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Local actors have growing prominence in climate governance but key capacities and powers remain with national policymakers. Coordination between national and local climate action is therefore of increasing importance. Underappreciated in existing academic and policy literature, coordination between actors at different scales can be affected not only by politics and institutional arrangements, but also by methods of data analysis. Exploring two datasets of GHG emissions by local area in England—one of consumption-based emissions and the other of territorial emissions—this paper shows the potential for a data scaling problem known as the modifiable areal unit problem and its possible consequences for the efficacy and equity implications of climate action. While this analysis is conceptual and does not identify specific instances of the modifiable areal unit problem or its consequences, it calls attention to methods of data analysis as possible contributors to climate governance challenges. Among other areas, future analysis is needed to explore how data scaling and other aspects of data processing and analysis may affect our understanding of non-state actors' contribution to climate action. Research highlights: Data scaling could affect the efficacy and equity of climate action in the UK. Enabling local actors can help to overcome the effects of data scaling. Relationships between methods of data analysis and governance need more attention. Research is needed on data scaling and the climate actions of non-state actors is needed. [ABSTRACT FROM AUTHOR]

Measuring agricultural productivity is a multiscale spatiotemporal problem that requires multiscale solutions. In Vietnam, rice comprises a substantial portion of the cultivated area and is a major export crop that supplies much of the global food system. Understanding the when and where of rice productivity is vital to addressing changes to yields and food security, yet descriptive summarizations will vary depending on the spatial or temporal scale of analysis. This paper explores rice trends across Vietnam over a 19-year period, giving specific attention to modifiable spatiotemporal unit problems by evaluating productivity across multiple time periods and administrative levels. A generalizable procedure and tools are offered for visualizing multiscale time-series remote sensing data in matrix and map form, not only to elucidate the effects of modifiable spatiotemporal unit problems, but also to demonstrate how these problems serve as a useful research framework. Remote sensing indices (e.g., LAI and EVI) were evaluated against national and provincial estimates across Vietnam during multiple crop production periods using the Pearson Correlation Coefficient (PCC) to establish a relationship. To overcome challenges posed by long-term observations masking emerging phenomena, time-series matrices and multi-spatial and multi-temporal maps were produced to show when, where, and how rice productivity across Vietnam is changing. Results showed that LAI and EVI are favorable indices for measuring rice agriculture in Vietnam. At the province scale, LAI compared to nationally reported production estimates reached a Pearson's r of 0.960; 0.974 for EVI during the spring crop production period. For questions such as, "What portion of Vietnam exhibits a negative linear trend in rice production?", the answer depends on how space and time are organized. At the province scale, 25.4% of Vietnam can be observed as exhibiting a negative linear trend; however, when viewed at the district scale, this metric rises to 45.7%. This research contributes to the discussion surrounding ontological problems of how agricultural productivity is measured and conveyed. To better confront how agriculture is assessed, adopting a multiscale framework can provide a more holistic view than the conventional single spatial or temporal selection. [ABSTRACT FROM AUTHOR]

Aim Location Taxon Methods Results Main Conclusions Accounting for biotic interactions in species distribution models is complicated by the fact that interactions occur at the individual‐level at unknown spatial scales. Standard approaches that ignore individual‐level interactions and focus on aggregate scales are subject to the modifiable aerial unit problem (MAUP) in which incorrect inferences may arise about the sign and magnitude of interspecific effects.Global (simulation) and North Carolina, United States (case study).None (simulation) and Aves (case study).We present a hierarchical species distribution model that includes a Markov point process in which the locations of individuals of one species are modelled as a function of both abiotic variables and the locations of individuals of another species. We applied the model to spatial capture‐recapture (SCR) data on two ecologically similar songbird species—hooded warbler (Setophaga citrina) and black‐throated blue warbler (Setophaga caerulescens)—that segregate over a climate gradient in the southern Appalachian Mountains, USA.A simulation study indicated that the model can identify the effects of environmental variation and biotic interactions on co‐occurring species distributions. In the case study, there were strong and opposing effects of climate on spatial variation in population densities, but spatial competition did not influence the two species' distributions.Unlike existing species distribution models, the framework proposed here overcomes the MAUP and can be used to investigate how population‐level patterns emerge from individual‐level processes, while also allowing for inference on the spatial scale of biotic interactions. Our finding of minimal spatial competition between black‐throated blue warbler and hooded warbler adds to the growing body of literature suggesting that abiotic factors may be more important than competition at low‐latitude range margins. The model can be extended to accommodate count data and binary data in addition to SCR data. [ABSTRACT FROM AUTHOR]

