| Title |
Teaching Data Justice: Algorithmic Bias and Critical Spatial Analysis in Urban Planning Education |
| ID_Doc |
43955 |
| Authors |
Acolin, A; Kim, AM |
| Title |
Teaching Data Justice: Algorithmic Bias and Critical Spatial Analysis in Urban Planning Education |
| Year |
2022 |
| Published |
|
| DOI |
10.1177/0739456X221116356 |
| Abstract |
As urban planners increasingly use technological advances to generate and analyze new data, we must take care to overcome biases embedded in them. We survey American planning programs and find that very few spatial analysis syllabi explicitly raise this issue or include readings or exercises to train students about the limitations and opportunities for critically handling new data streams. We conclude with suggestions for curricular strategies to help fill this pedagogical gap by incorporating (1) groundtruthing and fieldwork exercises; (2) exercises of comparative urban contexts and spatial patterns; and (3) digital participation and public discourse. |
| Author Keywords |
algorithmic bias; GIS; remote sensing; social media; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Social Science Citation Index (SSCI) |
| EID |
WOS:000854530800001 |
| WoS Category |
Regional & Urban Planning; Urban Studies |
| Research Area |
Public Administration; Urban Studies |
| PDF |
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