the measure of the size of an earthquake are magnitude and intensity [23]. The seismic energy released during an earthquake occurrence is measured by the magnitude, and it is commonly measured in a Richter scale. However, "Richter magnitude" (it is believed that it was named after Charles Richter who proposed the measure), may be regarded a measure only appropriate for earthquakes originating in California, USA [23]. Given this confusion, the moment magnitudes have become a universally appropriate scale (Mw) [23].

But what is the intensity of an earthquake? How is it measured? It is thought that intensity measures the consequences of an earthquake; that is, it is based on the observations made by people (as opposed to the Richter scale which employs instrumental measurements), and, consequently, it varies from place to place. The intensity is measured by what is known as the "Modified Mercalli Intensity Scale" (MMIS) [24].

What can we say about this in the context of the earthquakes that occurred in Mexico City in 2017? Was the Richter scale meaningful to the participants of the study? Was it meaningful to the residents of the capital city? The answer may be probably no. In fact, following the 2017 earthquakes, there was a debate on the mass media on this very issue. That is, it was a confusion among the residents of the city regarding the "intensity" felt by them during the two earthquakes that occurred on 7 and 19 September 2017. That is, for the earthquake on September 7 (magnitude of 8.1), with epicenter on the Pacific coast, the shaking was felt not that strong as the one on September 19 (with a magnitude of 7.1). Moreover, the consequences of the

**Figure 5.** *Responses on the magnitude of an earthquake.*

latter were severe in the city killing hundreds of people, but with a 7.1 of magnitude. These facts caused confusion among the residents.

For example, in an interview, the head of the "National Seismological Service" (SSN) argued that in relation to the earthquake on September 19, 2017

*"although it was smaller (the one on 19 September) than (the one on) the 7th of this month (September) and emitted less energy, "it was more intense for Mexico City because we are closer to the epicenter and because of the vulnerability of some areas (of the city) because are located in the lacustric soil." [25]*

Moreover, the head of the SSN went on saying that

*if people say it (the one in 19 September) was stronger than the one on 7 September or the one in '85 (1985), they are right, it is their perception, but it was smaller in size and energy released [25].*

The above clearly illustrates the difference between magnitude and intensity of an earthquake. Moreover, it may be argued that for the general public, it may be easier to understand the effects brought about, for example, by the two earthquakes just mentioned above in the context of the Mercalli Scale. Furthermore, it may be argued that people need to be well educated on these basic concepts. Also, it is of paramount importance to educate the residents of the capital city, for example, that damages caused by earthquakes depend on the distance from the epicenter, the type of soil, etc. Ultimately, the more effective is the communication on seismic risks and consequences, the better may be their preparedness to earthquakes.

#### **3.5 Education on seismic risk**

Respondents of the study were also asked whether they would like to be further educated on topics associated with seismic risk (the possible responses were "Yes" and "No"). The results showed that 83.5% of the students wanted to learn more on earthquakes and 16.5% showed no interest at all on this (**Figure 6**).

**Table 3** shows the results of the relationships of the variables considered in the analysis. Overall, when considering the age of the participants of the study, those whose age were 15, 16, and 17 years old showed more interest in learning more on earthquakes (69.5%, 1027/1479). On the other hand, women score a higher percentage than men in the willingness to learn more on seismic risk (i.e., 44.9%, 664/1479 vs. 38.6%, 571/1479). Finally, when considering the location of the participant schools, school 3 scored the highest percentage on their willingness to be educated on this very important and necessary subject (i.e., 19.6%, 290/1479).

Effectively, the results showed that there was (and still is) a great interest in learning more on seismic risk, for example, on what actions to take before, during, and after the occurrence of an earthquake. Moreover, the recent earthquakes in 2017 demonstrated that Mexico City's residents lacked an adequate preparation. Furthermore, it may be argued that organizations in charge of responding to the emergency (i.e., after the earthquake) were deficient in many respects, for example, the capacity of the rescue team in dealing with rescue during the emergency (e.g., in both aspects of "human" and infrastructure capacity). In most of the cases, the affected residents were themselves looking for their friends, relatives, etc., under the rubble. This clearly showed that emergency response teams need to be better prepared and well trained to cope with emergencies, such as those after the occurrence of the two earthquakes.