Through analysis and discussion of basic systemic properties of a rural municipality, this paper explores how aggregating data in planning and land use modeling can potentially obscure intricate real-world behavior. Complexity theory is applied as a theoretical framework for explaining this hypothesis. Thus, the aim of this study is to address the author's desire to understand systemic complexity when designing a data-driven decision-making model for rural planning. The novelty of this approach is two-fold: one, most studies on scalability issues in planning addresses spatial complexity, more so than systemic complexity within the complex system that the very act of planning strives to dictate. Two, although delimited to the scope of the study, the accessibility to and use of complete and valid socio-demographic data enables a rarely demonstrated accurate representation of an entire population. It is ultimately observed that on the disaggregated municipal level, systemic dispersion increases parallelly with population size, a correlation that is significantly influenced by gender ratio in any given parish – a characteristic that was not visible at the aggregated municipal level. In addition to advancing the understanding and placement of complexity science within spatial data science, these insights will make it easier to assess the generalizability of any given administrative unit by quantifying basic complexity attributes; in this case based on the correlation dispersion caused by the fragmentation of a municipality into its comprising parishes. [ABSTRACT FROM AUTHOR]

Measuring agricultural productivity is a multiscale spatiotemporal problem that requires multiscale solutions. In Vietnam, rice comprises a substantial portion of the cultivated area and is a major export crop that supplies much of the global food system. Understanding the when and where of rice productivity is vital to addressing changes to yields and food security, yet descriptive summarizations will vary depending on the spatial or temporal scale of analysis. This paper explores rice trends across Vietnam over a 19-year period, giving specific attention to modifiable spatiotemporal unit problems by evaluating productivity across multiple time periods and administrative levels. A generalizable procedure and tools are offered for visualizing multiscale time-series remote sensing data in matrix and map form, not only to elucidate the effects of modifiable spatiotemporal unit problems, but also to demonstrate how these problems serve as a useful research framework. Remote sensing indices (e.g., LAI and EVI) were evaluated against national and provincial estimates across Vietnam during multiple crop production periods using the Pearson Correlation Coefficient (PCC) to establish a relationship. To overcome challenges posed by long-term observations masking emerging phenomena, time-series matrices and multi-spatial and multi-temporal maps were produced to show when, where, and how rice productivity across Vietnam is changing. Results showed that LAI and EVI are favorable indices for measuring rice agriculture in Vietnam. At the province scale, LAI compared to nationally reported production estimates reached a Pearson’s r of 0.960; 0.974 for EVI during the spring crop production period. For questions such as, “What portion of Vietnam exhibits a negative linear trend in rice production?”, the answer depends on how space and time are organized. At the province scale, 25.4% of Vietnam can be observed as exhibiting a negative linear trend; however, when viewed at the district scale, this metric rises to 45.7%. This research contributes to the discussion surrounding ontological problems of how agricultural productivity is measured and conveyed. To better confront how agriculture is assessed, adopting a multiscale framework can provide a more holistic view than the conventional single spatial or temporal selection.

The global trend toward urbanization has spurred the widespread adoption of transit-oriented development (TOD). While previous research has extensively explored the relationship between land use and TOD ridership, much of it has focused on linear associations at a singular scale. Leveraging recent advancements in nonlinear modeling and the accessibility of open-source data, this study employs a comprehensive two-step methodology. Firstly, K-means clustering algorithm categorizes TOD sites in Shenzhen into three distinct clusters, providing a site-based understanding of their characteristics. Subsequently, a Light Gradient Boosting Machine (LightGBM) classification model, complemented by SHapley Additive exPlanations (SHAP) values for interpretation, quantitatively evaluates the influence of mixed land use on TOD ridership across various catchment areas. As for the findings, we discover that land-use factors have different effects on TOD site patronage at different buffer radii and delve into the intricacies of these effects. Further results reveal non-linear relationships with varying degrees of positivity and negativity. For instance, residents and health sites positively impact patronage across all buffer radii, while certain commercial land uses exhibit a negative influence. The study demonstrates how the importance of different land-use structures varies across these clusters, shedding light on the nuanced impacts of land use on TOD catchment areas. Our research optimizes land-use mixes based on predominant cluster characteristics by offering actionable recommendations for urban managers.

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics—how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets. RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. This perspective discusses the challenges associated with spatial analysis and proposes how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets. [ABSTRACT FROM AUTHOR]

The evaluation of landscape ecological risk (LER) in a river basin holds significant importance for the overall ecological environmental protection of the basin and subsequent sustainable development. The Shiyang River basin, a typical arid inland river basin, was selected as the research object, and the optimal scale of LER research was explored. Multi-period land use data was used to build an LER assessment model and reveal the temporal and spatial changes of LER in the Shiyang River basin. The PLUS model was used to simulate the spatial distribution characteristics and change trends of LER under different scenarios in 2030. The results show that the LER in the Shiyang River Basin has obvious scale dependence, with optimal granularity and magnitude of 60 m and 4.5 km, respectively. LER is dominated by higher risk and high risk categories, with significant spatial differences, showing a trend of low in the southwest and high in the northeast. The LER of the Shiyang River Basin decreased from 2000 to 2020. It is expected that the LER value under different scenarios in 2030 will show a downward trend, and the LER value under the ecological conservation priority scenario will be the lowest. This study can provide a reference for LER assessment in arid inland river basins. [ABSTRACT FROM AUTHOR]

