Can volunteer crowdsourcing reduce disaster risk? A systematic review of the literature

Kankanamge, Nayomi; Yiğitcanlar, Tan; Goonetilleke, Ashantha; Kamruzzaman, Md.

doi:10.1016/j.ijdrr.2019.101097

Cited by 111 publications

(76 citation statements)

References 107 publications

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“…This together with encryption technologies such as blockchain [100], and a focus on data security at all levels of design [131], can alleviate individual concerns regarding data security and contribute to increased transparency and trust regarding online systems [83,141]. This would allow increased avenues for citizen engagement in policy decisions and citizen scientist engagement with policy development via crowdsourcing [163], along with other online services such as electronic voting [141] and smart contracts [115].…”

Section: Ai In the Governance Dimension Of Smart Citiesmentioning

confidence: 99%

“…Finally, AI systems can be used to both assist and analyze acceptance of new systems-particularly those associated with new technologies [109,163]. Online 'chatbots' can help residents navigate new websites and online platforms [109], with training and tips customized based on individualized needs and interests [164].…”

Section: Ai In the Governance Dimension Of Smart Citiesmentioning

confidence: 99%

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Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

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Section: Ai In the Governance Dimension Of Smart Citiesmentioning

confidence: 99%

Section: Ai In the Governance Dimension Of Smart Citiesmentioning

confidence: 99%

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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“…Social media analytics are started to be used in the health sector these days. During the past decades, social media has redesigned the relationships in communities without any commercial objectives (Kankanamge et al, 2019). People become more expressive in social media than face to face direct discussions.…”

Section: Social Media Analyticsmentioning

confidence: 99%

How can social media analytics assist authorities in pandemic-related policy decisions? Insights from Australian states and territories

Yiğitcanlar

Kankanamge

Preston

et al. 2020

Preprint

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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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“…Traditionally, emergency managers identified the disaster response period as a blind period, where victims needed to ensure their own safety and security [45]. Due to volunteer crowdsourcing, people share real-time, time critical and location specific information whenever a disaster occurs, which can help the emergency managers to take necessary actions to reduce the disaster risk, increase the real-time awareness and predict the direction [46]. In a project of crisis mapping, the researchers used crowdsourced event data from Tweets and Facebook posts to produce the crisis map, and the practice showed that people used the map not only for situational awareness reporting including multimedia such as photos and short videos, but also for offering both help with materials and personal assistance.…”

Section: Introductionmentioning

confidence: 99%

Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation

Chen

Yao

Zhai

et al. 2020

IJGI

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Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and night shelter demand at the plot-scale using point of interest (POI) data, and then analyzed the supply and demand status of shelters after an evacuation simulation built in Python programming language. Taking the downtown areas of Guangzhou, China as a case study, the results show that significant differences exist in the size and spatial distribution of shelter demand in daytime and nighttime, and the total demand is 7.929 million people, which is far larger than the resident population. The average evacuation time of all 16,883 routes is 12.6 min, and after the evacuation, 558 of 888 shelters exceed their capacity to varying degrees, accounting for 62.84% of the total, indicating that the shelters cannot completely receive the potential evacuees. The method proposed in this paper provides a direct quantitative basis for the number and size of new shelter resources being planned during urban renewal activities, and form a reference for land reuse and disaster prevention space organization in future urban planning.

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Can volunteer crowdsourcing reduce disaster risk? A systematic review of the literature

Cited by 111 publications

References 107 publications

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

How can social media analytics assist authorities in pandemic-related policy decisions? Insights from Australian states and territories

Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation

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