Thailand Domestic Tourism: Occupancy Rate: Southern: Chiang Mai data was reported at 68.340 % in May 2019. This records a decrease from the previous number of 69.870 % for Apr 2019. Thailand Domestic Tourism: Occupancy Rate: Southern: Chiang Mai data is updated monthly, averaging 69.870 % from Jan 2017 (Median) to May 2019, with 29 observations. The data reached an all-time high of 91.710 % in Dec 2018 and a record low of 61.760 % in Sep 2017. Thailand Domestic Tourism: Occupancy Rate: Southern: Chiang Mai data remains active status in CEIC and is reported by Ministry of Tourism and Sport. The data is categorized under Global Database’s Thailand – Table TH.Q020: Tourism Statistics: Domestic Occupancy Rate: By Region and Province.

In 2024, the occupancy rate of hotels in the province of Chiang Mai, Thailand, was almost ** percent, indicating an increase compared to the previous year. In that year, there were nearly *** million international tourists visiting Chiang Mai.

泰国 Domestic Tourism: Occupancy Rate: Southern: Chiang Mai在2019-05达68.340 %，相较于2019-04的69.870 %有所下降。泰国 Domestic Tourism: Occupancy Rate: Southern: Chiang Mai数据按月度更新，2017-01至2019-05期间平均值为69.870 %，共29份观测结果。该数据的历史最高值出现于2018-12，达91.710 %，而历史最低值则出现于2017-09，为61.760 %。CEIC提供的泰国 Domestic Tourism: Occupancy Rate: Southern: Chiang Mai数据处于定期更新的状态，数据来源于Ministry of Tourism and Sport，数据归类于Global Database的泰国 – Table TH.Q020: Tourism Statistics: Domestic Occupancy Rate: By Region and Province。

The objectives of this research are to: (1) analyze the behaviour of Thai tourists traveling in the northern region After Covid-19, and (2) propose guidelines for management Planning tourism marketing for Thai tourists in the northern region for government agencies and entrepreneurs. It is quantitative research using questionnaire surveys with 400 respondents in 17 northern provinces. It was found that the northern province that most Thai tourists visited the most is Chiang Mai. The post covid-19 tourist behaviour of the Thais can be concluded as follow: (1) Thai tourists will use cash for their tourism spending, (2) they will travel as free independent travellers (FIT), (3) they will travel with their family, (4) they will use private vehicle, (5) they will travel 2 times a year, (6) they are likely to travel during the weekends, (7) the travel purpose is for recreation and eating, (8) they like to take pictures, (9) the Thai tourists find travel information mostly from the Internet, and (10) they prefer to spend about 1,001-5,000 baht each trip.The researcher planned to collect questionnaires and interviews with Thai tourists by focusing on finding tourism behavior perception of tourism, the demand for tourism in the post-Covid-19 period, including the use of social media of tourists to analyze the behavior of Thai tourists.

Population/Samples are 400 Thai tourists in 17 northern provinces.

Research Tools: questionnaires and interview forms that has been checked for content validity and validity including through the Human Ethics Committee.Data analysis

    Quantitative data collection, the statistics

were used to analyze the data as follows: 1) descriptive statistics is the statistics used to describe the samples that the researcher has collected data by using frequency, percentage, mean, and standard deviation; and 2) inferential statistics is a statistic that is used to analyze sample data in order to bring the obtained values ​​to explain or refer to the population, including hypothesis testing using Multiple Regression Analysis (MRA)

Techsalerator’s Location Sentiment Data for Thailand

Techsalerator’s Location Sentiment Data for Thailand provides a comprehensive dataset that analyzes public sentiment across various locations in the country. This dataset is essential for businesses, researchers, and policymakers looking to understand public perception, consumer trends, and regional sentiment dynamics in Thailand.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for Thailand

Techsalerator’s Location Sentiment Data for Thailand delivers structured insights into sentiment trends across cities, provinces, and specific venues. The dataset is crucial for market research, social analytics, tourism, and urban planning.

