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The COVID-19 pandemic has exacerbated the food insecurity issue across the nation, especially for vulnerable population groups. As an important way to access food, online grocery shopping has gained substantial popularity during COVID-19. However, it is not clear how COVID-19 impacted both the in-store and online grocery shopping simultaneously and how the impacts varied among different population groups. To gain a comprehensive understanding of the impacts of COVID-19 on food access, we conducted two rounds of online survey during the initial and middle stages of COVID-19 in Maricopa County, Arizona, United States. In addition to the factors that have been examined in the literature, we examined how travel behavior, food environment, and lifestyle change affected both in-store and online shopping during COVID-19 when compared with before COVID-19. Analysis results suggest that the methods people used for grocery shopping varied with different stages of COVID-19 and among different population groups. Changes of people's daily life and travel behavior during COVID-19 are found to be associated with people's grocery shopping method selection. While people with better food environment were more likely to keep in-store grocery shopping, as the pandemic progressed, food environment became less important as a factor influencing the adoption of online grocery shopping. Food assistance program participants had a higher demand for the online grocery shopping service. However, the barriers associated with redeeming their benefits online prohibited them from becoming new online grocery shoppers during COVID-19. The study provides important insights into planning of food provision and formulating effective intervention strategies during future shocks. [ABSTRACT FROM AUTHOR]

Urban informatics appears to be a suitable area for the application of digital twins. Definitions of the term share some characteristics, but these definitions do not agree on what exactly constitutes a digital twin. The term has the potential to be misleading unless adequate attention is paid to the inherent uncertainty in any replica of a real system. The question of uncertainty is addressed, together with some of the issues that make its quantification problematic. Digital twins for urban informatics pose questions of purpose, governance, and ethics. In the final section the paper suggests some research issues that will need to be addressed if digital twins are to be successful. [ABSTRACT FROM AUTHOR]

Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal for specific types of pedestrians, particularly those with limited mobility, such as wheelchair users and older adults. Offering practical routing services to these users requires that pedestrian navigation systems provide further information on route geometry. Therefore, this article proposes a novel method for extracting and analyzing the geometry properties of the shortest pedestrian paths, with a focus on open geospatial data across four aspects: (a) similarity, (b) route curviness, (c) road turns and intersections, and (d) road gradients. Deriving from the Hausdorff distance, a metric called the "dissimilarity ratio" was developed, allowing us to determine whether pairs of routes show any tendencies to be similar to each other. Using the "sinuosity index", a segment-based technique quantified the route curviness based on the number and degree of the road turns along the route. Moreover, relying upon open elevation data, the road gradients were extracted to identify routes offering smoother motion and better accessibility. Lastly, the road turns and intersections were investigated as pedestrian convenience and safety indicators. A local government area of Greater Sydney in Australia was chosen as the case study. The analysis was implemented on OpenStreetMap (OSM) shortest pedestrian paths against Google Maps as a benchmark for real-world commercial applications. The similarity analysis indicated that over 90% of OSM routes were identical or roughly similar to Google Maps. In addition, while Spearman's rank correlation showed a direct relationship between route curviness and route length, r S (758) = 0.92, p < 0.001, OSM, on average, witnessed more tortuous routes and, consequently, shorter straight roads between turns. However, OSM routes could be more suitable for pedestrians when the frequency of intersections and road slopes are at the center of attention. Finally, the devised metrics in this study, including the dissimilarity ratio and sinuosity index, showed their practicability in translating raw values into meaningful indicators. [ABSTRACT FROM AUTHOR]

In this commentary we reflect on the potential and power of geographical analysis, as a set of methods, theoretical approaches, and perspectives, to increase our understanding of how space and place matter for all. We emphasize key aspects of the field, including accessibility, urban change, and spatial interaction and behavior, providing a high‐level research agenda that indicates a variety of gaps and routes for future research that will not only lead to more equitable and aware solutions to local and global challenges, but also innovative and novel research methods, concepts, and data. We close with a set of representation and inclusion challenges to our discipline, researchers, and publication outlets. [ABSTRACT FROM AUTHOR]