The benefits derived from the spatial concentration of economic units, resulting from the close positioning of firms, may stem from economies of scale, a technical labor pool, low transportation and communication costs, and technology transfers. However, for this concentration to be effectively evaluated and regional policies to be developed efficiently for economic growth and development, spatial concentration measurements need to be consistent. At this point, attention is drawn to the modifiable areal unit problem (MAUP), a problem encountered in spatial analysis arising from the use of aggregated data and administrative boundaries, which impacts spatial analysis results. This study outlines the MAUP issue in the literature, emphasizes proposed solutions, and evaluates the impact of MAUP on measurements of the spatial concentration of economic activities. In a general assessment, it can be concluded that an unrestricted spatial structure and the use of personalized spatial data are crucial for mitigating MAUP effects. Additionally, in studies related to the spatial concentration of economic activities, approaches utilizing cluster-based indicators tend to neglect MAUP, while distance-based approaches are noted to minimize MAUP effects from various perspectives. [ABSTRACT FROM AUTHOR]

In the context of rural development and transformation, it is crucial to identify the impact of rural multifunctionality on non-point source (NPS) pollution. This study applies the Soil and Water Assessment Tool (SWAT), geographical detector, and principal component analysis in Liyang, a typical hilly subbasin in China, in order to assess the rural multifunctional development that influences the spatial differentiation of NPS pollution and detect the interactive effects of rural multifunctionality. The R2 and NSE demonstrated that the calibrated SWAT model successfully simulated NPS pollution in Liyang. The village scale was identified as the optimal research scale for examining the rural multifunctional development on NPS pollution distribution. The rural multifunctional indicators, such as the proportion of vegetable farming, sowing area, and grain farming, would influence NPS distribution. The number of family farming cooperatives, the area of pond farming, and the nature reserves area were also significant. The rural multifunctionality in Liyang could be classified into five categories: grain production, mixed agriculture, ecological conservation, leisure tourism, and industry and business function. The superposition of rural multifunctionality has a strengthening effect on NPS pollution, especially when the ecological conservation function is combined with the grain production or modern agriculture function. The study could provide NPS pollution control strategies for policymaking in rural multifunctional development. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the built environment and the ride-hailing ridership is crucial to the prediction of the demand for ride-hailing and the formulation of the strategy for upgrading the built environment. However, the existing studies on ride-hailing ignore the scale effect and zone effect of the modifiable area unit problem (MAUP), and show a lack of consideration for the elastic relationship with spatial heterogeneity between built environment variables and ride-hailing ridership. Taking Chengdu as an example, this paper selects 12 independent variables based on the "5Ds" (density, diversity, design, destination accessibility and distance to transit) of the built environment, the dependent variables are the density of ride-hailing pick-ups in the morning and evening peak hours, and 11 spatial units are proposed according to different scales and zoning methods for the aggregation of built environment variables and ride-hailing pick-ups. With the goal of global optimal goodness-of-fit, we determined the optimal spatial unit by using the log-linear Ordinary Least-Squares (OLS) model. A multi-scale geographically weighted elastic regression (MGWER) model is formulated to explore the relative effect of the built environment on the ride-hailing ridership and spatial heterogeneity. The average value of positive elastic local regression coefficient of different variables is used to measure the relative positive impact of built environment factors, and the absolute value of the average value of negative elastic local regression coefficient is used to measure the relative negative impact of built environment factors. The results show that: (1) The MGWER model under the community unit division has the best global goodness-of-fit. (2) Different built environment variables have different elastic impacts on the demand for ride-hailing. For the morning peak hours and evening peak hours, the top three built environment factors with positive impacts are ranked as follows: commercial POI density > average house price > population density, and distance to CBD has the highest negative impacts on pick-up ridership. (3) The different local elasticity coefficients of the built environment factors at different stations are discussed, which indicate the spatial heterogeneity of the ride-hailing ridership. The optimal community zoning method can provide a basis for the zoning and scheduling management of ride-hailing. The results of the built environment variables with greater impact are conducive to the formulation of targeted urban renewal strategies in the process of adjusting the ridership of ride-hailing. [ABSTRACT FROM AUTHOR]

