scholarly journals Disparities in Spatial Prevalence of Feline Retroviruses due to Data Aggregation: A Case of the Modifiable Areal Unit Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Bimal K. Chhetri ◽  
Olaf Berke ◽  
David L. Pearl ◽  
Dorothee Bienzle
Keyword(s):  
Risk Factors ◽  
Spatial Scale ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Postal Code ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Scan Test ◽  
Study Results ◽  
Spatial Scan

The knowledge of the spatial distribution feline immunodeficiency virus and feline leukemia virus infections, which are untreatable, can inform on their risk factors and high-risk areas to enhance control. However, when spatial analysis involves aggregated spatial data, results may be influenced by the spatial scale of aggregation, an effect known as the modifiable areal unit problem (MAUP). In this study, area level risk factors for both infections in 28,914 cats tested with ELISA were investigated by multivariable spatial Poisson regression models along with MAUP effect on spatial clustering and cluster detection (for postal codes, counties, and states) by Moran’s I test and spatial scan test, respectively. The study results indicate that the significance and magnitude of the association of risk factors with both infections varied with aggregation scale. Further more, Moran’s I test only identified spatial clustering at postal code and county levels of aggregation. Similarly, the spatial scan test indicated that the number, size, and location of clusters varied over aggregation scales. In conclusion, the association between infection and area was influenced by the choice of spatial scale and indicates the importance of study design and data analysis with respect to specific research questions.

scholarly journals Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

2020 ◽  
Author(s):  
Andrea Araujo Navas ◽  
Frank Osei ◽  
Ricardo J. Soares Magalhães ◽  
Lydia R. Leonardo ◽  
Alfred Stein
Keyword(s):  
Risk Factors ◽  
Data Structure ◽  
Schistosoma Japonicum ◽  
Spatial Data ◽  
Environmental Drivers ◽  
Parameter Estimates ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Reliable Parameter ◽  
Spatial Support

Abstract Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m, and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models. Methods: We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics. Results: Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease. Conclusions: Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programs by providing reliable parameter estimates at the same spatial support, and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns. Keywords: schistosomiasis modelling; modifiable areal unit problem; uncertainty; Bayesian statistics; convolution model.

scholarly journals Spatial scale in environmental risk mapping: A Valley fever case study

2017 ◽  
Vol 6 (2) ◽  
Author(s):  
Heidi E. Brown ◽  
Wangshu Mu ◽  
Mohammed Khan ◽  
Clarisse Tsang ◽  
Jian Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Land Cover ◽  
Spatial Scale ◽  
Environmental Risk ◽  
Spatial Data ◽  
Risk Mapping ◽  
Valley Fever ◽  
Income Status ◽  
The Individual ◽  
Design And Methods

<em>Background</em>. Valley fever is a fungal infection occurring in desert regions of the U.S. and Central and South America. Environmental risk mapping for this disease is hampered by challenges with detection, case reporting, and diagnostics as well as challenges common to spatial data handling. <br /><em>Design and Methods.</em> Using 12,349 individual cases in Arizona from 2006 to 2009, we analyzed risk factors at both the individual and area levels. <br /><em>Results</em>. Risk factors including elderly population, income status, soil organic carbon, and density of residential area were found to be positively associated with residence of Valley fever cases. A negative association was observed for distance to desert and pasture/ hay land cover. The association between incidence and two land cover variables (shrub and cultivated crop lands) varied depending on the spatial scale of the analysis. <br /><em>Conclusions</em>. The consistence of age, income, population density, and proximity to natural areas supports that these are important predictors of Valley fever risk. However, the inconsistency of the land cover variables across scales highlights the importance of how scale is treated in risk mapping.

scholarly journals Modifiable Areal Unit Problem (MAUP): Analysis of Agriculture of the State of Paraná-Brazil

2021 ◽  
Vol 13 (2) ◽  
pp. 35-50
Author(s):  
Elizabeth Giron Cima ◽  
eimar Freire da Rocha-Junior ◽  
Miguel Angel Uribe-Opazo ◽  
Gustavo Henrique Dalposso
Keyword(s):  
Social Development ◽  
Spatial Data ◽  
Agricultural Production ◽  
Research Data ◽  
The State ◽  
Modifiable Areal Unit Problem ◽  
Grain Production ◽  
Areal Unit ◽  
Regional Centers ◽  
Socioeconomic Data

