Miss America Titleholders

Miss America over the years

About this dataset

Every year, young women from across the United States compete for the title of Miss America. The competition is open to women between the ages of 17 and 25, and includes a talent portion, an interview, and a swimsuit competition (which was removed in 2018). The winner is crowned by the previous year's titleholder and goes on to tour the nation for about 20,000 miles a month, promoting her particular platform of interest.

The Miss America dataset contains information on all Miss America titleholders from 1921 to 2022. It includes columns for the year of the pageant, the name of the crowned winner, her state or district represented, awards won, talent performed, and notes about her win

How to use the dataset

This dataset contains information on Miss America titleholders from 1921 to 2022. The data includes the name of the winner, her state or district, the city she represented, her talent, and the year she won

Research Ideas

• Miss America could be used to study changes in American culture over time. For example, the decline in the swimsuit competition could be seen as a sign of increasing body positivity in the US.
• The dataset could be used to study the effect of winning Miss America has on a woman's career. Does winning lead to more opportunities?
• The dataset could be used to study geographical patterns inMiss America winners. For example, are there any states that have produced more winners than others?

Acknowledgements

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: miss_america_titleholders.csv | Column name | Description | |:----------------------|:-----------------------------------------------------------------------| | year | The year the Miss America pageant was held. (Integer) | | crowned | The name of the Miss America titleholder. (String) | | winner | The name of the Miss America winner. (String) | | state_or_district | The state or district represented by the Miss America winner. (String) | | city | The city represented by the Miss America winner. (String) | | awards | The awards won by the Miss America winner. (String) | | talent | The talent performed by the Miss America winner. (String) | | notes | Notes about the Miss America winner. (String) |

File: eurovision_winners.csv | Column name | Description | |:--------------|:-------------------------------------------------------------------------| | Year | The year the pageant was held. (Integer) | | Date | The date the pageant was held. (Date) | | Host City | The city where the pageant was held. (String) | | Winner | The name of the pageant winner. (String) | | Song | The song performed by the pageant winner. (String) | | Performer | The name of the performer of the pageant winner's song. (String) | | Points | The number of points the pageant winner received. (Integer) | | Margin | The margin of points between the pageant winner and runner-up. (Integer) | | Runner-up | The name of the pageant runner-up. (String) |

The Arts and Cultural Production Satellite Account (ACPSA) is produced through the partnership between the United States Bureau of Economic Analysis (BEA) and the National Endowment for the Arts (NEA). Built with the BEA's input-output (I-O) accounts, the ACPSA provides detailed statistics that illustrate the impact of arts and cultural production on the United States economy. Specifically, this account provides an assessment of the arts and cultural sector's contributions to gross domestic product (GDP). For years 1998 to 2021, the ACPSA presents annual statistics about the following items: (1) Output of detailed arts and cultural commodities and the industries producing these commodities; (2) employment and compensation within these industries; (3) arts and cultural value added by industry; and (4) commodity-flow details for arts and cultural production products. In the data tables provided, the statistics fall under two broad categories: (1) core arts and cultural production and (2) supporting arts and cultural production. The core category contains the commodities in which the output primarily contributes to arts and culture. Performing arts, museums, design services, and arts education are included in the core category. The supporting category consists of commodities that support the core category through publication, dissemination of the creative process, or other supportive functions. This category contains event promotion, printing, and broadcasting. The seven national-level data tables provided for each year from 1998 to 2021 include: Table 1. Production of Commodities by Industry Table 2. Output and Value Added by Industry Table 3. Supply and Consumption of Commodities Table 4. Employment and Compensation of Employees by Industry Table 5. Total ACPSA-related Employment by Industry Table 6. Output by ACPSA Commodity Table 7. Real Output by Commodity For years 2001-2021, a state-level value added and employment data table is included. It contains value added by industry by state, estimates for each state annually of employment and compensation by industry, and comparisons with ACPSA employment and compensation by industry the same year. It also includes the annual total of employment in each state across the arts and cultural commodities industries. In addition, estimates of real value added by industry and estimates of real gross output and prices indexes by ACPSA commodity are provided in separate Excel files. The industries and commodities presented in the data are based on the 2007 North American Industrial Classification System (NAICS). Users are encouraged to review the Table Guide as it gives important information for all data tables. Also, users should review the NEA Guide to the U.S. Arts and Cultural Production Satellite Account and the latest Arts Data Profile Series reports dedicated to the ACPSA: The U.S. Arts and Cultural Production Satellite Account (1998-2021) and State-Level Estimates of the Arts' Economic Value and Employment (2001-2021).