Top 5 Key Data Fields

• Geographical Location – Identifies the specific area (city, province, or district) where sentiment data is collected.
• Sentiment Score – Measures public sentiment on a scale, categorizing opinions as positive, neutral, or negative.
• Source of Sentiment – Extracts data from various sources, including social media, reviews, surveys, and local news.
• Topic of Sentiment – Identifies key topics associated with sentiment trends, such as tourism, business, environment, and public services.
• Time Stamp – Provides the date and time of data collection to analyze trends over different periods.

Top 5 Location Sentiment Trends in Thailand

• Tourism Sentiment Shifts – Sentiment around popular tourist destinations fluctuates based on seasonal trends, travel policies, and visitor experiences.
• Urban vs. Rural Sentiment – Cities like Bangkok and Chiang Mai show different sentiment patterns compared to rural areas, influenced by infrastructure, services, and quality of life.
• Consumer Brand Perceptions – Local and international brands receive varying sentiment feedback depending on product launches, promotions, and customer service.
• Environmental Concerns – Public sentiment on pollution, deforestation, and sustainability efforts impacts governmental and corporate policies.
• Political and Social Discussions – Conversations on governance, policies, and public welfare generate distinct sentiment trends across different regions.

Top 5 Applications of Location Sentiment Data in Thailand

• Market Research and Consumer Insights – Helps businesses understand regional consumer preferences and brand sentiment.
• Tourism and Hospitality Analytics – Provides insights into visitor satisfaction, allowing for service improvements in hotels, attractions, and restaurants.
• Government and Policy Decision-Making – Supports public administration in assessing public opinion on policies and urban development.
• Real Estate and Urban Planning – Guides property development and infrastructure projects based on sentiment trends in various locations.
• Crisis Management and Public Relations – Enables companies and organizations to respond proactively to negative sentiment and reputation risks.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Thailand, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographical Location
• Sentiment Score
• Source of Sentiment
• Topic of Sentiment
• Time Stamp
• Sentiment Category (Positive, Neutral, Negative)
• Demographic Insights (Age, Gender, Interests)
• Industry-Specific Sentiment (Retail, Travel, Finance, etc.)
• Social Media Sentiment Analysis
• Public Review Aggregation

For organizations seeking in-depth sentiment insights across Thailand, Techsalerator’s dataset is an invaluable resource for strategic decision-making, marketing, and public engagement.

This dataset provides a per-second table that combines pedestrian GPS trajectories with synchronized streetscape “micro-barrier” counts for Old City area of Chiang Mai, Thailand. Each row represents one second along a fixed itinerary walked or wheeled by study participants. The data were collected to analyse inclusive mobility, in particular, differences between walking and manual wheelchair users in a heritage, tourism-intensive environment.

File and structure. “inclusive_mobility.csv” Columns: timestamp (ISO 8601, UTC), mode (walk/wheelchair), occasion (morning/afternoon/evening), lon, lat (WGS84, EPSG:4326), speed_kmh (instantaneous speed), and second-level barrier counts: persons_s, cars_s, motorcycles_s. Barrier counts are the sum of detections across 30 frames within each second. Timestamps are aligned exactly at 1-second resolution so that movement and context are directly comparable.

Coverage and quality: The time span covers multiple passes along the Old City route across different times of day (tourism corridors linking temples/landmarks). Coordinates fall within the Old City bounding box. The table contains no personally identifiable information; trajectories are anonymized. Per-second speed values reflect mixed pedestrian conditions (stop-and-go near obstacles/crossings). As with consumer GPS, small positional jitter is possible near buildings/trees; brief speed spikes may occur where satellite geometry is poor. No map-matching or smoothing is applied in this file unless noted.

Intended use: Transport geography and urban planning analyses of speed dynamics, route geometry, and mobility inequality; reproducible modelling (e.g., Bayesian state-space) linking behaviour to micro-barriers; teaching materials for R/Stan workflows. Researchers may divide persons, crass, or motorcyclists by ~30 to obtain approximate per-frame averages, or aggregate by segment/time window for modelling.

