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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]

Everyday mobility is critical to understand migrants' accessibility and inclusions to the destination cities. Existing research was insufficient to explain the everyday movement of relatively privileged skilled migrants from the Global North to the Global South, nor did it distinguish the role of one's capability and intentionality in shaping mobility. To fill in the research gap, we employ the capital-mobility framework to investigate how skilled U.S. migrants' capital accumulation and intentionality jointly affect their everyday mobility in Chinese cities, and the implications for their social inclusion in the local society. We use sketch mapping with semistructured interviews to conduct this study in the Pearl River Delta region, a pioneer of economic development in China. We find that although migrants' capital accumulation facilitated their everyday movement, COVID-19 disrupted such mobility. Whether migrants had intentions to move varied, shaped by geopolitical relations, urban environment, and migrants' own capital. The voluntary or involuntary nature of their everyday mobility also suggests whether their social inclusion or exclusion in the local society is by choice or constraint. Our study integrates geographic information systems visualization with qualitative data to highlight the spatial mobility and immobility at the individual level and generates policy implications for retaining global talent at the city level. [ABSTRACT FROM AUTHOR]

As of 2022, more than 1,900 cities around the world had implemented bike-sharing systems (BSSs) with docking stations. Because there is a limit on how far most people are willing to walk to and from BSS stations, maximal coverage models have been a popular approach to optimizing dock station locations. Existing maximal coverage models, however, mainly evaluate demand coverage based on proximity to individual stations without considering the fact that BSS users must have dock stations convenient to both their pickup and drop-off sites. Given this, traditional single-node maximal coverage models might not locate stations to maximize the number of complete-able trips and will also tend to overestimate the coverage of demand. This study proposes a flow termini coverage model (FTCM) to locate BSS stations by considering the flow-based BSS usage demands and network connectivity. The model is tested on the Youbike system in Taipei, one of the most heavily used dock-station BSSs worldwide. This study compares the FTCM with both the traditional single-station maximal coverage model and Taipei’s official planning strategy. Results suggest that the FTCM consistently covers more origin–destination trips, especially in areas with unbalanced BSS usage. We also provide discussion on the cost–benefit trade-offs for transportation planners to choose the most appropriate modeling approaches. The FTCM could also be adapted to optimizing micromobility hubs for parking dockless shared bikes and e-scooters. [ABSTRACT FROM AUTHOR]

Geoforensic science investigates the location and time of criminal occurrences by integrating multiple fields, including geography, criminology, ecology, biology, and geology. The ubiquity, durability, and spatial-temporal predictability make pollen a frequently used biomarker in geoforensic investigations to help determine the provenance of hard-to-trace items, including computers, counterfeit products, digging equipment, clothing, and undetonated explosives. The recently developed Geoforensic Interdiction (GOFIND) model links the pollen combination collected from a sample object with the probability of locations traversed by the object. Although the GOFIND model improves over the traditional single-site joint probability approach and can be used to identify multiple locations simultaneously, substantial limitations remain. In particular, GOFIND requires specifying the number of locations traversed by an object in advance—a priori knowledge that is almost impossible to obtain in real-world applications. This article aims to introduce the GOFIND + model that leverages detected and undetected pollen to establish a probabilistic relation between pollen and the corresponding species distribution in the environment. Our simulation tests using the USDA CropScape data for the state of Texas show that the GOFIND + model outperforms the GOFIND model in predictive accuracy. Further, GOFIND + does not require that users specify the number of geographical stops and sites a priori. Key Words: geoforensics, GOFIND+, pollen, spatial optimization. [ABSTRACT FROM AUTHOR]

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]

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]

