Modifiable Areal Unit Problem Research Articles

Nonlinear and threshold effects of the built environment on e-scooter sharing ridership

Understanding the relationship between the built environment and e-scooter sharing (ESS) usage is important because it could help planners determine the high-demand area and design an effective investment plan to promote the use of micromobility. Previous studies usually assume that the relationship is linear, which may lead to inaccurate ridership prediction and ineffective policy. Thus, this study explores the nonlinear and threshold effects of the built environment on ESS ridership in Los Angeles using the gradient boosting decision tree. Fourteen built environment and ten demographic variables are selected as independent variables. We find that the built environment accounts for 91.66% of the total relative importance. The ten most important variables are intersection density, road density, public transit station density, restaurant density, employment density, distance to the center, population density, proportion of park area, parking density, and bike lane density. The nonlinear and threshold effects of the built environment on ESS ridership are determined. By using two spatial analysis units (census tract and census block group) and four temporal analysis units (spring, summer, autumn, and winter), the modifiable areal unit problem and the modifiable temporal unit problem are revealed.

Understanding the Modifiable Areal Unit Problem and Identifying Appropriate Spatial Units while Studying the Influence of the Built Environment on the Traffic System State

Spatially aggregated data are prone to the effects of the modifiable areal unit problem (MAUP), which applies to built environments and traffic data. Although various studies have been carried out to explore the impact of built environment factors on traffic systems, few have considered MAUPs, which may result in statistical inconsistency. The purpose of this study is to assess the effects of MAUPs on statistical variables and geographically weighted regression results when evaluating the influence of the built environment on the traffic system state. Fifty sets of spatial configurations were created using the different aggregation criteria. The variance inflation factor and spatial autocorrelation of the variables, as well as the R2 and root mean squared error of the GWR model, were used to assess the MAUP effect. The results show that the index variation is more dependent on the scale of the spatial unit than on zoning type. In the case study presented, based on the available dataset, the optimal spatial unit size for analyzing the influence of the built environment on Jinan’s traffic system was 900 m × 900 m.

Virus containment measures and homicide in Mexico: An assessment of community strain theory

ObjectivesThis study examined whether Covid-19 virus containment measures moderated the relationship between community strain and homicide rates in Mexico City neighborhoods and police quadrants. MethodsWe tested the moderation effects hypothesis with the use of a mixed-effects regression to estimate fixed effects with random effects at different levels of aggregation. A sensitivity analysis was used to assess whether results of the moderation effects were affected by changes in the unit of analysis. ResultsWe found no evidence that virus containment measures moderated the relationship between community strain and observed changes in homicide rates. Moreover, although community strain measures were found to be statistically associated with homicide rates, the results were seemingly affected by the Modifiable areal unit problem (MAUP). ConclusionsFirst, the link being made in the literature between the homicide drop and the Covid-19 pandemic based on strain theory premises has no universal empirical basis. Second, although homicide rates dropped on average after containment measures were implemented, these had different effects across places, making arguments based on overall average change inexact. Third, we find evidence that community strain can predict homicide rates, but results are sensitive to the MAUP. Thus, community strain explanations of homicide rates may only apply in some areas of cities and conditional on the unit of analysis.

The modifiable areal unit problem in the analysis of the demand for urban freight transport

This document diagnoses the effects of the Modifiable Areal Unit Problem (MAUP) in the study of the demand for urban freight transport, by means of a comparison of different analysis scales to determine the relevance of working with different levels of detail. It is recommended to analyze the number of retail businesses, because they are the ones that mainly attract urban freight transport, and other variables of a socio-territorial type, considering three different spatial units: basic geostatistical area, electoral section, and regular 500-by-500-meter grids. For each scale, descriptive statistics parameters, correlation between variables, as well as global and local spatial autocorrelation (Moran-LISA’s I) were obtained to determine the impact of the MAUP. The results obtained show that the electoral sections do not result in an adequate scale, unless it is used as a complementary scale, since the statistical results indicate a greater variation. However, its cartographic representation allows seeing spatial groupings that are not observed in the other scales, which can be a valuable contribution. In the case of the Basic Geostatistical Area (BGA) scale and the 500-by-500-meter grids, data show less dispersion. The variables analyzed show a greater correlation and present a greater global spatial autocorrelation, which is why it is determined that they may be the most appropriate scales to model the transport of merchandise in the study area. The site that had no discrepancy, regardless of the scale of analysis, was the historic downtown of the city of Toluca, so it can be considered a priority site based on its demand for merchandise and territorial characteristics, where regulation and transport control alternatives can be incorporated, as well as the creation of infrastructure to make deliv-eries more efficient and to mitigate its negative impacts.

