COMMUNITIES’ LANGUAGE NEED-BASED WILDFIRE EVACUATION ALERTS
Authored in September 2021 by WEmap, a group of recent graduates from the University of California, Berkeley, in collaboration with FireSafe Marin and Zonehaven, supported by Wonder Labs’ Reimagining 2025: Living with Fire Design Challenge Program.
Authors:
Abby Gao (yue.gao@berkeley.edu)
Virginia Wong (virginia_wong@berkeley.edu)
Yuquan Zhou (yuquan_zhou@berkeley.edu)
Advisors:
Charlie Crocker (CEO @ Zonehaven)
Shefali Lakhina (Co-founder @ Wonder Labs)
Sukh Singh (Researcher @ UC Berkeley)
Thomas Azwell (Scientist @ UC Berkeley)
Urvashi Ahuja (Head of Experience @ Zonehaven)
Vivek Rao (Lecturer & Researcher @ UC Berkeley)
Community Partners:
Marimar Ochoa (Public Information Specialist, County of Marin)
Sofia Martinez (Diversity, Equity and Inclusion Analyst, County of Marin)
1. BACKGROUND
Wildfire is one of the most threatening disasters in California. In recent years, there’s been an increase in the number of fast-moving wildfire events that have caused extensive damage and long-term impacts on the wellbeing of communities and ecosystems. According to CAL FIRE, 2020 was one of the most severe fire seasons in California. There were 9,917 wildfires with an estimate of 4.2 million acres of land burned. Over 9,000 structures were destroyed, and 31 people were killed. As of this writing, the Caldor Fire has burned at least 177,260 acres and is just 14% contained. Immediate attention and mitigation strategies are required at the landscape scale. However, challenges are often manifested at a smaller scale – in terms of how people experience wildfires and how they respond to evacuation alerts.
This research originally stemmed from our team’s passion for creating a more inclusive evacuation planning and process. Our team was formed initially in February 2020 during a class that focuses on Innovation in Disaster Response at UC Berkeley. The course was sponsored by the Blum Center for Developing Economies’ Development Engineering Program to help foster new ideas of improving disaster response. With our interest in tackling wildfire problems in California, we had proposed the EVACmap (Figure 1), which is an interactive mapping application that can provide up-to-date information on wildfires and potential resources during evacuation. For more information, please visit here.
Figure 1. EvacMap prototypes developed in February 2020.
Our team identified the lack of communication between first responders, support groups, and the communities (evacuees) at different stages of a wildfire event. The drastic delaying of resources allocation and evacuation responses to wildfire had created negative impacts on evacuees. Accurate information about wildfires and its recommended reactions are often hard to find when emergencies strike. During our interviews with first responders and people who have experienced evacuation in California, we realized that people with different social vulnerabilities feel different levels of “disaster” mentally regardless of physical wildfire situations. After that class, our team would like to take this challenge, expand upon it, and find opportunities that we can actually apply to the disaster response field.
We want to unpack the problem further and decided to pivot our idea to target directly on evacuees, and more specifically the minority communities who are underrepresented and need most help during evacuation. And hence, the development of WEMap, a community need-based map that is heavily focused on inclusiveness and equity in wildfire evacuation planning.
The gap between wildfire alerts and the information delivery to minority communities is even larger among other stakeholders in a wildfire situation due to the lack of funding and resources from the government coupled with other social factors and physical capability of responding to emergencies, for example, people without access to technology will need to find other ways of receiving accurate wildfire alerts; disable community might find it hard to evacuate due to their physical constraints, etc. Therefore, our team would like to dive deeper into this problem space and make insightful recommendations that can be beneficial to this emerging problem.
Specifically, we want to focus on the socio-vulnerable groups in County Marin to acquire information effectively. Through Wonder Labs’ Reimagining 2025: Living with Fire Design Challenge, we were given an opportunity to incorporate our idea into an existing alert platform, Zonehaven AWARE. This community-centered research and industry augmentation are very exciting to our team as it can be further developed, released to the public to improve communication outreach, and ultimately achieve our goal to help communities through more inclusive evacuation planning. By publishing this project, we hope our insights and concepts generated from this research could inspire other future efforts on wildfire risk reduction to focus on the linguistic struggles for the minority communities.
