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In 2021, the US Surgeon General issued a national advisory citing an epidemic of isolation and loneliness. Even before the onset of the COVID-19 pandemic, approximately half of people in the US reported experiencing measurable levels of loneliness. Despite localized and select cross-sectional studies highlighting even higher increases in isolation/loneliness during the COVID-19 pandemic, additional research is needed, particularly for youth and young adults. This work examines patterns of isolation/loneliness across the US from 2016 to 2022 among individuals aged 24 and younger. Our study leverages a unique dataset, Crisis Text Line, which provides complete spatiotemporal coverage of crisis conversations in the US. We conducted a geospatial analysis using Kuldroff’s Space–Time SatScan to identify statistically significant clustering of elevated isolation/loneliness-related conversations. The statistical significance of spatiotemporal clusters was determined using Monte Carlo simulations (n = 9999). Results demonstrated local relative risk as high as 1.47 in high-risk populations in Southern, Midwest, and Atlantic states, indicating areas where the actual case count is 147% of the expected cases (p value < 0.01) from May to July 2020. Results also identified co-occurrence of isolation/loneliness and other crises concerns, including depression/sadness, anxiety, and multiple suicidality indicators, with higher rates among racial/ethnic minority, transgender and gender diverse, and younger individuals. This work makes a unique contribution to the literature by elucidating spatiotemporal disparities in isolation/loneliness among young people, providing much-needed knowledge as to where future public health interventions are immediately needed. [ABSTRACT FROM AUTHOR]

In March 1996, a jaguar (Panthera onca) named Border King was seen in Arizona's Peloncillo Mountains, followed by a sighting of a second male, Macho B, in September. The cats had crossed the U.S.-Mexico border and quickly came to symbolize a conservation success story in complicated geopolitical terrain. Two decades later, the Trump Administration's increased militarization of the borderlands prompted concerns about the deleterious impacts of border wall expansion for jaguar movement and survival. This study examines the expansion of border barriers, and potential impact on jaguar habitat. Using geospatial technologies and public data, we measure border barrier expansion between 2005 and 2021. We found that of the suitable jaguar habitat that touched the border in the study area (155 km), 86% (or 133 km) had been cut off by border barrier by 2021. We distinguish "wall" from other barriers, including vehicle barriers, using aerial imagery. Our results show although barriers built from 2006 to 2015 were triple the length of those built under Trump, the majority consisted of vehicle barriers, which animals may be able to cross. Trump era construction shifted vehicle barriers to restrictive walls limiting animal movement. We argue examining the type of barrier is crucial in understanding the potential for border "security" disruption to jaguar movement and futures in the borderlands. [ABSTRACT FROM AUTHOR]

The main objective of the current study was to use seven lots in Hartford, CT that are planned for community reuse to determine the optimal sampling density that allows for the detection of hotspots of lead pollution while limiting the labor of the sampling process. The sampling density was investigated using soil Pb measured by in situ X-ray Fluorescence as the indicator to evaluate soil health, with a new threshold of 200-mg/kg proposed by the USEPA in January of 2024. Even though this study takes place in an urban setting, where the new USEPA policy requires the use of a 100-mg/kg threshold for Pb due to the fact that there are other identifiable sources of the contaminant, only the 200-mg/kg threshold is discussed because it is evident from the analysis that compliance of a 100 mg/kg threshold in urban plots is highly unlikely (five out of seven sites would require complete site excavation prior to reuse). Using the inverse distance weighted geospatial interpolation of in situ pXRF determined lead measurements, grid sampling resolutions of 3-m, 4-m, 5-m, 6-m, 8-m, 10-m, and 12-m were compared. Ultimately, the case study finds that the largest grid resolution that can be implemented for soil screening to maintain hotspots of pollution to properly inform soil management decisions is a 6-m grid, or a density of approximately 1/36-m2. [ABSTRACT FROM AUTHOR]

