Kevin C. Desouza | technology

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Pathways to the Making of Prosperous Smart Cities

September 16, 2020/0 Comments/in Articles, Information Technology, innovation, Queensland University of Technology /by Kevin Desouza

New paper published with M. Hunter, B. Jacob, and T. Yigitcanlar in Journal of Urban Technology.

Pathways to the Making of Prosperous Smart Cities: An Exploratory Study on the Best Practice

In this paper, we examine the understudied issue of the pathways to smart cities. While the extant literature on smart cities offers several insights into what smart cities are, with a few notable exceptions, it has less to say about how they come to be. With this latter question in mind, we identify three pathways to smart cities: (1) a greenfield development pathway, (2) a neighborhood development pathway, and (3) a platform-oriented platform. Drawing on nine different case studies, we offer some insights into the way in which each of these pathways is, more or less, able to realize the desired smart-city objectives. While exploratory in nature, the study offers unique insights into the pathways to smart cities as well as areas for future research.

To access the paper, please click [here].

Your IQ on Emerging Technologies – PM Magazine

June 5, 2020/0 Comments/in China Institute for Urban Governance, Information Technology, innovation, Policy Informatics, Public Policy, Queensland University of Technology /by Kevin Desouza

My article in the current issue of PM Magazine outlines key questions to ask when considering digital transformation efforts in the public sector. To read the complete article, please click here.

Smart City Report – Australia

February 9, 2020/0 Comments/in big data, China Institute for Urban Governance, Information Technology, innovation, Queensland University of Technology /by Kevin Desouza

We published our report on smart cities in Australia. To download the report, please click [here]. The findings were feature in Brisbane Times. We published an article on the report for The Conversation.

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].

Hawaii International Conference on System Sciences – Best Paper Nomination

January 2, 2020/0 Comments/in Articles, Information Technology, innovation, Queensland University of Technology, Speaking /by Kevin Desouza

Lena Waizenegger (Auckland University of Technology), Isabella Seeber (Universität Innsbruck), Gregory Dawson (Arizona State University) and I have a paper accepted at the upcoming Hawaii International Conference on Information Systems.The paper has been nominated for a best paper award.

PhD Public Defence – Tim Huygh

December 2, 2019/0 Comments/in Information Technology, innovation, Queensland University of Technology, Speaking, Travel /by Kevin Desouza

Looking forward to visiting Antwerp, Belgium for Tim Huygh's PhD Defence.

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]

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.