This summer project could not have succeeded without our supportive advisors and community partners. Thank you all for your continuous support and providing us with valuable opportunities and connections.
2. TIMELINE/OVERVIEW
3. PROBLEM SPACE
Marin County has been through many wildfires in the past 100 years. And because of its geographic condition of being a peninsula composed of hills, ridges, and small mountains, Marin County was selected to be the area of focus of our case study. The aim of this research is to understand how residents from culturally and linguistically diverse backgrounds respond to wildfire alerts before and during evacuation.
Through our research and GIS mapping, our team targeted non-native English speakers and generated maps that are totally different from a Fire Hazard Severity Zone map, see Figure 2. Many minority communities are under the “Local Responsibility Area”(LRA) while some are unincorporated according to CAL FIRE. The “very high risk” zones are Larkspur, Mill Valley, and Novato, which were identified without the consideration of all the areas within Marin County.
Figure 2 Fire Hazard Severity Zone created by CAL FIRE
These minority communities, especially those with limited English proficiency are mostly concentrated on the east side of Marin county, which oftentimes can be overseen on maps geographically or falls into the “Local Responsibility Area” that might not get full support from the public fire services of the county of Marin during wildfire.
3.1 NON-NATIVE ENGLISH SPEAKERS CONCENTRATED IN EAST SIDE OF MARIN COUNTY
According to the 2019 census tract data, there are 105,298 households in Marin County, within which 4,588 households, about 4.375%, have limited English speaking skills. Using language barriers as our target focus, we overlaid the number of limited English speaking households with the Fire Hazard Severity Zone data. Figure 3 shows that limited English Speaking households are concentrated in the east side of Marin County, and are close or located in very high fire hazard severity zones, indicating their vulnerability to wildfire risks and to other disaster-related challenges.
Figure 3 Number of non-English Speaking households at Census Tract Level, Marin County, 2019
Data source: 2019 ACS 5- year estimates, Table S1602 (Number of limited English Speaking households)
Marin Geohub (Fire HAzard Severity Zone)
Among these 4588 households, we also acknowledge a large population of Spanish speakers (more than 55%). In addition, 25% of them speak other Indo-European languages, and about 20% speak Asian and Pacific languages. Since most of the non-native English speaking households speak Spanish, we further narrow our research down focusing on Spanish households, which shows a similar geographic pattern (Figure 4).
Figure 4. Number of Spanish speaking households in Marin County, 2019
Data source: 2019 ACS 5- year estimates, Table S1602 (Number of limited English Speaking households)
Marin Geohub (Fire HAzard Severity Zone)
3.2 SAN RAFAEL AS OUR FOCUS COMMUNITY
Language barriers are a major contributor to poor information dissemination and communication during wildfire events. Since it has significantly reduced the effectiveness of overall communication between different stakeholders, our group decided to target the non-native English speaking communities. Overlaying the Wildland Urban Interface (WUI) with census tracts, we want to identify communities that have more than 150 limited English speaking households to be considered as our linguistic needs-related “Wildfire vulnerability zones”. They are San Rafael, Marin City, and Novato (Figure 5). After communicating with our community partners, our team selected San Rafael as our primary case study region to do in-person surveys and interviews.
Figure 5. Linguistic needs-related Wildfire vulnerable regions in Marin County
Data source: 2019 ACS 5- year estimates, Table S1602 (Number of limited English Speaking households)
Marin Geohub (Urban Wildland Interface)
4. OUR APPROACH
Our team approached this problem space with design thinking methodologies we learned from school and intersect them with real data gathered. This is not a full research, but we believe by providing insightful recommendations, it fosters meaningful and beneficial dialogue within this emerging field of disaster response. We hope our project can contribute to just, inclusive, and equitable wildfire resilience outcomes.
5. METHODOLOGY
(GIS + SURVEY)
Both quantitative and qualitative methods were used in this community-based research to identify linguistic-related wildfire vulnerability during evacuation. Our team thinks that wildfire vulnerability of minority communities is comparatively higher given their social, geographic factors combined with wildfire hazard potential and adaptive capacity of people, and therefore, both methods will cohesively generate a more inclusive outcome.