Cholera, an acute waterborne diarrheal disease, remains a major global health challenge. Despite being curable and preventable, it can be fatal if left untreated, especially for children. Bangladesh, a cholera‐endemic country with a high disease burden, experiences two peaks annually, during the dry pre‐monsoon spring and the wet post‐monsoon fall seasons. An early warning system for disseminating cholera risk, which has potential to reduce the disease burden, currently does not exist in Bangladesh. Such systems can raise timely awareness and allow households in rural, riverine areas like Matlab to make behavioral adjustments with water usage and around water resources to reduce contracting and transmitting cholera. Current dissemination approaches typically target local government and public health organizations; however, the vulnerable rural populations largely remain outside the information chain. Here, we develop and evaluate the accuracy of an early warning system—CholeraMap that uses high‐resolution earth observations to forecast cholera risk and disseminate geocoded risk maps directly to Matlab's population via a mobile smartphone application. Instead of relying on difficult to obtain station‐based environmental and hydroclimatological data, this study offers a new opportunity to use remote sensing data sets for designing and operating a disease early warning system. CholeraMap delivers monthly, color‐coded geospatial maps (1 km × 1 km spatial resolution) with household and community cholera risk information. Our results demonstrate that the satellite‐derived local‐scale risk model satisfactorily captured the seasonal cholera pattern for the Matlab region, and a detailed high‐resolution picture of the spatial progression of at‐risk areas during outbreak months. Plain Language Summary: Cholera, an acute waterborne diarrheal disease, remains a major public health challenge in developing nations. An early warning system for disseminating cholera risk has the potential to reduce the disease burden in rural, riverine, and endemic countries like Bangladesh. Current dissemination approaches typically target local government and public health organizations but largely overlook the vulnerable rural populations. We develop and evaluate the accuracy of an early warning system—CholeraMap that uses satellite remote sensing data to forecast cholera risk and disseminate geocoded risk maps directly to the remote population of Matlab, Bangladesh via a mobile smartphone application. CholeraMap delivers monthly, color‐coded risk maps to provide users with household and community cholera risk information along with associated explanations of the risks. Our results satisfactorily captured the spatiotemporal progression of at‐risk areas during high outbreak months and were disseminated to the vulnerable population of Matlab via this novel smartphone application. Key Points: An early warning system for disseminating cholera risk has the potential to reduce disease burden in vulnerable endemic populationsThis study is the first attempt to combine satellite data sets and smartphone‐based dissemination for waterborne diarrheal disease applicationsCholeraMap captured the spatiotemporal patterns and high‐resolution view of the progression of cholera risk during outbreak months [ABSTRACT FROM AUTHOR]

This study examines the association between the location, relative to the coast, of an individual's primary residence and the homeowners' risk tolerance. Utilizing data from the 2021 National Financial Capability Study and employing a probit model, we analyzed how varying risk tolerance levels affect the likelihood of owning a home in a coastal ZIP code. The respondent's risk tolerance was classified as high, medium, or low according to their self-reported willingness to take financial risks. Our results suggest that individuals with lower risk tolerances are less likely to own a home within a coastal ZIP code. Specifically, homeowners with medium-risk tolerance are 2.91% less likely, and those with low-risk tolerance are 3.17% less likely to own a primary residence in a coastal ZIP code when compared to those with high-risk tolerance. These results are statistically and economically similar when using a logit model. These findings are both statistically significant and align with economic theory. The analysis also included various demographic and socioeconomic factors, finding that age, income, and certain employment statuses influence coastal homeownership. This research contributes to the understanding of home ownership location choices and risk tolerance. Our results provide policymakers with insights into the risk characteristics of individuals who prefer coastal areas as their primary residences. This information can inform future policy decisions by highlighting the societal and economic implications of regulations related to residential coastal development. [ABSTRACT FROM AUTHOR]

