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An intelligent tree planning approach using location-based social networks data
How do we make sure that all citizens in a city have access to enough green space? An increasing part of the world’s population lives in urban areas, where contact with nature is largely reduced to street trees and parks. As optional tree planting sites and financial resources are limited, determining the best planting site can be formulated as an optimization problem with constraints. Can we locate these sites based on the popularity of nearby venues? How can we ensure that we include groups of people who tend to spend time in tree deprived areas?
Currently, tree location sites are chosen based on criteria from spatial-visual, physical and biological, and functional categories. As these criteria do not give any insights into which citizens are benefiting from the tree placement, we propose new data-driven tree planting policies that take socio-cultural aspects as represented by the citizens’ behavior into account. We combine a Location Based Social Network (LBSN) mobility...
Show moreHow do we make sure that all citizens in a city have access to enough green space? An increasing part of the world’s population lives in urban areas, where contact with nature is largely reduced to street trees and parks. As optional tree planting sites and financial resources are limited, determining the best planting site can be formulated as an optimization problem with constraints. Can we locate these sites based on the popularity of nearby venues? How can we ensure that we include groups of people who tend to spend time in tree deprived areas?
Currently, tree location sites are chosen based on criteria from spatial-visual, physical and biological, and functional categories. As these criteria do not give any insights into which citizens are benefiting from the tree placement, we propose new data-driven tree planting policies that take socio-cultural aspects as represented by the citizens’ behavior into account. We combine a Location Based Social Network (LBSN) mobility data set with tree location data sets, both of New York City and Paris, as a case study. The effect of four different policies is evaluated on simulated movement data and assessed on the average, overall exposure to trees as well as on how much inequality in tree exposure is mitigated.
Show less- All authors
- Staalduinen, J.H. van; Tetteroo, J.; Gawehns, D.; Baratchi, M.
- Editor(s)
- Cao, L.; Kosters, W.A.; Lijffijt, J.; Rijn, J.N. van; Takes, F.W.
- Date
- 2021-05-20
- Title of host publication
- Communications in Computer and Information Science; Artificial intelligence and machine learning: 32nd Benelux conference, BNAIC/Benelearn 2020, Leiden, the Netherlands, November 19–20, 2020, revised selected papers
- Pages
- 157 - 171
- ISBN (print)
- 9783030766399
- ISBN (electronic)
- 9783030766405
Publication Series
- Name
- 1398
Conference
- Conference
- BNAIC/Benelearn 2020
- Date
- 2020-11-19 - 2020-11-20
- Location
- Leiden