Students were also asked in which ways they would like to learn more on the subject; the possible answers were the following: "books," "the Internet," "at school,"

**29**

**Figure 6.**

**Age**

**Gender**

**Schools**

**Table 3.**

*Learning more on seismic risk.*

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes*

**Yes, N (%) No, N (%)**

"civil protection," "radio-TV," and "others" (the results are shown in **Figure 7**). The frequency data showed that the participants considered "civil protection" as the preferred option to learn more on earthquakes (44.1%, 549/1245). This was followed by "school" (22%, 274/1245) and finally "the Internet" (20%, 249/1245). **Table 4**, on the other hand, shows the results of the relationships between variables considered in the analysis. In general, when considering the age of the participants of the study, those whose age was 16 years old showed more interest in

*Relationship between the variables regarding to the willingness to learn more on seismic risk.*

 35 (2.8) 10 (4.1) 272 (22.0) 58 (23.8) 424 (34.3) 82 (36.6) 331 (26.8) 64 (26.2) 133 (10.8) 22 (9.0) 40 (3.2) 8 (3.3)

Women 664 (53.8) 95 (38.9) Men 571 (46.2) 149 (61.1)

School 1 160 (13.0) 18 (7.4) School 2 173 (14.0) 29 (11.9) School 3 290 (23.5) 57 (23.4) School 4 244 (19.8) 56 (23.0) School 5 211 (17.1) 57 (23.4) School 6 157 (12.7) 27 (11.1)

*DOI: http://dx.doi.org/10.5772/intechopen.85556*

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes DOI: http://dx.doi.org/10.5772/intechopen.85556*

#### **Figure 6.**

*Earthquakes - Impact, Community Vulnerability and Resilience*

These facts caused confusion among the residents.

latter were severe in the city killing hundreds of people, but with a 7.1 of magnitude.

For example, in an interview, the head of the "National Seismological Service"

*"although it was smaller (the one on 19 September) than (the one on) the 7th of this month (September) and emitted less energy, "it was more intense for Mexico City because we are closer to the epicenter and because of the vulnerability of some* 

*if people say it (the one in 19 September) was stronger than the one on 7 September or the one in '85 (1985), they are right, it is their perception, but it was smaller in* 

The above clearly illustrates the difference between magnitude and intensity of an earthquake. Moreover, it may be argued that for the general public, it may be easier to understand the effects brought about, for example, by the two earthquakes just mentioned above in the context of the Mercalli Scale. Furthermore, it may be argued that people need to be well educated on these basic concepts. Also, it is of paramount importance to educate the residents of the capital city, for example, that damages caused by earthquakes depend on the distance from the epicenter, the type of soil, etc. Ultimately, the more effective is the communication on seismic risks

Respondents of the study were also asked whether they would like to be further educated on topics associated with seismic risk (the possible responses were "Yes" and "No"). The results showed that 83.5% of the students wanted to learn more on

**Table 3** shows the results of the relationships of the variables considered in the analysis. Overall, when considering the age of the participants of the study, those whose age were 15, 16, and 17 years old showed more interest in learning more on earthquakes (69.5%, 1027/1479). On the other hand, women score a higher percentage than men in the willingness to learn more on seismic risk (i.e., 44.9%, 664/1479 vs. 38.6%, 571/1479). Finally, when considering the location of the participant schools, school 3 scored the highest percentage on their willingness to be educated

Effectively, the results showed that there was (and still is) a great interest in learning more on seismic risk, for example, on what actions to take before, during, and after the occurrence of an earthquake. Moreover, the recent earthquakes in 2017 demonstrated that Mexico City's residents lacked an adequate preparation. Furthermore, it may be argued that organizations in charge of responding to the emergency (i.e., after the earthquake) were deficient in many respects, for example, the capacity of the rescue team in dealing with rescue during the emergency (e.g., in both aspects of "human" and infrastructure capacity). In most of the cases, the affected residents were themselves looking for their friends, relatives, etc., under the rubble. This clearly showed that emergency response teams need to be better prepared and well trained to cope with emergencies, such as those after the occurrence of the two earthquakes.

Students were also asked in which ways they would like to learn more on the subject; the possible answers were the following: "books," "the Internet," "at school,"

and consequences, the better may be their preparedness to earthquakes.

earthquakes and 16.5% showed no interest at all on this (**Figure 6**).

on this very important and necessary subject (i.e., 19.6%, 290/1479).