In the context of rural development and transformation, it is crucial to identify the impact of rural multifunctionality on non-point source (NPS) pollution. This study applies the Soil and Water Assessment Tool (SWAT), geographical detector, and principal component analysis in Liyang, a typical hilly subbasin in China, in order to assess the rural multifunctional development that influences the spatial differentiation of NPS pollution and detect the interactive effects of rural multifunctionality. The R2 and NSE demonstrated that the calibrated SWAT model successfully simulated NPS pollution in Liyang. The village scale was identified as the optimal research scale for examining the rural multifunctional development on NPS pollution distribution. The rural multifunctional indicators, such as the proportion of vegetable farming, sowing area, and grain farming, would influence NPS distribution. The number of family farming cooperatives, the area of pond farming, and the nature reserves area were also significant. The rural multifunctionality in Liyang could be classified into five categories: grain production, mixed agriculture, ecological conservation, leisure tourism, and industry and business function. The superposition of rural multifunctionality has a strengthening effect on NPS pollution, especially when the ecological conservation function is combined with the grain production or modern agriculture function. The study could provide NPS pollution control strategies for policymaking in rural multifunctional development.

Understanding the relationships between commuting demand and built environment (BE) features is advantageous in alleviating travel pressure during the commuting period. Numerous works have explored these relationships under either a single spatial unit or travel modes. However, few efforts have attempted to examine how variations in both spatial units and transport modes affect the relationships. To address the above issues, we investigate the impact of the modifiable areal unit problem (MAUP) on the modeling results across three partitioning schemes (i.e., hexagonal-based, square-based, and administrative boundary) and nine spatial units under four commuting modes (i.e., taxi, bike-sharing, ride-hailing, and bus). A geographically weighted regression is applied to estimate commuting demands considering spatial autocorrelation and heterogeneity. The experiment is conducted using smart card data and vehicle GPS trajectories collected in Shenzhen City, China. The contributions of this study include the following three parts. First, it evaluates the pros and cons of different spatial units for commuting demand modeling. Second, it summarizes the effects of the MAUP on relationships among land use, demographic, and traffic network characteristics and commuting demand according to the partitioning scale and schemes. Third, it examines the similarities and differences in the effect of the MAUP on commuting demand modeling among four travel modes. The findings of this study contribute to the division of traffic analysis zones and the allocation of commuting trips. [ABSTRACT FROM AUTHOR]

Handling multiple scales efficiently is one avenue for processing big remote sensing imagery data. Unfortunately, imagery is also affected by the infamous modifiable areal unit problem, which creates unpredictable errors at different scales. We developed a downsampling method that attempts to keep the data distribution in a downsampled image constant, reducing the modifiable areal unit problem. We tested our method against classic downsampling methods (mean, central pixel selection, random) under a range of typical remote sensing scenarios. Under our experimental conditions, our downsampling method consistently outperformed the classical downsampling methods within a 95% confidence level. The downsampling method can be used in most typical situations where downsampling is needed, but it is likely to shine when used as a pyramid building policy in geocomputing platforms, such as Google Earth Engine. [ABSTRACT FROM AUTHOR]

The disparity between total population values and how that population is spread across an area can generate issues of perception, as though choropleth maps are usually used to represent population, its density may not be equally spread across the territory. A prime example of this situation is Galicia in NW Spain, as it has the highest number of population settlements of the country with almost 50% of the total, and yet at the same time it has only around 4% of municipalities, which means that each local authority has on average dozens of borderless settlements within its limits. However, population maps of the region are normally produced at the municipal level, providing a poor representation of the demographic complexity of the area. Consequently, this paper provides an alternative way of displaying population density by using three submunicipal division levels obtained from official sources: census tracts, parishes and settlements. While census tracts and parishes have defined limits, settlements do not, so they are normally mapped by using punctual symbology. It is therefore necessary to generate some borders for those population entities which do not have them, as it can help understand how the population is distributed in more detail. In order to provide a suitable means of comparison with the other administrative divisions this paper presents a method to convert settlement data into polygons that includes the use of Voronoi tessellation, producing for the first time the most detailed population density choropleth map of the region at settlement level. Furthermore, the method can be applied anywhere in Spain and with non-census data. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the retail food environment and individual and community health is important for addressing questions about diet-related disease and food security. A wide range of methodological approaches have been used to understand this complex relationship, but the studies have resulted in a lack of consensus on how and whether the food retail environment influences health outcomes and food choice. Inconsistency in measuring the food environment could explain inconsistent results. We calculate five different boundaries of the food retail environment to investigate the robustness of boundary measures. These five boundaries include an administrative boundary (census tract of residence), two Euclidean boundaries, and two network area boundaries. We compare four different aspects of the food retail environment—availability, accessibility, affordability, and realized purchase behavior—across these five fundamentally different geographic boundary definitions. Using the National Food Acquisition and Purchase Survey, Nielsen TDLinx, and Information Resources, Inc. retail and consumer data, we find that food environment measures using administrative boundary definitions are statistically different from all other boundary definitions. We find general similarities within the two Euclidean distance and two network area boundary definitions. Only a small portion of food-at-home purchase events and expenditures by households occur within their own census tract boundary, suggesting the boundary definition has limitations and households are mobile in their food acquisitions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

