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“We embody, we learn, we release the idea of failure, because it is all data.”
**—adrienne maree brown
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Exploring the Cooling Intensity of Urban Parks in New York City
https://github.com/kalliannhale/towardTreeEquity
Supporting the growth of green infrastructure is critical ground for community resilience to the steepening creep of climate change. One of the most potent legacies we are cultivating for future generations is the expansion of our urban tree canopies. This refers to areas covered by trees when viewed from above, whether public or private. Urban tree canopies help cool city streets, filter pollutants, protect biodiversity, manage stormwater, and improve public health–on top of strictly profitable increases in property value.
Grievously, an inequitable distribution of these benefits, as well as increased vulnerability to the effects of climate change, is the logical consequence of historical disinvestment from ghettoized neighborhoods.
towardTreeEquity is a collaborative, equity-driven data science experiment promoting the democratic development of socio-technical systems for climate resilience. I facilitate weekly community reflection and design sessions centering the creation of a flexible spatial and statistical scaffold to empower citizen scientists of all levels of technical proficiency to imagine and subsequently realize their own critical social-environmental analyses.
Inspired by the principles of Emergent Strategy, first outlined by the pre-eminent political theorist adrienne maree brown [sic], we look to the natural world for solutions to seemingly intractable civic and infrastructural challenges.
For our pilot study, I attempted to replicate a study undertaken by five researchers at the College of Landscape Architecture at Nanjing Forestry University and published in the Science of the Total Environment in January of 2023: Using buffer analysis to determine urban park cooling intensity: Five estimation methods for Nanjing, China (Yi Xiao, Yong Piao, Chao Pan, Dongkun Lee, Bing Zhao).
While the cooling effect of urban parks has been widely recognized, our understanding of park cooling intensity (PCI) and its mechanisms remains incomplete (1). This study outlines a comprehensive methodology to asses the cooling intensity of urban parks and identify the key factors influencing this effect.
The researchers ultimately settled on turning point maximum-method (TPM-M), as it was and to replicate this, which required us to generate concentric buffer rings at 30m intervals outward from the park boundary, and pinpointing the median land surface temperature (LST). This process is critical for performing zonal statistics, and can be found in the align_crs.R module.
<------------------ 60m Buffer ------------------>
<--------- 30m Buffer --------->
[ Ring (30m to 60m) ]
[ Ring (0m to 30m) [ PARK ] ]
[ [ ] ]
[--------------------------------]
This method was found to better track how LST increases with distance from the park, and identifies the first local maximum, or turning point, in the LST difference curve; it was, therefore, most adaptive to real gradients in the LST. Thus, we adopted the difference between the LST of the first turning point of decline within the buffer and the LST of the park to assess the PCI (see the blue circle in Fig. 2a) (Yu et al., 2017).
$$ {PCI} = {LST} ={LST}{{buffer}} - {LST}{{park}} $$
While the researchers used mean LST, in our replication we opted for median LST, because that data was readily available, under the assumption tht it would be less sensitive to small, anomalous hot surfaces win a park’s boundary (i.e., parking lots, buildings, and paved pathways) that do not contribute to the cooling effect.