Abstract
“Deviation” is common in scientific research, referring to the phenomenon that the output of a process is different from the expected. Deviation may possess various appearances and definitions, e.g., deviation of an observation from the truth, the general trend, or the theoretical value under assumptions, etc. Although in many cases it is perceived by the researcher as unwanted, it may be an inspirer and facilitator, leading to new discoveries and insights from innovative pathways. This chapter initiates a discussion on what and how we can learn from deviations, particularly in urban science. We use several application examples featuring big data and deep learning to illustrate our points.
| Original language | English |
|---|---|
| Title of host publication | New Thinking in GIScience |
| Publisher | Springer Nature |
| Pages | 301-308 |
| Number of pages | 8 |
| ISBN (Electronic) | 9789811938160 |
| ISBN (Print) | 9789811938153 |
| DOIs | |
| Publication status | Published - 1 Jan 2022 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© Higher Education Press 2022.
Keywords
- Deep learning
- Deviation
- Quantitative analysis
- Street view imagery
- Urban science