Perez J., Fusco G., 2024, Potential of the 15-Minute Peripheral City: Identifying Main Streets and Population Within Walking Distance, ICCSA 2024, In: O. Gervasi, B. Murgante, C. Garau, D. Taniar, A.M. Rocha and M.N. Faginas Lago, eds. Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14817. Cham: Springer, pp.50-60. https://doi.org/10.1007/978-3-031-65238-7_4.
The paper delves into the challenges of pedestrian accessibility within the concept of the 15-minute city, especially in peripheral areas with less pedestrian-friendly street networks. Through geoprocessing algorithms, the study identifies main streets and assesses their demographic potential in areas near Lille and Nice, France. The workflow is divided into four steps:
- Identification of main streets through angular continuity.
- Calculation of morphological indicators on buildings.
- Application of machine learning to estimate the number of dwellings per building.
- Estimation of population potential within various walking distances from main streets.
The findings highlight a network of interconnected main streets with significant population potentials in the outskirts of both test areas. These streets could be pivotal in developing commercial activities and services, thereby supporting the vision of the 15-minute city in peripheral regions.
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