Given widespread concerns over human-mediated bee declines in abundance and species richness, conservation efforts are increasingly focused on maintaining natural habitats to support bee diversity in otherwise resource-poor environments. However, natural habitat patches can vary in composition, impacting landscape-level heterogeneity and affecting plant-pollinator interactions. Plant-pollinator networks, especially those based on pollen loads, can provide valuable insight into mutualistic relationships, such as revealing the degree of pollination specialization in a community; yet, local and landscape drivers of these network indices remain understudied within urbanizing landscapes. Beyond networks, analyzing pollen collection can reveal key information about species-level pollen preferences, providing plant restoration information for urban ecosystems. Through bee collection, vegetation surveys, and pollen load identification across ~350 km of urban habitat, we studied the impact of local and landscape-level management on plant-pollinator networks. We also quantified pollinator preferences for plants within urban grasslands. Bees exhibited higher foraging specialization with increasing habitat heterogeneity and visited fewer flowering species (decreased generality) with increasing semi-natural habitat cover. We also found strong pollinator species-specific flower foraging preferences, particularly for Asteraceae plants. We posit that maintaining native forbs and supporting landscape-level natural habitat cover and heterogeneity can provide pollinators with critical food resources across urbanizing ecosystems. [ABSTRACT FROM AUTHOR]

Distance is one of the most important concepts in geography and spatial analysis. Since distance calculation is straightforward for points, measuring distances for non-point objects often involves abstracting them into their representative points. For example, a polygon is often abstracted into its centroid, and the distance from/to the polygon is then measured using the centroid. Despite the wide use of representative points to measure distances of non-point objects, a recent study has shown that such a practice might be problematic and lead to biased coefficient estimates in regression analysis. The study proposed a new polygon-to-point distance metric, along with two computation algorithms. However, the efficiency of these distance calculation algorithms is low. This research provides three new methods, including the random point-based method, polygon partitioning method, and axis-aligned minimum areal bounding box-based (MABB-based) method, to compute the new distance metric. Tests are provided to compare the accuracy and computational efficiency of the new algorithms. The test results show that each of the three new methods has its advantages: the random point-based method is easy to implement, the polygon partitioning method is most accurate, and the MABB-based method is computationally efficient. [ABSTRACT FROM AUTHOR]

Review of the special issue papers The eight research papers included in this special issue cover a breath of literature review, new methodology, applications, and perspectives of scale. Wang and Wu discuss computational challenges in modeling spatial and temporal dimensions of big data (data captured at fine spatial and temporal scales) in discrete-choice models. To achieve this, the authors collected and reconciled multiple years of geospatial data related to urbanization and surveys of children's health from multiple sources and at different scales, into a uniform spatial frame to exploit their correlations. Following these methodological discussions, this special issue includes three interesting application papers that adopt scale-aware analysis. [Extracted from the article]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

We study the problem of synthesizing lockdown policies—schedules of maximum capacities for different types of activity sites—to minimize the number of deceased individuals due to a pandemic within a given metropolitan statistical area (MSA) while controlling the severity of the imposed lockdown. To synthesize and evaluate lockdown policies, we develop a multiscale susceptible, infected, recovered, and deceased model that partitions a given MSA into geographic subregions, and that incorporates data on the behaviors of the populations of these subregions. This modeling approach allows for the analysis of heterogeneous lockdown policies that vary across the different types of activity sites within each subregion of the MSA. We formulate the synthesis of optimal lockdown policies as a nonconvex optimization problem and we develop an iterative algorithm that addresses this nonconvexity through sequential convex programming. We empirically demonstrate the effectiveness of the developed approach by applying it to six of the largest MSAs in the United States. The developed heterogeneous lockdown policies not only reduce the number of deceased individuals by up to 45 percent over a 100 day period in comparison with three baseline lockdown policies that are less heterogeneous, but they also impose lockdowns that are less severe. [ABSTRACT FROM AUTHOR]

Urban networks have been widely examined using infrastructure connection and firm connection data. In particular, urban networks constructed based on firm connection data have been used to depict the circulation of capital, information, personnel, and products between cities. Existing studies on firm connection networks rely on either inter- or intrafirm relationships. However, there exist various important extra-firm relationships, such as those between firms and governments, research institutions, and nonprofit organizations. This study innovatively incorporates the extra-firm relationships between governments and firms into urban network construction to provide new insights into the field of urban network research. Using Northeast China as a case study, we construct government procurement activity (GPA) connection networks for both central projects and local projects at the regional and national scales. Social network analysis and regression analysis are used to analyze the network characteristics and factors associated with the network structure. The results show that three provincial capitals (Harbin, Changchun, and Shenyang) hold dominant positions across all the networks. While these capitals serve as GPA suppliers in the region, they are mainly GPA consumers nationally. The factors of spatial proximity, economic development, population, and provincial boundaries were found to be significantly associated with GPA connections. Our study provides important insights into the role of cities in firm-government supply-demand networks. This research can be used to help formulate effective strategies to improve a city's competitiveness and cities' cooperation in the GPA supply-demand network. [ABSTRACT FROM AUTHOR]