Even people from frequently studied cultural contexts differ in how they conceptualize compassion, partly because of differences in how much they want to avoid feeling negative. To broaden this past work, we include participants from an understudied cultural context and start to examine the process through which culture shapes compassion. Based on ethnographic and empirical studies that include Ecuadorians, we predicted that Ecuadorians would want to avoid feeling negative less compared to U.S. Americans. Furthermore, we hypothesized that because of these differences in avoided negative affect, compared to U.S. Americans, for Ecuadorians, a compassionate response would contain more emotion sharing, which in turn would be associated with conceptualizing a compassionate face as one that mirrors sadness more and expresses happiness (e.g., a kind smile) less. Using a reverse correlation task, participants in the U.S. and Ecuador selected the stimuli that most resembled a compassionate face. They also reported how much they wanted to avoid feeling negative and described what a compassionate response would entail. As predicted, compared to U.S. Americans, Ecuadorians wanted to avoid feeling negative less, they conceptualized a compassionate response as one that focused more on emotion sharing, and visualized a compassionate face as one that contained more sadness and less happiness. Furthermore, exploratory analyses suggest that wanting to avoid feeling negative and conceptualizations of a compassionate response as emotion sharing partly sequentially explained the cultural differences in conceptualizations of a compassionate face. What people regard as compassionate differs across cultures, which has important implications for cross-cultural counseling.

Cultural Nuances Dataset V1: Understanding Cross-Cultural Differences with Chain of Thought Reasoning

Description: Dive into the intricate world of cultural differences with the "Cultural Nuances Dataset V1." This open-source resource (MIT licensed) presents a carefully curated collection of question-and-answer pairs designed to train AI models in understanding the subtle yet significant variations in language, behavior, decision-making, and social norms across diverse cultures.… See the full description on the dataset page: https://huggingface.co/datasets/moremilk/CoT-Reasoning_Cultural_Nuances.

Cultural Items Dataset for HW1 of the NLP course (2025)

This is the dataset for the first homework of the 2025 edition of the NLP course at Sapienza University. The dataset is a collection of Wikidata Items classified as:

Cultural Agnostic: the item is commonly known/used worldwide and no culture claims the item. Cultural Representative: the item is originated in a culture and/or claimed by a culture as their own, but other cultures know/use it or have similar items. Cultural… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/nlp2025_hw1_cultural_dataset.

Comprehensive dataset of 2 Cultures in Louisiana, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This study tests the malleability of thinking styles across cultures. Participants from China, Hong Kong, and the United States were randomly assigned to one of four conditions to manipulate thinking styles (analytical, holistic, intuitive, and control). Participants first responded to a scale measuring four thinking styles (analytical, holistic, intuitive, and normative). They then read a message to induce one of these thinking styles and responded to four scenarios regarding their decision related to the scenario, the difficulty in making the decision, their confidence in their decision, and the perceived realism of the scenario. Participants then responded to the same scale measuring the four thinking styles. Results supported the expectation that people in different cultures use predominantly different thinking styles to make decisions. The manipulation of thinking styles, however, changed people’s thinking in complex rather than direct ways. Analytical thinking, which is the predominant style used by U.S. Americans, was not as malleable as the other styles, and the American participants were less changeable in their style than participants from China and Hong Kong. In other words, and in summary, some thinking styles and some cultures seem to be more malleable than others. Implications of these results for understanding culture and cognition are discussed.

