Journal cover Journal topic
AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Articles | Volume 3
AGILE GIScience Ser., 3, 30, 2022
AGILE GIScience Ser., 3, 30, 2022
10 Jun 2022
10 Jun 2022

Handling the MAUP: methods for identifying appropriate scales of aggregation based on measures on spatial and non-spatial variance

Alexis Comber1,2, Paul Harris3, Kristina Bratkova1,2, Hoang Huu Phe4, Minh Kieu5, Quang Thanh Bui6, Thi Thuy Hang Nguyen7, Eric Wanjau2, and Nick Malleson1,2 Alexis Comber et al.
  • 1School of Geography, University of Leeds, Leeds, UK
  • 2Leeds Institute for Data Analytics, University of Leeds, UK
  • 3Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, UK
  • 4RD Consultants, Hanoi City, Vietnam
  • 5Faculty of Engineering, University of Auckland, New Zealand
  • 6Faculty of Geography, VNU University of Science, Hanoi, Vietnam
  • 7VNU Vietnam Japan University, Vietnam National University, Hanoi, Vietnam

Keywords: Aggregation, Scale, Spatial Support, Ecological Fallacy

Abstract. The Modifiable Areal Unit Problem or MAUP is frequently alluded to but rarely addressed directly. The MAUP posits that statistical distributions, relationships and trends can exhibit very different properties when the same data are aggregated or combined over different reporting units or scales. This paper explores a number of approaches for determining appropriate scales of spatial aggregation. It examines a travel survey, undertaken in Ha Noi, Vietnam, that captures attitudes towards a potential ban of motorised transport in the city centre. The data are rich, capturing travel destinations, purposes, modes and frequencies, as well as respondent demographics (age, occupation, housing etc) including home locations. The dataset is highly dimensional, with a large n (26339 records) and a large m (142 fields). When the raw individual level data are used to analyse the factors associated with travel ban attitudes, the resultant models are weak and inconclusive - the data are too noisy. Aggregating the data can overcome this, but this raises the question of appropriate aggregation scales. This paper demonstrates how aggregation scales can be evaluated using a range of different metrics related to spatial and non-spatial variances. In so doing it demonstrates how the MAUP can be directly addressed in analyses of spatial data.

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