The coronavirus disease 2019 pandemic triggered nationwide school closures in March 2020, putting millions of children in the United States who were reliant on subsidized school meals at risk of experiencing hunger. In response, the US Department of Agriculture mobilized the Summer Food Service Program and Seamless Summer Option program to provide emergency free school meals. There is a need to investigate the effectiveness of these programs in covering underresourced communities during the pandemic. This study assessed associations between meal distribution and census tract demographics (ie, poverty level, race/ethnicity, and deprivation level based on social deprivation index score). An observational study using longitudinal meal distribution data collected over an 18-month period following school closures (March 2020 to August 2021). Monthly meal distribution data were collected for community sites serving 142 census tracts within 4 urban New Jersey cities predominantly populated by people with low incomes and from racial and ethnic minority groups. Main outcome measures were the number of meals served monthly by Summer Food Service Program and Seamless Summer Option meal sites. A 2-part multivariable regression approach was used to analyze the data. In the first step, logistic regression models showed that high-deprivation tracts were more likely to serve meals during the observed period (odds ratio 3.43, 95% CI 1.001 to 11.77; P = 0.0499). In the second step, among tracts that served any meals during the observed period, mixed effects negative binomial regression models showed that high-poverty and high-deprivation tracts served comparatively more meals (incidence rate ratio [IRR] 2.83, 95% CI 2.29 to 3.51; P < 0.001 and IRR 1.94, 95% CI 1.65 to 2.28; P < 0.001, respectively). Findings show that meal distribution during the pandemic was higher within census tracts with higher poverty and deprivation levels, indicating that underresourced communities with higher need had more free meals available during this unprecedented public health emergency. [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]

The worldwide growing demand for energy has imposed much pressure on energy supply and the environment. Solar energy, as one of the clean and renewable resources, provides a great potential for helping to meet the growing energy demand and reduce the environmental impacts. How to make the best use of a solar photovoltaic (PV) system has received much attention in recent years. Integrating geographic information systems (GIS), this paper proposes a new spatial optimization problem, the maximal PV panel coverage problem (MPPCP), for solar PV panel layout design. Suitable installation areas are first delineated in GIS. Then the MPPCP is used to identify the best spatial configurations of multiple PV panels. Different orientations and alignment scenarios are incorporated in the model to account for installation constraints while achieving the goal of maximal energy production. The new problem is applied to locate solar PV arrays on a rooftop with limited suitable installation areas. Results show that flexible panel alignments increase the maximal energy production by up to 6%. Model performance and computational efficiency are discussed. The research provides important insights into the spatial layout design of solar PV panels at various scales. • GIS finds the suitable areas for solar PV panel installation. • Layout design maximizes the energy production potential of a solar PV system. • The new method has been applied to identify the optimal panel layout on a rooftop. • Flexible panel alignments increase the maximal energy production by up to 6%. • Model 1 is more computational tractable requiring less problem-solving time. [ABSTRACT FROM AUTHOR]

Choropleth mapping provides a powerful way to visualize geographical phenomena with colors, shadings, or patterns. In many real-world applications, geographical data often contain uncertainty. How to incorporate such uncertainty into choropleth mapping is challenging. Although a few existing methods attempt to address the uncertainty issue in choropleth mapping, there are limitations to widely applying these methods due to their strong assumption on the distribution of uncertainty and the way in which similarity or dissimilarity is assessed. This article provides a new classification scheme for choropleth maps when data contain uncertainty. Considering that in a choropleth map, units in the same class are assigned with the same color or pattern, this new approach assumes the existence of a representative value for each class. A maximum likelihood estimation–based approach is developed to determine class breaks so that the overall within-class deviation is minimized while considering uncertainty. Different methods—including linear programming, dynamic programming, and an interchange heuristic—are developed to solve the new classification problem. The proposed mapping approach has been applied to map the median household income data from the American Community Survey and simulated disease occurrence data. Test results show the effectiveness of the new approach. The linkage between the new approach and the existing methods is also discussed. Key Words: choropleth mapping, map classification, maximum likelihood estimation, uncertainty. [ABSTRACT FROM AUTHOR]

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]