(SSN) argued that in relation to the earthquake on September 19, 2017

*areas (of the city) because are located in the lacustric soil." [25]*

Moreover, the head of the SSN went on saying that

*size and energy released [25].*

**3.5 Education on seismic risk**

**28**

*Learning more on seismic risk.*


**Table 3.**

*Relationship between the variables regarding to the willingness to learn more on seismic risk.*

"civil protection," "radio-TV," and "others" (the results are shown in **Figure 7**). The frequency data showed that the participants considered "civil protection" as the preferred option to learn more on earthquakes (44.1%, 549/1245). This was followed by "school" (22%, 274/1245) and finally "the Internet" (20%, 249/1245).

**Table 4**, on the other hand, shows the results of the relationships between variables considered in the analysis. In general, when considering the age of the participants of the study, those whose age was 16 years old showed more interest in

#### *Earthquakes - Impact, Community Vulnerability and Resilience*

#### **Figure 7.**

*Preferences in learning more on seismic risk.*


#### **Table 4.**

*Preferences in learning more on earthquakes.*

learning more on earthquakes through "civil protection" organization (35.5%), followed by "school" (33.6%), "the Internet" (32.1%), and "books" (31.4%). Further, women score a higher percentage than men in the willingness to learn more on seismic risk through "civil protection" (i.e., 56.5% vs. 43.5%). Finally, when considering the location of the participant schools, school 3 scored the highest percentage on their willingness to be educated by "civil protection" on the subject (i.e., 23.5%).

**31**

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes*

The results clearly showed the need to educate the residents of the capital city, that is, not only students but also the general public, on issues such as those topics covered here and others relevant to specific actions to take before, during, and after

This chapter has presented some of the results of a cross-sectional study conducted in Mexico City in 2015–2016. The approach has been the application of a questionnaire to a sample size of *n* = 1489. Six high schools participated in the study are located within the seismic zones of the city. Some of the results and conclusions

a.About 95% of the students have experienced an earthquake, and 71% considered that earthquakes cannot be predicted; however, 29% did not know this fact.

c.In relation to the question related to how the size of an earthquake is measured, as expected, 96% of the participants considered the "Richter" scale; however, this raises the question as to whether the participants really know the meaning

d.One of the key conclusions is associated with the need to educate the inhabitants of the capital city on a more realistic scale of the size of an earthquake; this

e.More generally, the residents of the city should be educated on these basic concepts. Moreover, the more effective is the communication on risks and consequences, the better may be their preparedness to earthquakes.

f. It appears that civil protection should take the lead in designing an education program on seismic risk, which could be implemented at schools; moreover, it

Some future research is needed, such as that associated with an inferential

This research was supported by the following grants: CONACYT-No: 248219;

b.About 82.2% of students were all aware of the likelihood of an earthquake occurrence sometime in the future; the occurrence of the two strong earth-

quakes that hit the city in 2017 confirmed their perception.

could be the "Modified Mercalli Intensity Scale" (MMIS).

should be implemented with urgency.

SIP-IPN-20196424 and 20180064; and UANL-PTC-1046.

The authors declare that they have no competing interests.

analysis of the collected data.

**Acknowledgements**

**Conflict of interest**

*DOI: http://dx.doi.org/10.5772/intechopen.85556*

an earthquake occurrence.

**4. Conclusions**

are given below:

of the scale.

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes DOI: http://dx.doi.org/10.5772/intechopen.85556*

The results clearly showed the need to educate the residents of the capital city, that is, not only students but also the general public, on issues such as those topics covered here and others relevant to specific actions to take before, during, and after an earthquake occurrence.

## **4. Conclusions**

*Earthquakes - Impact, Community Vulnerability and Resilience*

learning more on earthquakes through "civil protection" organization (35.5%), followed by "school" (33.6%), "the Internet" (32.1%), and "books" (31.4%). Further, women score a higher percentage than men in the willingness to learn more on seismic risk through "civil protection" (i.e., 56.5% vs. 43.5%). Finally, when considering the location of the participant schools, school 3 scored the highest percentage on their willingness to be educated by "civil protection" on the subject (i.e., 23.5%).

