Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.
Background and objectives: Due to COVID-19, various countries introduced lockdowns and limited citizen movements. These restrictions triggered an increased use of digital technologies and platforms by the public. This provides an opportunity for the authorities to capture public perceptions on COVID-19 from social media channels to make informed decisions. The use of social media analytics during pandemics for decision-making, however, is an understudied area of research. Thus, this study aims to generate insights into how social media analytics can assist authorities in pandemic-related policy decisions. Methods: This study involved a social media analysis approach-i.e., systematic geo-Twitter analysis-that contains descriptive, content, sentiment, and spatial analyses. Australian states and territories are selected as the case study context for the empirical investigation. This study collected 96,666 geotagged tweets (originated from Australia between 1 January and 4 May 2020), and analysed 35,969 of them after data cleaning. Results: The findings disclose that: (a) Social media analytics is an efficient approach to capture the attitudes and perceptions of the public during a pandemic; (b) Crowdsourced social media data can guide interventions and decisions of the authorities during a pandemic, and; (c) Effective use of government social media channels can help the public to follow the introduced measures/restrictions. Conclusion: The findings are invaluable for authorities to understand community perceptions and identify communities in needs and demands in a pandemic situation, where authorities are not in a position to conduct direct and lengthily public consultations.
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