Skip to main content
SpringerLink
Log in

Temporal aggregation bias and Gerrymandering urban time series

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Attempts to simulate a distribution which is closer to log-normal at each 5-min set of observations while maintaining the observed median and upper IQR changed the log-mean insignificantly and so the effect on TAB was inconclusive.

References

  1. Openshaw S (1984) “The Modifiable Areal Unit Problem”, in CATMOG. GeoBooks, Norwich

    Google Scholar 

  2. Forest B (2018) Electoral geography: From mapping votes to representing power. Geogr Compass 12:1–17

    Article  Google Scholar 

  3. Çöltekin A, De Sabbata S, Willi C, Vontobel I, Pfister S, Kuhn M, Lacayo M (2011) “Modifiable temporal unit problem”, in ISPRS/ICA workshop “Persistent problems in geographic visualization”(ICC2011). France, Paris

    Google Scholar 

  4. Eichenbaum MS, Christiano LJ (1985) Temporal Aggregation and Structural Inference in Macroeconomics. Carn-Roch Conf Ser Public Policy 26:63–130

    Google Scholar 

  5. Yule GU, Kendal MG (1950) An Introduction to the Theory of Statistics. Griffin, London

    Google Scholar 

  6. Gehlke CE, Biehl K (1934) Certain effects of grouping upon the size of the correlation coefficient in census tract material. J Am Stat Assoc 29:169–170

    Google Scholar 

  7. Openshaw S, Taylor P (1979) A million or so correlation coefficients: three experiments on the modifiable areal unit problem. Stat Appl Spatial Sci. Pion, London 127–144

  8. Flowerdew R (2011) How serious is the Modifiable Areal Unit Problem for analysis of English census data? Popul Trends 145:106–118

    Article  Google Scholar 

  9. Openshaw S, Rao L (1995) Algorithms for reengineering 1991 Census geography. Environ Plan A 27:425–446

    Article  Google Scholar 

  10. Shellman SM (2004) Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis. Polit Anal 12:97–104

    Article  Google Scholar 

  11. Cheng T, Adepeju M (2014) Modifiable temporal unit problem (MTUP) and its effect on space-time cluster detection. PloS One 9:e100465

  12. Lutkepohl H (1984) Forecasting Contemporaneously Aggregated Vector ARMA Processes. Journal of Busniess & Economic Statistics 2(3):201–214

    Google Scholar 

  13. Rosanna RJ, Seater JJ (1985) Temporal Aggregation and Economic Time Series. Journal of Business & Economic Statistics 13(4):441–451

    Google Scholar 

  14. Eglund P, Quigley JM, Redfearn CL (1999) The Choice of Methodology for Computing Housing Price Indexes: Comparisons of Temporal Aggregation and Sample Definition. J Real Estate Financ Econ 19(2):91–112

    Article  Google Scholar 

  15. Ramirez OA (2012) “Conclusive Evidence on the Benefits of Temproal Disaggregation to Improve the Precision of Time Series Model Forecasts,” in Agricultural & Applied Economics Association's Annual Meeting, Seattle, WA

  16. Salles R, Mattos P, Dubois Iorgulescu AM, Bezerra E (2016) Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean. Ecol Info 36:94–105

  17. Percoco M (2015) Temporal aggregation and spatio-temporal traffic modeling. J Transp Geogr 46:244–247

    Article  Google Scholar 

  18. Alarcon Falconi TM, Estrella B, Sempertegui F, Naumova EN (2020) Effects of Data Aggregation on Time Series Analysis of Seasonal Infections. Int J Environ Res Public Health 17

  19. Duque JC,  Artı́s M, Ramos R (2006) The ecological fallacy in a time series context: evidence from Spanish regional unemployment rates. J Geogr Syst 8(9):391–410

  20. Jong R, Bruin S (2012) Linear trends in seasonal vegetation time series and the modifiable temporal unit problem. Biogeosciences 9(1):71–77

  21. Griffith DA, Wong DWS, Whitfield T (2003) Exploring Relationships Between the Global and Regional Measures of Spatial Autocorrelation. J Reg Sci 43(4):683–710

    Article  Google Scholar 

  22. Galić Z, Mešković E, Osmanović D (2017) Distributed processing of big mobility data as spatio-temporal data streams. GeoInformatica, vol. 21, pp. 263–291, 01 4 

  23. Hunkeler U, Truong HL, Stanford-Clark A (2008) “MQTT-S — A publish/subscribe protocol for Wireless Sensor Networks,” in 2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE '08)

