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16 pages, 2656 KiB

Open AccessArticle

Open Data Insights from a Smart Bridge Datathon: A Multi-Stakeholder Observation of Smart City Open Data in Practice

by Sage Cammers-Goodwin

Smart Cities 2023, 6(2), 676-691; https://doi.org/10.3390/smartcities6020032 - 21 Feb 2023

Cited by 3 | Viewed by 2129

“Open Data” efforts are growing, especially in Europe, where open data are seen as a possible ethical driver of innovation. As smart cities continue to develop, it is important to explore how open data will affect the stakeholders of smart public spaces. Making data open and accessible not only has a managerial and technical component but also creates opportunities to shift power dynamics by granting individuals (and entities) access to data they might not otherwise be able to obtain. The scope of those who could access these data is wide, including data-illiterate citizens, burgeoning startups, and foreign militaries. This paper details the process of making data “open” from the MX3D smart bridge in Amsterdam through a “datathon”. The development and outcomes of opening the data and the event itself bring us closer to understanding the complexity of open data access and the extent to which it is useful or empowering for members of the public. While open data research continues to expand, there is still a dearth of studies that qualitatively detail the process and stakeholder concerns for a modern smart city project. This article serves to fill this gap. Full article

(This article belongs to the Topic Smart Cities: Infrastructure, Innovation, Technology, Governance and Citizenship)

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12 pages, 1457 KiB

Open AccessArticle

Predicting Site Energy Usage Intensity Using Machine Learning Models

by Soualihou Ngnamsie Njimbouom, Kwonwoo Lee, Hyun Lee and Jeongdong Kim

Sensors 2023, 23(1), 82; https://doi.org/10.3390/s23010082 - 22 Dec 2022

Viewed by 2448

Abstract

Climate change is a shift in nature yet a devastating phenomenon, mainly caused by human activities, sometimes with the intent to generate usable energy required in humankind’s daily life. Addressing this alarming issue requires an urge for energy consumption evaluation. Predicting energy consumption is essential for determining what factors affect a site’s energy usage and in turn, making actionable suggestions to reduce wasteful energy consumption. Recently, a rising number of researchers have applied machine learning in various fields, such as wind turbine performance prediction, energy consumption prediction, thermal behavior analysis, and more. In this research study, using data publicly made available by the Women in Data Science (WiDS) Datathon 2022 (contains data on building characteristics and information collected by sensors), after appropriate data preparation, we experimented four main machine learning methods (random forest (RF), gradient boost decision tree (GBDT), support vector regressor (SVR), and decision tree for regression (DT)). The most performant model was selected using evaluation metrics: root mean square error (RMSE) and mean absolute error (MAE). The reported results proved the robustness of the proposed concept in capturing the insight and hidden patterns in the dataset, and effectively predicting the energy usage of buildings. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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30 pages, 1049 KiB

Open AccessArticle

Collaboration between Government and Research Community to Respond to COVID-19: Israel’s Case

by Mor Peleg, Amnon Reichman, Sivan Shachar, Tamir Gadot, Meytal Avgil Tsadok, Maya Azaria, Orr Dunkelman, Shiri Hassid, Daniella Partem, Maya Shmailov, Elad Yom-Tov and Roy Cohen

J. Open Innov. Technol. Mark. Complex. 2021, 7(4), 208; https://doi.org/10.3390/joitmc7040208 (registering DOI) - 1 Oct 2021

Cited by 3 | Viewed by 2827

Abstract

Triggered by the COVID-19 crisis, Israel’s Ministry of Health (MoH) held a virtual datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel’s research community was invited to offer insights to help solve COVID-19 policy challenges. The Datathon was designed to develop operationalizable data-driven models to address COVID-19 health policy challenges. Specific relevant challenges were defined and diverse, reliable, up-to-date, deidentified governmental datasets were extracted and tested. Secure remote-access research environments were established. Registration was open to all citizens. Around a third of the applicants were accepted, and they were teamed to balance areas of expertise and represent all sectors of the community. Anonymous surveys for participants and mentors were distributed to assess usefulness and points for improvement and retention for future datathons. The Datathon included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the three winning teams are currently considered by the MoH as potential data science methods relevant for national policies. Based on participants’ feedback, the process for future data-driven regulatory responses for health crises was improved. Participants expressed increased trust in the MoH and readiness to work with the government on these or future projects. Full article

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