Treety: A Data-driven Approach to Urban Canopy Development

• Published in: 2018 IEEE International Smart Cities Conference (ISC2) pp. 1 - 7
• Main Authors: Mannan, Sonia, Callenes-Sloan, Joseph
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2018
• Subjects: Climate change; Data models; Machine Learning; Meteorology; Smart Cities; Sustainable development; Temperature sensors; Urban areas; Urban Development; Vegetation
• DOI: 10.1109/ISC2.2018.8656956

Cities are facing increasingly significant sustainability issues in the face of growing populations and climate change. At the center of many cities plans to address sustainability issues, are plans for urban canopy development (i.e. increases the number of trees in cities). In this paper, we propose an approach to educate residents about the environmental and socio-economic benefits of trees in urban settings and incentivize tree growth in cities. In addition to environmental benefits, trees have been shown to have many significant socio-economic benefits (e.g. real estate values, pedestrian traffic, motor traffic, and many other aspects of urban life) [1], [2], [3]. Our approach can also be leveraged to encourage resident participation in city tree development programs, such as Free Tree Programs [4]. By quantifying and displaying tree environmental and economic benefits at the selected locations on an interactive map, citizens and policymakers can determine the best locations to plant trees in the city. The system aggregates data from the distributed city sensors to model tree benefits and provide an overall score which quantifies the benefit of planting a tree(s) at a given location(s). The system models tree benefits by taking historical data from the pedestrian, environmental, and traffic sensors to predict the potential impacts (e.g. carbon reduction, evapotranspiration, average pedestrian traffic, and average vehicular traffic, property values, ) of a tree(s) at a given location(s) [1], [2], [3], [5]. By using the system, citizens learn about the positive effects of having trees in their neighborhood and policy makers may also better conduct city planning and develop urban policies/strategies to maximize the impact of trees in their communities. Results show moderate to strong correlations between a sample of key socio-economic parameters and trees.