**30**

**Table 4.**

*Preferences in learning more on earthquakes.*

**Figure 7.**

**Gender**

**Age**

**Schools**

*Preferences in learning more on seismic risk.*

**Books, N (%)**

Women 21 (41.2) 86 (34.5) 149

Men 30 (58.8) 163 (65.5) 125

**The Internet, N (%)**

**School, N (%)**

(54.4)

(45.6)

 6 (11.8) 11 (4.4) 8 (2.9) 15 (2.7) 0 (0.0) 0 (0.0) 12 (23.5) 59 (23.7) 53 (19.3) 124 (22.6) 25 (21.4) 2 (40.0) 16 (31.4) 80 (32.1) 92 (33.6) 195 (35.5) 34 (29.1) 2 (40.0) 14 (27.5) 66 (26.5) 73 (26.6) 139 (25.3) 37 (31.6) 1 (20.0) 3 (5.9) 24 (9.6) 42 (15.3) 59 (10.7) 14 (12.0) 0 (0.0) 0 (0.0) 9 (3.6) 6 (2.2) 17 (3.1) 7 (6.0) 0 (0.0)

School 1 7 (13.7) 22 (8.8) 31 (11.3) 89 (16.2) 7 (6.0) 2 (40.0) School 2 4 (7.8) 22 (8.8) 60 (21.9) 65 (11.8) 15 (12.8) 1 (20.0) School 3 10 (19.6) 80 (32.1) 54 (19.7) 129 (23.5) 28 (23.9) 1 (20.0) School 4 15 (29.4) 47 (18.9) 48 (17.5) 107 (19.5) 23 (19.7) 0 (0.0) School 5 7 (13.7) 51 (20.5) 52 (19.0) 100 (18.2) 25 (21.4) 1 (20.0) School 6 8 (15.7) 27 (10.8) 29 (10.6) 59 (10.7) 19 (16.2) 0 (0.0)

**Civil protection, N (%)**

**Radio-TV, N (%)**

310 (56.5) 70 (59.8) 5 (100.0)

239 (43.5) 47 (40.2) 0 (0.0)

**Others, N (%)**

This chapter has presented some of the results of a cross-sectional study conducted in Mexico City in 2015–2016. The approach has been the application of a questionnaire to a sample size of *n* = 1489. Six high schools participated in the study are located within the seismic zones of the city. Some of the results and conclusions are given below:


Some future research is needed, such as that associated with an inferential analysis of the collected data.

#### **Acknowledgements**

This research was supported by the following grants: CONACYT-No: 248219; SIP-IPN-20196424 and 20180064; and UANL-PTC-1046.

### **Conflict of interest**

The authors declare that they have no competing interests.

## **Author details**

Tatiana Gouzeva1 \*, Diego Padilla-Pérez<sup>2</sup> , Daniel Velázquez-Martínez<sup>3</sup> , Vladimir Avalos-Bravo4 and José Lourdes Félix-Hernández<sup>5</sup>

1 Grupo de investigación SARACS, Instituto Politécnico Nacional, Mexico

2 Centro de Desarrollo Aeroespacial, Instituto Politécnico Nacional, Mexico

3 Departamento de Ingeniería Industrial y Administración, Universidad Autónoma de Nuevo León, Monterrey, Mexico

4 SEPI-ESIQIE, SARACS, Instituto Politécnico Nacional, Mexico

5 Departamento de Ingeniería Civil, Universidad Juárez Autónoma de Tabasco, Mexico

\*Address all correspondence to: tatianagouzeva@hotmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**33**

*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes*

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[Accessed: 12 February 2019]

[9] Alexander D. Natural Hazards. New York, London: Taylor & Francis Group; 1993. 632 p. ISBN: 978-1-857-28094-4

[10] Anderson JW. Cultural adaptation

[11] Palm R. Urban earthquake hazards: The impacts of culture on perceived risk and response in the USA and Japan. Applied Geography. 1998;**18**:35-46. DOI:

to threatened disaster. Human Organization. 1967;**27**:298-307

10.1016/S0143-6228(97)00044-1

ijdrr.2018.06.004

[12] Suri K. Understanding historical, cultural and religious frameworks of mountain communities and disasters in Nubra valley of Ladakh. International Journal of Disaster Risk Reduction. 2018;**31**:504-513. DOI: 10.1016/j.

[13] Lindell M, Whitney DJ. Correlates of household seismic hazard adjustment adoption. Risk Analysis. 2000;**20**:13-25.

[14] Joohannesdottir G, Gısladottir G.

volcanic hazard in southern Iceland: Vulnerability and risk perception. Natural Hazards and Earth System Sciences. 2010;**10**:407-420. DOI: 10.5194/nhess-10-407-2010

[15] Yamada M, Heaton T, Beck J. Realtime estimation of fault treatment rupture extent using near source versus far-source classification.