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

The 2015 American Housing Survey marks the first release of a newly integrated national sample and independent metropolitan area samples. The 2015 release features many variable name revisions, as well as the integration of an AHS Codebook Interactive Tool available on the U.S. Census Bureau Web site. This data collection provides information on representative samples of each of the 15 largest metropolitan areas across the United States, which are also included in the integrated national sample (available as ICPSR 36801). The metropolitan area sample also features representative samples of 10 additional metropolitan areas that are not present in the national sample. The U.S. Department of Housing and Urban Development (HUD) and the U.S. Census Bureau intend to survey the 15 largest metropolitan areas once every 2 years. To ensure the sample was representative of all housing units within each metro area, the U.S. Census Bureau stratified all housing units into one of the following categories: (1) A HUD-assisted unit (as of 2013); (2) Trailer or mobile home; (3) Owner-occupied and one unit in structure; (4) Owner-occupied and two or more units in structure; (5) Renter-occupied and one unit in structure; (6) Renter-occupied and two or more units in structure; (7) Vacant and one unit in structure; (8) Vacant and two or more units in structure; and (9) Other units, such as houseboats and recreational vehicles. The data are presented in three separate parts: Part 1, Household Record (Main Record); Part 2, Person Record; and Part 3, Project Record. Household Record data includes questions about household occupancy and tenure, household exterior and interior structural features, household equipment and appliances, housing problems, housing costs, home improvement, neighborhood features, recent moving information, income, and basic demographic information. The Household Record data also features four rotating topical modules: Arts and Culture, Food Security, Housing Counseling, and Healthy Homes. Person Record data includes questions about personal disabilities, income, and basic demographic information. Finally, Project Record data includes questions about home improvement projects. Specific questions were asked about the types of projects, costs, funding sources, and year of completion.

Previous research indicates that cultural variations exist in conceptualizations of compassion, potentially attributable to the extent to which individuals in diverse cultural settings want to avoid (versus accept) feeling negative emotions and the significance they place on emotional sharing as a component of compassion. The present study investigates the conceptualization of compassion among individuals in Mexico and the United States, aiming to understand why these cultural differences occur. We hypothesized that Mexicans (1) would want to avoid feeling negative less, (2) would consequently regard emotion sharing as a more critical element of a compassionate response, and (3) would therefore conceptualize a compassionate face as one that mirrors sadness more and expresses happiness less compared to U.S. Americans. Participants from Mexico and the United States engaged in a reverse correlation task, selecting stimuli that most closely resembled a compassionate face. The selected images were aggregated and coded for the extent of sadness and happiness depicted. Additionally, participants indicated how much they wanted to avoid feeling negative and, by using an open-ended format, described what a compassionate response would entail in their view. These responses were coded for whether or not they focused on emotion sharing. Consistent with our hypotheses, Mexicans, who want to avoid feeling negative less compared to U.S. Americans, place greater importance on emotion sharing in a compassionate response. This variation is associated with Mexicans conceptualizing a compassionate face as one that portrays more sadness and less happiness compared to U.S. Americans. People in different cultural contexts have different views about what compassion might entail. Understanding and embracing these cultural differences in compassion can help us navigate our increasingly multicultural world, fostering more meaningful connections and guiding our actions with more humility and sensitivity.

In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

Institutions of higher education (IHE) throughout the United States have a long history of acting out various levels of commitment to diversity advancement, equity, and inclusion (DEI). Despite decades of DEI “efforts,†the academy is fraught with legacies of racism that uphold white supremacy and prevent marginalized populations from full participation. Furthermore, politicians have not only weaponized education but passed legislation to actively ban DEI programs and censor general education curricula (https://tinyurl.com/antiDEI). Ironically, systems of oppression are particularly apparent in the fields of Ecology, Evolution, and Conservation Biology (EECB)–which recognize biological diversity as essential for ecological integrity and resilience. Yet, amongst EECB faculty, people who do not identify as cis-heterosexual, non-disabled, affluent white males are poorly represented. Furthermore, IHE lack metrics to quantify DEI as a priority. Here we show that only 30.3% of US-faculty posi..., Here we investigated the (lack of) process in faculty searches at IHE for evaluating candidates’ ability to advance DEI objectives. We quantified the prevalence of required diversity statements relative to research and/or teaching statements for all faculty positions posted to the Eco-Evo Jobs Board (http://ecoevojobs.net) from January 2019 - May 2020 as a proxy for institutional DEI prioritization (Supplement). We also mapped the job posts that required diversity statements geographically to gauge whether and where diversity is valued in higher education across the US. Data analysis We pulled all faculty jobs posted on Eco-Evo jobs board (http://ecoevojobs.net) from Jan 1, 2019, to May 31, 2020. For each position, we recorded the Location (i.e., state), Subject Area, Closing Date, Rank, whether or not the position is Tenure Track, and individual application materials (i.e., Research statement, Teaching statement, combined Teaching and Research statement, Diversity statement, Mentorship..., Google Sheets or Excel is required to open Lafferty et al. Data_File.xlsx Sankey Flow Show (THORTEC Software GmbH: www.sankeyflowshow.com) used to create the Sankey diagram Figure 2 produced in R

