{"id":{"repo_id":"arkansas","oai_identifier":"oai:scholarworks.uark.edu:etd-5248"},"canonical_url":"https://search.dev.ndltd.org/etd/arkansas/oai:scholarworks.uark.edu:etd-5248","repository":{"repo_id":"arkansas","name":"University of Arkansas","base_url":"https://scholarworks.uark.edu/do/oai/"},"display":{"title":"Landscapes of Danger: A Geospatial Analysis of Perceived and Realistic Risk in Bryce Canyon National Park","abstract":"<p>The quantification of risk has inspired a wide breath of literature from the physical sciences, social sciences, and interdisciplinary disciplines like geography. Many attempts to estimate risk via natural hazards either focus on quantifying realistic risk or perceived risk of lay persons, with very little overlap between these paradigms. Due to this, a considerable knowledge gap exists within perceived risk and natural hazards research. This study aims to provide a comprehensive, risk estimation and assessment strategy through a multi-hazard risk assessment of Bryce Canyon National Park (BRCA). This case study analyzed knowledge of risk among visitors with perception surveys and Likert-based scales, in addition to identifying high risk areas of the park through Geographic Information Systems (gis). With a sample size of 254, a systematic stratified sampling method was implemented at specific sites in the park chosen for their distinctive viewsheds, accessibility, and popularity. To identify risky areas, two fuzzy logic models were built: one to identify areas susceptible to rockfall and another to identify areas susceptible to landslides/slumps. Overall, respondents reported feeling largely unconcerned when ranking their perception of various risks within the park (µ = 2.1, σ = .78), however, perception gaps and demographic influences were revealed on individual event types. When asked to identify dangerous areas of the park, participants tended to select locations in the main amphitheater – the most highly trafficked area of the park – even though the fuzzy logic models showed a wider range of locations were susceptible to mass wasting events.</p>","abstract_html":"&lt;p&gt;The quantification of risk has inspired a wide breath of literature from the physical sciences, social sciences, and interdisciplinary disciplines like geography. Many attempts to estimate risk via natural hazards either focus on quantifying realistic risk or perceived risk of lay persons, with very little overlap between these paradigms. Due to this, a considerable knowledge gap exists within perceived risk and natural hazards research. This study aims to provide a comprehensive, risk estimation and assessment strategy through a multi-hazard risk assessment of Bryce Canyon National Park (BRCA). This case study analyzed knowledge of risk among visitors with perception surveys and Likert-based scales, in addition to identifying high risk areas of the park through Geographic Information Systems (gis). With a sample size of 254, a systematic stratified sampling method was implemented at specific sites in the park chosen for their distinctive viewsheds, accessibility, and popularity. To identify risky areas, two fuzzy logic models were built: one to identify areas susceptible to rockfall and another to identify areas susceptible to landslides/slumps. Overall, respondents reported feeling largely unconcerned when ranking their perception of various risks within the park (µ = 2.1, σ = .78), however, perception gaps and demographic influences were revealed on individual event types. When asked to identify dangerous areas of the park, participants tended to select locations in the main amphitheater – the most highly trafficked area of the park – even though the fuzzy logic models showed a wider range of locations were susceptible to mass wasting events.&lt;/p&gt;","abstract_has_math":false,"creators":["Francis, Tia"],"institution":null,"degree_name":"Master of Science in Geography (MS)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Davidson, Fiona M.","Tullis, Jason A."],"advisors":["Paradise, Thomas R."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-05-01T07:00:00Z","date_published":"2020-05-01T07:00:00Z","updated_at":"2026-07-24T01:00:09Z","subjects":["Cartography","Fuzzy logic","Hazards","Risk","Risk assessment","Risk perception","Environmental Public Health","Geographic Information Sciences","Human Ecology","Human Geography","Nature and Society Relations","Spatial Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/3698","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Davidson, Fiona M.","Tullis, Jason A."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Paradise, Thomas R."]