Bulletin of the Seismological Society of America. 2007;**97**(6):1890-1910. DOI:

10.1785/0120060243

DOI: 10.1111/0272-4332.00002

People living under threat of

*DOI: http://dx.doi.org/10.5772/intechopen.85556*

[1] Ismail N, Okazaki K, Ochiai C, Fernandez G. Livelihood in Banda Aceh, Indonesia after the 2004 Indian Ocean tsunami. International Journal of Disaster Risk Reduction. 2018;**28**:439-449. DOI: 10.1016/j.

[2] Klinger Y, Ji C, Shen ZK, Bakun WH. Introduction to the special issue on the 2008 Wenchuan, China, earthquake. Bulletin of the Seismological Society of America, Seismological Society of America. 2010;**100**(5B):2353-2356. DOI:

[3] Goto K, Ikehara K, Goff J, Chague-Goff C, Jaffe B. The 2011 Tohoko-oki tsunami-three years on. Marine Geology. 2014;**358**:2-11. DOI: 10.1016/j.

[4] Goda K, Kiyota T, Pokhrel RM, Chiaro G, Katagiri T, Sharma K, et al. The 2015 Gorkha Nepal earthquake damage survey. Frontiers in Built Environment. 2015;**1**(8):1-15. DOI:

[5] SSN. Sismos [Internet]. Mexico City. Mexico: 2018. Available from: http:// www.ssn.unam.mx/ [Accessed: 12

[6] SSN. Sismo de Tehantepec (2017-09- 07 23, 49 MW 8.2) [Internet]. Mexico City. Mexico: 2017. Available from: http://www.ssn.unam.mx/sismicidad/ reportes-especiales/2017/SSNMX\_rep\_ esp\_20170907\_Tehuantepec\_M82.pdf

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esp\_20170919\_Puebla-Morelos\_M71.pdf

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de 2017, Puebla-Morelos (M 7.1) [Internet]. Mexico City. Mexico: 2017. Available from: http://www. ssn.unam.mx/sismicidad/reportesespeciales/2017/SSNMX\_rep\_

[Accessed: 15 February 2018]

**References**

ijdrr.2017.09.003

10.1785/0120100172

margeo.2014.08.008

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*Knowledge and Perception on Seismic Risk of Students in Mexico City Before the 2017 Earthquakes DOI: http://dx.doi.org/10.5772/intechopen.85556*

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*Earthquakes - Impact, Community Vulnerability and Resilience*

**32**

Mexico

**Author details**

Tatiana Gouzeva1

Vladimir Avalos-Bravo4

de Nuevo León, Monterrey, Mexico

\*, Diego Padilla-Pérez<sup>2</sup>

provided the original work is properly cited.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

and José Lourdes Félix-Hernández<sup>5</sup>

3 Departamento de Ingeniería Industrial y Administración, Universidad Autónoma

5 Departamento de Ingeniería Civil, Universidad Juárez Autónoma de Tabasco,

1 Grupo de investigación SARACS, Instituto Politécnico Nacional, Mexico

2 Centro de Desarrollo Aeroespacial, Instituto Politécnico Nacional, Mexico

4 SEPI-ESIQIE, SARACS, Instituto Politécnico Nacional, Mexico

\*Address all correspondence to: tatianagouzeva@hotmail.com

, Daniel Velázquez-Martínez<sup>3</sup>

,

[1] Ismail N, Okazaki K, Ochiai C, Fernandez G. Livelihood in Banda Aceh, Indonesia after the 2004 Indian Ocean tsunami. International Journal of Disaster Risk Reduction. 2018;**28**:439-449. DOI: 10.1016/j. ijdrr.2017.09.003

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[4] Goda K, Kiyota T, Pokhrel RM, Chiaro G, Katagiri T, Sharma K, et al. The 2015 Gorkha Nepal earthquake damage survey. Frontiers in Built Environment. 2015;**1**(8):1-15. DOI: 10.3389/fbuil.2015.00008

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[12] Suri K. Understanding historical, cultural and religious frameworks of mountain communities and disasters in Nubra valley of Ladakh. International Journal of Disaster Risk Reduction. 2018;**31**:504-513. DOI: 10.1016/j. ijdrr.2018.06.004

[13] Lindell M, Whitney DJ. Correlates of household seismic hazard adjustment adoption. Risk Analysis. 2000;**20**:13-25. DOI: 10.1111/0272-4332.00002

[14] Joohannesdottir G, Gısladottir G. People living under threat of volcanic hazard in southern Iceland: Vulnerability and risk perception. Natural Hazards and Earth System Sciences. 2010;**10**:407-420. DOI: 10.5194/nhess-10-407-2010

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**35**

Section 3

Vulnerability
