Neighborhood physical disorder, or the deterioration of urban environments, is associated with negative mental and physical health outcomes. Eleven trained raters used CANVAS, a web-based system for conducting reliable virtual street audits, to collect data on nine indicators of physical disorder using Google Street View imagery of 532 block faces in New York City, New York, USA. We combined the block face indicator data into a disorder scale using item response theory; indicators ranged in severity from presence of litter, a weak indicator of disorder, to abandoned cars, a strong indicator. Using this scale, we estimated disorder at the center point of each sampled block. We then used ordinary kriging to interpolate estimates of disorder levels throughout the city. The resulting map condenses a complex estimation process into an interpretable visualization of the spatial distribution of physical disorder in New York City.

邻里身体紊乱或城市环境恶化与负面的身心健康结果有关。 11 名经过培训的评估员使用 CANVAS（一种基于网络的系统，用于进行可靠的虚拟街道审核），利用美国纽约州纽约市 532 个街区的 Google 街景图像收集九项身体疾病指标的数据。我们利用项目反应理论将块面指标数据组合成无序量表；指标的严重程度各不相同，从存在垃圾（这是一项较弱的混乱指标）到废弃的汽车（这是一项较强的指标）。使用这个尺度，我们估计了每个采样块中心点的混乱程度。然后，我们使用普通克里金法来插值整个城市的混乱程度的估计值。由此产生的地图将复杂的估计过程浓缩为纽约市身体疾病空间分布的可解释可视化。