| Title |
Learning from the Informality. Using GIS Tools to Analyze the Structure of Autopoietic Urban Systems in the "Smart Perspective" |
| ID_Doc |
39530 |
| Authors |
Di Pinto, V; Rinaldi, AM; Rossini, F |
| Title |
Learning from the Informality. Using GIS Tools to Analyze the Structure of Autopoietic Urban Systems in the "Smart Perspective" |
| Year |
2021 |
| Published |
Isprs International Journal Of Geo-Information, 10, 4 |
| DOI |
10.3390/ijgi10040202 |
| Abstract |
This paper explores the link between the current vision of the "smart city" and the notion of urban autopoiesis understood as self-organized/managed urban systems. It seeks to highlight how the use of GIS analysis, applied to the study of informal settlements, can provide useful information to understand the smart city paradigm. The paper argues the key idea that a smart city should not be seen only as a high-tech urban environment because the transition to smartness will need major changes in its inner structure. Using a combination of quantitative and qualitative GIS analysis methods, this study examines the case of the BaSECo Compound, one of the densest informal settlements in Metro Manila (Philippines), with the aim of both generating a comprehensive morphological analysis of this dynamic urban area as well as contributing to the configurational theory of the smart city. The results suggest that the analysis of autopoietic urban systems could expand our understanding of how the structure of the city could evolve to accommodate the needs of its citizens and creating more resilient and inclusive communities. |
| Author Keywords |
smart city; urban autopoiesis; informal settlements; GIS; configurational analysis; urban morphology |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000643081300001 |
| WoS Category |
Computer Science, Information Systems; Geography, Physical; Remote Sensing |
| Research Area |
Computer Science; Physical Geography; Remote Sensing |
| PDF |
https://www.mdpi.com/2220-9964/10/4/202/pdf?version=1616740075
|