, Reference Information

Provenance for this README

• File name: README_Dataset-Academic-JEDI.txt
• Authors: Erin A. McKenney
• Other contributors: Diana J. R. Lafferty, Tru Hubbard, Sarah Trujillo, DeAnna Beasley
• Date created: 2023-06-08
• Date modified: 2023-10-18

Dataset Attribution and Usage

• Dataset Title: Data for the article “A path forward: creating an academic culture of justice, equity, diversity and inclusionâ€
• Persistent identifier: DOI:10.5061/dryad.cfxpnvxbb
• License: Use of these data is covered by the following license:
  • Title: CC0 1.0 Universal (CC0 1.0)
  • Specification: https://creativecommons.org/publicdomain/zero/1.0/; the authors respectfully request to be contacted by researchers interested in the re-use of these data so that the possibility of collaboration can be discussed.

Methodological Information

• All data were collected by the authors.
• Methods of data collection/generation: see manuscript and Supplemental Materials f...

U.S. Census BlocksThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Census Blocks in the United States. A brief description of Census Blocks, per USCB, is that "Census blocks are statistical areas bounded by visible features such as roads, streams, and railroad tracks, and by nonvisible boundaries such as property lines, city, township, school district, county limits and short line-of-sight extensions of roads." Also, "the smallest level of geography you can get basic demographic data for, such as total population by age, sex, and race."Census Block 1007Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Census Blocks) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 69 (Series Information for 2020 Census Block State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (U.S. Census Blocks - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: What are census blocksFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

alielfilali01/MA-Culture-Vision-v0.1 dataset hosted on Hugging Face and contributed by the HF Datasets community

The zip file contains fourteen Parquet [1] files of two kinds, for each of the seven years between 2015 and 2021 included: - region_counts: for every word found, gives how many times it appeared, regardless of capitalization ("count" column), how many times it appeared with at least one capitalized letter ("count_upper"), in how many different counties it appeared ("nr_cells"), and whether we considered it to be a proper noun ("is_proper") - raw_cell_counts: gives the count for every word by county, regardless of capitalization.

These counts were obtained from geo-tagged Tweets posted those years within the contiguous US, which were collected through the through the streaming API of Twitter, and more specifically using the “statuses/filter” end-point [2]. See the project's paper for more details on methodology, and the code repository to reproduce the analysis.

The two text files are our lists of excluded word forms.

According to a report published by UNESCO in February 2022, cultural and creative industries accounted for 3.1 percent of the global gross domestic product and 6.2 percent of global employment in 2020. That year, the coronavirus (COVID-19) pandemic set unprecedented challenges for this market. Overall, due to the pandemic, it was estimated that cultural and creative industries worldwide lost around 750 billion U.S. dollars in gross value added (GVA) and 10 million jobs in 2020.

The MFA (Many Faces of Anger) dataset includes 200 in-the-wild videos from North American and Persian cultures with fine-grained labels of: 'annoyed', 'anger', 'disgust', 'hatred' and 'furious' and 13 related emojis.

