The Modifiable Aerial Unit Problem (MAUP) influences the interpretation of spatial data in that forms of spatial aggregation creates scale and segmentation ecological fallacies. This paper explores the extent to which similar scalar and segmentation issues affect the analysis of temporal data. The analogy of gerrymandering in spatial data, which is the purposeful segmentation of space such that the underlying aggregations prove a specific point, is used to demonstrate segmentation and aggregation effects on time series data. To do so, the paper evaluates real-time sound monitoring data for Dublin, Ireland at multiple aggregation scales and segmentations to determine their effects with respect to compliance with European Union regulations concerning acceptable decibel levels. Like the MAUP, increasing scales of temporal aggregation remove extremes at more local scales, which has the effect of reducing measurements of non-compliance. Similarly, and unlike the spatial equivalent, because of circadian human social patterns, segmentation of temporal measurements also has a predictable, and gerrymander-able, effect on the measurement of compliance with ambient sound limits. The effect is computed as the Temporal Aggregation Bias and strategies which could justify gerrymandering of sound monitoring data are presented.

可修改航空单元问题 (MAUP) 影响空间数据的解释，因为空间聚合的形式会产生尺度和分割生态谬误。本文探讨了类似的标量和分割问题对时态数据分析的影响程度。空间数据中的 gerrymandering 类比是有目的的空间分割，使得底层聚合证明一个特定的点，用于演示对时间序列数据的分割和聚合效果。为此，本文评估了爱尔兰都柏林的多个聚合规模和分段的实时声音监测数据，以确定它们在遵守欧盟关于可接受分贝水平的法规方面的影响。和 MAUP 一样，增加时间聚合的规模会消除更多局部规模的极端情况，这具有减少不合规测量的效果。类似地，并且与空间等效物不同，由于昼夜节律的人类社会模式，时间测量的分割对环境声音限制的符合性测量也具有可预测的和可区分的影响。该效果被计算为时间聚合偏差，并提出了可以证明对声音监测数据进行划分的策略。