Choropleth mapping provides a simple but effective visual presentation of geographical data. Traditional choropleth mapping methods assume that data to be displayed are certain. This may not be true for many real-world problems. For example, attributes generated based on surveys may contain sampling and non-sampling error, and results generated using statistical inferences often come with a certain level of uncertainty. In recent years, several studies have incorporated uncertain geographical attributes into choropleth mapping with a primary focus on identifying the most homogeneous classes. However, no studies have yet accounted for the possibility that an areal unit might be placed in a wrong class due to data uncertainty. This paper addresses this issue by proposing a robustness measure and incorporating it into the optimal design of choropleth maps. In particular, this study proposes a discretization method to solve the new optimization problem along with a novel theoretical bound to evaluate solution quality. The new approach is applied to map the American Community Survey data. Test results suggest a tradeoff between within-class homogeneity and robustness. The study provides an important perspective on addressing data uncertainty in choropleth map design and offers a new approach for spatial analysts and decision-makers to incorporate robustness into the mapmaking process. [ABSTRACT FROM AUTHOR]

Distance is an important and basic concept in geography. Many theories, methods, and applications involve distance explicitly or implicitly. While measuring the distance between two locations is a straightforward task, many geographical processes involve areal units, where the distance measurement can be complicated. This research investigates distance measurement between a location (point) and an area (polygon). We find that traditional polygon‐to‐point distance measurements, which involve abstracting a polygon into a central or representative point, could be problematic and may lead to biased estimates in regression analysis. To solve this issue, we propose a new polygon‐to‐point distance metric along with two algorithms to compute the new distance metric. Simulation analysis shows the effectiveness of the new distance metric in providing unbiased estimates in linear regression. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The fundamental basis for territorial political representation is the presumption that people share greater common interests with others in closer geographic proximity than with people farther away. This principle is found in U.S. legal requirements that districts for the House of Representatives and state legislatures should not needlessly divide "communities of interest" and should be "reasonably compact." We propose a new objective standard to evaluate spatial fairness of redistricting plans: the extent to which a delineation minimizes total distance between all pairs of people assigned to the same district. To date, the legal standard of compactness has primarily been thought of in terms of the shapes of political districts, but boundary shape may have little correspondence with how populations are actually clustered and dispersed. Inter‐person separation, by contrast, provides a direct, intuitive metric for evaluating the congruence of districting plans with the territorial basis of political representation. To operationalize an inter‐person separation standard, we propose a model and present a heuristic method for delineating comparator districting schemes. We apply the standard to the current U.S. Congressional Districts in the states of Arizona and North Carolina to demonstrate how inter‐person separation could be used to develop and vet future redistricting plans. [ABSTRACT FROM AUTHOR]

Traffic congestion represents an ongoing serious issue in many large cities. Many public facilities, such as hospitals, tend to be centrally located to ensure they are most accessible to local residents; as a result, they may contribute significantly to a city's traffic congestion. In this study, a multi‐objective spatial optimization model was provided to help formulate hospital relocation plans, taking into account both traffic congestion and hospital accessibility. Using intra‐urban movement data, we proposed a method to estimate the area‐wide traffic congestion caused by hospital visits and to identify potential hospitals to be relocated. An NSGA‐II (Non‐dominated Sorting Genetic Algorithm II) algorithm was applied to solve the hospital relocation optimization problem; we applied our model to study optimal hospital relocation plans in Beijing. Analysis results provide a tradeoff between traffic congestion relief and hospital accessibility. We discussed plans that significantly reduce traffic congestion while maintaining a high level of hospital accessibility. Our study has significant policy implications and provides insights for future facility planning and transportation planning. [ABSTRACT FROM AUTHOR]

Abstract: The p‐median problem (PMP) is one of the most applied location problems in urban and regional planning. As an NP‐hard problem, the PMP remains challenging to solve optimally, especially for large‐sized problems. A number of heuristics have been developed to obtain PMP solutions in a fast manner. Among the heuristics, the Teitz and Bart (TB) algorithm has been found effective for finding high‐quality solutions. In this article, we present a spatial‐knowledge‐enhanced Teitz and Bart (STB) heuristic method for solving PMPs. The STB heuristic prioritizes candidate facility sites to be examined in the solution set based on the spatial distribution of demand and service provision. Tests based on a range of PMPs demonstrate the effectiveness of the STB heuristic. This new algorithm can be incorporated into current commercial GIS packages to solve a wide range of location‐allocation problems. [ABSTRACT FROM AUTHOR]

Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Analysis results are often found to vary with the way we abstract geographical space. When continuous geographic phenomena are abstracted, processed, and stored in a digital environment, some level of discretization is often employed. Information loss in a discretization process brings about uncertainty/error, and as a result research findings may be highly dependent on the particular discretization method used. This article examines one spatial problem concerning how to achieve the maximal regional coverage given a limited number of service facilities. Two widely used geographical space abstraction approaches are examined, the point-based representation and the area-based representation, and issues associated with each representation scheme are analyzed. To accommodate the limitations of the existing representation schemes, a mixed representation strategy is proposed along with a new maximal covering model. Experiments are conducted to site warning sirens in Dublin, Ohio. Results demonstrate the effectiveness of the mixed representation scheme in finding high-quality solutions when the regional coverage level is medium or high. [ABSTRACT FROM AUTHOR]

Copyright of Papers in Regional Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Precipitation stations are important components of a hydrological monitoring network. Given their critical role in rainfall forecasting and flood warnings, along with limited observation resources, determining the optimal locations to deploy precipitation stations presents an important problem. In this paper, we use a maximal covering location problem to identify the best precipitation station sites. Considering the terrain conditions and the characteristics of a rainfall network, the original maximal covering location model is modified with the introduction of a set of additional constraints. The minimum density requirement is used to determine a precipitation station’s coverage range, and three weighting schemes are used to evaluate each demand object’s covering priority. As a typical mountainous watershed with high annual precipitation, the Jinsha River Basin is selected as the study area to test the applicability of the proposed method. Results show that the proposed method is effective for precipitation station configuration optimization, and the model solution achieves higher coverage than the real-world deployment. Compared with the commercial solver CPLEX, a genetic algorithm-based heuristic can significantly reduce the computation time when the problem size is large. Several deployment strategies are also discussed for establishing the optimal configuration of precipitation stations. [ABSTRACT FROM AUTHOR]

This article introduces measures to quantify spatial autocorrelation for vectors. In contrast to scalar variables, spatial autocorrelation for vectors involves an assessment of both direction and magnitude in space. Extending conventional approaches, measures of global and local spatial associations for vectors are proposed, and the associated statistical properties and significance testing are discussed. The new measures are applied to study the spatial association of taxi movements in the city of Shanghai. Complications due to the edge effect are also examined. [ABSTRACT FROM AUTHOR]

ABSTRACT Demand for service in location modelling is often evaluated based on the spatial proximity of fixed and static reference locations of demand (e.g. home) to a facility, which ignores person-specific activity-travel patterns and the temporal changes in demand for service throughout the day. To address these limitations, this study draws upon recent developments in space-time measures of individual accessibility to explore the spatial and temporal structures of demand by considering individuals' space-time constraints and impact of existing urban structures. Based on a time-geographic framework, eight space-time demand measures were developed and compared with three conventional location-based demand measures for 12 hospitals through an empirical study conducted in Columbus, Ohio. The results show that geographic proximity between clients' home and facilities may not be an effective indicator for service demand, and conventional demand measures tend to underestimate potential demand for service in most situations. The study concludes that space-time demand measures that take into account people's activity-travel patterns in space-time would lead to better estimation of demand for service in most cases. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the Association of American Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current method for delineating U. S. metropolitan and micropolitan statistical areas (collectively referred to as core-based statistical areas- CBSAs) is to cluster counties based on the strength of commuting interactions between outlying areas and urban cores. The social and economic integration of a metropolitan or a micropolitan statistical area is operationalized using what we believe is an outdated, monocentric perspective on functional spatial structure: one that fixates solely on inward and reverse commuting. We propose a new spatial optimization model for delineating CBSAs that can better account for polycentric urban structure by considering all intercounty commuting linkages. Our model seeks to find the boundaries that maximize the containment of entire webs of intra- CBSA intercounty commuting. We apply the proposed method to delineate alternative sets of metropolitan and micropolitan statistical areas for comparison with the federal government's currently defined (2010 Office of Management and Budget [ OMB] standards) official CBSAs. El método actual para delimitar áreas estadísticas micropolitanas y metropolitana en los Estados Unidos (denomidado genéricamente áreas estadísticos nucleares o core-statistical areas- CBSA) consiste en agrupar condados en base a la fuerza de las interacciones de los desplazamientos residencia-trabajo entre las zonas periféricas y los núcleos urbanos. La integración social y económica de un área estadística metro y micro politana se pone en operación usando un enfoque funcional que los autores consideran obsoleto que se basa en una estructura espacial urbana monocéntrica. Dicho enfoque se limita únicamente a los desplazamientos hacia el interior y de vuelta. Los autores proponen un nuevo modelo de optimización espacial para delinear CBSAs que respresenta mejor una estructura urbana policéntrica que considera todos los desplazamientos entre condados. El modelo busca encontrar los límites que maximizan la inclusión de redes enteras de desplazamientos intra CBSA y entre condados. El método propuesto es aplicado para delinear conjuntos alternativos de áreas estadísticas metropolitanas y micropolitanas y es comparado con los límites oficiales actuales de CBSA proporcionados por gobierno federal (de acuerdo a los lineamientos de la Oficina de Administración y Presupuesto u Office of Management Budget-OMB) en 2010). 目前用于描述美国大都市区与居住区统计区域（共同地被称为基于核心的统计区域-CBSAs）的方法，主要是基于边远地区和城市核心区间的相互作用强度对郡县进行聚类。大都市区或居住区统计区域社会与经济一体化的运作方式，采用的是一种功能空间结构上落伍的单中心视角，即仅关注内部交换或反向交换。本文提出了一种新的空间优化模型用于描述CBSAs，将所有内部通勤联系考虑在内，能更好地解释多中心城市结构。该模型力图发现能最大化包含内部CBSA通勤联系网的边界。最后，将该模型应用于大都市区和居住区的统计区域描述，并与联邦政府目前定义的官方CBSAs（管理与预算 [OMB]标准2010办公室）进行了对比研究。 [ABSTRACT FROM AUTHOR]