A Bayesian shared-effects modeling framework to quantify the modifiable areal unit problem

The modifiable areal unit problem (MAUP) refers to the effects caused by modifying the units of analysis in the context of spatial statistical analyses on areal data. The problem was formulated decades ago, but it is still of interest due to its complexity. Usually, studies on the MAUP are experimental, focusing mainly on analyzing the variation of model estimates or statistical properties as a consequence of varying the units of analysis. Indeed, the theoretical advances in this research line are scarce, leaving the MAUP as an issue to be considered in practice if spatial analyses are carried out at different scales or with multiple zonations of the study area. In this paper, a Bayesian shared-effects modeling framework is proposed for quantifying the MAUP, both globally and locally. In a global sense, the model described allows measuring the discrepancy in a covariate effect between two different spatial scales. If a global MAUP effect is identified, posterior predictive checks can be computed to detect those spatial units that contribute the most to such discrepancies. The methodology proposed is applied first to a traffic accident dataset recorded in Valencia, Spain, including multiple environmental and sociodemographic covariates. Five different partitions of the city based on the structure of census tracts are considered to analyze MAUP effects. A second application is also shown, considering a dataset about COVID-19 death counts in the Greater London area at two different aggregation levels. This second dataset includes multiple socioeconomic and health indicators as well.

Characterizing mobility patterns of private electric vehicle users with trajectory data

Human mobility pattern analysis has received rising attention. However, little is known about the mobility patterns of private Electric Vehicle (EV) users. In response, this paper characterized mobility patterns of private EV users using a unique one-month dataset containing moving trajectories of 76,774 actual private EVs in January 2018 in Beijing. Specifically, we first explored the diversity, regularity, spatial extent, and uniqueness of EV users’ mobility patterns. The results suggested that most EV users had both regular travel and activity patterns (the mean travel and activity entropies were 2.17 and 1.83, respectively) with special preferences towards some specific activity locations relative to all the locations they visited (the mean number of activity locations visited was 13.57 in one month). Furthermore, they tended to perform activities within a small geographical area (the mean radius of gyration was 7.60 km) and have a short daily travel distance (the mean value was 37.35 km) relative to their electric driving range. Further, we associated EV users’ mobility patterns with the built environment through ordinary least squares and geographically weighted regression models, particularly considering the so-called modifiable areal unit problem (MAUP). Due to the MAUP, most of the statistically significant built environment variables varied across spatial analysis units (SAUs). Gymnasia was the only variable statistically associated with the mobility patterns for all SAUs; while the variables related to residence and workplace were not statistically associated.

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

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.

Optimization Framework for Spatiotemporal Analysis Units Based on Floating Car Data

Spatiotemporal scale is a basic component of geographical problems because the size of spatiotemporal units may have a significant impact on the aggregation of spatial data and the corresponding analysis results. However, there is no clear standard for measuring the representativeness of conclusions when geographical data with different temporal and spatial units are used in geographical calculations. Therefore, a spatiotemporal analysis unit optimization framework is proposed to evaluate candidate analysis units using the distribution patterns of spatiotemporal data. The framework relies on Pareto optimality to select the spatiotemporal analysis unit, thereby overcoming the subjectivity and randomness of traditional unit setting methods and mitigating the influence of the modifiable areal unit problem (MAUP) to a certain extent. The framework is used to analyze floating car trajectory data, and the spatiotemporal analysis unit is optimized by using a combination of global spatial autocorrelation coefficients and the coefficients of variation of local spatial autocorrelation. Moreover, based on urban hotspot calculations, the effectiveness of the framework is further verified. The proposed optimization framework for spatiotemporal analysis units based on multiple criteria can provide suitable spatiotemporal analysis scales for studies of geographical phenomena.

Endogenous local labour markets, regional aggregation and agglomeration economies

ABSTRACT We examine the role of spatial unit size in measuring the urban wage premium. Using Dutch administrative data, we define local labour markets (LLMs) based on employees’ commuting outcomes, gender and educational attainment. We show that high-educated employees and male employees have a relatively large LLM. Using a continuum of regional aggregations, we find that urban wage premium estimates increase over the level of aggregation, also for different subgroups of employees. We show that the modifiable areal unit problem (MAUP) does not explain this pattern, consistent with stronger agglomeration externalities at higher regional aggregation levels.

Do spatiotemporal units matter for exploring the microgeographies of epidemics?

From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamps and spatial scales. This study examines three cities with localized COVID-19 resurgence after the first wave of the pandemic in mainland China to estimate the differential impact of spatiotemporal unit on exploring the influencing factors of epidemic spread at the microscale. The quantitative analysis results suggest that future spatial epidemiology research should give greater attention to the “symptom onset” timestamp instead of only the “confirmed” data and that “spatial transmission” should not be confused with “spatial sprawling” of epidemics, which can greatly reduce comparability between epidemiology studies. This research also highlights the importance of considering the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP) in future studies.

