Using geographically weighted models to explore how crowdsourced landscape perceptions relate to landscape physical characteristics
Using geographically weighted models to explore how crowdsourced landscape perceptions relate to landscape physical characteristics
复制标题
DOI：
10.1016/j.landurbplan.2020.103904
复制
发表时间：
2020-11
影响因子：
9.1
通讯作者：
Yi-Min Chang Chien;S. Carver;A. Comber
中科院分区：
文献类型：
--
作者：
Yi-Min Chang Chien;S. Carver;A. Comber
本研究探讨了景观野生性的正式衡量标准（即人类人工物品的缺失、土地覆盖的感知自然性、远离机械化通道和地形的崎岖不平）如何与英国“风景或非景观”数据中捕获的景观审美质量的众包衡量标准相关联。对多元线性回归（MLR）和两种空间变系数模型：地理加权回归（GWR）和多尺度地理加权回归（MGWR）进行了评价。MLR为国家数据分析提供了一个基线模型，显示了空间自相关残差的存在，并表明地理加权模型可能是合适的。发现标准GWR加剧了协变量之间的局部共线性，过度拟合和欠拟合模型都具有高度变化和局部化的结果。这是由于其单一的“一刀切”带宽，以及假设目标变量和预测变量之间的所有关系都在相同的空间尺度上运行。MGWR通过确定特定于参数的带宽，减轻标准GWR中发现的局部共线性问题，从而使系数估计在空间上更加稳定和一致，从而放宽了这一假设。研究结果还表明，一些协变量（如偏远程度）与景观感知质量之间的关系在空间上变化不大，而其他协变量之间存在明显的梯度关系。例如，北部和西部的自然性更强，南部和东部的崎岖性更强，苏格兰和北部没有人工制品的程度比英格兰和南部要弱。总体而言，在景观因子与公众感知的统计关系中，MGWR比GWR对空间异质性更为敏感。这些发现提供了对这些关系如何在空间上变化的细微理解，强调了这些方法在景观尺度分析中支持政策和规划的价值。本文的讨论部分将MGWR作为默认的地理加权模型，以评估在景观研究中使用众包数据的潜力。在这样做时，它说明了如何使用这些方法来探索主观和客观的景观评价。
This study explores how formal measures of landscape wildness (i.e. absence of human artefacts, perceived naturalness of land cover, remoteness from mechanised access, and ruggedness of the terrain) correlate with crowdsourced measures of landscape aesthetic quality as captured in Scenic-Or-Not data for Great Britain. It evaluates multiple linear regression (MLR) and two spatially varying coefficients models: geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MLR provided a baseline model in an analysis of national data, exhibiting the presence of spatially autocorrelated residuals and suggesting that geographically weighted models may be appropriate. A standard GWR was found to exacerbate local collinearity between covariates, both overfitting and underfitting the model with highly varied and localised results. This was due to its single one-size-fits-all bandwidth and the assumption that all relationships between the target and predictor variables operate over the same spatial scale. MGWR relaxes this assumption by determining parameter-specific bandwidths, mitigating the local collinearity issues found in a standard GWR and resulting in more spatially stable and consistent coefficient estimates. The findings also indicated that the relationship between some covariates (such as remoteness) and perceived landscape quality varied little spatially, while clear gradients were found for other covariates. For example, naturalness was stronger in the north and west, ruggedness was stronger in the south and east, and the absence of human artefacts was weaker in Scotland and the north than in England and the south. Overall, the study showed that MGWR is more sensitive than GWR to the spatial heterogeneity in the statistical relationships between landscape factors and public perceptions. These findings provide nuanced understandings of how these relationships vary spatially, underscoring the value of such approaches in landscape scale analyses to support policy and planning. The discussion section of this paper considers the MGWR as the default geographically weighted model, assessing the potential for the use of crowdsourced data in landscape studies. In so doing, it illustrates how such approaches could be used to explore both subjective and objective landscape evaluations.