The vector assignment p-median problem (VAPMP) (Weaver and Church in Transp Sci 19(1):58-74, ) was one of the first location-allocation models developed to handle split assignment of a demand to multiple facilities. The underlying construct of the VAPMP has been subsequently used in a number of reliable facility location and backup location models. Although in many applications the chance that a facility fails may vary substantially with locations, many existing models have assumed a uniform failure probability across all sites. As an improvement, this paper proposes a new model, the expected p-median problem as a generalization of existing approaches by explicitly considering site-dependent failure probabilities. Multi-level closest assignment constraints and two efficient integer linear programming (ILP) formulations are introduced. While prior research generally concludes that similar problems are not integer-friendly and cannot be solved by ILP software, computational results show that our model can be used to solve medium-sized location problems optimally using existing ILP software. Moreover, the new model can be used to formulate other reliable or expected location problems with consideration of site-dependent failure probabilities. [ABSTRACT FROM AUTHOR]

With the direct linkage to a travel map system, geostatistical techniques have been recently adopted for urban travel time estimation. Some important traffic characteristics of urban transportation networks, however, have not been adequately addressed in these studies. As an improvement over the existing studies, this study incorporates the directional effect of traffic into several commonly used geostatistical models for travel time estimation. We show that model performance can be significantly enhanced when flow specific properties are explicitly considered in constructing the associated interpolation models. The developed methodology is applied to a set of traffic data collected in the city of Tucson, Arizona during the rush hours. Results demonstrate an average of 20 % reduction in RMSE compared with those by the traditional approaches. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the Association of American Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Uncertainties and errors associated with aggregation have long been recognized in the study of spatial problems. In facility location modeling, while much has been done to examine the aggregation of large datasets of discrete points, errors and uncertainties involved in aggregating continuous spatial units are not well understood. This study focuses on the effects of aggregating continuous spatial units into discrete points within the context of the location set covering problem. We propose new measures to understand and quantify errors associated with a continuous aggregation scheme. In a real-world application, the proposed methods can be used to suggest an appropriate aggregation scheme before the application of the location model. We demonstrate the concepts developed here with an empirical study of siting emergency warning sirens in the city of Dublin, OH. [ABSTRACT FROM PUBLISHER]

