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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]

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]

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]

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]

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]

Despite the potential for pollen to provide key geographic information, law enforcement agencies have historically underutilized forensic palynology. Pollen samples are rarely collected at crime scenes or on objects of interest, and experts needed to manually identify pollen and its morphological characteristics are often in short supply. Fortunately, the advent of DNA barcoding and metabarcoding is poised to change this, enabling law enforcement agencies to easily and quickly identify individual plant species from mixed pollen samples. However, determining the location of pollen deposition based on identified plant species remains challenging, requiring comprehensive plant geodatabases and complex analytical approaches. This type of analysis is also complicated because pollen samples from forensic objects are not comprehensive. Furthermore, while detecting common plant pollen in a sample is likely, common plants provide less explanatory power due to their ubiquity. Thus, we propose a network-based approach to construct a categorization of 'forensically-useful' plants – those species that characterize geographically distinct plant mixes quickly for forensic applications and thus provide significant explanatory power to a geolocation model. The developed tool can better inform more detailed forensic search models, significantly reducing their overall computational burden and enabling high-fidelity forensic computational tools to operate faster, at a higher resolution, and over a more extensive study area. • Geolocation of forensic pollen samples is a persistent challenge in palynology • Recent machine learning models are effective but computationally intensive • Modified social network analytical tools can be used to link pollen to place • These tools allow for a first-pass geolocation filter [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

• 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]

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]