National cultures and cultural differences provide a crucial component of the international business (IB) research context. We conducted a bibliometric study of articles published in seven leading IB journals over a period of three decades to analyze how national culture has been impacting IB research. Co-citation mappings permit us to identify the ties binding works dealing with culture and cultural issues in IB. We identify two main clusters of research, each comprising two sub-clusters, with Hofstede’s (1980) work delineating much of the conceptual and empirical approach to culture-related studies. One main cluster entails works on the conceptualization of culture and its dimensions and the other cluster focuses on cultural distance. This conceptual framework captures the extant IB research incorporating culture-related concepts and influences.

Indian Cultural Dataset

This dataset contains various Indian cultural elements including:

Cultural Elements Folks and Regional Stories Historical Events Mythology Regional Elements Value Systems & Teachings

  Dataset Structure

The dataset is organized into the following directories:

Cultural Elements/ folks and regional stories/ Historical events/ mythology/ Regional element/ Value Systems & Teachings/

  Content

The dataset includes PDF and text files… See the full description on the dataset page: https://huggingface.co/datasets/ombhojane/indian_cultural_raw_dataset.

Snapshot img

Cultural Tourism Market Size 2025-2029

The cultural tourism market size is forecast to increase by USD 8.41 billion, at a CAGR of 18.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing number of individuals seeking unique, immersive experiences to alleviate stress and enrich their personal growth. This trend is further fueled by the burgeoning adoption of advanced technologies such as augmented reality (AR) and virtual reality (VR) in cultural tourism, enabling travelers to explore historical sites and artifacts in a more engaging and interactive manner. However, this market faces challenges as well. Overtourism, or the excessive concentration of tourists in specific locations, poses a threat to the preservation of cultural heritage sites and the local communities that rely on tourism.
Addressing this issue through sustainable tourism practices and effective crowd management strategies is essential for companies seeking to capitalize on the opportunities presented by the market while mitigating potential risks. By focusing on innovative solutions that cater to the evolving needs and preferences of travelers, while respecting and preserving cultural heritage, businesses can differentiate themselves and thrive in this dynamic and growing market.

What will be the Size of the Cultural Tourism Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the increasing demand for authentic and immersive experiences. Crowd control and tourism infrastructure remain key concerns as cultural heritage sites attract large numbers of visitors. Digital guides and mobile applications enhance the visitor experience, offering GPS navigation, augmented reality, and interactive exhibits. Economic impact is a significant factor, with art galleries, language courses, and adventure tourism contributing to local economies. Visitor management systems and travel advisories ensure responsible tourism practices, while travel agencies and tourist information centers facilitate seamless travel experiences. Visa requirements and health precautions are essential considerations for tourists.

Sustainable tourism initiatives, such as waste management and cultural preservation, minimize environmental impact. Experiential tourism and educational tourism provide unique learning opportunities, while medical tourism caters to health-conscious travelers. Social media marketing and community-based tourism foster authentic connections with local communities. Cultural exchange programs promote cross-cultural understanding. Wellness tourism and religious tourism cater to specific niche markets, offering spiritual and rejuvenating experiences. Immersive technologies, such as virtual reality and tourist guides, bring history to life. Rural tourism and urban tourism offer diverse experiences, appealing to various travel preferences. Tourism policies and online booking platforms shape the industry, ensuring efficient and accessible travel experiences.

Ongoing trends include the integration of technology and the emphasis on sustainable and responsible tourism practices. The market continues to unfold, offering endless opportunities for exploration and discovery.

How is this Cultural Tourism Industry segmented?

The cultural tourism industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Domestic cultural tourism
  International cultural tourism


Service

  Cultural eco-tourism
  Indigenous cultural tourism
  Socio-cultural tourism


Application

  Leisure
  Religious pilgrimage
  Education
  Research


Traveler Type

  Solo Travelers
  Group Travelers
  Families


Geography

  North America

    US


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The domestic cultural tourism segment is estimated to witness significant growth during the forecast period.

In the dynamic the market, domestic tourism is experiencing a significant surge, fueled by the quest for genuine experiences, technological innovations, and government incentives promoting local heritage. Mobile applications serve as essential tools, granting travelers instant access to detailed guides, maps, and cultural information for their destinations. This convenience and ease of use enhance the planning and navigation process for cultural tours. Virtual Reality (VR) and Augmented Reality (AR) applications have gained popularity, offering immersive experiences that enable u