Achieving the greatest coverage using limited resources has long been a concern for regional planners. Since the 1970s, a variety of models have been studied and relied upon. Finding ways to best represent geographical space remains a challenge to many researchers. Solutions suggested by models vary greatly with different space representation schemes. For example, in the past, points have been widely adopted to represent spatial demand for coverage. However, this simple abstraction of geographical space could bring about inaccuracies and uncertainties, and often compromises its solution quality. Considering that demand can be area based, which is beyond points, objects of different shapes have been proposed as an alternative for representation. With advances in geographic information systems ( GISs), the new representation scheme using objects has recently received much attention. Compared with the straightforward point-based abstraction, spatial object representation poses considerable challenges to both model formulation and computation capability. This article revisits model development for the problem of regional coverage maximization and proposes a new formulation where coverage of spatial demand is implicitly modeled. Model testing is conducted through an application to warning siren siting in Dublin, Ohio, which has been studied by other researchers. Results demonstrate the effectiveness of the new model when compared with the existing models. Uno de los objetivos comunes en la planificación regional es lograr la máxima cobertura de un servicio con recursos limitados. Desde la década de los 70s se vienen utilizado una serie de modelos conocidos. Sin embargo, la representación adecuada del espacio geográfico de dichos modelos sigue siendo un problemática para muchos investigadores. Las soluciones derivadas de los modelos varían considerablemente de acuerdo a los diferentes esquemas de representación utilizados. El uso de puntos por ejemplo, ha sido ampliamente utilizado en el pasado como esquema de representación espacial de la demanda a ser cubierta por el servicio. Sin embargo, esta simple abstracción del espacio geográfico puede provocar imprecisiones e incertidumbres que a menudo afectan la calidad de la solución matemática del modelo. En contraste, si la demanda se define como un área que abarca una extensión que va más allá de los puntos es posible proponer-como lo han hecho varios recientemente- el uso de objetos espaciales (de formas variadas) como una alternativa sensata de representación. Con los avances en los sistemas de información geográfica (SIG), este nuevo esquema de representación -que usa objetos especiales- han recibido considerable atención. En contraste con esquemas sencillos como el basado en puntos, la representación de objetos espaciales presenta varios desafíos en cuanto a la formulación y computación del modelo. El presente artículo reseña el desarrollo del modelamiento del problema de máxima cobertura regional ( problem of regional coverage maximization) y propone una nueva formulación en la cual la cobertura de la demanda en el espacio es modelada de manera implícita. La evaluación del modelo se realiza a través de una aplicación que usa datos de la ubicación sirenas de alarma en Dublín, Ohio. Los resultados demuestran la eficacia del nuevo modelo en comparación con los modelos existentes. 利用有限资源实现最大覆盖一直广受区域规划者关注。自20世纪70年代以来，发展了大量针对这一问题的模型研究。寻找最佳的地理空间表达方式仍是研究者面临的一大挑战。不同模型对于不同的空间表达模式变化很大。比如过去空间覆盖需求多采用点集表达。然而，这种简单的地理空间抽象方法可能会引起不精确性和不确定性，通常也会导致解决方案质量下降。考虑到需求可能是基于区域而非基于点，不同形状的对象已经被当作空间表达可替代的方式。随着GIS技术的进步，基于对象空间表达的新方案近来备受关注。较之于直接基于点的空间抽象，空间对象表达在模型的形式化表达和计算性能上均面临相当大的挑战。本文回顾了区域最大覆盖问题模型研究的发展历程，提出了一个新的蕴含空间覆盖建模需求的模型。以曾被诸多学者研究过的俄亥俄州都柏林的报警器选址问题为例对模型进行检验，结果显示新模型较现有模型更为有效。 [ABSTRACT FROM AUTHOR]

A simple method for vehicle re-identification to generate vehicle passage times with loop data is developed. The method departs from other existing methods for vehicle passage time estimation: 1) It handles vehicle signatures one at a time and evaluates each vehicle observed only once. 2) The commonly used prespecified time window is replaced by a dynamic list of vehicles to be matched. 3) Vehicle matching is based on a combined estimation model that integrates spot traffic data with spatial vehicle data. The performance of the algorithm was tested with field data. Furthermore, to examine the effect of some of the assumptions on the performance of the algorithm, we compared the result with that obtained from an offline optimization model based on a spatial constraint that considers as many vehicles as possible for matching. The proposed method is particularly suitable for real-time applications since it can be easily implemented with little calibration effort and is computationally efficient. [ABSTRACT FROM PUBLISHER]

As an emerging technology, wireless communication revolutionizes the way data are shared and transferred. In particular, wireless mesh network (WMN) technology allows data transmission from one node to another without extensive cabling. In this article, spatial characteristics of maximal covering problems are explored, and a novel spatial optimization model is proposed for WMN topology planning. The model selects the optimal locations for network infrastructure to achieve the maximal coverage of spatial demand. Additionally, important WMN design requirements have been accounted for, including network topology and throughput capacity. The validity of the model is tested through a WMN deployment developed for an emergency medical service application in Tucson, Arizona. [ABSTRACT FROM AUTHOR]

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The Prairie Pothole Region in the United States contains millions of seasonal, semi-permanent, or permanent lakes and wetlands that typically range in size from 0.1 to 10 ha. These lakes and wetlands are vulnerable to climate change, especially in our study area in South Dakota, in which a period of deluge following a sharp drought considerably expanded the areal extent of prairie pothole lakes during the last decade of the twentieth century. Preliminary estimates of lake areas, determined using LANDSAT 5 and 7 images, had appreciable errors especially for the smallest of these lakes. This article describes a new sub-pixel approach integrated with a CART (Classification and Regression Tree) model using a GIS (Geographical Information System) to quantify mixed water pixels along lake boundaries to improve area estimations for pothole lakes. Errors in estimated area were typically 10% or less for lakes greater than 1 ha in size. An analysis of lakes in our study area demonstrates how lake area changed with the transition from drought to deluge. Small lakes exhibited a distinct seasonal variation in contrast to large lakes that tended to follow precipitation trends more broadly. The total water area of lakes is consistent with broad variation in rainfall. [ABSTRACT FROM AUTHOR]