Following up the previously published systematic review on the same topic and realizing that new studies and evidence had emerged on the matter, we conducted an update on the same research terms. With the objective of updating the information relating environmental risk factors on neurodegenerative diseases and the geographic approaches used to address them, we searched PubMed, Web of Science and Scopus for all scientific studies considering the following three domains: neurodegenerative disease, environmental atmospheric factor and geographical analysis, using the same keywords as in the previously published systematic review. From February 2020 to February 2023, 35 papers were included versus 34 in the previous period, with dementia (including Alzheimer's disease) being the most focused disease (60.0%) in this update, opposed to multiple sclerosis on the last review (55.9%). Also, environmental pollutants such as PM2.5 and NO2 have gained prominence, being represented in 65.7% and 42.9% of the new studies, opposed to 9.8% and 12.2% in the previous review, compared to environmental factors such as sun exposure (5.7% in the update vs 15.9% in the original). The mostly used geographic approach remained the patient's residence (82.9% in the update vs 21.2% in the original and 62.3% in total), and remote sensing was used in 45.7% of the new studies versus 19.7% in the original review, with 42.0% of studies using it globally, being the second most common approach, usually to compute the environmental variable. This review has been registered in PROSPERO with the number CRD42020196188. [ABSTRACT FROM AUTHOR]

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning and development. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by rapid population growth. Over the past decade, multiple projects have been undertaken to produce regional and national land cover maps. However, using different classification systems and legends has made updating and sharing land cover information challenging. This has resulted in the inefficient use of human and financial resources. The development of the Land Cover Meta Language (LCML) based on International Organization for Standardization (ISO) standards offers an opportunity to create a standardized classification system. This system would enable easier integration of regional and national data, efficient management of information, and better resource utilization in West Africa. This article emphasizes the process and the need for multistakeholder collaboration in developing a standardized land cover classification system for West Africa, which is currently nonexistent. It presents the survey data collected to evaluate historical, current, and future land cover mapping projects in the region and provides relevant use cases as examples for operationalizing a standardized land cover classification legend for West Africa. [ABSTRACT FROM AUTHOR]

Abstract Following up the previously published systematic review on the same topic and realizing that new studies and evidence had emerged on the matter, we conducted an update on the same research terms. With the objective of updating the information relating environmental risk factors on neurodegenerative diseases and the geographic approaches used to address them, we searched PubMed, Web of Science and Scopus for all scientific studies considering the following three domains: neurodegenerative disease, environmental atmospheric factor and geographical analysis, using the same keywords as in the previously published systematic review. From February 2020 to February 2023, 35 papers were included versus 34 in the previous period, with dementia (including Alzheimer’s disease) being the most focused disease (60.0%) in this update, opposed to multiple sclerosis on the last review (55.9%). Also, environmental pollutants such as PM2.5 and NO2 have gained prominence, being represented in 65.7% and 42.9% of the new studies, opposed to 9.8% and 12.2% in the previous review, compared to environmental factors such as sun exposure (5.7% in the update vs 15.9% in the original). The mostly used geographic approach remained the patient’s residence (82.9% in the update vs 21.2% in the original and 62.3% in total), and remote sensing was used in 45.7% of the new studies versus 19.7% in the original review, with 42.0% of studies using it globally, being the second most common approach, usually to compute the environmental variable. This review has been registered in PROSPERO with the number CRD42020196188.

Abstract An integrated approach using geophysical and geospatial techniques was employed to model the surface and subsurface water-bearing strata and assess aquifer vulnerability in the Sehnsa town, Kotli district, State of Azad Kashmir, Pakistan. The inadequate scientific studies in the hilly terrain with such complex geological conditions has led to the failure of the boreholes for groundwater extraction. For the evaluation of groundwater potential and subsurface lithology, 30 vertical electrical soundings (VES) stations utilizing the Schlumberger electrode configuration were completed, modeled and analyzed spatially. Numerous geoelectrical parameters like true resistivity, thickness of subsurface layers and Dar-Zarrouk parameters were evaluated. The subsurface lithology delineated comprised topsoil, clayey sand, sandstone, and boulder clays which closely resemble to the borehole lithologs available in the study area. The inversion model confirms the presence of patches of high-resistivity sandstone in the southwestern part of the study area with the maximum thickness of the aquifer up to 140 m. Most aquifers were classified as unconfined with Q–type resistivity curves. The protective overburden capacity of the aquifers is rated as poor at VES 1, 3–5, 8, 10–16, 18, 19, 22–25, 27 and 30 whereas the moderate category was found at VES 2, 9 and 20 and excellent at VES 7 and 28, respectively. Therefore, the VES stations with poor and moderate ratings of overburden protective capacity are vulnerable for surface contaminants. The aquifer recharge was associated with rainfall and partly from the Poonch River. The effective integration of geophysical and geospatial techniques in this study provides sufficient information about the regional water resources and gives a preliminary model that can facilitate efficient water resource management in the area. These approaches can be successfully applied to diverse geographical and hydrogeological sites due to their versatility and reliability.

