Areal Unit Research Articles

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.

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases.

Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.

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.

Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories.

The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students.

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.

Point prevalence survey is useful for introducing effective surveillance of healthcare-associated infections

SummaryBackgroundIn Finland, the surveillance of healthcare-associated infections (HAI) became obligatory by the renewed Communicable Diseases Act on the 1st March 2017.AimTo introduce HAI surveillance protocol (HALT-2 by ECDC) in primary care hospitals in the largest hospital district in Finland, and to measure the burden of HAIs and antimicrobial use patterns for improvement.MethodsTwo identical point prevalence surveys (PPS) were organized in autumn 2015 and in spring 2017. The infection control persons (ICP) in the hospitals were inducted to the HAI definitions and the study protocol to collect the data with questionnaires on the study days. The data were checked and analyzed by the areal infection control unit. The hospitals were provided feedback of the results and HAI prevention methods.FindingsIn 2015, 2218 patients from 22 hospitals and in 2017, 2343 patients from 25 hospitals were studied. The prevalence of HAI was 11% in both surveys (ranges per hospital 4–24% and 4–31%, respectively). Of all HAIs, 37% originated from referring hospitals. Respiratory tract, urinary tract and skin were the most frequent sites of infection. One fourth of all patients received at least one systemic antimicrobial. The process showed that recognition of HAIs may be difficult for non-experienced ICPs.ConclusionsThe HALT-2 protocol proved useful in introducing HAI surveillance and prevention in primary care hospitals with active patient transfer from other hospitals and relatively high prevalence of HAI and antimicrobial use. For annually repeated surveys, slightly shorter electronic questionnaires are essential.

The modifiable areal unit problem in the relationship between jobs–housing balance and commuting distance through big and traditional data

Jobs–housing balance aims to reduce commuting distance and alleviate traffic congestion as a transport policy. However, the measurement of jobs–housing balance is affected by the spatial areal unit employed in analysis and planning. The mechanism by which commuting distance is affected by jobs–housing balance remains controversial for several reasons, including inconsistency of spatial analysis units and whether socio-demographic attributes are controlled. Cellphone data exhibit superiority in identifying the journey-to-work trips for large sample sizes and entail high spatial precision that can be integrated into different analysis units. By combining cellphone data and traditional survey data with detailed socio-demographic attributes aggregated at different analysis units, this study examines the scale and zoning issues of jobs–housing relationships in Shanghai, China. When using large analysis units, the effect of jobs–housing balance was expanded and that of different socio-demographic attributes changed variously and unpredictably. Furthermore, the uniformity of the spatial analysis unit influenced the effect of jobs–housing balance on commuting distance. This study substantiates the effect of the modifiable areal unit problem (MAUP) on the relationship between jobs–housing balance and commuting distance considering socio-demographic attributes and demonstrates that the changing rules of the effects of that balance on the commuting distance are different from those of socio-demographic attributes as the scale changes. Consequently, this work enriches the literature on the MAUP in relation to the jobs–housing balance and obtains a thorough understanding of the MAUP effects through combining big and traditional data.

The effects of population aggregation in geospatial electrification planning

The introduction of geospatial data into modelling efforts carries many advantages but also introduces numerous challenges. A common challenge is the Modifiable Areal Unit Problem (MAUP), describing how results change as the spatial aggregation of data changes. Here, we have studied MAUP in geospatial least-cost electrification modelling. We do this by assessing the effects of using 26 different population bases each for Benin, Malawi and Namibia. We use the population bases to generate 2080 electrification scenarios per country and conducting a global sensitivity analysis using the Delta Moment-Independent Measure. We identify population aggregation to be highly influential to the model results with regards to method of aggregation (delta values of 0.06–0.24 depending on output studied), administrative division (0.05–0.14), buffer chosen in the clustering process (0.05–0.32) and the minimum number of neighbours within the buffer required for clustering (0.05–0.19). Based on our findings, we conclude that geospatial electrification studies are not robust concerning the choice of population data. We suggest, that modelers put larger emphasis on different population aggregation methods in their sensitivity analyses and that the methods chosen to conduct sensitivity analysis are global in nature (i.e. moving all inputs simultaneously through their possible range of values).