How to represent geographic space has long been an issue in location modelling. Facilities, demand and/or the region of interest are often abstracted using aggregated points. However, substantial errors can be introduced, and obtained solutions could be dependent on the degree of aggregation. To address this, geographic representation of space is undergoing renewed research interest in spatial analysis and modelling. In this article the maximal coverage problem is studied, with a particular focus on demand coverage representation. Due to the limitations of existing modelling approaches for examining the coverage of space, there exist significant discrepancies between what is modelled and actual geographic coverage. In order to accurately reflect the mechanism of maximal coverage for spatial objects (points, lines or polygons), we introduce a new model explicitly accounting for joint service provided by multiple facilities. The new model can be viewed as an extension of existing approaches, but also a generalisation. An application to warning siren siting is carried out to demonstrate the merits of this new approach. [ABSTRACT FROM AUTHOR]

Facility placement and associated service coverage are major concerns in urban and regional planning. In this paper an approach is detailed for the problem of covering spatial demand for service, where potential facilities are located in the continuous plane. It is shown that weighted demand, represented as points, lines or polygons, can be optimally served by a finite number of potential facility locations, called the polygon intersection point set (PIPS). The developed approach is an extension of a point-based abstraction of demand to more general representations (e.g. points, lines or polygons). An empirical analysis of warning siren siting in Ohio is carried out, highlighting the applicability of this approach. [ABSTRACT FROM AUTHOR]

In the past few years, station-free bike sharing systems (SFBSSs) have been adopted in many cities worldwide. Different from conventional station-based bike sharing systems (SBBSSs) that rely upon fixed bike stations, SFBSSs allow users the flexibility to locate a bike nearby and park it at any appropriate site after use. With no fixed bike stations, the spatial extent/scale used to evaluate bike shortage/surplus in an SFBSS has been rather arbitrary in existing studies. On the one hand, a balanced status using large areas may contain multiple local bike shortage/surplus sites, leading to a less effective rebalancing design. On the other hand, an imbalance evaluation conducted in small areas may not be meaningful or necessary, while significantly increasing the computational complexity. In this study, we examine the impacts of analysis scale on the SFBSS imbalance evaluation and the associated rebalancing design. In particular, we develop a spatial optimization model to strategically optimize bike rebalancing in an SFBSS. We also propose a region decomposition method to solve large-sized bike rebalancing problems that are constructed based on fine analysis scales. We apply the approach to study the SFBSS in downtown Beijing. The empirical study shows that imbalance evaluation results and optimal rebalancing design can vary substantially with analysis scale. According to the optimal rebalancing results, bike repositioning tends to take place among neighboring areas. Based on the empirical study, we would recommend 800 m and 100/200 m as the suitable scale for designing operator-based and user-based rebalancing plans, respectively. Computational results show that the region decomposition method can be used to solve problems that cannot be handled by existing commercial optimization software. This study provides important insights into effective bike-share rebalancing strategies and urban bike transportation planning. [ABSTRACT FROM AUTHOR]

Low-socioeconomic-status (SES) consumers tend to be more price sensitive than their high-SES counterparts. Nonetheless, various economic-related burdens, such as mobility costs and lack of information, often hinder their ability to attend to scarcity—a phenomenon called "ghetto tax." The current research moves a step further to show that even when very poor consumers can exert price sensitivity and are fully informed, a "psychological ghetto tax" often discourages them from doing so. Across five studies, the authors demonstrate that, relative to (1) high-SES consumers or (2) contexts of intragroup interaction, low-SES consumers are willing to pay higher prices and to accept lower-value rewards to avoid commercial settings that require intergroup interaction (e.g., poor consumers in a high-end shopping mall). This effect is driven by the poor consumers' heightened expectations of discrimination in upscale commercial settings, a concern virtually nonexistent among wealthy consumers. Companies' inclusion statements emphasizing customer equality and/or customer diversity can serve as safety cues against stigmatized identities and increase low-SES consumers' price sensitivity. [ABSTRACT FROM AUTHOR]