The way the researcher groups his research data will influence the result of his work. In the literature, this phenomenon is treated as a Problem of the Modifiable Areal Unit. The objective of this article was to analyze the three spatial levels by Municipalities, Regional Centers and Mesoregions using the following data: gross domestic product, effective agricultural production, grain production and gross value of agricultural production for the state of Paraná-Brazil in the period since 2012 until 2015. The methodological procedure studied data from the Paranaense Institute for Economic and Social Development of the above-named variables collected on the website of the Paranaense Institute for Economic and Social Development of the 399 municipalities, 23 regional centers and 10 mesoregions. The results found show the presence of the Modifiable Areal Unit Problem, presenting different results for each level of grouping. The study revealed the problem of the modifiable areal unit is a relevant occurrence and it should be disregarded by researchers who work with clusters of spatial data in their studies. The results found allow a better understanding of the scale effect and demonstrate the efficiency of spatial analysis in socioeconomic data.

scholarly journals geospatial analysis of pertussis and its risk factors in southern Ontario from 2005–2016

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Taha Abdulhakim Elghamudi ◽  
Olaf Berke
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Poisson Regression ◽  
Regression Models ◽  
Incidence Rates ◽  
Predictor Variables ◽  
Southern Ontario ◽  
Scan Test ◽  
Spatial Poisson Regression ◽  
Spatial Scan

Introduction: Pertussis, commonly known as whooping cough, is a bacterial respiratory tract infection caused by Bordetella pertussis. Pertussis affects more than 48 million people worldwide annually, most of whom are under the age of 5. Hypothesis & Objectives: The hypothesis being investigated is that pertussis incidence, between 2005 and 2016, is not equally distributed across public health units in southern Ontario. We aim to identify disease cluster locations and associate geospatial fluctuations in incidence rates with putative risk factors. Materials and Methods: Data was sourced from Public Health Ontario on pertussis incidence in southern Ontario for all ages, specifically for each public health unit’s geographical area. A choropleth map was generated using data smoothed by empirical Bayesian estimation in a spatial analysis context. Following the creation of an incidence map for southern Ontario, the spatial scan test was applied to elucidate the existence of any disease clusters at a public health unit level. Moran’s I was used to determine whether there was evidence of any spatial dependence in pertussis incidence. Finally, putative risk factors were assessed in Poisson regression models and spatial Poisson regression models as potential predictor variables. Results and Discussion: The flexible spatial scan test identified three spatial clusters where incidence rates of pertussis were higher than expected. A spatial Poisson regression model was fit that included predictor variables of socioeconomic status and population density. For every 100 people/km2 increase in population density there was a significant 6% increase in pertussis incidence (p=0.03). Interestingly, vaccination rates were not found to be predictive of pertussis incidence nor did the variable improve the model. This epidemiological study identifies where pertussis incidence is clustered and what variables it is associated with, both of which are valuable for public health purposes and as a reference for future research into pertussis.

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.

scholarly journals Modifiable Areal Unit Problem: the issue of determining the relationship between microparameters and a macroparameter

2019 ◽  
Vol 10 (3) ◽  
pp. 393-417
Author(s):  
Michał Barnard Pietrzak
Keyword(s):  
Spatial Data ◽  
Value Added ◽  
Homogeneous System ◽  
Economic Research ◽  
Technical Equipment ◽  
Modifiable Areal Unit Problem ◽  
Entire Area ◽  
Areal Unit ◽  
The Impact ◽  
The Relationship