Exploring the spectrum of urban area key figures using data from Finland and proposing guidelines for delineation of urban areas

Urban area definitions are commonly used worldwide to reflect countries’ urban population percentages. The measurements are based on local factors and differ widely across countries, making them non-comparable. This is well acknowledged among specialists; however, they are commonly used in everyday practice as universal measurements, and even compared with each other. The problem is that we do not know the degree of error in such comparisons. For this purpose, in the study presented here we analysed and categorised different European national urban area definitions, testing them in the case of Finland. Definitions from 27 European countries were divided into two main categories according to the end result areal unit of the definition and further into seven subcategories based on the criteria used. Thirteen different definitions in the case of Finland were tested using spatial analyses with GIS. The results indicate that urban population percentage varies widely according to different definitions, making their comparability infeasible. The difference is even greater in the case of urban area ratio and population densities of urban areas between the two main categories. The results prove that definitions based on LAU areas cannot illustrate urban areas and their densities coherently. In light of a literature review on certain relational urban area delineation methods and the case study, desirable characteristics for the delineation of urban areas were highlighted. Consequently, a constant, structured evaluation of urbanity measurements and their underlying logic is necessary to enable unambiguous discourse on urban area in urban sciences. The results could help in formulating the applicability of the concept of urban area in scientific and popular communication and media.

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

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.

An extended spatiotemporal exposure index for urban racial segregation

ABSTRACT The Segregation Index quantifies the degree of segregation of social groups or classes. Because of the increasing use of fine-grained spatiotemporal activity and flow data, the conventional segregation measurements’ inclusiveness is challenged. We add population flow to the conventional place-based spatial exposure index to identify spatiotemporal segregation changes. Specifically, we considered the population-flow network, hierarchical structure, and time. In Chicago’s demonstration case study, we first used the time-dependent Twitter Origin-Destination flow matrices and their hierarchical structure information to estimate interactions between areal units at the neighborhood level. Then we computed the new population composition of units based on their interactions with other units and estimated the proposed spatiotemporal exposure index for different times. Finally, we systematically compared their differences with the conventional indices at global and local scales to see how population-flow patterns affect the exposure index. The results show that the population-flow patterns reflect valuable information in neighborhood interactions in temporal and spatial dimensions, but it is missing information in the conventional segregation computations. Furthermore, we emphasize that the hierarchical structures of flow patterns and the choice of appropriate parameters are also important factors for a rational segregation evaluation.

Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level

As vulnerable road users, elderly pedestrians are more likely to be injured in road crashes due to declining physical and perceptual capabilities. Most previous studies on the influence of the built environment on elderly pedestrian safety focused on intersections or areal units. Using a district of Shanghai as the study area, this research investigated the effects of the built environment at the road segment level with elderly pedestrian collision, taxi tracking point, point of interest, street view image, open street map, land use, housing price, and elderly population datasets. In particular, this research employed both Poisson and geographically weighted Poisson regression (GWPR) models to account for spatial nonstationarity. The Poisson model indicates that green space, sidewalks, and junctions on the roads significantly affected elderly pedestrian safety, and roads around nursing homes, schools, bus stops, metro stations, traditional markets, and supermarkets were hazardous for elderly pedestrians. The results of the GWPR model suggest that the influence of factors varied across the study area. Green space could decrease the risk of elderly pedestrian collisions only in areas without congested environments. Separations need to be installed between roadways and sidewalks to improve elderly road safety.