The objective of the study was to evaluate the potential geospatial relationship between agricultural pesticide use and two cancer metrics (pediatric cancer incidence and total cancer incidence) across each of the 11 contiguous states in the Western United States at state and county resolution. The pesticide usage data were collected from the U.S. Geological Survey Pesticide National Synthesis Project database, while cancer data for each state were compiled from the National Cancer Institute State Cancer Profiles. At the state spatial scale, this study identified a significant positive association between the total mass of fumigants and pediatric cancer incidence, and also between the mass of one fumigant in particular, metam, and total cancer incidence (P‐value < 0.05). At the county scale, the relationship of all cancer incidence to pesticide usage was evaluated using a multilevel model including pesticide mass and pesticide mass tertiles. Low pediatric cancer rates in many counties precluded this type of evaluation in association with pesticide usage. At the county scale, the multilevel model using fumigant mass, fumigant mass tertiles, county, and state predicted the total cancer incidence (R‐squared = 0.95, NSE = 0.91, and Sum of square of residuals [SSR] = 8.22). Moreover, this study identified significant associations between total fumigant mass, high and medium tertiles of fumigant mass, total pesticide mass, and high tertiles of pesticide mass relative to total cancer incidence across counties. Fumigant application rate was shown to be important relative to the incidence of total cancer and pediatric cancer, at both state and county scales. Plain Language Summary: The objective of the study was to evaluate the associations between agricultural pesticide usage and cancer incidence among adults and children. The analysis was conducted in the 11 neighboring states in the western U.S. We collected the information on agricultural pesticide usage from the U.S. Geological Survey Pesticide National Synthesis Project database. The data on cancer incidence among adults and children were collected from the National Cancer Institute State Cancer Profiles. We find that the agricultural pesticide type, namely fumigants, are associated with the cancer incidence among adults and children in the western U.S. The most predominant fumigant, metam was also found to be associated with cancer incidence among adults. A model was developed in this study to predict cancer incidence among adults using pesticide usage information. The study infers that the agricultural pesticide type, fumigants are important relative to cancer incidence among adults and children. Key Points: This study analyzed the geospatial relationship between cancer metrics and pesticide usage in the 11 western states of the United StatesA multilevel model was employed, which accurately estimated the cancer incidence at county spatial resolutionFumigants were found to be statistically significantly associated with cancer incidence at both state and county spatial scales [ABSTRACT FROM AUTHOR]

Purpose – Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors. Design/methodology/approach – This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering. Findings – As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands. Originality/value – This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.

Abstract The objective of the study was to evaluate the potential geospatial relationship between agricultural pesticide use and two cancer metrics (pediatric cancer incidence and total cancer incidence) across each of the 11 contiguous states in the Western United States at state and county resolution. The pesticide usage data were collected from the U.S. Geological Survey Pesticide National Synthesis Project database, while cancer data for each state were compiled from the National Cancer Institute State Cancer Profiles. At the state spatial scale, this study identified a significant positive association between the total mass of fumigants and pediatric cancer incidence, and also between the mass of one fumigant in particular, metam, and total cancer incidence (P‐value

Since the construction of the 21st Century Maritime Silk Road, islands in China have undergone more rapid urbanization, commercialization, and tourism. The interactions between natural and anthropogenic processes, however, have severely disturbed island ecosystems. Therefore, it is crucial to understand these interactions and identify key areas for island ecosystem protection and restoration by assessing the spatiotemporal patterns of landscape ecological risk. In this study, we investigated the optimal spatial scale needed to analyze spatiotemporal changes in landscape ecological risk on Haitan Island from 2000 to 2020. The landscape index varied with the change in spatial size, and the optimal grid size and scale based on semivariogram analysis were 75 m and 500 × 500 m, respectively. The spatial patterns of land use and landscape ecological risk have significantly changed owing to urban development on Haitan Island, with large amounts of cropland and forest being replaced by impervious land. The overall threat level of landscape ecological risk has decreased, and lowest-risk areas have gradually become the dominant type in the region. However, the landscape fragmentation and degree of water loss is increasing, and the landscape ecological risk of offshore areas cannot be ignored. The spatial clustering characteristics of landscape ecological risk in each period were significant, and the hotspots in the spatial distribution of the landscape ecological risk index were mainly coastal areas. The impacts of human activities on regional landscape patterns and ecosystems are increasing in intensity, and population density and distance to urban centers are the main drivers. This study provides a theoretical reference for selecting an optimal scale of analysis for ecological risk assessment in island landscapes and can be used as a reference in island spatial regulation and environmental impact assessments.

