Collaborative Research: Improving Small Area Population Estimation with High-Resolution Remote Sensing

小区域人口估计对于理解和应对许多社会、政治、经济和环境问题至关重要。人口的规模和分布往往是州和地方政府资源分配的关键决定因素。在美国，通过全国人口普查，每十年只有一个日期可以获得详细的人口数据。这种频率通常不能满足像德克萨斯州奥斯汀这样的快速增长地区的需求，在这些地区，值得注意的是发生了当地人口普查间的变化。该项目的总体目标是开发一种成本效益高的方法，以产生详细的(人口普查区级)、及时的(人口普查间)和准确的人口估计数。这将通过利用新出现的高空间分辨率遥感技术，如高空间分辨率卫星图像和光探测和测距技术，以自动方式改进对住房单位的估计来实现。然后，将采用传统的住房单位方法来计算人口普查区级的人口数量。将进行两次单独的验证：包括一次家庭特别普查，并与2010年人口普查进行比较，以评估根据开发的遥感方法得出的人口估计的准确性。该项目的目的是协同以往在遥感和人口地理这两个领域中分别存在的努力，以寻求解决办法，以便在人口普查区块一级及时提供人口估计数。这项研究将对这两个领域的技术进步作出重大贡献，并展示在处理人口估计问题上将这两个领域联系起来的可行方案。拟议的方法将为人口预测提供更具成本效益的解决方案，因为与人口普查局费力收集的十年一次的调查数据相比，该方法主要由遥感数据驱动。此外，该项目的结果将与来自奥斯汀和附近快速发展地区的城市规划者和官员进行广泛讨论。

Small-area population estimates are essential for understanding and responding to many social, political, economic, and environmental problems. The size and distribution of the population often are key determinants for resource allocation for state and local governments. In the United States, detailed population data are only available for one date per decade through the national census. This frequency often does not meet the needs of rapid growth areas like Austin, Texas where noteworthy local inter-censal population changes occur. The overall objective of this project is to develop a cost-effective method to generate detailed (census block level), timely (inter-census) and accurate population estimates. This will be accomplished by development of an improved estimation of housing units in an automatic fashion from emerging high-spatial resolution remote sensing technologies, such as high spatial resolution satellite imagery and Light Detection And Ranging (LiDAR). A conventional Housing Unit (HU) method will then be employed to calculate population counts at census block level. Two separate validations: including a household special census, and comparisons to 2010 Census, will be performed to evaluate the accuracy of population estimates derived from the developed remote sensing methods. This project is intended to synergize previous efforts that separately existed in two fields: remote sensing and population geography, in order to seek solutions for providing timely population estimates at census block level. The research will make significant contributions to the technical advancement of the two fields as well as demonstrate a viable scheme for linking the two fields in addressing population estimation. The proposed method will provide a more cost-effective solution for population projections since the method is mainly driven by remote sensing data as compared to the laboriously collected decennial and survey data of the Census Bureau. In addition, the results of the project will be discussed extensively with city planners and officials from Austin and nearby rapidly growing localities.