Research background: One of the issues considered by economists such as Tinbergen (1939), Klein (1946), May, (1946), Theil (1965), Pawłowski (1969), Bołt et al. (1985) was to determine the mechanism of transition between the results of microeconomics and the theory of macroeconomics. As part of this research, Pawłowski (1969) raised the problem of establishing the relationship between microparameters and a macroparameter. In the presented article, Pawłowski's problem was expanded to include spatial economic research, where micro-dependencies and spatial macro-dependencies were analysed. Purpose of the article: The purpose of the article is to establish the relationship between the microparameters set for SGM agricultural macroregions and the macroparameter referring to the whole area of Poland, where the parameters describe the economic dependencies regarding the impact of the size of farms in established region on their technical equipment. In the study, the economic relationships analysed in the case of individual SGM agricultural macroregions were defined as spatial micro-dependencies, and in the case of the entire area of Poland as spatial macro-dependencies. Methods: The methodological part of the article describes the concepts of Modifiable Areal Unit Problem, causal homogeneity of spatial data, homogeneous system of sets of areal units, area and sub-areas of conclusions. The concepts of micro-dependencies and spatial macro-dependencies are presented. Basic equations allowing to determine the evaluation of the spatial macroparameter as a linear combination of spatial microparameters were also presented. Findings & Value added: In the first stage of the study, spatial micro-dependencies were identified for subsequent SGM agricultural macroregions. In the second stage of the study, the relationship between spatial microparameters for single macroregions and the spatial macroparameter for Poland was determined. Establishing the relationship allowed to determine the macroparameter estimate for the whole area of Poland.

Effects of the Modifiable Areal Unit Problem on the Delineation of Traffic Analysis Zones

2009 ◽  
Vol 36 (4) ◽  
pp. 625-643 ◽  
Author(s):  
José Manuel Viegas ◽  
L Miguel Martinez ◽  
Elisabete A Silva
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Transportation Planning ◽  
Traffic Analysis ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Transportation Demand ◽  
Planning Models ◽  
Demand Models ◽  
And Performance

Transportation analysis is typically thought of as one kind of spatial analysis. A major point of departure in understanding problems in transportation analysis is the recognition that spatial analysis has some limitations associated with the discretization of space. Among them, modifiable areal units and boundary problems are directly or indirectly related to transportation planning and analysis through the design of traffic analysis zones (TAZs). The modifiable boundary and the scale issues should all be given specific attention during the specification of a TAZ because of the effects these factors exert on statistical and mathematical properties of spatial patterns (ie the modifiable areal unit problem—MAUP). The results obtained from the study of spatial data are not independent of the scale, and the aggregation effects are implicit in the choice of zonal boundaries. The delineation of zonal boundaries of TAZs has a direct impact on the reality and accuracy of the results obtained from transportation forecasting models. In this paper the MAUP effects on the TAZ definition and the transportation demand models are measured and analyzed using different grids (in size and in origin location). This analysis was developed by building an application integrated in commercial GIS software and by using a case study (Lisbon Metropolitan Area) to test its implementabiity and performance. The results reveal the conflict between statistical and geographic precision, and their relationship with the loss of information in the traffic assignment step of the transportation planning models.

Are Regional Differences in Psychological Characteristics and Their Correlates Robust? Applying Spatial-Analysis Techniques to Examine Regional Variation in Personality

2021 ◽  
pp. 174569162199832
Author(s):  
Tobias Ebert ◽  
Jochen. E. Gebauer ◽  
Thomas Brenner ◽  
Wiebke Bleidorn ◽  
Samuel D. Gosling ◽  
...  
Keyword(s):  
Spatial Data ◽  
The United States ◽  
Psychological Characteristics ◽  
Big Five Personality ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Analysis Techniques ◽  
Personality Differences ◽  
Psychological Differences ◽  
Spatial Dependencies

There is growing evidence that psychological characteristics are spatially clustered across geographic regions and that regionally aggregated psychological characteristics are related to important outcomes. However, much of the evidence comes from research that relied on methods that are theoretically ill-suited for working with spatial data. The validity and generalizability of this work are thus unclear. Here we address two main challenges of working with spatial data (i.e., modifiable areal unit problem and spatial dependencies) and evaluate data-analysis techniques designed to tackle those challenges. To illustrate these issues, we investigate the robustness of regional Big Five personality differences and their correlates within the United States (Study 1; N = 3,387,303) and Germany (Study 2; N = 110,029). First, we display regional personality differences using a spatial smoothing approach. Second, we account for the modifiable areal unit problem by examining the correlates of regional personality scores across multiple spatial levels. Third, we account for spatial dependencies using spatial regression models. Our results suggest that regional psychological differences are robust and can reliably be studied across countries and spatial levels. The results also show that ignoring the methodological challenges of spatial data can have serious consequences for research concerned with regional psychological differences.

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