우리나라 디지털 행정구역 경계 파일의 일반화에 관한 연구: 주제도 제작용 기본도를 중심으로

The main objective of this study is to offer properly generalized digital files of Korea administrative boundaries for thematic mapping. In a theoretical concern, the digital generalization model proposed by McMaster and Shea is intensively reviewed, and two major simplification algorithms, the Douglas-Peuker algorithm and the Visvalingam-Whyatt algorithm, are thoroughly compared. As a result, a modified version of the Visvalingam-Whyatt algorithm is selected mainly because it is better in preserving the contiguity property among the original areal units and in performing the smoothing operation while simplifying map features. The generalization framework is applied to the 2020 boundary file of the most detailed administrative areal units in South Korea targeting for 1:4,000,000 and 1:8,000,000 map scales. By comparing the generalized maps created at the 8 different vertex retention rates, it is concluded that the 0.2% retention rate map is suitable for the 1:4,000,000 scale and the 0.05% retention rate map is for the 1:8,000,000 scale. This study is expected to help people avoid the use of the far too detailed raw boundary files and/or the far too scant generalized files, and can be seen as a cornerstone of a broader project to construct a standardized set of administrative boundary files which are designed in compliance with different map scales that people freely use for their needs.

Estimating σ2 for the Classical Linear Regression Model (CLRM) with the Presence of the Modifiable Areal Unit Problem (MAUP)

In a classical linear regression model (CLRM), the magnitude of disturbances is characterized by σ2. When individual observations are aggregated into regions, the modifiable areal unit problem (MAUP) appears. The presence of the MAUP brings significant challenges to estimating σ2, as the traditional ordinary least square estimator at the individual level, s2, becomes downward biased at the aggregate level. Based on the information available before and after the aggregation process, three estimators of σ2 at the aggregate level are proposed in this study: the trace estimator, the harmonic estimator, and the arithmetic estimator. Endorsed by Monte–Carlo simulations, these estimators provide significantly better estimates than directly borrowing s2 at the aggregate level, but each achieves a different trade‐off between the availability of required information and the accuracy of estimates. The findings provide a solid foundation for inferential statistics, such as constructing confidence intervals and performing hypothesis testing for CLRMs at the aggregate level.

The Spatial Distribution Characteristics of the Cultivated Land Quality in the Diluvial Fan Terrain of the Arid Region: A Case Study of Jimsar County, Xinjiang, China

Environmental constraints are not only important aspects that affect the cultivated land quality but also necessary factors that shall be considered when evaluating the cultivated land quality scientifically. Moreover, identifying the quality condition of cultivated land accurately is the premise for guaranteeing food security. Based on the case study of diluvial fan terrain in Jimsar County, Xinjiang in the arid region of Northwest China, this study utilizes a geographic information system spatial analysis and a multifactor comprehensive evaluation method and constructs a comprehensive evaluation index system for cultivated land quality on account of three dimensions, namely soil properties, farming conditions, and natural environmental conditions. To reduce the Modifiable Areal Unit Problem (MAUP) effect and improve the accuracy of the quality evaluation results of cultivated land, this study compares the spatial interpolation methods of Inverse Distance Weighted Matrix (IDW), Ordinary Kriging (OK), and Spline Functions (Spline) based on different cultivated land evaluation units. Through the assessment on the comparison results, we finally adopted large-scale cultivated land as the quality evaluation unit of cultivated land and Ordinary Kriging (OK) as the spatial interpolation method. The results indicated that the average grade of the quality index of cultivated land in the diluvial fan terrain of Jimsar County is 6.66 at the middle or lower level; the quality of cultivated land and natural environment conditions reduce with the rise of elevation of the diluvial fan terrain, indicating a vertical zonality differentiation rule; the farming conditions keep sliding from the middle part of diluvial fan terrain to the edge of the diluvial fan terrain and the piedmont slope. The major factors affecting the quality of the cultivated land include the soil capacity, soil pH, soil organic matter, the quantity of straw returning to the field, source of irrigation water, water delivery method, part of the diluvial fan, groundwater level depth, and geomorphic type. Therefore, the measures to improve the quality of the cultivated land are put forward, mainly including improving the soil, carrying out land consolidation projects, and developing highly efficient water-saving irrigation agriculture. This study provides favorable references and directions for the sustainable utilization and quality improvement of cultivated land resources in arid regions.

