扩大你的人口：统计缩减以提高社会经济普查数据的空间分辨率
Magnify Your Population: Statistical Downscaling to Augment the Spatial Resolution of Socioeconomic Census Data
摘要：
人口和社会经济属性的精细分辨率估计对于规划和政策制定至关重要。虽然已作出若干努力，以制作精细的网格化人口估计数，但社会经济特征通常无法在比普查单位更精细的尺度上获得，这可能会掩盖当地的异质性和差异。在本文中，我们提出了一种新的统计降尺度方法来获得关键社会经济属性的细尺度估计。该方法利用人口和地理广泛的协变量，可在多个尺度和额外的人口普查协变量，只有在粗分辨率，这是包括在模型中的层次内的“向前学习”的方法。对于每个选定的社会经济变量，随机森林模型在源人口普查单位上进行训练，然后用于生成精细尺度的网格化预测，然后进行调整，以确保与粗略的人口普查数据保持最佳一致性。作为一个案例研究，我们将这种方法应用于美国的人口普查数据，将选定的社会经济变量在块组级别上缩小到300空间分辨率的网格。该方法的准确性进行评估，在两个空间尺度，首先计算一个伪交叉验证系数的决定，在块组水平的预测，然后，仅为广泛的变量，也为（未经调整的）预测计数块组的总和。基于这些分数和对缩小比例的地图的检查，我们得出结论，我们的方法能够提供比现有人口普查数据更准确，更平滑，更详细的社会经济估计。
英文摘要：
Fine resolution estimates of demographic and socioeconomic attributes are crucial for planning and policy development. While several efforts have been made to produce fine-scale gridded population estimates, socioeconomic features are typically not available at scales finer than Census units, which may hide local heterogeneity and disparity. In this paper we present a new statistical downscaling approach to derive fine-scale estimates of key socioeconomic attributes. The method leverages demographic and geographical extensive covariates available at multiple scales and additional Census covariates only available at coarse resolution, which are included in the model hierarchically within a "forward learning" approach. For each selected socioeconomic variable, a Random Forest model is trained on the source Census units and then used to generate fine-scale gridded predictions, which are then adjusted to ensure the best possible consistency with the coarser Census data. As a case study, we apply this method to Census data in the United States, downscaling the selected socioeconomic variables available at the block group level, to a grid of ~300 spatial resolution. The accuracy of the method is assessed at both spatial scales, first computing a pseudo cross-validation coefficient of determination for the predictions at the block group level and then, for extensive variables only, also for the (unadjusted) predicted counts summed by block group. Based on these scores and on the inspection of the downscaled maps, we conclude that our method is able to provide accurate, smoother, and more detailed socioeconomic estimates than the available Census data.
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