Ecological security pattern (ESP) provide a solution to balance ecosystems and urban development. The modifiable areal unit problem (MAUP) is a well-known geographical issue that affects the understanding of ecological processes and the results of spatial modeling. However, only a few studies have explicitly clarified the effects of MAUP on ESP. Herein, we propose new methods for identifying ecological sources and mapping the resistance surface, as well as a framework for analyzing the effects of MAUP on ESP. Our results indicate that (1) the datasets used for identifying ecological sources should possess a grain size of smaller than 300 × 300 m2. (2) The resistance surface directly influenced the results of the ecological corridors and nodes. (3) MAUP could generate uncertainties in ESP policymaking. Additionally, this study developed comprehensive network analysis tools based on graph machine learning to analyze the effect of MAUP on ESP policymaking, which can provide important landscape planning. The network analysis approaches may help policymakers gain a comprehensive understanding of the ESP network to improve the credibility of policies. • Identifying ecological sources considering ecological quality and human disturbance. • Comprehensive network analysis tools were developed using graph machine learning. • Analysing the effects of modifiable areal unit problem on ecological security pattern. • Modifiable areal unit problem creates uncertainty in ecological planning policy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Background In disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in ‘single-aggregation disease maps’ whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated. Results We introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016. Conclusions The ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.

Objective: To examine the impact of the modifiable areal unit problem (MAUP) in an investigation of factors associated with ED demand in Perth, Western Australia, in 2016. Furthermore, to advocate a means of avoiding this impact. Methods: ED presentations were classified as: urgent medical, non‐urgent medical, urgent trauma or non‐urgent trauma. In each group, sex‐stratified, age‐adjusted multivariate associations with socio‐economic status and distance to the nearest ED and general practitioner (GP) were estimated. Modelling was undertaken using different sets of spatial units: Australian Bureau of Statistics (ABS) Statistical Areas Level 1 (SA1s) and numerous aggregate‐level zonations of SA1s (ABS SA2s and others). Results: Estimates obtained using the different units often varied widely: for seven (30%) of 24 strata defined by combinations of sex, ED type and covariate, the smallest and largest effect sizes differed in terms of direction; further, for 11 (65%) of the remaining 17 strata, the largest effect size was at least twice as high as the smallest. This demonstrates the MAUP's impact and that analyses based on a single set of spatial units are unreliable. To resolve the observed variation, we highlight the SA1‐level estimates. Conclusions: When formulating interventions targeting reduced ED utilisation, policy planners should be guided by evidence based on analysis of appropriate spatial units. This ideal is undermined by the widespread lack of acknowledgement of the MAUP in studies examining drivers of ED demand using spatially aggregated data. To avoid the MAUP, only estimates obtained through examining a minimal geographic unit should be relied upon. [ABSTRACT FROM AUTHOR]

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 programmes 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.

Handling multiple scales efficiently is one avenue for processing big remote sensing imagery data. Unfortunately, imagery is also affected by the infamous modifiable areal unit problem, which creates unpredictable errors at different scales. We developed a downsampling method that attempts to keep the data distribution in a downsampled image constant, reducing the modifiable areal unit problem. We tested our method against classic downsampling methods (mean, central pixel selection, random) under a range of typical remote sensing scenarios. Under our experimental conditions, our downsampling method consistently outperformed the classical downsampling methods within a 95% confidence level. The downsampling method can be used in most typical situations where downsampling is needed, but it is likely to shine when used as a pyramid building policy in geocomputing platforms, such as Google Earth Engine.

Understanding the influence mechanisms of dockless bike-sharing usage is essential for land use planning and bike scheduling strategy implementation. Although various studies have been carried out to explore the impact of built environment (BE) factors on bike-sharing usage, few studies have examined the modifiable areal unit problem (MAUP). Moreover, previous studies mainly focused on the separate effect of each factor but neglected the interactions between these factors. Taking Shenzhen, China as the case, this study fills these two gaps by employing the geographical detector method to examine the MAUP in dockless bike-sharing usage as well as the interactive effects of BE factors. The results revealed that the influences of most BE variables are sensitive to the spatial areal units, which have informed urban planners what built-environment factors should be paid more attention to at certain spatial scales. Additionally, through the comparisons between single effect and interactive effect, this study revealed some interesting findings that can provide scientific basis for temporal rebalance strategy for the innovative and high-density metropolis in China. [ABSTRACT FROM AUTHOR]

Recent accounts of American politics focus heavily on urban–rural gaps in political behavior. Rural politics research is growing but may be stymied by difficulties defining and measuring which Americans qualify as "rural." We discuss theoretical and empirical challenges to studying rurality. Much existing research has been inattentive to conceptualization and measurement of rural geography. We focus on improving estimation of different notions of rurality and provide a new dataset on urban–rural measurement of U.S. state legislative districts. We scrutinize construct validity and measurement in two studies of rural politics. First, we replicate Flavin and Franko (2020, Political Behavior, 845–864) to demonstrate empirical results may be sensitive to measurement of rural residents. Second, we use Mummolo and Nall's (2017, The Journal of Politics, 45–59) survey data to show rural self-identification is not well-captured with objective, place-based classifications, suggesting a rethinking of theoretical and empirical accounts of rural identity. We conclude with strategies for operationalizing rurality using readily available tools. [ABSTRACT FROM AUTHOR]

In the previous two reports in this series, we discussed the history and current status of quantitative geography. In this final report, we focus on the future. We argue that quantitative geographers are most helpful when we can simplify difficult problems using our distinct domain expertise. To do this, we must clarify the theory underpinning core conceptual problems in quantitative geography. Then, we examine the social forces that are shaping the future of quantitative geography. We conclude with criteria for how quantitative geography might succeed in addressing these challenges. [ABSTRACT FROM AUTHOR]

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.

