scholarly journals How serious is the Modifiable Areal Unit Problem for analysis of English census data?

2011 ◽  
Vol 145 (1) ◽  
pp. 106-118 ◽  
Author(s):  
Robin Flowerdew
Keyword(s):  
Census Data ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit

scholarly journals Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0221070 ◽  
Author(s):  
Daniele Da Re ◽  
Marius Gilbert ◽  
Celia Chaiban ◽  
Pierre Bourguignon ◽  
Weerapong Thanapongtharm ◽  
...  
Keyword(s):  
Predictive Models ◽  
Census Data ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Livestock Census

scholarly journals ASSESSING MODIFIABLE AREAL UNIT PROBLEM IN THE ANALYSIS OF DEFORESTATION DRIVERS USING REMOTE SENSING AND CENSUS DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 77-80 ◽  
Author(s):  
J. F. Mas ◽  
A. Pérez Vega ◽  
A. Andablo Reyes ◽  
M. A. Castillo Santiago ◽  
A. Flamenco Sandoval
Keyword(s):  
Environmental Variables ◽  
Census Data ◽  
Spatial Configuration ◽  
Rate Of Change ◽  
Population Census ◽  
Modifiable Areal Unit Problem ◽  
Land Use Land Cover ◽  
Economic Variables ◽  
Areal Unit ◽  
Socio Economic Variables

In order to identify drivers of land use / land cover change (LUCC), the rate of change is often compared with environmental and socio-economic variables such as slope, soil suitability or population density. Socio-economic information is obtained from census data which are collected for individual households but are commonly presented in aggregate on the basis of geographical units as municipalities. However, a common problem, known as the modifiable areal unit problem (MAUP), is that the results of statistical analysis are not independent of the scale and the spatial configuration of the units used to aggregate the information. In this article, we evaluate how strong MAUP effects are for a study on the deforestation drivers in Mexico at municipality level. This was done by taking socio-economic variables from the 2010 Census of Mexico along with environmental variables and the rate of deforestation. As population census is given for each human settlement and environmental variables are obtained from high resolution spatial database, it was possible to aggregate the information using spatial units (”pseudo municipalities”) with different sizes in order to observe the effect of scale and aggregation on the values of bivariate correlations (Pearsons r) between pairs of variables. We found that MAUP produces variations in the results, and we observed some variable pairs and some configurations of the spatial units where the effect was substantial.

scholarly journals Downscaling livestock census data using multivariate predictive models: sensitivity to modifiable areal unit problem

2019 ◽  
Author(s):  
Daniele Da Re ◽  
Marius Gilbert ◽  
Celia Chaiban ◽  
Pierre Bourguignon ◽  
Weerapong Thanapongtharm ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Goodness Of Fit ◽  
Census Data ◽  
Spatial Clustering ◽  
A Priori ◽  
Analytical Framework ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Administrative Units ◽  
Correlation Statistics

AbstractThe analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori scales and shapes choices could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson’s r correlation statistics and RMSE were carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneus distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson’s r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products.

Exploring the correlation between city size and residential segregation: comparing Chilean cities with spatially unbiased indexes

2020 ◽  
Vol 32 (2) ◽  
pp. 569-588
Author(s):  
Matias Garreton ◽  
Agustin Basauri ◽  
Luis Valenzuela
Keyword(s):  
Residential Segregation ◽  
Urban Studies ◽  
Census Data ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
City Size ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Disadvantaged Groups ◽  
Widespread Phenomenon

Urban segregation is a widespread phenomenon with profound social implications, and one that presents difficult measurement challenges. Segregation indexes may be affected by scale or zoning biases of the modifiable areal unit problem (MAUP). In this article, we develop a methodology that relies on spatial clustering algorithms to simultaneously cope with both kinds of MAUP biases, and we test it with complete census data for most Chilean cities. We find a robust correlation between segregation and city size, contesting previous claims about the spuriousness of this relationship. We also show that socioeconomic polarization is a widespread phenomenon in Chile and that it is not just a problem of disadvantaged groups’ concentration. Based on these results, we suggest that area-based desegregation policies should be generally reinforced, and complemented in big Chilean cities with housing-mix policies. We argue that using spatially unbiased segregation indexes could improve comparative urban studies.