5.1 NARROW DOWN OUR PROBLEM SPACE THROUGH GIS ANALYSIS
Since there are many factors involved in a wildfire situation, our team would like to investigate this problem at a much smaller scale. To begin, we conducted some Geographic Information System (GIS) analysis to identify the area of focus. GIS is a quick tool for overlaying massive datasets and visualizing them spatially. All maps generated in our research are created using open-source data found online.
GIS dataset extracted from:
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Marin County’s data banks:
- Marin Geohub and MarinMap for Urban Wildland Interface and Fire Severity Map shapefiles
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Data from United States Census Bureau:
- Data of limited English speaking households at census tract level in Marin County
- 2020 TIGER/Line Shapefiles of the census tract boundaries in Marin County
5.2 IDENTIFY NEEDS AND INSIGHTS THROUGH INTERVIEWS AND SURVEYS
To understand the actual needs of communities in wildfire events, quantitative data is not enough. Many data were filtered and simplified by different factors such as ages, location, gender, income, etc. And therefore, through conducting in-person interviews and online surveys, our team will be able to gather primary sources about the struggles people are experiencing and identify pain points of the vulnerable population. With the support of our community partners in Marin County, our team will be able to conduct outreach within the community and interview some residents in Marin County.
5.3 ANALYZE DATA GATHERED THROUGH DIAGRAMS AND SETTING UP ARCHETYPES
As designers, we would like to use design thinking methodologies and attempts to translate insights and data gathered visually. By generating affinity maps and identifying three behavior archetypes, we will be able to form connections between people and how they receive emergency alerts. After that, we will be able to define the core problem by creating empathy maps and user journey maps that inform us with solutions that help relieve the pain points identified.
5.4 BRAINSTORM, CONDENSE, AND PRODUCE
As we identify the core problem, our team will brainstorm a number of possible creative concepts that could potentially mitigate the linguistic struggles people are experiencing. An impact-effort matrix will be used to sort out these ideas by listing out pros and cons of each top idea. From that, we will be able to select the top ideas as our recommendation to this problem space.
5.5 PROTOTYPE
The prototypes and recommendations generated from this project for Zonehaven AWARE will be based on our insights. These prototypes are examples of how our concepts could be applied to real evacuation products, which can benefit minority groups, particularly non-native English speakers. We hope these prototypes could inspire more creative and impactful solutions in the disaster response field.
6. SURVEY GENERATION, INTERVIEWS AND OUTCOME
Our goal is to create an inclusive survey that not only provides insights to our objective but also creates a channel for residents of Marin, particularly the minority community to voice their concerns. And therefore, we develop our questionnaire with our community partners, see Appendix, which are most familiar with the needs of locals. They have also provided us with some valuable lessons on word choice, setting the tones, giving out incentives, and how to conduct community outreach in person.
Since our target group is Spanish speakers with limited English proficiency, our survey has two language options both in English and Spanish. Both were generated with the support from our community partners. Despite the challenges of the pandemic, we still decided to do both in-person and online surveys as face-to-face communication increases the accuracy of the answers and also provides us with a better understanding of the struggles of communities through their body language, articulation, and so on. On July 22nd, 2021, our team went to a Spanish speaking community center in Marin County (330 Bellam Blvd, San Rafael, CA) to send out 20 surveys and conduct 5 interviews. During the in-person community outreach, our team was able to talk with both native and non-native speakers in Marin County and understand their opinions on wildfire alerts and their concerns over wildfire preparation.
We also sent out online surveys on different Reddit groups, for example, r/wildfire (a subreddit for wildland firefighters), r/Marin, and r/CaliforniaDisasters, as well as CALIFORNIA WILDFIRE SEASON group on Facebook.
Our team received 798 online responses, which exceeded our expectations. These primary sources of data allow us to compare and understand people with diverse cultural backgrounds and their knowledge about wildfire and alerts systems. After cleaning out duplicate and contradictory responses, the number of valid responses is 700 in which 86 people are non-native English speakers.
7. INSIGHTS
Based on the answers people provided in surveys, our team generated insights that might be useful to understand people’s evacuation behaviors during a wildfire event. The survey result only reflects the opinions of some people in Marin county and might contain biases. Our team has tried our best to ensure the survey reaches audiences as wide as possible during the pandemic.