Analyzing multi-scale spatial point patterns in a pyramid modeling framework

ABSTRACT Many spatial analysis methods suffer from the scaling issue identified as part of the Modifiable Areal Unit Problem (MAUP). This article introduces the Pyramid Model (PM), a hierarchical data framework integrating space and spatial scale in a 3D environment to support multi-scale analysis. The utility of the PM is tested in examining quadrat density and kernel density, which are commonly used measures of point patterns. The two metrics computed from a simulated point set with varying scaling parameters (i.e. quadrats and bandwidths) are represented in the PM. The PM permits examination of the variation of the density metrics computed at all different scales. 3D visualization techniques (e.g. volume display, isosurfaces, and slicing) allow users to observe nested relations between spatial patterns at different scales and understand the scaling issue and MAUP in spatial analysis. A tool with interactive controls is developed to support visual exploration of the internal patterns in the PM. In addition to the point pattern measures, the PM has potential in analyzing other spatial indices, such as spatial autocorrelation indicators, coefficients of regression analysis and accuracy measures of spatial models. The implementation of the PM further advances the development of a multi-scale framework for spatio-temporal analysis.

Analysing geographic variation in lower urgency emergency department presentations using readily – available administrative boundaries and a novel spatial smoothing technique

ABSTRACT Misallocation of finite healthcare resources can occur when guided by maps that are produced using aggregate-level administrative units. Such maps are affected by the modifiable areal unit problem (MAUP), which describes how patterns may change depending on the particular choice of mapping unit. Smoothing can help avoid this unintended yet detrimental phenomenon. This paper compares the utility of aggregate-level administrative units and smoothing for exploring variation in lower urgency emergency department (ED) presentations across metropolitan Perth, Western Australia. Rates of such presentations were mapped using Australian Bureau of Statistics Statistical Areas Levels 1, 2, and 3 (SA1-3s) and the recently proposed Overlay Aggregation Method (OAM). Resulting maps were compared based on their ability to represent local variation in rates and optimise the targeting and logistical efficiency of geographically targeted resource allocation. SA1-level variation in rates was increasingly obscured by SA2s and SA3s. OAM helped avoid this pitfall, facilitating stable identification of SA1-resolution, high-rate regions while preserving privacy, mitigating the MAUP, and balancing the targeting and logistical efficiency of planned resource allocation to those regions. Routine application of smoothing can help avoid issues undermining maps of lower urgency ED presentations and other health outcomes that are based on aggregate-level administrative units.

Modeling Spatial Riding Characteristics of Bike-Sharing Users Using Hotspot Areas-Based Association Rule Mining

This study aims to investigate the spatial riding characteristics under different demand scenarios using association rule mining with hotspot detection, and to establish the subordinate rules between bike-sharing demand and land elements and between land elements. To reduce deviation from modifiable areal unit problem (MAUP) and improve objectivity and accuracy, we impose spatial constraints using the hotspot detection model instead of the square grid and traditional traffic zone. The bike-sharing trajectory-based kernel density algorithm is employed to explore the optimum analysis locations and the analysis areas with the relatively high demand. More importantly, the research featured here involves five demand scenarios for the differentiation of riding characteristics. The results show that the most significant influencers on bike-sharing demand include financial insurance facilities, dining facilities, and landscapes. As for characteristics of riding destination, the combinations between landscapes and financial insurance facilities, between landscapes and companies/enterprises, and between companies/enterprises and financial insurance facilities are more likely to be visited simultaneously. These findings make us understand urban spatial structure in response to traffic plan and provide evidence for bike-sharing dispatch optimization.

Multivariate Hierarchical Analysis of Car Crashes Data Considering a Spatial Network Lattice

AbstractRoad traffic casualties represent a hidden global epidemic, demanding evidence-based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a case study of a major city (Leeds, UK), in which 5862 crashes of different severities were recorded over an 8-year period (2011–2018). We consider a family of Bayesian hierarchical models that include spatially structured and unstructured random effects to capture the dependencies between the severity levels. Results highlight roads that are more prone to collisions, relative to estimated traffic volumes, in the north-west and south of city centre. We analyse the modifiable areal unit problem (MAUP), proposing a novel procedure to investigate the presence of MAUP on a network lattice. We conclude that our methods enable a reliable estimation of road safety levels to help identify ‘hotspots’ on the road network and to inform effective local interventions.