Abstract Background All analyses of spatially aggregated data are vulnerable to the modifiable areal unit problem (MAUP), which describes the sensitivity of analytical results to the arbitrary choice of spatial aggregation unit at which data are measured. The MAUP is a serious problem endemic to analyses of spatially aggregated data in all scientific disciplines. However, the impact of the MAUP is rarely considered, perhaps partly because it is still widely considered to be unsolvable. Results It was originally understood that a solution to the MAUP should constitute a comprehensive statistical framework describing the regularities in estimates of association observed at different combinations of spatial scale and zonation. Additionally, it has been debated how such a solution should incorporate the geographical characteristics of areal units (e.g. shape, size, and configuration), and in particular whether this can be achieved in a purely mathematical framework (i.e. independent of areal units). We argue that the consideration of areal units must form part of a solution to the MAUP, since the MAUP only manifests in their presence. Thus, we present a theoretical and statistical framework that incorporates the characteristics of areal units by combining estimates obtained from different scales and zonations. We show that associations estimated at scales larger than a minimal geographical unit of analysis are systematically biased from a true minimal-level effect, with different zonations generating uniquely biased estimates. Therefore, it is fundamentally erroneous to infer conclusions based on data that are spatially aggregated beyond the minimal level. Instead, researchers should measure and display information, estimate effects, and infer conclusions at the smallest possible meaningful geographical scale. The framework we develop facilitates this. Conclusions The proposed framework represents a new minimum standard in the estimation of associations using spatially aggregated data, and a reference point against which previous findings and misconceptions related to the MAUP can be understood.

The modifiable areal unit problem (MAUP) is a cause of statistical and visual bias when aggregating data according to spatial units, particularly when spatial units may be changed arbitrarily. The MAUP is a concern in vector-borne disease research when entomological metrics gathered from point-level sampling data are related to epidemiological data aggregated to administrative units like counties or ZIP Codes. Here, we assess the statistical impact of the MAUP when calculating correlations between randomly aggregated cases of anaplasmosis in New York State during 2017 and a geostatistical layer of an entomological risk index for Anaplasma phagocytophilum in blacklegged ticks (Ixodes scapularis Say, Acari: Ixodidae) collected during the fall of 2017. Correlations were also calculated using various administrative boundaries for comparison. We also demonstrate the impact of the MAUP on data visualization using choropleth maps and offer pycnophylactic interpolation as an alternative. Polygon simulations indicate that increasing the number of polygons decreases correlation coefficients and their variability. Correlation coefficients calculated using ZIP Code tabulation area and Census tract polygons were beyond 4 standard deviations from the mean of the simulated correlation coefficients. These results indicate that using smaller polygons may not best incorporate the geographical context of the tick-borne disease system, despite the tendency of researchers to strive for more granular spatial data and associations., (© The Author(s) 2023. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

This study fully addressed the modifiable areal unit problem (MAUP) that was well-known in geography but generally ignored by safety analysis. The basic issue of MAUP was introduced firstly with a case study to explicitly demonstrate the existence of the problem in macro level crash modeling, and then four potential strategies, i.e., using disaggregate data as possible, capturing spatial non-stationarity, designing optimal zoning systems, conducting sensitivity analysis to report the scope and magnitude of MAUP, were proposed and illustrated in an integrated way, followed by the future research directions. Results revealed that more efforts are desired to calibrate the state-of-art modeling technique at various levels of aggregation based on spatial homogeneity in traffic safety, transport characteristics, and demographical factors. The awareness of this problem in traffic safety domain is expected to the delineation of basic spatial units (e.g. the traffic safety analysis zones), as well as to provide new insights into the nature of MAUP in statistics and geography.