Location models have been widely used to support locational decisions for various service provision. One common objective of location models has been to ensure maximal accessibility of sited facilities to demand populations. Accessibility evaluation in location models often assumes that trips originate from fixed locations (usually home) and are single purpose. These assumptions contradict the empirical evidence that suggests trips also commonly originate from non-home locations and may involve multiple stops. In this study, a new multi-objective location model is developed that extents the classic p -median problem (PMP) to account for a more realistic assessment of accessibility. Based on the individual accessibility assessment, notions of trip chaining and activity space are incorporated into the model development. In addition to fixed home locations, stops along chained trips are allowed for potential service site visits, and activity space is introduced as an additional dimension to evaluate accessibility of alternative opportunities. The effectiveness of the new model is demonstrated using an application in Tucson, AZ. [ABSTRACT FROM AUTHOR]

Adequate access to healthy food has become a social issue due to the recent Great Recession and heightened levels of unemployment. Geographers have focused their attention on how to accurately evaluate food access and how to identify and delineate food deserts; that is, low-income neighborhoods where affordable and healthy food is lacking or limited. Findings of recent food access studies are, however, dramatically inconsistent. We argue that spatial scale and the level of aggregation used in constructing food access measures could account for a major portion of the varying results. We draw on an empirical study in the Tucson, Arizona, metropolitan area, to examine how varying geographic scales and aggregation methods affect food access assessment. We also provide an analysis to show how spatial scale and aggregation practices lead to inconsistent conclusions about food access and designation of food deserts. [ABSTRACT FROM AUTHOR]

Demographic and socio-economic information provided by the American Community Survey (ACS) have been increasingly relied upon in many planning and decision making contexts due to its timely and current estimates. However, ACS estimates are well known to be subject to larger sampling errors with a much smaller sample size compared with the decennial census data. To support the assessment of the reliability of ACS estimates, the US Census Bureau publishes a margin of error at the 90% confidence level alongside each estimate. While data error or uncertainty in ACS estimates has been widely acknowledged, little has been done to devise methods accounting for such error or uncertainty. This article focuses on addressing ACS data uncertainty issues in choropleth mapping, one of the most widely used methods to visually explore spatial distributions of demographic and socio-economic data. A new classification method is developed to explicitly integrate errors of estimation in the assessment of within-class variation and the associated groupings. The proposed method is applied to mapping the 2009–2013 ACS estimates of median household income at various scales. Results are compared with those generated using existing classification methods to demonstrate the effectiveness of the new classification scheme. [ABSTRACT FROM AUTHOR]

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]

Activity space represents an important concept for understanding human activity-travel. The existing activity space delineation approaches are limited in fully characterizing real-world travel behaviors. To address the issue, this research proposes a new time geography based approach to more accurately portray activity spaces of urban travelers. The proposed approach takes into account the full complexity of real-world travel and underlying urban structures. Results of an empirical study are presented based on the 2008 Add-on National Household Travel Survey conducted in Tucson, Arizona. Activity spaces of 1164 sample travelers are delineated and analyzed. Results show the effectiveness of the new approach in more realistically depicting urban activity-travel. [ABSTRACT FROM AUTHOR]

Choropleth maps visualize areal geographical data by grouping data into a few map classes and assigning different colors, shades, or patterns. Recent studies show that data uncertainty, commonly observed in real-life applications, should also be accounted for when determining the best classification scheme. Due to data uncertainty, a few studies note that map units might be placed in a wrong class, and the concept of map robustness has been introduced to minimize such misplacement. Recently, an algorithm has been developed to integrate robustness into the design of the optimal map classification scheme. However, the existing algorithm has two limitations: first, it is only suitable for certain robustness metrics. Second, when identifying the optimal class breaks, the existing algorithm requires predefined candidate class break values, which might lead to sub-optimal solutions. This paper resolves these issues by proposing a new model, namely, the Continuous Robust Map Classification Problem (CRMCP), and the associated solution approach. The CRMCP allows mapmakers to customize robustness metrics according to their data and applications. In addition, a particle swarm optimization algorithm is developed to solve the CRMCP. The model and algorithm are tested using American Community Survey data. Test results suggest that the new approach can find better solutions than the existing algorithm. The study improves the usability of choropleth maps when uncertain geographical attributes are involved and allows spatial analysts and decision-makers to incorporate robustness into the mapmaking process more flexibly. • Robustness is a new perspective in determining the class break values for choropleth maps. • CRMCP allows flexible robustness definition and continuous class break values for mapping uncertain geographical attributes. • A particle swarm optimization algorithm is developed to solve the CRMCP model. • The proposed algorithm outperform the existing ones in terms of solution quality, efficiency, and versatility. [ABSTRACT FROM AUTHOR]

