Kevin C. Desouza

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Child Care Providers and Yelp Consumer Reviews

January 9, 2020/0 Comments/in Arizona State University, Articles, big data, innovation, Policy Informatics, Public Policy, Queensland University of Technology /by Kevin Desouza

New paper published in Early Childhood Research Quarterly. To access the paper, please click [LINK].

Knowledge Cities World Summit 2019, Florianópolis, Brazil

October 29, 2019/0 Comments/in Information Technology, innovation, Intrapreneurship, Policy Informatics, Public Policy, Queensland University of Technology, Speaking, Travel /by Kevin Desouza

Looking forward to my visit to Florianópolis, Brazil. I will deliver a masterclass on governance and innovation in smart cities as part of a Council on Australia Latin America Relations (COALAR) grant funded by the Department of Foreign Affairs and Trade, Australia at the KCWS2019: SUSTENTABILIDADE E INOVAÇÃO NA ERA DO CONHECIMENTO.

The Growing Fragility of Cities – 2019 ICMA Annual Conference

October 21, 2019/0 Comments/in Articles, big data, Information Technology, innovation, Policy Informatics, Public Policy, Speaking, Travel /by Kevin Desouza

I will present findings from my research on fragile cities at the 2019 Annual ICMA Conference. This research was funded by ICMA as part of my research fellowship. Michael Hunter collaborated with me on this research project.

Resiliency, cybersecurity, and creating more sustainable places are all topics being discussed by local government practitioners and scholars. Could it be that as local governments are increasing reliance on technology that they are becoming more fragile in the absence of considering some of the socio-economic consequences? Join this roundtable to offer your thoughts and opinions.

UN Women Australia – Big Data and Artificial Intelligence

October 15, 2019/0 Comments/in Articles, big data, Information Technology, innovation, Policy Informatics, Public Policy, Queensland University of Technology, Speaking, Travel /by Kevin Desouza

For more information, visit [LINK]

Cities Are Surprisingly Fragile – Scientific American

June 25, 2019/0 Comments/in big data, China Institute for Urban Governance, Information Technology, innovation, Policy Informatics, Public Policy, Queensland University of Technology /by Kevin Desouza

AI and India – Brookings #TechTank

April 22, 2019/0 Comments/in Articles, big data, Information Technology, innovation, Policy Informatics, Public Policy, Queensland University of Technology /by Kevin Desouza

As artificial intelligence technologies take over larger number of tasks, India will face unique impacts of automation relative to other countries. With its large and young population, advances in AI will affect India in aspects from jobs to quality of life. Incidentally, the Indian economy is currently ill-equipped to face the advent of automation and AI. To read more...

Spatial-Temporal Effect of Household Solid Waste on Illegal Dumping – Journal of Cleaner Production

April 17, 2019/0 Comments/in big data, China Institute for Urban Governance, Information Technology, innovation, Policy Informatics, Public Policy, Queensland University of Technology, Shanghai Jiao Tong University, Speaking, Travel /by Kevin Desouza

Along with colleagues Wenting Yang and Bo Fan, at Shanghai Jiao Tong University, I have a paper accepted at Journal of Cleaner Production.

Spatial-Temporal Effect of Household Solid Waste on Illegal Dumping

Illegal dumping is an increasingly costly problem with profoundly negative consequences for the livability and sustainability of our communities. The problem of illegal dumping is particularly acute in the developing world. While the literature is rich in descriptive studies on illegal dumping, few studies leverage large-scale spatial-temporal data through innovative analytical tools to study the actual dynamics of household illegal waste dumping. Our study aims to fill this gap by developing a multilevel theoretical model with which to illustrate the impact of illegal dumping. We explore the spatial-temporal distribution of illegal dumping cases using data mining. Next, we integrate datasets reflecting different levels into a hierarchical data structure organized by membership function. We then use a hierarchical generalized linear model to validate our multilevel model. The results indicate that the spatial factors have a significant relationship with illegal dumping, whereas the direct influence of temporal and community-level factors on illegal dumping is insignificant. Furthermore, the moderating effect of management level and public order on the relationship between spatial features and illegal dumping is significant. Based on our results, we offer several suggestions for preventing illegal dumping.