Modelling changes in biodiversity have become a necessary component of smart urban planning practices. However, concepts such as biodiversity are often evaluated using area-based composite indices, the results of which are heavily reliant on specific parameters chosen. This paper explores the design and implementation of a butterfly biodiversity index by comparing two widely accepted modelling techniques: principal component analysis and spatial multi-criteria decision analysis (MCDA). A high degree of scale dependency has been demonstrated in previous studies exploring the use of area-based composite measures. To evaluate the impact of scale, each model was assessed at two different spatial resolutions. The outcomes were analyzed, mapped and compared using ordinary least squares, geographically weighted regression and global Moran's I to evaluate relative biodiversity patterns across the City of Toronto, Canada. Findings indicate that the impact of spatial scale was significant, whereby the coarser resolution models were found to be more highly correlated with biodiversity, compared to the finer resolution models. The results of this study contribute to a growing body of literature that explores key conceptual questions regarding the robustness of GIS-based MCDA, the impact of scale in urban ecology studies, and the use of composite indices to manage spatial ecological data. [ABSTRACT FROM AUTHOR]

The paper discusses the features of applying multiscale approach in studies of spatial heterogeneity. We analyze socioeconomic indicators for different scales of spatial organization in Russia: municipalities, regions (federal subjects), and economic areas ('economicheskiy rayon'). It is established that more discrete levels of subdivisions, in accordance with statistics theory, have higher levels of heterogeneity. Based on our calculations, we demonstrate that the evaluation of heterogeneity indices for the same territories when applying different grids is combined with partial distortion of these indices. Such errors are explained by the continuity of the geographical space and the failure tos unambiguously determine the true geographical boundaries. We propose a generalization coefficient, a proportion of heterogeneity indices at different scale levels, which enables to assess multiscale spatial heterogeneity. The coefficient provides a means of distinguishing scale levels with the greatest diversity of territories. In addition, this coefficient can be used to differentiate between 'statistical' heterogeneity, which is explained by the number of elements in a system, and 'actual' (geographical) heterogeneity. A case study of estimating heterogeneity for E.E. Leizerovich's microzoning grid revealed that the 'actual' heterogeneity level can be significantly lower than the one, evaluated using traditional calculations. We provide examples of practical use for the coefficient, e.g., it enables to assess the validity of typologies and elaborate more detailed projections for discrete grids (so called 'small areas'). [ABSTRACT FROM AUTHOR]

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 programmes 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. [ABSTRACT FROM AUTHOR]

• Land use has effect on residents' shopping in metro station areas nearest to home. • Modifiable Areal Unit Problem (MAUP) influences this effect. • Effect of service land use is different with general land use in attracting shopping. • Service land use accumulates greater effect with scales other than 800 m. • Potential social benefit of catchment area land use planning deserves more attention. Land use planning has been emphasised by both planners and researchers in providing incentives for local shopping. This study advances the body of knowledge by investigating land use and shopping trips in the catchment area for the metro station nearest to home, a unique context that serves both as a shopping destination and a transit origin for further shopping opportunities. Negative binomial and Tobit regressions were applied respectively to examine the effect of land use density on weekly shopping frequency in the station area nearest to home, and its share of individual's total shopping. The impact of the Modifiable Areal Unit Problem (MAUP) on these statistical relationships was addressed, with land use densities objectively measured in seven spatial scales using a radial buffer scheme. Results from all regressions suggest the existence of MAUP in influencing the sign, significance and size of the station area land use effects on shopping trips. A nuanced disaggregation of service types identifies both complementary and competitive relations among services, even under the same generic category, such as dining. The results show that model adopting 400 m radial buffer as measurement scale accumulates greater effect of service land use on station area shopping frequency, while model adopting the 1200 m scale accumulates greater effect of service land use on the station area shopping's share of individual's total shopping. This paper presents the sensitivity of effect of land use on shopping trips in station areas to the spatial scale of land use measurement. It provides pragmatic guidelines for station area planning to augment retail attractiveness and the social benefits of the catchment area for the metro station. [ABSTRACT FROM AUTHOR]

Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in 'near real time'. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n  =   1,653) and Middle Tennessee, USA (n  =   2,779), and northern Alberta, Canada (n  =   809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases: 1, a null model that assumes cluster growth is strictly proportional to cluster size, i.e. no variation in transmission rates among individuals; and 2, a weighted model that incorporates individual-level covariates, such as recency of diagnosis. The optimal threshold maximizes the difference in information loss between models, where covariates are used most effectively. Optimal TN93 thresholds varied substantially between data sets, e.g. 0.0104 in Alberta and 0.016 in Seattle and Tennessee, such that the optimum for one population would potentially misdirect prevention efforts in another. For a given population, the range of thresholds where the weighted model conferred greater predictive accuracy tended to be narrow (±0.005 units), and the optimal threshold tended to be stable over time. Our framework also indicated that variation in the recency of HIV diagnosis among clusters was significantly more predictive of new cases than sample collection dates (ΔAIC > 50). These results suggest that one cannot rely on historical precedence or convention to configure genetic clustering methods for public health applications, especially when translating methods between settings of low-level and generalized epidemics. Our framework not only enables investigators to calibrate a clustering method to a specific public health setting, but also provides a variable selection procedure to evaluate different predictive models of cluster growth. [ABSTRACT FROM AUTHOR]