This research presents a study of visits to farmers' markets, a rapidly growing urban phenomenon in the U.S., from a geographic perspective. Although the social and economic impacts of farmers' markets have received considerable attention recently, examining farmers' market access in space-time is still lacking in the existing analytical frameworks. This study challenges conventional food access measures that have been primarily focused on the spatial separation between markets and consumers' homes and proposes a more realistic space-time based strategy. A survey was conducted on twelve markets in Tucson, Arizona. Analysis results show that majority of market patrons went to a market that was different from the one nearest to their homes, and the market choice, including the geographic location and the associated market operating time, was highly affected by patrons' other daily activities. The particular types of activities combined with farmers' market trips were found to vary temporally and fluctuate based on patron's employment characteristics. Our study suggests that conventional food access measures should be used in caution to assess farmers' market access as these measures can lead to an overestimate of the travel consumers are willing or even able to incur. [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]

Covering models have been used in a wide range of modeling and geospatial analysis applications ranging from planning emergency services to natural reserve design. One topic in coverage modeling that has received considerable research attention is addressing uncertainty due to facility unavailability and service disruptions. In this article, we propose a covering model that maximizes the expected coverage of demand by considering the possibility of facility failures. Unlike existing models that assume a uniform failure probability across all sites in an area, the proposed model can account for spatially varying failure probabilities and describes better the underlying geographic processes that cause facility failures. The model is posed as a spatial optimization problem using integer linear programming. We compare two different formulations of the covering model and discuss their properties. The proposed model formulations have been tested computationally using a warning sirens data set that has been widely used in assessing covering models. We conclude with a summary of findings as well as possible directions of future research. [ABSTRACT FROM PUBLISHER]

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]

This article discusses spatial optimization in geography, focusing on contributions of geographers in explicit geographical contexts. An overview of spatial optimization is given, as well as illustrative examples. Many of the individuals contributing to this area of the discipline are identified, demonstrating the breadth of academic institutions spanning the globe where spatial optimization is represented in the research and curriculum of geographers. The article provides a characterization of what a spatial optimization problem is, but also properties, relationships, and challenges behind this. The ultimate purpose of this article is to highlight the spatial optimization subspecialty within geography and in doing so, highlight the need for continued spatial model development and application in the discipline. Further, there is also a need for research focused on techniques to solve spatial optimization problems, particularly in the context of geographic information systems. [ABSTRACT FROM PUBLISHER]

Abstract: The recent drive among consumers to purchase fresh, healthy and environmentally friendly food has brought about a renewed interest in farmers’ markets. However, ensuring the success of a farmers’ market is not an easy task. Unlike general grocery stores, farmers’ markets often have very limited hours of service and are distributed sparsely in space. Both spatial and temporal constraints that people experience in their daily lives limit their accessibility to a farmers’ market. This research incorporates such constraints in the service provision planning of farmers’ markets. In particular, two models are proposed to select locations and the associated service schedules for a fixed number of farmers’ markets. A case study is conducted in Tucson, Arizona to demonstrate the merits of the new approach. [Copyright &y& Elsevier]

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]

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]

