The Modifiable Areal Unit Problem (MAUP) is one of the most fundamental and stubborn problems in geospatial analysis. When using spatially aggregated data (i.e. data, such as points, which have been separated or grouped into certain areas), the observed patterns and relationships can change dramatically depending on the scale and configuration of the spatial units chosen, potentially leading to biased interpretations, misleading conclusions, and ethically problematic outcomes (Wong, 2009). First recognised by Gehlke and Biehl in 1934, the term MAUP was not officially coined until 1979, when Openshaw and Taylor tested different boundary systems on the same area and evaluated the correlation between their results. The problem arises because statistical results will not remain consistent when analyses are performed on the same data split into different area units or aggregated on a different spatial scale. These are known individually as the zoning and scale effects:

1. Zoning: The way in which areas and boundaries are defined will generate changes in results for the same analysis.
2. Scale: The same data grouped at larger or smaller scales will give different results for the same analysis.

Let’s look at an example:

Census tracts are geographic areas defined for the purpose of taking census data. In the United States, census tracts are designed to be “as homogeneous as possible with respect to population characteristics, economic status, and living conditions” and average around 4,000 inhabitants (US Census Bureau, 2018). Figure 1 shows U.S. census tract #864, Queens County, New York (no. 36081086400). In 2020, 2,867 people lived in this 0.4 square mile area. Comparing the census tract boundary to the aerial imagery, we see that the majority of the population lives in the northwest corner of the area (Figure 2). Thus, the population is not evenly distributed throughout the census tract.

When does this become a problem?

If we want to do spatial analyses on this area, problems may arise. For example, when deciding where to build public infrastructure, such as a new school, hospital, or public transport infrastructure, urban planners use census data to place these services within reach of the majority of people. When using census tracts as the areal unit, planners often assume an even distribution of people within these spaces and will extract the midpoint of the area to use in any analysis. This is problematic because populations are not evenly distributed within census tracts, thus any analysis will misrepresent the true distance that people must travel to access services. Of course, a trade-off must be made between accuracy and computational resources: it would take a lot of energy and complex calculations to measure each individual’s distance to reach their nearest school, hospital or bus stop.

Figure 1: U.S. census tract #864, Queens County, New York, no. 36081086400 (Source: United States Census Bureau, 2020).

Figure 2: Satellite imagery of U.S. census tract #864, Queens County, New York, no. 36081086400 (outlined in red) (Source: Google Maps, 2025).

When the MAUP becomes an Ethical Issue

The MAUP is inherently derived from social and political biases. The most basic issue is that someone, somewhere, has produced the units of analysis which we use in geospatial analyses. Humans are all subject to bias, whether intentional or unintentional, making the MAUP a highly important issue for spatial analysts to be aware of. The MAUP isn’t just a technical problem: it has real ethical implications wherever spatial data is used to inform political, social or economic decision-making. For example…

1. Disenfranchisement in Service Delivery: As discussed in the previous section, the MAUP can lead to inequitable access to services when population data is poorly defined and/or boundaries are manipulated. In today’s technical world, urban planners increasingly rely on accurate spatial data to make informed decisions about public service distribution.
2. Misrepresentation of Communities: Aggregating data into arbitrary units can mask the true characteristics of smaller or more vulnerable populations. Policymakers may overlook areas of need, leading to inequitable resource allocation like underfunding schools, health services or infrastructure in marginalised neighbourhoods. For example, a study on poverty in Pennsylvania found that poverty rates are often reported using census-based areal units: when aggregated at finer scales, different nuances and pockets of poverty were unmasked (Hayward and Parent, 2009).
3. Bias in Research and Policy: Different choices of geographic units can produce conflicting statistical results. Researchers or institutions may cherry-pick unit boundaries to support a preferred narrative or policy outcome, intentionally or unintentionally introducing misleading conclusions.

Gerrymandering

Gerrymandering is defined as the “manipulation of an electoral constituency’s boundaries so as to favour one party or class” (Oxford Dictionary). This unjust political practice is used by one party to gain an unfair advantage over its rivals (political or partisan gerrymandering) or to weaken the voting power of minority groups (racial gerrymandering). Figure 3 gives a simple visual explanation of how gerrymandering works. The image shows 50 voters, with 60% leaning blue and 40% leaning red. If votes were counted at this level, blue would win based on the majority. However, grouping the voters can dramatically alter the results. In the jagged partitioning scenario, the 50 voters are divided into five sets of 10. The group boundaries are drawn to maximise the power of red voters, giving them a majority in three of the five groups, and leaving blue with only two. In the horizontal partitioning scenario, red voters are packed into a few districts and the blue voters are spread more efficiently, giving all groups a blue majority. This demonstrates how district boundaries can be strategically drawn to favour one party, allowing them to win more seats than their share of the vote would suggest they should. If you want to try this out for yourself, have a go at Gerrymander – a voting district game by GameTheory.

Figure 3: An example of how voters can be divided into groups to influence electoral outcomes (Source: Scientific American, 2022).

The MAUP is directly related to the concept of gerrymandering. The way we zone or group data, like electoral boundaries, can significantly alter the results of statistical analyses, like determining electoral seats.