  24. Andrienko G, Andrienko N (2008) “Spatio-temporal Aggregation for Visual Analysis of Movements,” in IEEE Symposium on Visual Analytics Science and Technology Essentially, October 21 - 23, Columbus, Ohio, USA

  25. Ashby MPJ, Bowers KJ (2013) A comparison of methods for temporal analysis of aoristic crime. Crime Sci 2:1

    Article  Google Scholar 

  26. Huang Q, Wong DWS (2015) Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty: An Example Using Twitter Data. Ann Assoc Am Geogr 105(9):1179–1197

  27. Silva TH, Melo POSV, Viana AC, Almeida JM, Salles J, Loureiro AAF (2013) “Traffic Condition Is More Than Colored Lines on a Map: Characterization of Waze Alerts,” in Social Informatics, Cham

  28. Horner MW (2004) Spatial dimensions of urban commuting: a review of major issues and their implications for future geographic research. Prof Geogr 56:160–173

    Google Scholar 

  29. Wen C, Teng Z, Chen J, Wu Y, Gong R, Pu J (2016) “Socialradius: visual exploration of user check-in behavior based on social media data,” in International Conference on Cooperative Design, Visualization and Engineering

  30. Dzogang F, Goulding J, Lightman S, Cristianini N (2017) “Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases,” in Advances in Intelligent Data Analysis XVI, Cham

  31. Fotheringham AS, Wong DWS (1991) The modifiable areal unit problem in multivariate statistical analysis. Environ Plan A 23:1025–1044

    Article  Google Scholar 

  32. Dark SJ, Bram D (2007) The modifiable areal unit problem (MAUP) in physical geography. Prog Phys Geogr 31(10):471–479

  33. Duque JC, Laniado H, Polo A (2018) S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem. PLoS ONE 13(11):1–25

    Article  Google Scholar 

  34. Kwan M-P (2012) The uncertain geographic context problem. Ann Assoc Am Geogr 102:958–968

    Article  Google Scholar 

  35. Morrill R (2018) “Electoral geography and gerrymandering: Space and politics,” in Reordering The World: Geopolitical Perspectives On The 21st Century, G. J. Demko, Ed., Routledge, 117–138

  36. Forest B (2001) Mapping Democracy: Racial Identiy and the Quandry of Political Representation. Ann Assoc Am Geogr 91(1):143–166

    Article  Google Scholar 

  37. Phillips P, Lee I (2011) “Crime analysis through spatial areal aggregated density patterns,” GeoInformatica, vol. 15, pp. 49–74, 01 1 

  38. Kennedy SS (2017) Electoral Integrity: How Gerrymandering Matters. Public Integrity 19:265–273

    Article  Google Scholar 

  39. European Commission (2002) “Directive 2002/49/EC of the European Parliament and of the Council of 25 June 2002 relating to the assessment and management of environmental noise,” 

  40. Berglund B, Lindvall T, Schwela DH (1999) “Guidelines for Community Noise,” 

  41. Dublin Agglomeration (2018) “Environmental Noise Action Plan December 2018 - July 2023,”

  42. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389

    Article  MathSciNet  Google Scholar 

  43. Basner M, Babisch W, Davis A, Brink M, Clark C, Janssen S, Stansfeld S (2014) Auditory and non-auditory effects of noise on health. The lancet 383:1325–1332

    Article  Google Scholar 

  44. Ising H, Kruppa B (2004) “Health effects caused by noise: evidence in the literature from the past 25 years,” Noise and Health 6:5

  45. European Commission (2017) “Future Brief: Noise abatement approaches,” Sci Environ Policy

  46. Navrud S (2002) The State-Of-The-Art on Economic Valuation of Noise. Agricultural University of Norway, Dept. of Economics and Social Sciences

    Google Scholar 

  47. Working Group on Health and Socio-Economic Aspects (2003) “Valuation of Noise,” European Commission

  48. Peuquet DJ (2001) Making space for time: Issues in space-time data representation. GeoInformatica 5:11–32

    Article  Google Scholar 

Download references

Acknowledgements

The author gratefully acknowledges funding from Science Foundation Ireland under the Investigator’s Award Program. Award number: 15/IA/3090.

Author information

Authors and Affiliations

  1. Iontas Building, National Centre for Geocomputation, Maynooth University, County Kildare, Maynooth, Ireland

    Samuel Stehle

Authors
  1. Samuel Stehle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samuel Stehle.

Ethics declarations

Conflicts of interest

The author declares that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stehle, S. Temporal aggregation bias and Gerrymandering urban time series. Geoinformatica 26, 233–252 (2022). https://doi.org/10.1007/s10707-021-00452-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-021-00452-z

Keywords

Search

Navigation