Abstract Background This paper focuses on the period from 2019 to 2021 and investigates the factors associated with the high prevalence of C-section deliveries in South India. We also examine the nuanced patterns, socio-demographic associations, and spatial dynamics underlying C-section choices in this region. A cross-sectional study was conducted using large nationally representative survey data. Methods National Family Health Survey data (NFHS) from 2019 to 2021 have been used for the analysis. Bayesian Multilevel and Geospatial Analysis have been used as statistical methods. Results Our analysis reveals significant regional disparities in C-section utilization, indicating potential gaps in healthcare access and socio-economic influences. Maternal age at childbirth, educational attainment, healthcare facility type size of child at birth and ever pregnancy termination are identified as key determinants of method of C-section decisions. Wealth index and urban residence also play pivotal roles, reflecting financial considerations and access to healthcare resources. Bayesian multilevel analysis highlights the need for tailored interventions that consider individual household, primary sampling unit (PSU) and district-level factors. Additionally, spatial analysis identifies regions with varying C-section rates, allowing policymakers to develop targeted strategies to optimize maternal and neonatal health outcomes and address healthcare disparities. Spatial autocorrelation and hotspot analysis further elucidate localized influences and clustering patterns. Conclusion In conclusion, this research underscores the complexity of C-section choices and calls for evidence-based policies and interventions that promote equitable access to quality maternal care in South India. Stakeholders must recognize the multifaceted nature of healthcare decisions and work collaboratively to ensure more balanced and effective healthcare practices in the region.

Abstract Unionized workers often use striking and picketing to raise attention to their grievances, dissuade customers from patronizing a business, and pressure employers in negotiations. Despite its wide use and recognition in popular culture, the effects of picketing and striking on retail business are not well understood. Adjacent literature has used cell phone tracking and other digital geo-tagging techniques to measure the effects of factory closures, COVID-19 restrictions, and stimulus payments on store patronage and economic activity. This article provides a case study using mobile geolocation data to quantify the loss of store foot traffic due to striking with picketing by analyzing the 2022 King Soopers strike in Colorado, USA. Using the historic foot traffic data of the past two years for 118 King Soopers locations, 78 of which went on strike, two SARIMA models were trained, and their predicted foot traffic values were compared to the actual values during the strike period. This technique indicates an average 47% decrease in foot traffic for striking stores and a 14% decrease in foot traffic for nonstriking locations.

Mapping groundwater potential zones helps in precisely planning well drilling. The Meki Catchment is currently experiencing irrigation expansion and water scarcity challenges, creating a strong need for groundwater exploitation. The goal of this study was to identify groundwater potential zones (GWPZs) in the Meki Catchment using geospatial technologies to enhance water resource management and support sustainable development in the region. Eight thematic components were used in this analysis. Analytical Hierarchy Process (AHP) method was applied to ensure consistency in judgments through paired comparisons. Weighted overlay analysis was then used to evaluate the GWPZs. The study found that out of the total Meki Catchment area (226,333.26 ha), 30.68% (69,448.31 ha) has high potential, and 34% (76,963.33 ha) has good potential. Validation was conducted using ground truth data, considering existing water point field data from borewells and springs. The results of this study can help prioritize strategies to ensure the sustainable use of groundwater in the area. Drilling organizations can utilize these findings as a resource for feasibility studies in groundwater prospecting projects to locate well sites. The validation results demonstrate that potential groundwater zones can be identified more accurately using GIS and RS approaches at a lower cost.