Estrogen promotes growth of estrogen receptor-positive (ER+) breast tumors. However, epidemiological studies examining the prognostic characteristics of breast cancer in postmenopausal women receiving hormone replacement therapy reveal a significant decrease in tumor dissemination, suggesting that estrogen has potential protective effects against cancer cell invasion. Here, we show that estrogen suppresses invasion of ER+ breast cancer cells by increasing transcription of the Ena/VASP protein, EVL, which promotes the generation of suppressive cortical actin bundles that inhibit motility dynamics, and is crucial for the ER-mediated suppression of invasion in vitro and in vivo. Interestingly, despite its benefits in suppressing tumor growth, anti-estrogenic endocrine therapy decreases EVL expression and increases local invasion in patients. Our results highlight the dichotomous effects of estrogen on tumor progression and suggest that, in contrast to its established role in promoting growth of ER+ tumors, estrogen has a significant role in suppressing invasion through actin cytoskeletal remodeling. Whilst estrogen is known to be tumorigenic in some breast cancer, in some contexts it can be protective against invasion and dissemination. Here, the authors show estrogen can promote generation of Suppressive Cortical Actin Bundles that can inhibit motility dynamics through EVL-mediated actin cytoskeletal remodeling. [ABSTRACT FROM AUTHOR]

This article explores Hangzhou's multifaceted role in shaping Chinese Buddhist culture and contributing to knowledge production. As a vital hub of Chinese material and intellectual culture, Hangzhou's significant contributions to Buddhism are emphasized, shedding light on its key role in disseminating Buddhist teachings and preserving knowledge. The study delves into the rich history of Buddhist reference works, particularly leishu, showcasing how these compilations were pivotal in organizing and transmitting Buddhist wisdom. The article connects Hangzhou's intellectual legacy to the broader context of Chinese Buddhism, emphasizing its crucial position in the development and dissemination of Buddhist doctrines. Additionally, it highlights ongoing academic efforts to compile an Encyclopedia of Hangzhou Buddhist Culture, underscoring Hangzhou's continued importance in contemporary Buddhist scholarship. [ABSTRACT FROM AUTHOR]

The production of stone sculptures in the form of statues, steles, and pagodas was arguably one of the most prominent forms of Buddhist devotion in early medieval China. The abundant Buddhist imagery and devotional inscriptions adorning their surfaces provide rich information on their creation, commission, consecration, and worship, which is essential for comprehending the local community's religious landscape. This paper employs a network analysis approach to visualize and analyze the spatial arrangement of Buddhist images on the surfaces of pagodas from Nannieshui 南涅水, Shanxi 山西 Province. Network analysis reveals that Nannieshui pagodas were commissioned and worshipped differently from other kinds of stone sculpture, as shown by its emphasis on the spatial and sequential order of Buddhist images depicted on the exterior of these pagodas. [ABSTRACT FROM AUTHOR]

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The market for local food has grown considerably over the course of thirty years. At the same time, even robust local food systems may reproduce injustices that have long marred corporatized food systems. In this paper, we explore the relationship of environmental justice to local food systems by asking two questions. First, are farmers markets (FMs) more likely to be found in wealthier, whiter communities? Second, are FMs providing people who are not members of socially privileged groups with an opportunity to buy food grown in accordance with organic principles? Drawing on survey data from FMs in New Jersey, we find that, although community demographics are strongly associated with FM location, social privilege is not associated with where organic farmers sell produce, once the lens of analysis is narrowed to just communities with an FM. But although food grown without agricultural chemicals is widely available at FMs in the study area, organic farmers who accept payment via WIC or SNAP are not more likely to sell at FMs in lower-income communities, and fewer organic farmers than non-organic farmers accept payment via WIC or SNAP. The presence of organic farmers at FMs in a wide range of communities suggests that expanding access to locally sourced fruits and vegetables may be able to play a small but not insignificant role in reducing exposure to agricultural chemicals. However, organic food would be more accessible at FMs in low-income communities if more organic farmers participated in nutrition assistance programmes like WIC and SNAP. [ABSTRACT FROM AUTHOR]

Based on the spatial analysis and GIS modeling of the distribution of religious sites in Greater China, we have developed the concept of regional religious systems (RRSs) as a novel way of understanding and studying the spatial distribution patterns of religious sites and their relationship with other social and cultural factors. This essay further explores theoretical issues such as its center–periphery relations in existing administrative and economical hierarchies. Drawing on our current project on RRSs in the Hangzhou region and various available studies about pre-modern Chinese religion, the author explains the spatial characteristics of RRSs, such as the role of transportation, trade and pilgrimage routes in the formation of RRSs. Using Chinese Buddhism as an example, the author argues that RRSs in Greater China should be treated as a spatial formation without an internal hierarchical structure because the political and administrative hierarchy prevents the formation of a strong religious hierarchy. [ABSTRACT FROM AUTHOR]