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