Analyzing the groundwater potential zone is a fundamental first step in investigating groundwater resources in arid and semi-arid regions. This study examined the groundwater potential zone of the Fafen-Jerer sub-basin by applying geographic information system (GIS) and remote sensing (RS). The study used ten influencing factors, including geology, geomorphology, slope, soil, lineament density, drainage density, land use, land cover, topographic wetness index, topographic roughness index, and rainfall, to identify potential sites. Based on their effect on groundwater recharge, the sub-class of each influencing factor was identified and evaluated. The weights were determined using the multi-criteria decision-making method’s analytical hierarchy process technique. At the conclusion of the investigation, the region was divided into four potential zones: low, moderate, high, and extremely high. The study region comprises 84% moderate potential zones, 14% high groundwater potential zones, and 2% low and extremely high potential zones. area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the groundwater potential zone, and the findings show extremely good performance (AUC = 0.87). The study provides recommendations for stakeholders and water management professionals to develop a strategy for water resource management based on the conjunctive use of surface and groundwater.

Unionized workers often use striking and picketing to raise attention to their grievances, dissuade customers from patronizing a business, and pressure employers in negotiations. Despite its wide use and recognition in popular culture, the effects of picketing and striking on retail business are not well understood. Adjacent literature has used cell phone tracking and other digital geo-tagging techniques to measure the effects of factory closures, COVID-19 restrictions, and stimulus payments on store patronage and economic activity. This article provides a case study using mobile geolocation data to quantify the loss of store foot traffic due to striking with picketing by analyzing the 2022 King Soopers strike in Colorado, USA. Using the historic foot traffic data of the past two years for 118 King Soopers locations, 78 of which went on strike, two SARIMA models were trained, and their predicted foot traffic values were compared to the actual values during the strike period. This technique indicates an average 47% decrease in foot traffic for striking stores and a 14% decrease in foot traffic for nonstriking locations. [ABSTRACT FROM AUTHOR]

Background: This paper focuses on the period from 2019 to 2021 and investigates the factors associated with the high prevalence of C-section deliveries in South India. We also examine the nuanced patterns, socio-demographic associations, and spatial dynamics underlying C-section choices in this region. A cross-sectional study was conducted using large nationally representative survey data. Methods: National Family Health Survey data (NFHS) from 2019 to 2021 have been used for the analysis. Bayesian Multilevel and Geospatial Analysis have been used as statistical methods. Results: Our analysis reveals significant regional disparities in C-section utilization, indicating potential gaps in healthcare access and socio-economic influences. Maternal age at childbirth, educational attainment, healthcare facility type size of child at birth and ever pregnancy termination are identified as key determinants of method of C-section decisions. Wealth index and urban residence also play pivotal roles, reflecting financial considerations and access to healthcare resources. Bayesian multilevel analysis highlights the need for tailored interventions that consider individual household, primary sampling unit (PSU) and district-level factors. Additionally, spatial analysis identifies regions with varying C-section rates, allowing policymakers to develop targeted strategies to optimize maternal and neonatal health outcomes and address healthcare disparities. Spatial autocorrelation and hotspot analysis further elucidate localized influences and clustering patterns. Conclusion: In conclusion, this research underscores the complexity of C-section choices and calls for evidence-based policies and interventions that promote equitable access to quality maternal care in South India. Stakeholders must recognize the multifaceted nature of healthcare decisions and work collaboratively to ensure more balanced and effective healthcare practices in the region. [ABSTRACT FROM AUTHOR]