Ethics and privacy: No face data or re-identification is included. Barrier variables are object categories only. Please consult the associated article for ethics/IRB notes.

Citation and versioning. Cite the Mendeley Data DOI for this dataset (include version), and the Zenodo DOI for the archived code release that reproduces the analysis. Use a permissive license (e.g., CC BY 4.0) to enable reuse.

In this paper, we analyze the factors affecting tourist involvement in coffee tourism after covid-19 pandemic in Thailand. The coffee tourism data is collected. Using Stop word removal, stemming, dimensionality reduction, Min-max normalization the data can be preprocessed. Feature can be extracted by using Linear Discriminant Analysis (LDA). Bat Algorithm is used for feature selection. Stochastic Neuro Fuzzy Decision Tree (SNF-DT) is used for Data analysis. Figure 2 indicates an overall methodology used. A. Dataset This study comprised 6,485,791 Thai and international visitors who visited Chiang Rai, Chiang Mai, Mae Hong Son, and Lampang provinces were included in this research. This study includes tourists from Thailand and abroad who visited coffee tourism destinations and participated in coffee-related tourism activities such as a tour of the cultivation and production area or a coffee tasting. The sample was selected using grab sampling. Based on ninety five percent confidences, the sample size was determined to be 10,000 participants with a margin of error of 0.05. The final result was 398, however it was rounded up to 400 to make data gathering. The proportion of people that responded is also shown for each province. Table 1 show that Sample Group in Each Province.

The Thai real estate market, valued at $54.90 billion in 2025, exhibits robust growth potential, projected at a 5.41% Compound Annual Growth Rate (CAGR) from 2025 to 2033. This expansion is fueled by several key factors. Firstly, Thailand's burgeoning tourism sector continuously drives demand for hospitality and residential properties, particularly in popular destinations like Bangkok, Phuket, and Pattaya. Secondly, a growing middle class and increasing urbanization contribute significantly to the residential segment's growth. Furthermore, government initiatives aimed at infrastructure development and foreign investment further stimulate market activity. The industrial and logistics segment also experiences strong growth due to Thailand's strategic position in Southeast Asia's manufacturing and supply chains. However, challenges such as fluctuating interest rates, potential economic slowdowns, and regulatory changes present potential restraints on market growth. The market is relatively concentrated, with major players like Pruksa Real Estate, LPN Development, and Sansiri dominating the landscape. While the residential segment currently holds the largest market share, the hospitality and industrial segments are poised for significant growth in the coming years. The diverse geographic distribution of projects across major cities reflects the balanced growth pattern of the market. The forecast period (2025-2033) anticipates a steady increase in market value, driven by continued economic growth and investment in infrastructure. The segmentation by property type (residential, office, retail, hospitality, industrial and logistics) and major cities provides granular insights into market dynamics. While the provided data focuses on Thailand, the global context underscores the interconnectedness of real estate markets. International investment and tourism play significant roles in shaping the Thai real estate landscape, reflecting global economic trends and investor confidence. Analyzing the competitive landscape reveals a mix of established developers and emerging players, fostering innovation and competition within the sector. Understanding these interwoven factors is crucial for navigating the complexities and opportunities presented by the dynamic Thai real estate market. Recent developments include: January 2024: Sansiri Public Company Limited, a Thai real estate developer, designated Phuket as a strategic location to launch 16 new projects with a total value of THB 15 billion (USD 416.6 million) over the next five years. Sansiri is also expected to establish a new regional headquarters in Phuket to provide excellent after-sales services, reaffirming its commitment to serving both local and international customers.December 2023: Saudi Arabia and Thailand collaborated to enhance innovation and entrepreneurship, strengthening their bilateral ties. The bilateral trade relationship witnessed an uptick, with Riyadh hosting a four-day trade show in August featuring over 100 manufacturers and entrepreneurs from Thailand showcasing products across various sectors.. Key drivers for this market are: The Rise in e-commerce and digitalization. Potential restraints include: The Rise in e-commerce and digitalization. Notable trends are: Growth in Tourism is Driving the Market.