Trees play an integral role in the “green” framework of an urban ecosystem. However, just as they are beneficial to the environment, they can pose a significant risk to utility infrastructure networks, particularly in severe weather events. The objectives of this research were to explore the effect of scale and spatial variation on the relationships between trees and utility assets for vegetation-related power outages through the incorporation of remote sensing and geographic information system (GIS) analysis. Tree location and structural metrics derived from airborne Light Detection and Ranging (LiDAR) data were combined with regional utility network GIS data to test the prediction analysis capabilities of global and local statistics at multiple scales. Pearson’s correlation was carried out to examine the relationships between tree structure and utility asset variables to vegetation-related power outages, including the effect of the resolution, or grid-cell size, on those relationships. To test the performance of global and local regression modeling on outage prediction, ordinary least square (OLS) and geographically weighted regression (GWR) models were evaluated using four explanatory variables (utility wire length, utility pole count, tree canopy area, maximum tree height) at four different grid cell scales (50 m, 500 m, 1 km, 2 km). In general, Pearson’s correlation demonstrated the strongest positive relationship between explanatory variables and power outages when only aggregating 50-m grid cells exhibiting co-location of trees and utility assets to 2-km grid cells. Local regression models performed better than global models at all scales, with GWR producing the highest adjusted R² and lowest Akaike information criterion (AIC) values of 0.955 and 3213, respectively. Additionally, the performance of OLS and GWR models increased with scale as both models produced the highest adjusted R² at 2-km grid-cell scale. GWR model outputs demonstrated unique spatial patterning across the study area. This research demonstrated the effect of scale and spatial variation on regression analysis for the estimation of tree-related power outages.

树木在城市生态系统的“绿色”框架中扮演着不可或缺的角色。然而，正如它们对环境有益一样，它们可能对公用事业基础设施网络构成重大风险，尤其是在恶劣天气事件中。这项研究的目的是通过结合遥感和地理信息系统（GIS），探索尺度和空间变化对树木和公用事业资产之间关系的影响，这些关系用于与植被有关的电力中断。从机载光检测和测距（LiDAR）数据得出的树的位置和结构指标与区域公用事业网络GIS数据相结合，以测试全球和本地统计数据在多个尺度上的预测分析能力。进行了Pearson的相关性，以检查树木结构和公用事业资产变量与植被相关的停电之间的关系，包括分辨率或网格大小对这些关系的影响。为了测试全局和局部回归模型对停电预测的性能，使用四个解释变量（公用电线长度，公用电线杆数，树冠面积，最大树高）评估了普通最小二乘（OLS）和地理加权回归（GWR）模型）在四个不同的网格像元规模（50 m，500 m，1 km，2 km）。通常，当仅将展示树木和公用事业资产并置的50米网格单元聚集到2公里网格单元时，皮尔逊相关性证明了解释变量与停电之间最强的正相关关系。²和最低的Akaike信息标准（AIC）值分别为0.955和3213。此外，OLS和GWR模型的性能随比例增加，因为两个模型在2 km的网格单元比例下产生的调整后的R ²最高。GWR模型输出显示了整个研究区域的独特空间格局。这项研究证明了规模和空间变化对回归分析的影响，该回归分析用于估计与树木有关的电力中断。