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Modifiable temporal unit problem

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The Modified Temporal Unit Problem (MTUP) is a source of statistical bias that can occur in time series and spatial analysis when using temporal data that has varying temporal resolutions.[1][2] In such cases, choosing an appropriate temporal unit (e.g., days, months, years) can affect the analysis results and lead to inconsistencies or errors in statistical hypothesis testing.[3]

Introduction

The MTUP is closely related to the Modifiable areal unit problem or MAUP, in that they both relate to the scale of analysis and the issue of choosing an appropriate analysis.[2][4] While the MAUP refers to the choice of spatial enumeration units, the MTUP arises because different temporal units have different properties and characteristics, such as the number of periods they contain or the amount of detail they provide.[2][4][5] For example, if you have daily sales data for a product, you can aggregate it into weekly, monthly, or yearly sales data. In this case, using monthly data instead of daily data can result in losing important information about the timing of events, and using yearly data can obscure short-term trends and patterns.[3] However, the daily data in the example may have too much noise, temporal autocorrelation, or be inconsistent with other datasets.[1] With only daily data, conducting an analysis accurately at the hourly rate would not be possible. In addition, the Modifiable Temporal Unit Problem can also arise when the time units are irregular or when the data is missing for some periods. In such cases, the choice of the time unit can affect the amount of missing data, which can impact the accuracy of the analysis and forecasting.

Overall, the Modifiable Temporal Unit Problem highlights the importance of carefully considering the time unit when analyzing and forecasting time series data.[1] It is often necessary to try different time units and evaluate the results to determine the most appropriate choice.[1]

Suggested solutions

To address the MTUP, it is important to consider the temporal resolution of the data and choose the most appropriate temporal unit based on the research question and the goals of the analysis.[1] In some cases, it may be necessary to aggregate or interpolate the data to a consistent temporal unit. Additionally, it may be helpful to use multiple temporal units or to present results for different temporal units to demonstrate the sensitivity of the results to the choice of temporal unit.[1]

See Also

References

  1. ^ a b c d e f Cheng, Tao; Adepeju, Monsuru; Preis, Tobias (27 June 2014). "Modifiable Temporal Unit Problem (MTUP) and Its Effect on Space-Time Cluster Detection". PLoS ONE. 9 (6). doi:10.1371/journal.pone.0100465.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  2. ^ a b c Jong, R. de; Bruin, S. de (5 January 2012). "Linear trends in seasonal vegetation time series and the modifiable temporal unit problem". Biogeosciences. 9: 71–77. doi:10.5194/bg-9-71-2012.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  3. ^ a b Deckard, Mica; Schnell, Cory (22 October 2022). "The Temporal (In)Stability of Violent Crime Hot Spots Between Months and The Modifiable Temporal Unit Problem". Crime & Delinquency. doi:10.1177/00111287221128483.
  4. ^ a b Chen, Xiang; Ye, Xinyue; Widener, Michael J.; Delmelle, Eric; Kwan, Mei-Po; Shannon, Jerry; Racine, Racine F.; Adams, Aaron; Liang, Lu; Peng, Jia (27 December 2022). "A systematic review of the modifiable areal unit problem (MAUP) in community food environmental research". Urban Informatics. 1. doi:10.1007/s44212-022-00021-1. S2CID 255206315. Retrieved 27 December 2022.
  5. ^ Openshaw, Stan (1983). The Modifiable Aerial Unit Problem (PDF). GeoBooks. ISBN 0-86094-134-5.
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