},{"key":"dc:creator","label":"Author","values":["Francis, Tia"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-06-18T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Geography (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cartography","Fuzzy logic","Hazards","Risk","Risk assessment","Risk perception","Environmental Public Health","Geographic Information Sciences","Human Ecology","Human Geography","Nature and Society Relations","Spatial Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/3698"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["<p>The quantification of risk has inspired a wide breath of literature from the physical sciences, social sciences, and interdisciplinary disciplines like geography. Many attempts to estimate risk via natural hazards either focus on quantifying realistic risk or perceived risk of lay persons, with very little overlap between these paradigms. Due to this, a considerable knowledge gap exists within perceived risk and natural hazards research. This study aims to provide a comprehensive, risk estimation and assessment strategy through a multi-hazard risk assessment of Bryce Canyon National Park (BRCA). This case study analyzed knowledge of risk among visitors with perception surveys and Likert-based scales, in addition to identifying high risk areas of the park through Geographic Information Systems (gis). With a sample size of 254, a systematic stratified sampling method was implemented at specific sites in the park chosen for their distinctive viewsheds, accessibility, and popularity. To identify risky areas, two fuzzy logic models were built: one to identify areas susceptible to rockfall and another to identify areas susceptible to landslides/slumps. Overall, respondents reported feeling largely unconcerned when ranking their perception of various risks within the park (µ = 2.1, σ = .78), however, perception gaps and demographic influences were revealed on individual event types. When asked to identify dangerous areas of the park, participants tended to select locations in the main amphitheater – the most highly trafficked area of the park – even though the fuzzy logic models showed a wider range of locations were susceptible to mass wasting events.</p>"]},{"key":"dc:title","label":"Title","values":["Landscapes of Danger: A Geospatial Analysis of Perceived and Realistic Risk in Bryce Canyon National Park"]}]}],"canonical_facts":{"dc:contributor":["Davidson, Fiona M.","Tullis, Jason A."],"dc:contributor.advisor":["Paradise, Thomas R."],"dc:creator":["Francis, Tia"],"dc:date":["2020"],"dc:date.available":["2021-06-18T07:00:00Z"],"dc:description.abstract":["<p>The quantification of risk has inspired a wide breath of literature from the physical sciences, social sciences, and interdisciplinary disciplines like geography. Many attempts to estimate risk via natural hazards either focus on quantifying realistic risk or perceived risk of lay persons, with very little overlap between these paradigms. Due to this, a considerable knowledge gap exists within perceived risk and natural hazards research. This study aims to provide a comprehensive, risk estimation and assessment strategy through a multi-hazard risk assessment of Bryce Canyon National Park (BRCA). This case study analyzed knowledge of risk among visitors with perception surveys and Likert-based scales, in addition to identifying high risk areas of the park through Geographic Information Systems (gis). With a sample size of 254, a systematic stratified sampling method was implemented at specific sites in the park chosen for their distinctive viewsheds, accessibility, and popularity. To identify risky areas, two fuzzy logic models were built: one to identify areas susceptible to rockfall and another to identify areas susceptible to landslides/slumps. Overall, respondents reported feeling largely unconcerned when ranking their perception of various risks within the park (µ = 2.1, σ = .78), however, perception gaps and demographic influences were revealed on individual event types. When asked to identify dangerous areas of the park, participants tended to select locations in the main amphitheater – the most highly trafficked area of the park – even though the fuzzy logic models showed a wider range of locations were susceptible to mass wasting events.</p>"],"dc:identifier":["https://scholarworks.uark.edu/etd/3698"],"dc:subject":["Cartography","Fuzzy logic","Hazards","Risk","Risk assessment","Risk perception","Environmental Public Health","Geographic Information Sciences","Human Ecology","Human Geography","Nature and Society Relations","Spatial Science"],"dc:title":["Landscapes of Danger: A Geospatial Analysis of Perceived and Realistic Risk in Bryce Canyon National Park"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Geography (MS)"]},"updated_at":"2026-07-24T01:00:09Z"}