7.1 BASED ON GENDER, AGE, AND LANGUAGE SKILLS
There are more male (59.9%) than female participants (44.2%) in our sample. In which, 91.7% of male participants feel prepared to be evacuated in the event of a wildfire. Comparatively, women, transgender women, transgender men, and the two-spirit group feels less prepared during an evacuation. Although people might have different interpretations on “preparedness” (e.g where to go when emergency strikes, what to bring and what resources they could find), Figure 6 reflects how common wildfire evacuation has become due to the frequency of wildfire events in Marin County.
Figure 6. Evacuation preparedness diagrams based on gender
Notes: N = 419 for men; N = 231 for women; N = 17 for transgender women; N = 17 for transgender men; N = 7 for two-spirit; N = 10 for those who prefer not to respond or describe as others
In terms of age, most people that are under 45 years old feel prepared for evacuation (90%), while only 81.5% of the people above 45 years old are prepared, see Figure 7. The result reflects a delay in evacuation response in people older than 45 years of age. Our team believes that the slower response time might be caused by various reasons including not being familiar with new technology such as mapping tools, alerts subscriptions, having more responsibility in terms of protecting their family and their properties, as well as needing more evacuation guidance.
Figure 7: Evacuation preparedness diagrams based on age
Notes: N = 419 for men; N = 254 for responders under 24 years old; N = 417 for responders between 25 to 44 years old; N = 27 for responders above 45 years old (2 missing responses);
In addition, from Figure 8, we have noticed that native English speakers also feel more prepared to evacuate (90.6%) than the non-native English speakers (85.9%). Our team believes that linguistic barriers might be one of the factors that create this delay in disaster response among non-native English speakers. Therefore, we would like to further investigate and understand the challenges that are associated with understanding the information from alert systems from a non-native English speaker standpoint.
Figure 8: Evacuation preparedness diagrams comparison between Native English Speakers and Non-native English speakers.
Notes: N = 615 for native English speakers; N = 85 for non-native English speakers.
7.2 PROBLEM-RELATED INSIGHTS
7.2.1 VARIOUS CHALLENGES RECEIVING ALERTS
One of our survey questions asks about challenges that people encounter when receiving alerts. About 37.7% of the non-native English speakers mentioned the need for an alert translation or linguistically adjusted alerts (Figure 9). Another 37.7% of the same group claimed to have technical difficulties, which they don’t know how to receive or subscribe to the wildfire alerts. Some people from the native English-speaking group have similar responses, but the percentage is comparatively lower (24.4%), indicating that the process of subscribing to receive alerts is more challenging to those who are non-native English speakers. Consequently, our team believes that non-native English speakers rely on their personal contacts as their primary wildfire communication resources during a wildfire event.
Figure 9: Non-native English speakers' challenges when receiving alerts
Notes: N = 85, it is a multiple-choice selection, so one responder could select more than one answer
7.2.2 WORD OF MOUTH IS THE MOST EFFECTIVE WAY DURING WILDFIRE EVACUATION FOR NON-NATIVE ENGLISH SPEAKERS
During our research, we also noticed that there are several ways to receive official alerts or information about the wildfire. However, we want to pinpoint which particular method did non-native English speakers use to improve the existing system from its root problem. From the survey results, see Figure 10, a larger portion of non-native English speakers mentioned that they received emergency information through their personal contracts outside and within their neighborhood, including family, friends, and neighbors. Some received theirs through social media platforms, which these platforms may not be considered as official alerts according to Marin County. The delay of evacuation response might appear from inaccurate information within the community or inaccessibility to the official alerts system.
Figure 10: Method used to receive emergency alerts from non-native English Speakers
Notes: N = 84 (1 missing response), it is a multiple-choice selection, so one responder could select more than one answer
During the interviews, people also mentioned that sometimes email alerts don't attract people's attention immediately, but pop-up alerts do. 69.9% of the non-native English speakers participants in our survey said they subscribed to Alert Marin. However, the subscription rate is still lower than the native English speakers (73.1%). On the other side of the spectrum, 3.6% of the non-native English speakers have no idea what Alert Marin is, which is twice the percentage of the native English speakers. The correlation between English proficiency and alert messages has reflected a communication gap between these alert platforms and the non-native English speaking communities during a wildfire event that the emergency alerts are harder to reach non-native English speakers than native English speakers (Figure 11).