Many government agencies and corporations face locational decisions, such as where to locate fire stations, postal facilities, nature reserves, computer centers, bank branches, and so on. To reach such location-related decisions, geographical information systems (GIS) are essential for providing access to spatial data and analysis tools. Moreover, geographic insights can be gained from GIS as they enable capabilities for better reflecting problems of interest in location modeling. The resulting models can be complex, however, and hence computationally challenging to solve. This article examines an important model for regional service coverage maximization. This model is solved heuristically using a genetic algorithm. The new heuristic innovatively incorporates problem-specific knowledge by exploring the geographical structure of the problem under study. Comparative application results demonstrate important nuances of the new genetic algorithm, enhancing overall performance. [ABSTRACT FROM AUTHOR]

Abstract: It is not uncommon to see maps and more advanced statistical or spatial analysis reported in the media. Geographical information systems (GIS) have no doubt facilitated access to and analysis of spatial information. This article is the outgrowth of contact by the Boston Globe and subsequent published articles examining structure fires and the location of fire stations in Massachusetts. The questions asked by the Boston Globe are important geographical ones regarding location specific urban/rural service provision issues, and to answer these questions requires the use of spatial analysis. This article focuses on the use of methods for answering the questions raised in the context of applying academic research, an issue that continues to be a challenge. [Copyright &y& Elsevier]

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]

Geoforensics leverages various spatial analytical, biological, ecological, and geological techniques to improve criminal investigations, including efforts to thwart terrorism, evaluate humanitarian crises or identify the origin of fraudulent goods. In addition to investigating what took place, geoforensic efforts often focus on determining where and when the event occurred. Forensic palynology is a promising subfield of geoforensics that uses pollen to link persons or objects to particular places and times. This promise is driven by pollen's strong utility as a biomarker. It is ubiquitous, durable and demonstrates a relatively predictable distribution in time and space. As a result, pollen often provides important clues for determining the provenance of hard-to-trace items, such as computers, fraudulent goods, digging tools, clothing, and undetonated explosives during geoforensic investigations. One major limitation that has reduced the implementation of forensic palynology is the lack of computational tools that relate pollen species to geographic locations. The purpose of this paper is to introduce a robust geocomputational framework that uses pollen sample composition to identify the relative likelihood of potential origin locations for improving geoforensic efforts. Using USDA CropScape data for the state of Texas, our results suggest that this framework allowed for multiple potential origin sites to be identified simultaneously, with solution properties that were better than a random process and offer a possible alternative to the single site joint probability approaches commonly used in the field of forensic palynology. • Pollen is small, durable and ubiquitous – making it an excellent biomarker for geoforensics • Pollen can be linked to specific places at specific times • We present geocomputational framework that uses pollen to identify potential origin locations for objects of interest • GOFIND offers an alternative to the single site, joint probability approaches commonly used in forensic palynology. [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]

Access to a variety of affordable and healthy food has been a critical component in sustainable food-system planning. Research on food accessibility and food deserts (low-income areas with no or limited access to healthy food) can have important policy implications for alleviating health disparities. In the existing food access literature, supermarkets or large chain grocery stores have typically been used as the basis for measuring food access. Independent/non-chain grocers are often left out. We propose a multidimensional accessibility based assessment method to examine whether and how independent grocers help shape the food landscape and their locational strategy. A food desert elimination optimization model is formulated to evaluate the effectiveness of relying on small, full-service grocers for servicing food desert neighborhoods. The empirical study conducted in Tucson, Arizona indicates that, while full-service independent grocers fill some gaps left by chain markets, such stores are more helpful for improving food access diversity; this is reflected by the co-locating patterns of chain and non-chain stores. A few independent stores do, however, primarily serve food deserts in racially/ethnically diverse neighborhoods where higher proportions of residents rely on public assistance program and have limited mobility. Our case study suggests that small, independent grocers may have significant potential to aid in servicing areas with no or limited access to healthy food, particularly if policy incentives are provided. • We examined the role of full service independent grocery stores in shaping urban foodscape. • A spatial optimization model was developed to maximally eliminate food deserts. • Independent grocers are found to fill in the gaps left behind by chain stores in some communities while in many more add to the diversity rather than proximity. • They tend to serve more racially/ethnically diversified and dense neighborhoods. • Promoting small independent grocers in un- or underserved areas has great potential to improve regional food accessibility. [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]