This pioneering study explores the geospatial and temporal patterns of natural and human-induced disasters from 1900 to 2024, providing essential insights into their global distribution and impacts. Significant trends and disparities in disaster occurrences and their widespread consequences are revealed through the utilization of the comprehensive international EM-DAT database. The results showed a dramatic escalation in both natural and man-made (technological) disasters over the decades, with notable surges in the 1991–2000 and 2001–2010 periods. A total of 25,836 disasters were recorded worldwide, of which 69.41% were natural disasters (16,567) and 30.59% were man-made (technological) disasters (9269). The most significant increase in natural disasters occurred from 1961–1970, while man-made (technological) disasters surged substantially from 1981–1990. Seasonal trends reveal that floods peak in January and July, while storms are most frequent in June and October. Droughts and floods are the most devastating in terms of human lives, while storms and earthquakes cause the highest economic losses. The most substantial economic losses were reported during the 2001–2010 period, driven by catastrophic natural disasters in Asia and North America. Also, Asia was highlighted by our research as the most disaster-prone continent, accounting for 41.75% of global events, with 61.89% of these events being natural disasters. Oceania, despite experiencing fewer total disasters, shows a remarkable 91.51% of these as natural disasters. Africa is notable for its high incidence of man-made (technological) disasters, which constitute 43.79% of the continent's disaster events. Europe, representing 11.96% of total disasters, exhibits a balanced distribution but tends towards natural disasters at 64.54%. Examining specific countries, China, India, and the United States emerged as the countries most frequently affected by both types of disasters. The impact of these disasters has been immense, with economic losses reaching their highest during the decade of 2010–2020, largely due to natural disasters. The human toll has been equally significant, with Asia recording the most fatalities and Africa the most injuries. Pearson's correlation analysis identified statistically significant links between socioeconomic factors and the effects of disasters. It shows that nations with higher GDP per capita and better governance quality tend to experience fewer disasters and less severe negative consequences. These insights highlight the urgent need for tailored disaster risk management strategies that address the distinct challenges and impacts in various regions. By understanding historical disaster patterns, policymakers and stakeholders can better anticipate and manage future risks, ultimately safeguarding lives and economies. [ABSTRACT FROM AUTHOR]

Delivering value from digital concepts such as Digital Twins is necessary to address systemic national and global issues, such as achieving Net Zero. However, there is still a lack of consensus over what a Digital Twin (DT) is and efforts to clarify this do not consider the Geospatial perspective. With the aspiration for national- and international-scale DTs, it is important that the Geospatial community understands its role in supporting the realisation of the value of these DTs. Here, a systematic literature review is used to gather DT case studies that use, or are inferred to use, elements of the Geospatial discipline. A total of 77 DT case studies about smart cities, manufacturing, energy, construction and agriculture are reviewed in full, and 24 Geospatial DT dimensions are defined and then compared with existing DT dimensions. The results indicate a considerable use of Geospatial Science in DTs that is not explicitly stated, meaning that there are possibly missed opportunities for collaboration between the Geospatial and DT communities. We conclude that the role of Geospatial Science in DTs is larger than stated and needs to be understood further. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of rooftop photovoltaic (PV) systems in mitigating energy vulnerability in the urban context. Based on a geospatial data-driven approach, it combines georeferenced assessment of solar potential and high-resolution demand data with energy vulnerability indicators for both heating and cooling needs, to identify priority areas for supporting PV deployment. Results show more than 50% saving potential in the energy bill for the selected priority areas. The mismatch between PV supply and demand supports the development of demand-aggregating collective self-consumption approaches such as solar energy communities, whose challenges and opportunities are discussed. [ABSTRACT FROM AUTHOR]