Figure 11: Awareness of county's emergency alerts comparison between non-native and native English speakers
Notes: N = 83 for non-native English speakers (2 missing responses) N = 610 for native English speakers (5 missing responses)
7.2.3 ENGLISH PROFICIENCY VARIES AMONG NON-NATIVE ENGLISH SPEAKERS
Since English proficiency impacted how alerts systems deliver information, we asked people to rate their English proficiency level. Among those non-native English speakers, 60% of them stated that they can speak English fluently, who might be able to translate alerts information and spread them within the community, while 30% of them claim to have intermediate English abilities, who could communicate using English. However, they may need some extra help when encountering an emergency situation. In addition, about 8% of them only have an elementary level of English capabilities, indicating English alerts may not be helpful to them. (Figure 12)
Figure 12: English proficiency among non-native English speakers
Notes: N = 85
Similar to the comparison we made earlier about what channel did people use to receive alerts between the non-native and native English speakers, we also made another comparison between non-native English speakers who speak fluently and those who only have elementary to intermediate levels of English. The result shows that non-native English speakers who speak fluent English have a higher percentage (around 10-20% more) of using public alerts and mobile phone application alerts systems as their primary sources of wildfire information compared to those who have only elementary or intermediate English level, see Figure 13. The comparison illustrates our assumptions about the correlation between English proficiency and how people receive emergency alerts.
Figure 13: Method used to receive emergency alerts comparison between non-native English speakers with different English proficiency levels
Notes: N = 51 for fluent non-native English speakers; N = 33 for intermediate and elementary native English speakers (1 missing response); it is a multiple-choice selection, so one responder could select more than one answer
7.2.4 AN URGE FOR LANGUAGE TRANSLATION IN ALERTS SYSTEM
We then ask for any improvement in community-wide actions, 38.8% of the non-native English speakers responded that they would like to see better emergency alerts and notifications, and 9.4% of them would like to receive information in a language that they could understand, stressing the need for better and more accessible alerts that is in a language that people could understand even in an emergency situation, see Figure 14.
Figure 14. Wanted improvement in community-wide actions from non-native English speakers
Notes: N = 85, it is a multiple-choice selection, so one responder could select more than one answer
7.2.5 SEEKING FOR WILDFIRE-RELATED INFORMATION IN ALERTS
Other than challenges encountered, people with different linguistic and cultural backgrounds expect different information from the alert systems. A wide variety of information was mentioned, including:
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Fire perimeter and rate of spread information (i.e., where is the actual location of the fire, their proximity to the fire, what areas need to be evacuated, etc)
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How to react to emergencies or wildfires.
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Air quality
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Evacuation routes, road closure, and traffic information
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Power outage
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Drinking water supplies
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Improvement in community-wide actions, such as possible home hardening, structural defense, available insurance, and evacuation drills
8. GENERATING QUESTIONS THROUGH AFFINITY MAPPING
Figure 15 is an affinity diagram generated by insights gathered from our survey and interview. From the data, we realize that the difficulties people were experiencing have a close correlation to their English proficiency, and how language barriers can significantly delay effective alerts and information delivery. Therefore, we lay out all information on this diagram using three categories that we created for people within the non-native English-speaking community. For those who are fluent in English and are able to translate information to their communities members are the “Translators'' (green), the intermediate English speaking group will be the “Autodidacts” (blue) and the “Late Receivers” (yellow) are people with limited English speaking skills. Through affinity mapping, three “How might We” (HMW) questions were raised. How might we help people acquire information in an effective way? How might we notify people more efficiently? And finally, how might we help people understand alerts in their preferred language?
Figure 15. Affinity mapping
9. IDENTIFYING ARCHETYPES, AND CREATING EMPATHY MAPS
To better understand how these three types of people respond to a wildfire alert, we set some limitations for each type based on logical assumptions derived from their answers in surveys and interviews. We summarize each type’s characteristics according to their behaviors, language skills, channels of communication, goals, and pain points (Figure 16).