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]

We introduce network science methods to uncover inherent characteristics of functional regions. An aggregate spatial interaction network is constructed based on a large mobile phone data set including 431 million mobile calls made by 10 million anonymous customers over one month and the geographic locations of the mobile base towers involved in each call. We use Thiessen polygons (termed 'cells') as the unit of analysis to approximate the service area of each mobile base tower. Major findings encompass the following three aspects. First, cells with high betweenness centrality are linearly distributed in space, which closely aligns with major transportation corridors. We find that this pattern can be explained by analysing the characteristics of calling activities on transportation networks. Second, we detect a two-level hierarchy of communities that correspond well to county and prefecture-level administrative unit boundaries. Lastly, almost every community identified at the two hierarchical levels contains a cell with high betweenness. These cells are located near the political and economic centres and play the role of hubs in the regional socio-economic system. This research demonstrates that networks built from mobile phone data provide new understandings of spatial interactions and regional structures. [ABSTRACT FROM AUTHOR]

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]

Summary Streamflow stations are typical hydrometric stations that serve as the basic components of hydrological monitoring networks. To effectively obtain information for predicting the magnitude and frequency of future floods and droughts, an optimal configuration of streamflow stations should be established to maximize the sensing capability with limited resources. In this paper, we propose a method to site streamflow stations in space to maximize the total area for streamflow monitoring. Considering the special regulations for deploying streamflow stations, a modified maximal covering location problem (MCLP) model is introduced. The effective coverage range of a streamflow station is determined based on the minimum density required and the site-specific terrain slope. The candidate sites are assumed to be continuously distributed along a river, and the river network is abstracted as a series of line-based river segments. The covering priority for each segment can be determined by the river length, the drainage area, or the level of flooding risk. The hydrometric network intersection point set (HNIPS) is proposed to identify finite candidate sites along a river. By narrowing the continuous search space to a discrete point set, this siting problem is solved using the MCLP-based model and HNIPS. The Jinsha River Basin is selected as a study area to test the proposed streamflow station siting method. Results show that the proposed method is effective in prescribing the optimal configuration of streamflow stations and the model solution achieves better coverage than that of the real-world deployment. The applicability of the proposed optimal siting method using HNIPS is analyzed. The criteria for candidate site selection and impacts of different weighting schemes applied to river segments are also discussed. [ABSTRACT FROM AUTHOR]

• A novel maximum cover location problem is developed for rooftop PV panel layout. • High resolution images of three distinct rooftop structures are collected with a UAV. • Rooftop structure, panel size, and irradiance determine optimal PV panel layout. • Results highlight the importance of considering panel layout for energy estimates. • The framework provides a spatially explicit optimized panel configuration for each rooftop. Recent years have seen a substantial increase in energy produced by renewable sources. The International Energy Agency (IEA) expects a large portion of future growth in renewable energy to come from solar, especially rooftop photovoltaic (PV) systems. Studies focused on estimating rooftop solar energy potential generally use the total area available for PV installation as determined by solar irradiance availability. This process can lead to substantial over- or under-estimation of energy estimates. Only a few studies have incorporated the spatial layout of PV panels in the solar energy generation estimates, and none have simultaneously considered PV panel size, orientation, and rooftop structure. We address this limitation with a new spatially explicit optimization framework to enhance the accuracy of rooftop solar energy assessments. We consider both the roof's structural configuration and the shape and size of the panels in a novel maximum cover spatial optimization model. After applying the framework to three different types of rooftops (flat roof, pitched roof, and complex roof), we find that conventional methods can lead to a nearly 60% overestimation of energy potential compared to the optimized panel layout. Our work illustrates the importance of considering panel size and rooftop characteristics and offers a mechanism for designing more efficient rooftop PV systems. [ABSTRACT FROM AUTHOR]

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]

