National Administrative Department of Statistics, Colombia.
Leverhulme Centre for Demographic Science, University of Oxford.
Popul Stud (Camb). 2024 Mar;78(1):3-20. doi: 10.1080/00324728.2023.2190151. Epub 2023 Mar 28.
Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.
有效的政府服务依赖于准确的人口数量来分配资源。在哥伦比亚和全球范围内，人口普查在偏远地区和发生武装冲突的地区都具有挑战性。在人口普查准备阶段，哥伦比亚国家统计管理局开展了社会制图工作坊，社区代表在其所在地区估算了住房和人口数量。我们重新利用了这些信息，将其与遥感建筑物数据和其他地理空间数据相结合。为了估算建筑物数量和人口规模，我们开发了分层贝叶斯模型，使用附近的全面普查计数进行训练，并使用 10 倍交叉验证进行评估。我们比较了模型，以评估社区知识、遥感建筑物及其组合对模型拟合的相对贡献。社区模型无偏但不精确；卫星模型更精确但有偏差；组合模型在整体准确性方面表现最佳。结果再次证实了遥感建筑物数据在人口估计方面的强大功能，并强调了纳入当地知识的价值。