Figure 16. Three archetypes of non-native English speaker
Since people react to wildfire evacuation differently, our team would like to understand non-native English speakers' emotions and feelings upon receiving emergency alerts. Using the above characteristics identified, our team made 3 personas and imagined how they would respond during a wildfire event. Figure 17 - 19 are a series of empathy maps that show the “pains” and “gains” of each persona through understanding what they do, say, feel, think and hear in an emergency situation. The translators’ experience in evacuation is comparatively less stressful in an evacuation situation than the autodidacts and late receivers who struggle to understand alerts and confirm their accuracy.
Figure 17. Empathy map (Translator)
Figure 18. Empathy map (Autodidacts)
Figure 19. Empathy map (Late receivers)
10. SYNTHESIS THROUGH USER JOURNEY MAP
Figure 20. User journey map pre- and during wildfire
The fluctuating emotions throughout different stages of evacuation were mapped using a user journey map. Translators and autodidacts often feel worried upon receiving the alerts, but soon feel calmer when translators receive updated wildfire information and translate them to autodidacts. From the perspective of the translators, they feel good about helping people using their English skills whereas the autodidacts feel calmer when they are able to receive trusted wildfire sources from their neighbors. However, late receivers usually don’t receive and understand the alerts as fast as the others. They need help with translation and information search, and they feel lost because of their limited language skills. Searching for related and reliable information can be tiring and affects people’s emotions. Since people tend to have different concerns about emergency situations (insight we discovered from the survey results), the information seeking time varies and often delays their disaster response.
11. DEFINE THE PROBLEM
Converging what we have analyzed so far, the biggest takeaway is that English proficiency is highly correlated with how people react and respond to emergency alerts. Although challenges might vary based on people’s experience, language barriers are a major factor that delays wildfire responses. It is also an area that is currently lacking research and improvement within the disaster response field. It is not just about translating information into a specific language but understanding the needs of people and providing them with relevant information in a language they understand. It has also become more obvious that within non-native English speakers, people have different priorities based on their English proficiency and cultural background when it comes to wildfire evacuation and receiving alerts. Therefore, we redefined our problem space and generated a new “How might we” (HMW) question - How might we deliver timely and relevant wildfire information based on communities’ linguistic needs in a language they preferred?
12. IDEATION
The following are augmentation recommendations for Zonehaven Aware, these concepts are created based on our research findings and might need further exploration on their implementation feasibility.
12.1 DISPLAY ZONE INFORMATION BASED ON COMMUNITIES’ LANGUAGES PREFERENCES
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We recommend Zonehaven to gather information about people’s language preferences and frequently-search phrases. The platform should automatically display or suggest relevant wildfire information based on the most “clicked” language and topics on the information card.
12.2 INCLUDE WILDFIRE-RELATED RESOURCES ON ALERTS
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To improve resource allocation and reduce the overall burden of public services during wildfire events, we suggest adding wildfire-related information that includes resources listed in our survey insights, see section 7.2.5. When there is an emergency alert, people will be able to also see useful links to resources.
12.3 ADDING INTERACTIVE FEATURES TO PLATFORM
Volunteers sign up to help
Late receivers need assistance
When an emergency strikes, alerts might not be able to be officially translated right away. Since there are people who can translate within the community, such as the “translators” we identify and those who are native English speakers, we recommend having a function that volunteers can sign up on Zonehaven to help translate information and help their neighbors (e.g calling the late receivers via phone to translate evacuation orders). Our team thinks this collective effort is meaningful and effective. This will also ensure safer evacuation.
13. PROTOTYPES - AUGMENTING ZONEHAVEN AWARE
Zonehaves Aware serves community residents and provides reliable sources for first-order evacuation updates and preparation resources. On Zonehaven AWARE, a county is divided into different evacuation zones (Figure 21). Residents will find information about their zone, familiarize themselves with local fire and law agencies in their area, and identify resources that will help them prepare.
Figure 21. Existing interface of Zonehaven AWARE showing the divided “zones”
Zonehaven AWARE is available online as a website application. Currently, it is only in English and has limited resources linked to each information card. Our prototypes use the interface of Zonehaven AWARE as the base and incorporate our concepts and recommendations on top of existing features.
