Design thinking for city dashboard development: Recommendations from a study of smart asset management in Sydney, Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The city dashboard has become an integral component of smart city asset management systems. It leverages data collected from multiple sources to monitor performance and enable evidence‐based decision making. This article investigates the use of a design thinking framework to develop a functional and easy to understand city dashboard. The Smart Social Spaces project is used as a case study to illustrate how design thinking can be employed to develop an asset management dashboard, enabling efficient management of public space and infrastructure. The article profiles the unique collaboration between a local government, a multi‐disciplinary team of university academics, and a street furniture designer and manufacturer, all located in Sydney, Australia. We unpack some of the design practice nuances that led this project to receive national awards and international recognition, and most importantly, created a user‐friendly system to track and maintain public micro assets. We conclude with lessons learnt and recommendations for dashboard development through a design thinking process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it