• The MAUP explains how and why changing the shapes and sizes of electoral districts (the areal units) can lead to very different political outcomes, even though the underlying data (voter preferences) remain the same.
• In Figure 3, the left panel shows the overall distribution of votes, which is neutral and accurate.
• The horizontal and jagged partitioning panels use the same data, but the zoning and scale effects are mobilised. Looking at smaller units, and separating them into different districts, produces opposite electoral outcomes.

Gerrymandering is a strategic exploitation of the MAUP for political gain. It reveals the inherent instability and potential for bias in analysis or decision based on arbitrarily defined geographic units: especially when those units are drawn with political intent. 

The U.S. Supreme Court ruled intentional gerrymandering illegal in 1986. However, it’s often very difficult to spot deliberate gerrymandering and set concrete rules and regulations for fair districting (Bischoff, 2022). One of the most well-known examples of gerrymandering in the U.S. is Maryland’s third congressional district (Figure 4). At first glance, the district appears extremely jagged. There is a general assertion that district borders should be “compact”: the more jagged a boundary, the larger the perimeter, the more likely an area is to be gerrymandered. That is the case here. Maryland’s District 3 was changed in 2022, after a judge ruled it to be “an extreme gerrymander” (Boehm, 2022).

Figure 4: Maryland U.S. District 3, from 2013 until 2022 (Source: Scientific American, 2022).

How can Geospatial Analysts Manage the MAUP?

For Geographic Information System (GIS) practitioners, the MAUP is a fundamental issue to be critically aware of. Here are three important steps that analysts can take to ensure they account for the MAUP:

1. Use multiple zoning and scale configurations: Analyse data at different aggregations to identify patterns and test the sensitivity of results to changing unit boundaries.
2. Choose meaningful units of analysis: Where possible, base units on functional or community-relevant boundaries (e.g. neighbourhoods, health districts), as arbitrary or artificial boundaries may misrepresent spatial patterns. Actively choose boundaries and scales based on the aim of the analysis.
3. State the limitations of your analysis: Identify where the analysis may be misleading on account of the MAUP, and clearly state this on any work produced. It is also great practice to collaborate with other GIS practitioners, especially those from different backgrounds, as they may spot issues that you miss.

References

Bischoff, M., 2022. Geometry Reveals the Tricks behind Gerrymandering. Scientific American, 10 November. Available at: https://www.scientificamerican.com/article/geometry-reveals-the-tricks-behind-gerrymandering/

Boehm, E., 2022. Judge Tosses Maryland’s Highly Gerrymandered Congressional Map. Reason, 25 March. Available at: https://reason.com/2022/03/25/judge-tosses-marylands-highly-gerrymandered-congressional-map/#:~:text=Calling%20it%20%22an%20extreme%20gerrymander%2C%22%20a%20Maryland%20judge,new%20one%20before%20the%20end%20of%20the%20month.

Duignan, B., 2025. Gerrymandering. Politics, Britannica, 13 May. Available at: https://www.britannica.com/topic/gerrymandering

Gehlke, C. E., and K. Biehl, 1934. Certain Effects of Grouping Upon the Size of the Correlation Coefficient in Census Tract Material. Journal of the American Statistical Association Supplement 29: 169-170.

Hayward, P. and Parent, J., 2009. Modeling the influence of the modifiable areal unit problem (MAUP) on poverty in Pennsylvania. Pennsylvania Geographer 47(1):120-135.

Jones, R., 2011. The Modifiable Areal Unit Problem in GIS. Cartographica Blog, 19 May. Available at: https://blog.cartographica.com/the-modifiable-areal-unit-problem-in-gis.html

Openshaw, S. and P. J. Taylor, 1979. A Million or so Correlation Coefficients: Three Experiments on the Modifiable Areal Unit Problem. In N. Wrigley, ed. Statistical Applications in the Spatial Sciences, 127-144. London: Pion.

United States Census Bureau, 2018. Census Tracts and Block Numbering Areas, Chapter 10. Available at: https://www2.census.gov/geo/pdfs/reference/GARM/Ch10GARM.pdf#:~:text=Census%20tracts%20are%20small%2C%20relatively%20permanent%20geographic%20entities,8%2C000%20residents%20and%20boundaries%20that%20follow%20visible%20features.

United States Census Bureau, 2020. 2020 Census Demographic Data Map Viewer. Online resource. Available at: https://maps.geo.census.gov/ddmv/map.html

Wagner, R., 2022. Modifiable Areal Unit Problem — The Spatial Data Scientist’s nightmare. Medium, 2 March. Available at: https://medium.com/@raulcosta12332/modifiable-areal-unit-problem-the-spatial-data-scientists-nightmare-31f8fbbbbe7e

Wong, D. W., 2004. The Modifiable Areal Unit Problem (MAUP). D. G. Janelle et al. (eds.), WorldMinds: Geographical Perspectives on 100 Problems, 571-575. 

© 2004 Kluwer Academic Publishers. Available at: https://blogs.ubc.ca/advancedgis/files/2020/09/Wong2004_Chapter_TheModifiableArealUnitProblemM.pdf
Wong, D.W., 2009. Modifiable Areal Unit Problem, Editor(s): Rob Kitchin, Nigel Thrift, International Encyclopedia of Human Geography, Elsevier, pp. 169-174. Available at: https://www.sciencedirect.com/topics/earth-and-planetary-sciences/modifiable-areal-unit-problem