Abstract: Background: Clandestine laboratory operators commonly extract ephedrine and pseudoephedrine—precursor chemicals used to synthesize methamphetamine—from over-the-counter cold/allergy/sinus products. To prevent this activity, two states, Oregon in 07/2006 and Mississippi in 07/2010, implemented regulations classifying ephedrine and pseudoephedrine as Schedule III substances, making products containing them available by prescription only. Using simple pre-regulation versus post-regulation comparisons, reports claim that the regulations have substantially reduced clandestine laboratory seizures (an indicator of laboratory prevalence) in both states, motivating efforts to implement similar regulation nationally. This study uses ARIMA-intervention time-series analysis to more rigorously evaluate the regulations’ impacts on laboratory seizures. Methods: Monthly counts of methamphetamine clandestine laboratory seizures were extracted from the Clandestine Laboratory Seizure System (2000—early 2011) for Oregon, Mississippi and selected nearby states (for quasi-control). Findings: Seizures in Oregon and nearby western states largely bottomed out months before Oregon''s regulation, and changed little thereafter. No significant impact for Oregon''s regulation was found. Mississippi and nearby states generally had elevated seizures before Mississippi''s regulation. Mississippi experienced a regulation-associated drop of 28.9 seizures (50.2%) in the series level (p <0.01), while nearby states exhibited no comparable decline. Conclusions: Oregon''s regulation encountered a floor effect, making any sizable impact infeasible. Mississippi, however, realized a substantial impact, suggesting that laboratories, if sufficiently extant, can be meaningfully impacted by prescription precursor regulation. It follows that national prescription precursor regulation would have little impact in western states with low indicated laboratory prevalence, but may be of significant use in regions facing higher indicated prevalence. [Copyright &y& Elsevier]

Abstract: This paper provides a new perspective on human motion with an investigation of whether and how patterns of human mobility inside cities are affected by two urban morphological characteristics: compactness and size. Mobile phone data have been collected in eight cities in Northeast China and used to extract individuals’ movement trajectories. The massive mobile phone data provides a wide coverage and detailed depiction of individuals’ movement in space and time. Considering that most individuals’ movement is limited within particular urban areas, boundaries of urban agglomerations are demarcated based on the spatial distribution of mobile phone base towers. Results indicate that the distribution of human’s intra-urban travel in general follows the exponential law. The exponents, however, vary from city to city and indicate the impact of city sizes and shapes. Individuals living in large or less compact cities generally need to travel farther on a daily basis, and vice versa. A Monte Carlo simulation analysis based on Levy flight is conducted to further examine and validate the relation between intra-urban human mobility and urban morphology. [Copyright &y& Elsevier]

The two major rainfall observation techniques, ground-based measurements and remote sensing, have distinct coverage characteristics. Large-scale spatial coverage and long-term temporal coverage cannot be achieved simultaneously by using only ground-based precipitation stations or space-borne sensors. Given the temporal discontinuity of satellite coverage and limited ground-based observation resources, we propose a method for siting precipitation stations in conjunction with satellite-based rainfall sensors to maximize the total spatial-temporal coverage of weighted demand in a continuous observation period. Considering the special principles of deploying precipitation stations and the requirement for continuous coverage in space and time, a time-continuous maximal covering location problem (TMCLP) model is introduced. The maximal spatial coverage range of a precipitation station is determined based on the minimum density required and the site-specific terrain conditions. The coverage of a satellite sensor is calculated for each time period when it passes overhead. The polygon intersection point set (PIPS) is refined to identify finite candidate sites. By narrowing the continuous search space to a finite dominating set and discretizing the continuous observation period to sequential sub-periods, the siting problem is solved using the TMCLP model and refined PIPS. According to specific monitoring purposes, different weighting schemes can be used to evaluate the coverage priority of each demand object. The Jinsha River Basin is selected as the study region to test the proposed method. Satellite-borne precipitation radar is used to evaluate the satellite coverage. The 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. The applicability of the proposed method, site selection criteria, deployment strategies in different observation modes, and the impacts of different weighting schemes are also discussed. [ABSTRACT FROM AUTHOR]