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provide. For example, daily sales data for a product can be aggregated 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. However, the daily data in the example may have too much noise, temporal autocorrelation, or be inconsistent with other datasets. 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.
64:
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influence future observations, while low autocorrelation suggests that current values are independent of past values. This concept is often used in time series analysis to understand patterns, trends, and dependencies within a time-ordered dataset, helping to make predictions and infer the underlying dynamics of a system over time. By adjusting the temporal unit used to bin the data in the analysis, temporal autocorrelation can be addressed.
20:
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Temporal autocorrelation refers to the degree of correlation or similarity between values of a variable at different time points. It examines how a variable's past values are related to its current values over a sequence of time intervals. High temporal autocorrelation implies that past observations
106:
The impact of MTUP on crime analysis can be significant, as it can affect the accuracy and reliability of crime data and its conclusions about crime patterns and trends. For example, suppose the temporal unit of analysis is changed from days to weeks. In that case, the number of reported crimes may
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of analysis and the issue of choosing an appropriate analysis. 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
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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. In some cases, it may be necessary to aggregate or interpolate the data to a consistent temporal unit.
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The MTUP can affect our understanding of the incidence and prevalence of diseases or health outcomes in different populations over time, resulting in incorrect or incomplete conclusions about the public health situation. The timeframe chosen for collecting and analyzing public health data is
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The MTUP can also have an impact on food accessibility. This issue arises when the temporal unit of analysis is changed, leading to changes in the patterns and trends observed in food accessibility data. For example, if food accessibility data is analyzed from different years or aggregated
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Overall, the
Modifiable Temporal Unit Problem highlights the importance of carefully considering the time unit when analyzing and forecasting time series data. It is often necessary to try different time units and evaluate the results to determine the most appropriate choice.
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differently, then the results of a study are likely to be impacted. This can affect our understanding of the availability of food in different areas over time, and can result in incorrect or incomplete conclusions about food accessibility.
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decrease or increase, even if the underlying pattern remains constant. This can lead to incorrect conclusions about the effectiveness of crime prevention strategies or the overall level of crime in a given area.
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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.
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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).
550:"COVID-19 Deaths in the United States: Shifts in Hot Spots over the Three Phases of the Pandemic and the Spatiotemporally Varying Impact of Pandemic Vulnerability"
359:
Deckard, Mica; Schnell, Cory (22 October 2022). "The
Temporal (In)Stability of Violent Crime Hot Spots Between Months and The Modifiable Temporal Unit Problem".
51:. In such cases, choosing a temporal unit (e.g., days, months, years) can affect the analysis results and lead to inconsistencies or errors in
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Flowchart illustrating selected units of time. The graphic also shows the three celestial objects that are related to the units of time.
593:"Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem"
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Park, Yoo Min; Kearney, Gregory D.; Wall, Bennett; Jones, Katherine; Howard, Robert J.; Hylock, Ray H. (26 August 2021).
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397:"A systematic review of the modifiable areal unit problem (MAUP) in community food environmental research"
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471:"Temporal autocorrelation: a neglected factor in the study of behavioral repeatability and plasticity"
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514:"Measuring spatio-temporal autocorrelation in time series data of collective human mobility"
321:"Linear trends in seasonal vegetation time series and the modifiable temporal unit problem"
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263:"Modifiable Temporal Unit Problem (MTUP) and Its Effect on Space-Time Cluster Detection"
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Mitchell, David J; Dujon, Antoine M; Beckmann, Christa; Biro, Peter (1 November 2019).
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Tuson, M.; Yap, M.; Kok, M. R.; Murray, K.; Turlach, B.; Whyatt, D. (27 March 2019).
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Table showing quantitative relationships between common units of time
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Cheng, Tao; Adepeju, Monsuru; Preis, Tobias (27 June 2014).
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Gao, Yong; Cheng, Jing; Meng, Haohan; Liu, Yu (2019).
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something that needs to be considered by researchers.
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319:Jong, R. de; Bruin, S. de (5 January 2012).
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75:or MAUP, in that they both relate to the
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190:Neighborhood effect averaging problem
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230:Uncertain geographic context problem
554:Int. J. Environ. Res. Public Health
150:Boundary problem (spatial analysis)
71:The MTUP is closely related to the
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439:The Modifiable Aerial Unit Problem
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225:Tobler's second law of geography
518:Geo-spatial Information Science
220:Tobler's first law of geography
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39:that occurs in time series and
654:Geographic information systems
180:Geographic information systems
53:statistical hypothesis testing
29:Modified Temporal Unit Problem
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531:10.1080/10095020.2019.1643609
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73:modifiable areal unit problem
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659:Problems in spatial analysis
280:10.1371/journal.pone.0100465
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414:10.1007/s44212-022-00021-1
16:Source of statistical bias
610:10.1186/s12942-019-0170-3
373:10.1177/00111287221128483
175:Facility location problem
145:Arbia's law of geography
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88:Temporal autocorrelation
436:Openshaw, Stan (1983).
361:Crime & Delinquency
567:10.3390/ijerph18178987
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498:10536/DRO/DU:30135712
488:10.1093/beheco/arz180
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338:10.5194/bg-9-71-2012
200:Spatial epidemiology
205:Technical geography
195:Torsten Hägerstrand
129:Suggested solutions
475:Behavioral Ecology
367:(6–7): 1312–1335.
170:Ecological fallacy
111:Food accessibility
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401:Urban Informatics
155:Coastline paradox
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138:See also
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419:S2CID
102:Crime
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33:MTUP
27:The
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333:doi
285:PMC
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
