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    Unleashing Hidden Business Insights: Harnessing Unstructured Big Data through Text Analysis, NLP, and Visualizations for Budgetary Decisions in Governmental Organizations
    (2024-01-01)
    Kongthong, Chanwit
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Intakosum, Sarun
    Processing Thai language texts can be a challenge due to the complexities of the language, particularly texts from social media and online platforms. This paper introduces an analysis and visualization framework specifically designed to tackle the intricacies associated with processing the Thai language data within the context of online textual content, by utilizing natural language processing (NLP) and visualization techniques. The objectives of this study were to develop an effective Thai text data analysis and visualization framework that allows us to effectively and automatically get a better understanding of the content embedded in Thai textual data. The methodology initiated with a review of existing analysis frameworks and visualization techniques with a specific focus on Thai. The data collection phase encompassed a diverse corpus of Thai text data gathered from online sources. The selected data underwent preprocessing to address language-specific challenges. The proposed Thai analysis and visualization framework consists of multiple stages. Each stage is tailored to accommodate the intricacies of the Thai language, facilitating improved information extraction and text comprehension. The proposed visualization techniques utilize interactive graphs, such as bar charts, line charts, pie charts and donut charts, to offer intuitive and insightful representations of the processed data. Results from our case study show the effectiveness of our Thai analysis framework and visualization techniques in capturing crucial information from online contents written in Thai from governmental organizations.
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    Comparative Foot Traffic Analysis During Normal Periods and Firework Events Using Wi-Fi Sensors
    (2024-01-01)
    Traganmaturapot, Peerada
    ;
    Sonehara, Noboru
    ;
    Hiruma, Nobuharu
    ;
    Cooharojananone, Nagul
    ;
    Kodate, Akihisa
    Recent observations indicate an increase in the frequency of crowd crush incidents, highlighting the urgent need for effective mitigation strategies. Addressing this issue necessitates a comprehensive understanding of the factors influencing visitor decision-making to prevent adverse outcomes such as crowd crushes. This study introduces a Streamlit dashboard designed to visualize foot traffic data in the Sendagaya area and integrates contextual data from 10 key factors, including events and locations, points of interest (POIs), periods of time, online search activity, height of buildings, temperature and weather conditions, currency exchange rates, earthquakes, number of international flight arrivals, and hotel room rates. This integration facilitates comparative analysis of foot traffic patterns during standard periods versus periods coinciding with significant events, such as the 2023 Jingu Gaien Fireworks Festival, to assess their impact on congestion levels. Conducted exclusively in Sendagaya, the study utilized 8 Wi-Fi sensors throughout August 2023, encompassing 3 key stages: data collection, preprocessing, and dashboard development. The analysis revealed significant determinants-including events, points of interest, time periods, and online activity-that influence visitor foot traffic, while other factors exhibited no discernible impact. These findings have important implications for enhancing decision-making processes, preparedness measures, risk management strategies, and data-driven policymaking for sustainable tourism development.
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    Foot Traffic Analysis Using Wi-Fi Sensor During the Tokyo 2020 Olympics and Paralympics
    (2023-01-01)
    Traganmaturapot, Peerada
    ;
    Sonehara, Noboru
    ;
    Hiruma, Nobuharu
    ;
    Cooharojananone, Nagul
    ;
    Jirapongwanich, Jirakit
    Various real-world factors, such as time, weather, distance, environment, the COVID-19 pandemic, or even protests, can all impact human decision-making. However, restrictions and unexpected occurrences may also influence people's decisions regarding their path at any given time. These factors can lead to challenges in managing foot traffic at largescale events. In response to these challenges, this paper proposes a data-driven web-based foot traffic management supporting dashboard for large-scale events based on limited pedestrian count data, consisting of sensor name, latitude, longitude, MAC address, Datetime, and RSSI, collected by Wi-Fi sensors around the Sendagaya area during the Tokyo 2020 Olympics and Paralympics. The results confirmed that our proposed web-based dashboard contributes to human behavior understanding and decision-supporting policymaking for foot traffic management, which improves the design of spectator movement between transportation and venues in large-scale events. Furthermore, the dashboard is valuable from various perspectives, including preventing crowd crushing, redesigning areas to increase engagement in the shopping district, and improving traffic management.