Data accurately representing the population distribution at the subnational level within countries is critical to policy and decision makers for many applications. Call data records (CDRs) have shown great promise for this, providing much higher temporal and spatial resolutions compared to traditional data sources. For CDRs to be integrated with other data and in order to effectively inform and support policy and decision making, mobile phone user must be distributed from the cell tower level into administrative units. This can be done in different ways and it is often not considered which method produces the best representation of the underlying population distribution. Using anonymised CDRs in Namibia between 2011 and 2013, four distribution methods were assessed at multiple administrative unit levels. Estimates of user density per administrative unit were ranked for each method and compared against the corresponding census-derived population densities, using Kendall’s tau-b rank tests. Seasonal and trend decomposition using Loess (STL) and multivariate clustering was subsequently used to identify patterns of seasonal user variation and investigate how different distribution methods can impact these. Results show that the accuracy of the results of each distribution method is influenced by the considered administrative unit level. While marginal differences between methods are displayed at “coarser” level 1, the use of mobile phone tower ranges provided the most accurate results for Namibia at finer levels 2 and 3. The use of STL is helpful to recognise the impact of the underlying distribution methods on further analysis, with the degree of consensus between methods decreasing as spatial scale increases. Multivariate clustering delivers valuable insights into which units share a similar seasonal user behaviour. The higher the number of prescribed clusters, the more the results obtained using different distribution methods differ. However, two major seasonal patterns were identified across all distribution methods, levels and most cluster numbers: (a) units with a 15% user decrease in August and (b) units with a 20–30% user increase in December. Both patterns are likely to be partially linked to school holidays and people going on vacation and/or visiting relatives and friends. This study highlights the need and importance of investigating CDRs in detail before conducting subsequent analysis like seasonal and trend decomposition. In particular, CDRs need to be investigated both in terms of their area and population coverage, as well as in relation to the appropriate distribution method to use based on the spatial scale of the specific application. The use of inappropriate methods can change observed seasonal patterns and impact the derived conclusions.

准确代表国家内次国家级人口分布的数据对于许多应用的政策和决策者来说至关重要。呼叫数据记录 (CDR) 在这方面表现出了巨大的潜力，与传统数据源相比，它提供了更高的时间和空间分辨率。为了将 CDR 与其他数据集成，并为有效地告知和支持政策和决策制定，必须将手机用户从蜂窝塔级别分配到管理单元。这可以通过不同的方式完成，并且通常不考虑哪种方法可以产生潜在人口分布的最佳表示。使用 2011 年至 2013 年间纳米比亚的匿名 CDR，在多个行政单位级别评估了四种分配方法。使用 Kendall 的 tau-b 秩检验对每种方法对每个行政单位的用户密度估计值进行排名，并与相应的人口普查得出的人口密度进行比较。使用黄土 (STL) 和多元聚类的季节性和趋势分解随后被用于识别季节性用户变化的模式，并研究不同的分布方法如何影响这些。结果表明，每种分配方法结果的准确性受到所考虑的行政单位级别的影响。虽然方法之间的边际差异显示在“粗略”级别 1，但使用手机信号塔范围为纳米比亚在更精细的级别 2 和 3 提供了最准确的结果。使用 STL 有助于识别底层分布方法的影响进一步分析，随着空间尺度的增加，方法之间的共识程度降低。多元聚类提供了有价值的见解，了解哪些单位具有相似的季节性用户行为。规定的聚类数越高，使用不同分布方法获得的结果差异越大。然而，在所有分布方法、级别和大多数集群数量中都确定了两种主要的季节性模式：(a) 8 月份用户减少 15% 的单位和 (b) 12 月份用户增加 20-30% 的单位。这两种模式都可能部分与学校假期和人们去度假和/或探亲访友有关。本研究强调了在进行季节性和趋势分解等后续分析之前详细调查 CDR 的必要性和重要性。尤其是，CDR 需要根据其面积和人口覆盖范围进行调查，以及根据特定应用的空间规模使用的适当分布方法。使用不适当的方法可能会改变观察到的季节性模式并影响得出的结论。