The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions

Spatial data are used in many scientific domains including analyses of Ecosystem Services (ES) and Natural Capital (NC), with results used to inform planning and policy. However, the data spatial scale (or support) has a fundamental impact on analysis outputs and, thus, process understanding and inference. The Modifiable Areal Unit Problem (MAUP) describes the effects of scale on analyses of spatial data and outputs, but it has been ignored in much environmental research, including evaluations of land use with respect to ES and NC. This paper illustrates the MAUP through an ES optimisation problem. The results show that MAUP effects are unpredictable and nonlinear, with discontinuities specific to the spatial properties of the case study. Four key recommendations are as follows: (1) The MAUP should always be tested for in ES evaluations. This is commonly performed in socio-economic analyses. (2) Spatial aggregation scales should be matched to process granularity by identifying the aggregation scale at which processes are considered to be stable (stationary) with respect to variances, covariances, and other moments. (3) Aggregation scales should be evaluated along with the scale of decision making (e.g., agricultural field, farm holding, and catchment). (4) Researchers in ES and related disciplines should up-skill themselves in spatial analysis and core paradigms related to scale to overcome the scale blindness commonly found in much research.

The role of spatial scale in drought monitoring and early warning systems: a review

Drought is a costly natural disaster characterized by water shortages that impact water availability, agriculture, ecosystems, and the economy. The driving mechanisms of drought operate on a wide range of spatial scales, from the movement of soil water on a hillslope to global atmospheric circulation. Additionally, drought impacts vary across spatial scales, from drought induced crop stress on a specific agricultural field to widespread continental water shortages. As a result, multiscalar drought monitoring and early warning systems are needed to utilize observational data sets obtained at different spatial scales and to communicate drought impacts to various levels of decision-makers in government and industry. However, scaling must be employed to translate information across scales, either to fix incongruencies in the spatial scale of input data sets or to modify the model output scale. These scaling techniques have several challenges and limitations that hinder drought accuracy and interpretability, such as the Modifiable Areal Unit Problem (MAUP) and increased model uncertainty. This paper reviews the role of spatial scale in drought monitoring and early warning systems, the associated challenges, and techniques to minimize their impact. Finally, this review identifies several knowledge gaps and future directions.

Geographic Boundaries and Local Economic Conditions Matter for Views of the Economy

AbstractThe link between objective facts and politically relevant beliefs is an essential mechanism for democratic accountability. Yet the bulk of empirical work on this topic measures objective facts at whatever geographic units are readily available. We investigate the implications of these largely arbitrary choices for predicting individual-level opinions. We show that varying the geographic resolution—namely aggregating economic data to different geographic units—influences the strength of the relationship between economic evaluations and local economic conditions. Finding that unemployment claims are the best predictor of economic evaluations, especially when aggregated at the commuting zone or media market level, we underscore the importance of the modifiable areal unit problem. Our methods provide an example of how applied scholars might investigate the importance of geography in their own research going forward.

Impact of Australian Bureau of Statistics data perturbation techniques on the precision of Census population counts, and the propagation of this impact in a geospatial analysis of high-risk foot hospital admissions among an Indigenous population

ABSTRACT Precise population counts facilitate reliable estimation of differences in disease risk between population sub-groups or geographic regions, which informs rational public health decision-making. In Australia, Census population counts obtainable via the Australian Bureau of Statistics’ (ABS’) TableBuilder tool, which are widely used, are routinely perturbed to protect privacy. We examine this perturbation’s impact on: the precision of TableBuilder population counts, and a geospatial analysis of high-risk foot (HRF) hospital admissions among Western Australian (WA) Indigenous people. The former is severely compromised, disproportionately so for Indigenous people and under the currently employed ‘perturbation without additivity’ (PWoA) approach. E.g., based on fine-resolution, PWoA-affected data, the Indigenous population total for ‘Inner City’ WA was >50% lower than its true total. Imprecise calculation of WA Indigenous HRF admission counts, and misrepresentation of high-rate regions of such admissions in Perth, resulted. An approach suggested to manage this - examination of coarse-resolution data - is inadequate, partly due the modifiable areal unit problem. However, a novel map overlay technique alleviates this issue. We recommend that the ABS review the PWoA approach and consider implementing different statistical disclosure control techniques or reverting to the previously employed ‘perturbation with additivity’ approach, prior to releasing data from the 2021 Australian Census.

The effect of pre-aggregation scale on spatially adaptive filters

Choropleth mapping continues to be a dominant mapping technique despite suffering from the Modifiable Areal Unit Problem (MAUP), which may distort disease risk patterns when different administrative units are used. Spatially adaptive filters (SAF) are one mapping technique that can address the MAUP, but the limitations and accuracy of spatially adaptive filters are not well tested. Our work examines these limitations by using varying levels of data aggregation using a case study of geocoded breast cancer screening data and a synthetic georeferenced population dataset that allows us to calculate SAFs at the individual-level. Data were grouped into four administrative boundaries (i.e., county, Zip Code Tabulated Areas, census tracts, and census blocks) and compared to individual-level data (control). Correlation assessed the similarity of SAFs, and map algebra calculated error maps compared to control. This work describes how pre-aggregation affects the level of spatial detail, map patterns, and over and under-prediction.

Spatial Analysis of COVID-19 Vaccination: A Scoping Review.

Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O’Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques.