Measuring human-scale urban form and its determinants of inequality.

我们建议综合和开发机器学习和统计方法，利用图像定量评估城市空间不平等。通过测量建筑环境的相似性，我们从街道的人的角度来看待不平等，研究城市规划作为经济，环境和社会包容或排斥的潜在机制的作用。使用城市伦敦作为我们的第一个城市布局，我们将做到这一点：-利用信息编码的像素分布从谷歌街景图像，以定义地理定位图像之间的相似性度量。聚类相似的图像，从而创建视觉上均匀的图像集，跨越空间，并在空间上限定邻域。使用聚类图像结合卫星图像和道路网络等其他数据源创建街区地理空间地图。- 开发在空间流行病学中使用图像的方法，不仅通过定义新的邻域边界，而且还通过使用图像作为空间模型中的协变量。- 包括在我们的分析中重点关注图像特征的可解释性，以便得出有关建筑环境中不平等的决定因素的结论。我们相信，通过开发使用图像分析城市不平等的方法，我们将首先;以无监督的方式量化人类规模的城市形态的空间分布，开发可转移到世界各地城市的方法，其次;开发将图像协变量纳入贝叶斯模型的方法，可转移到广泛的领域应用，最后;将城市形态视为城市不平等的决定因素，有助于我们更广泛地理解人类规模的城市形态及其在经济、环境和社会三个方面的表现。

We propose synthesising and developing methods from machine learning and statistics to quantitatively assess spatial urban inequalities with the use of imagery. Through measuring similarity of the built environment, we approach inequalities from the human-scale view of the street, examining the role of urban planning as a potential mechanism for economic, environmental and social inclusion or exclusion. Using the city of London as our first urban layout, we will do this by:- harnessing information encoded in distribution of pixels from Google Street View images to define similarity metric between geo-located images. Clustering similar images and thus creating visually homogeneous sets of images, across space, and also spatially conditioned to define neighbourhoods.- creating geospatial map of neighbourhoods using clustered images combined with additional data sources such as satellite images and road networks. - developing methods for the use of imagery in spatial epidemiology, not only by defining new neighbourhood boundaries, but also, the use of imagery as covariates in spatial models. - including throughout our analysis a focus on interpretability of image features, in order to draw conclusions about determinants of inequality in the built environment. We believe that by developing methods for analysing urban inequalities using imagery, we will first; quantify the spatial distribution of human-scale urban form in an unsupervised manner, developing methods which are transferable to cities worldwide, second; develop methodologies for incorporating image covariates in Bayesian models, which are transferable to a wide range of domain applications, and lastly; examine urban form as a determinant of urban inequality, contributing to our broader understanding of human-scale urban form and its performance in three dimensions: economic, environmental and social.