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In the United States, every state has a tourism website. These sites highlight the main attractions of the state, travel tips, and blog posts among other relevant information. The funding for these websites often comes from occupancy taxes, a form of taxes that comes from tourists who stay in hotels and visit attractions. Therefore, current and past tourists fund the efforts to draw future tourists into the state. Since state tourism is funded by the success of past tourism efforts, it is important for researchers to spend their time and resources on finding out what efforts were successful and which weren’t. With this comes the importance of seeing trends in past tourism endeavors. By examining past tourism websites, patterns can be drawn about information that changed, from season to season and year to year. These patterns can be used to see what researchers deemed as successful tourism efforts, and help guide future state tourism decisions. Our client, Dr. Florian Zach of the Howard Feiertag Department of Hospitality and Tourism Management, wants to use this historical analysis on state tourism information to help with his research on trends in state tourism website content. Iterations of the California state tourism website, among other sites, are stored as snapshots on the Internet Archive and can be accessed to see changes in websites over time. Our team was given Parquet files of these snapshots dating back to 2008. The goal of the project was to assist Dr. Zach by using the California state tourism website, visitcalifornia.com, and these snapshots as an avenue to explore data extraction and visualization techniques on tourism patterns to later be expanded to other states’ tourism websites. Python’s Pandas library was utilized to examine and extract relevant pieces of data from the given Parquet files. Once the data was extracted, we used Python’s Natural Language Processing Toolkit to remove non-English words, punctuation, and a set of unimportant “stop words”. With this refined data, we were able to make visualizations regarding the frequency of words in the headers and body of the website snapshots. The data was examined in its entirety as well as in groups of seasons and years. Microsoft Excel functions were utilized to examine and visualize the data in these formats. These data extraction and visualization techniques that we became familiar with will be passed down to a future team. The research on state tourism site information can be expanded to different metadata sets and to other states. 

The Food and Drug Administration recommends against washing raw chicken due to the risk of transferring dangerous food-borne pathogens through splashed drops of water. Many cooks continue to wash raw chicken despite this warning, however, and there is a lack of scientific research assessing the extent of microbial transmission in splashed droplets. Here, we use large agar plates to confirm that bacteria can be transferred from the surface of raw chicken through splashing. We also identify and create a phylogenetic tree of the bacteria present on the chicken and the bacteria transferred during splashing. While no food-borne pathogens were identified, we note that organisms in the same genera as pathogens were transferred from the chicken surface through these droplets. Additionally, we show that faucet height, flow type, and surface stiffness play a role in splash height and distance. Using high-speed imaging to explore splashing causes, we find that increasing faucet height leads to a flow instability that can increase splashing. Furthermore, splashing from soft materials such as chicken can create a divot in the surface, leading to splashing under flow conditions that would not splash on a curved, hard surface. Thus, we conclude that washing raw chicken does risk pathogen transfer and cross-contamination through droplet ejection, and that changing washing conditions can increase or decrease the risk of splashing.