Selection of the set of areal units for economic regional research on the land use: a proposal for Aggregation Problem solution

The knowledge of the spatial development of phenomena is crucial in the case of research in economics, geological survey, mining, earth resources and geography. In the literature one can diagnose an important methodological and implementation gap concerning the selection of the set of areal units within the Aggregation Problem. The issue relates to determining boundaries of areal units (regions), whose properties are described by spatial data. The boundaries of areas should be established in such a way that a given analyzed phenomenon is influenced by the same main causes. Only in this case, the analyzed spatial data will properly reflect the impact of main causes, the properties of phenomena and dependencies between them. This means that determining the proper boundaries of areas is a necessary condition for receiving correct conclusions (e.g. delimiting metropolitan areas, assessing mineral resource potential and deposits, or assessing the dynamics of surface processes). From this perspective, the main objective of the article is presenting the proposal for solving the Aggregation Problem, where as the case study the economic analysis of agrarian resources and structure is used. The solution to the problem will lead to establishing the system of macroregions, where the obtained proposal of a system of four sets of areal units is important from the point of view of spatial research. The main added value of the research and its specific contribution to the literature is based on the fact that the proposed solution to the Aggregation Problem can be considered as universal, which is not limited to selected scientific disciplines. The methodology presented in the article can be effectively applied to other spatial research in the field of geology and mining, where the most appropriate research field is the issue of locating areas with appropriate properties or areas which are affected by given analised phenomena.

Scaling Occupancy Estimates up to Abundance for Wolves

ABSTRACTManagement of wildlife populations often requires reliable estimates of population size or distribution. Estimating abundance can be logistically difficult, and occupancy models have been used as a less expensive proxy for abundance estimation. Another alternative is to use independent estimates of home‐range size and mean group size to directly scale occupancy estimates up to abundance. We used simulations to explore when scaling occupancy up to abundance is reliable, and as an example we applied an occupancy approach to estimate abundance of wolves (Canis lupus) from roadside snow‐tracking surveys in northern Wisconsin, USA, in 2016 and 2018. Estimates of wolf abundance were plausible and compared favorably with independent estimates produced by territory mapping, and snow‐tracking data requirements were lower than for territory mapping. Simulation results suggested that reasonable abundance estimates could be obtained under some conditions but also that severe positive bias could result under other conditions, especially when populations were small and dispersed, home range size was small, and areal sampling units were large. Positive bias in abundance estimates occurs because of closure assumption violations when tracks from a single wolf or pack are detected in >1 sample unit, and the sum of the sample unit areas where tracks were detected exceed the sum of the home range areas. Bias was minimized when sampling units were small relative to home range size or when sampling units were route segments that approximate point sample units, and when home ranges were highly aggregated. We conclude that, although caution is warranted when scaling occupancy estimates up to abundance, scaled occupancy models can provide feasible and reliable estimates of abundance, assuming home range size and mean group size are accurately known or estimated, sampling units are appropriately chosen, and covariates that aggregate home ranges can be used to accurately predict occupancy probability. © 2021 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.

Understanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models.

In order to find useful intervention strategies for the novel coronavirus (COVID‐19), it is vital to understand how the disease spreads. In this study, we address the modeling of COVID‐19 spread across space and time, which facilitates understanding of the pandemic. We propose a hybrid data‐driven learning approach to capture the mobility‐related spreading mechanism of infectious diseases, utilizing multi‐sourced mobility and attributed data. This study develops a visual analytic approach that identifies and depicts the strength of the transmission pathways of COVID‐19 between areal units by integrating data‐driven deep learning and compartmental epidemic models, thereby engaging stakeholders (e.g., public health officials, managers from transportation agencies) to make informed intervention decisions and enable public messaging. A case study in the state of Colorado, USA was performed to demonstrate the applicability of the proposed transmission modeling approach in understanding the spatio‐temporal spread of COVID‐19 at the neighborhood level. Transmission path maps are presented and analyzed, demonstrating their utility in evaluating the effects of mitigation measures. In addition, integrated embeddings also support daily prediction of infected cases and role analysis of each area unit during the transmission of the virus.