A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning
Abstract
1. Introduction
2. Literature Review
2.1. Walking Methodologies
2.2. Informative Routing Optimization
2.3. Enhancing Walking Methodologies with Machine Learning
3. Data and Methods
3.1. Hypothesis
3.2. Methodology
Algorithm 1 Randomised Points Sample Set Along Clustered Map Lines |
1: procedure RandomSampleSet(set size, sample size, points, map, clusters) 2: define set S 3: repeat 4: sample ← qgis.RandomPointsAlongLines(sample size, points, map, clusters) 5: S ←sample 6: until set size 7: return S 8: end procedure |
Algorithm 2 Distance Matrix Set from Randomised Points Sample Set |
1: procedure DistanceMatrixSet(sample set) 2: define set DMS 3: for each s ∈ sample set do 4: matrix ← qgis.DistanceMatrix(s) 5: DMS ← matrix 6: end for 7: return DMS 8: end procedure |
Algorithm 3 Distance Filter Evaluation of Points Sample Set |
1: procedure EvaluateFilterDistance(sample set, dmatrix set) 2: define set of tuples EFD 3: for each (s, d) ∈ (sample set, dmatrix set) do 4: dist ← 0 5: for each p ∈ {s − last(s)} do 6: dist ← dist + qgis.proximity(p, p + 1) 7: end for 8: EFD ← (dist, s, d) 9: end for 10: return EFD 11: end procedure |
Algorithm 4 Shortest Route Selection Algorithm |
Require: map, a qgis map with identified streets. clusters, a qgis map with clustered streets. Ensure: route, an optimised randomly selected route. 1: samples ← RandomSampleSet(1000, 5, 1, map, clusters) 2: dmatrices ← DistanceMatrixSet(samples) 3: tuple ← EvaluateFilterDistance(samples, dmatrices) 4: smallest ← min(tuple) 5: msample ← unpack(smallest) 6: mccord ← qgis.geometry(msample) 7: route ← qgis.shortest path(mccord) 8: end procedure |
3.3. Data
4. Results
5. Discussion
5.1. Potential for Rapid Methodologies
5.2. Potential Integration into Walking Videos and Go-Alongs
5.3. Disposition of the Walker
5.4. Physical, Social and Political Corporalities
5.5. Cyborg Walk and Hathaway’s Cyborg
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Valenzuela-Levi, N.; Gálvez Ramírez, N.; Nilo, C.; Ponce-Méndez, J.; Kristjanpoller, W.; Zúñiga, M.; Torres, N. A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning. Land 2024, 13, 1211. https://doi.org/10.3390/land13081211
Valenzuela-Levi N, Gálvez Ramírez N, Nilo C, Ponce-Méndez J, Kristjanpoller W, Zúñiga M, Torres N. A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning. Land. 2024; 13(8):1211. https://doi.org/10.3390/land13081211
Chicago/Turabian StyleValenzuela-Levi, Nicolás, Nicolás Gálvez Ramírez, Cristóbal Nilo, Javiera Ponce-Méndez, Werner Kristjanpoller, Marcos Zúñiga, and Nicolás Torres. 2024. "A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning" Land 13, no. 8: 1211. https://doi.org/10.3390/land13081211
APA StyleValenzuela-Levi, N., Gálvez Ramírez, N., Nilo, C., Ponce-Méndez, J., Kristjanpoller, W., Zúñiga, M., & Torres, N. (2024). A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning. Land, 13(8), 1211. https://doi.org/10.3390/land13081211