13.1 [CONCEPT 1] INSTANT TRANSLATING INFORMATION IN COMMUNITY-PREFERRED LANGUAGE
The goal of this concept design is to allow instant translation of wildfire alerts into the community's preferred language. This feature will require gathering data on the platform to help improve the user interface. The demonstration video (Figure 22 and 23) shows a new “translation” button that people can toggle between different languages while English is still the default language.
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Figure 22. Demonstration clip showing possible translation features.
Figure 23. Translation button on the information card.
We also recommend Zonehaven develop a translation chart for frequently-used phrases in alerts as the translation data. In addition, displaying the translation chart to the communities will help people understand what might appear in alerts so that they get prepared. People will be able to understand the different status of their zone that might happen, and what information they could get on Zonehaven AWARE.
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Figure 24. Translation Chart showing different statuses a zone might be in.
13.2 [CONCEPT 2 ] WILDFIRE-RELATED RESOURCE LINKS ON ALERTS
The goal of this concept is to provide relevant information to residents in Marin County with different cultural and linguistic backgrounds. To make it more relevant, this feature will require more research on the needs of different communities or gathering data of most-searched topics. Figure 24 is an illustration showing extra information under the “useful links” section and how the new information card could look like.
Figure 25. Useful links that direct people to external wildfire resources
13.3 [CONCEPT 3] SIGN UP TO VOLUNTEER OR REQUESTING NON-EMERGENCY HELP
The goal for this concept is to reduce the burden of official alerts generators by having a function that volunteers can contribute to translate information and help those in need. The demonstration video (Figure 26) below shows options for volunteering and requesting non-emergency help from the platform. This concept will foster collaboration between authorities, organizations, and communities, and develop a closer relationship.
Figure 26. User flow on Zonehaven AWARE to offer or request help
13.3.1 REQUESTING ASSISTANCE THROUGH PLATFORM
Figure 27 shows how people can request non-emergency assistance through Zonehaven Aware platform when an emergency happens. The form also has different language translations available. The simple form will ask questions such as how they would like to be notified or what kind of help they need, to help to connect them with the next right and available volunteer.
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Figure 27. An illustration showing the instant translation of the request assistance form
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Figure 28. An illustration showing the volunteers registration form
13.3.2 SIGN UP TO OFFER HELP THROUGH THE PLATFORM
These collaborative features allow people who think they are qualified to help during wildfire evacuation. Translators or native speakers could sign up on Zonehaven to be volunteers. After signing up, they can be guided to a page where there is more information about wildfires, a short introduction to their role and Zonehaven Volunteer’s portal to connect with people in need. Figure 28 shows what that form will look like on the platform.
14. LIMITATION AND ASSUMPTION MADE
As mentioned previously, our team tried our best to ensure the accuracy and unbiased survey answer through cleaning up survey data and asking questions with neutral wordings. We hope to acquire geographic information such as zip code in our future research and survey so that we can further reduce invalid and irrelevant responses. This will also enable us to add complexity to our problem space through visualizing the data spatially using GIS analysis.
In this project, we assume all non-native English speakers, regardless of their native languages, have similar needs in terms of understanding English alerts. We also assume people expect to receive alerts in their native language, which is one of our criteria during the cleaning up process. These assumptions helped us set boundaries within the problem space. However, more research is needed to prove the statements and understand the actual needs of the community.
15. CONCLUSION
This open-source research created insightful recommendations for the existing alerts platform - Zonehaven AWARE based on data gathered through GIS analysis, surveys and interviews. Using design thinking methodologies, the mapping exercise informs our three augmentation concepts which we hope to provide valuable inputs to the existing platform and have a positive impact to our target non-native English speaking community. This research aims to contribute and inspire the disaster response field and hopes to create a more inclusive evacuation planning. Since there is little research focusing on linguistic struggles for minority communities, this research will ignite dialogues and change how the alerts system and platform is designed.
During this journey, our team built successful partnerships with Zonehaven, Wonder Labs and Firesafe Marin and we hope to continue developing the linguistic features for Zonehaven AWARE, our next steps could be to perform user tests and create iterations to fine tune these new linguistic features.
APPENDIX
SURVEY
CONSENT FORM