Modifiable Areal Unit Problem

2021 ◽  
pp. 854-855
Author(s):  
Martin A. Andresen
Keyword(s):  
Modifiable Areal Unit Problem ◽  
Areal Unit

scholarly journals Exploring the Modifiable Areal Unit Problem in Spatial Water Assessments: A Case of Water Shortage in Monsoon Asia

Water ◽  
2015 ◽  
Vol 7 (12) ◽  
pp. 898-917 ◽  
Author(s):  
Aura Salmivaara ◽  
Miina Porkka ◽  
Matti Kummu ◽  
Marko Keskinen ◽  
Joseph Guillaume ◽  
...  
Keyword(s):  
Water Shortage ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Monsoon Asia

Metrics of Urban Form and the Modifiable Areal Unit Problem

2005 ◽  
Vol 1902 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Ming Zhang ◽  
Nishant Kukadia
Keyword(s):  
Data Aggregation ◽  
Urban Form ◽  
Mode Choice ◽  
Empirical Modeling ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Block Level ◽  
A Cell ◽  
Using Data ◽  
Network Pattern

There is growing interest in incorporating urban form indicators into transportation planning and travel analysis. These indicators typically are measured at a certain level of spatial aggregation (e.g., traffic analysis zone) and therefore are subject to the modifiable areal unit problem (MAUP) known primarily in the statistical and geographic literature but generally overlooked by transportation researchers. The presence of the MAUP can cause serious inconsistency in analytical results and consequently misinform policy making. This study diagnoses the MAUP in measuring urban form through empirical modeling of travel mode choice in the Boston, Massachusetts, region. Using data aggregated in grids with five cell sizes and at the transportation analysis zone, the census block group, and the block level, the study explores the sensitivity of coefficient estimates for population density, network pattern, and land use balance to data aggregation in predicting mode choice decisions. Having confirmed the presence of the MAUP, the study discusses three approaches for dealing with it. Using a grid with a cell size of 1/2 mi appears to be the most desirable method of data aggregation among the eight methods studied. The suggested improvements in methodology will help advance the inquiry on the link between urban form and travel.

scholarly journals S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207377 ◽  
Author(s):  
Juan C. Duque ◽  
Henry Laniado ◽  
Adriano Polo
Keyword(s):  
Statistical Test ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit

scholarly journals Changing boundaries: Overcoming modifiable areal unit problems related to unemployment data in South Africa

2016 ◽  
Vol Volume 112 (Number 3/4) ◽  
Author(s):  
Gina Weir-Smith ◽  
◽  
Keyword(s):  
South Africa ◽  
Rural Areas ◽  
Census Data ◽  
Metropolitan Areas ◽  
Geographic Area ◽  
Areal Unit ◽  
Areal Interpolation ◽  
Unemployment Data ◽  
Municipal Boundaries ◽  
Longitudinal Comparison

Abstract The longitudinal comparison of census data in spatial format is often problematic because of changes in administrative boundaries. Such shifting boundaries are referred to as the modifiable areal unit problem (MAUP). This article utilises unemployment data between 1991 and 2007 in South Africa to illustrate the challenge and proposes ways to overcome it. Various censuses in South Africa use different reporting geographies. Unemployment data for magisterial districts of census 1991 and 1996 were re-modelled to the 2005 municipal boundaries. This article showed that areal interpolation to a common administrative boundary could overcome these reporting obstacles. The results confirmed more accurate interpolations in rural areas with standard errors below 3300. Conversely, the largest errors were recorded in the metropolitan areas. Huge increases in unemployment between 1996 and 2001 statistics were also evident, especially in the metropolitan areas. Although such areas are more complex in nature, making it more difficult to accurately calculate census data, the increase in unemployment could also be the result of census taking methods. The article concludes that socio-economic data should be available at the smallest possible geographic area to ensure more accurate results in interpolation. It also recommends that new output areas be conceptualised to create a seamless database of census data from 1991 to 2011 in South Africa.

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