Knowledge of land is of central importance to manage the impact of mankind upon the environment. The understanding and treatment of land vary greatly across different regions and communities, making the description of land highly specific to each locality. To address the larger global issues, such as world food production or climate change mitigation, one needs to have a common standardized understanding of the biosphere cover and use of land. Different governments and institutions established national systems to describe thematic content of land within their jurisdictions. These systems are all valid and tuned to address various national needs. However, their integration at regional or global levels is lacking. Integrating data from widely divergent sources to create world datasets not only requires standards, but also an approach to integrate national and regional land cover classification systems. The ISO 19144 series, developed through the collaboration between the Food and Agriculture Organization of the United Nations (FAO) and the International Organization for Standardization (ISO), offers a meta-language for the integration of disparate land classification systems, enhancing interoperability, data sharing, and national to global data integration and comparison. This paper provides an overview of classification system concepts, different stages for the development of standards in ISO and the status of different standards in the ISO 19144 series. It also explores the critical role of the ISO 19144 series in standardizing land cover and land use classification systems. Drawing on practical case studies, the paper underscores the series' potential to support global sustainable development goals and lays out a path for its future development and application. Using these standards, we gain not only a tool for harmonizing land classification, but also a critical level for advancing sustainable development and environmental stewardship worldwide. [ABSTRACT FROM AUTHOR]

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully‐connected neural networks. Plain Language Summary: Many machine learning methods have been applied to geospatial data, that is data that has a spatial dimension and corresponds to particular regions on Earth. However, many of these methods do not account for the fact that data recorded at nearby locations will be correlated. We apply a deep learning method called a graph neural network to overcome this issue. Our application to West Nile virus forecasting in Illinois could aid mosquito surveillance and abatement efforts within the state. This shows that graph neural networks could be a good option for other geospatial data, since they outperform a range of baseline methods in our particular problem. Key Points: Many machine learning methods applied to environmental problems do not account for spatial dependenceWe apply a spatially aware graph neural network model to forecast West Nile virusGraph neural networks applied to irregularly sampled geospatial data can outperform a range of baseline methods [ABSTRACT FROM AUTHOR]

Abstract Cholera, an acute waterborne diarrheal disease, remains a major global health challenge. Despite being curable and preventable, it can be fatal if left untreated, especially for children. Bangladesh, a cholera‐endemic country with a high disease burden, experiences two peaks annually, during the dry pre‐monsoon spring and the wet post‐monsoon fall seasons. An early warning system for disseminating cholera risk, which has potential to reduce the disease burden, currently does not exist in Bangladesh. Such systems can raise timely awareness and allow households in rural, riverine areas like Matlab to make behavioral adjustments with water usage and around water resources to reduce contracting and transmitting cholera. Current dissemination approaches typically target local government and public health organizations; however, the vulnerable rural populations largely remain outside the information chain. Here, we develop and evaluate the accuracy of an early warning system—CholeraMap that uses high‐resolution earth observations to forecast cholera risk and disseminate geocoded risk maps directly to Matlab's population via a mobile smartphone application. Instead of relying on difficult to obtain station‐based environmental and hydroclimatological data, this study offers a new opportunity to use remote sensing data sets for designing and operating a disease early warning system. CholeraMap delivers monthly, color‐coded geospatial maps (1 km × 1 km spatial resolution) with household and community cholera risk information. Our results demonstrate that the satellite‐derived local‐scale risk model satisfactorily captured the seasonal cholera pattern for the Matlab region, and a detailed high‐resolution picture of the spatial progression of at‐risk areas during outbreak months.

In March 1996, a jaguar (Panthera onca) named Border King was seen in Arizona’s Peloncillo Mountains, followed by a sighting of a second male, Macho B, in September. The cats had crossed the U.S.-Mexico border and quickly came to symbolize a conservation success story in complicated geopolitical terrain. Two decades later, the Trump Administration’s increased militarization of the borderlands prompted concerns about the deleterious impacts of border wall expansion for jaguar movement and survival. This study examines the expansion of border barriers, and potential impact on jaguar habitat. Using geospatial technologies and public data, we measure border barrier expansion between 2005 and 2021. We found that of the suitable jaguar habitat that touched the border in the study area (155 km), 86% (or 133 km) had been cut off by border barrier by 2021. We distinguish “wall” from other barriers, including vehicle barriers, using aerial imagery. Our results show although barriers built from 2006 to 2015 were triple the length of those built under Trump, the majority consisted of vehicle barriers, which animals may be able to cross. Trump era construction shifted vehicle barriers to restrictive walls limiting animal movement. We argue examining the type of barrier is crucial in understanding the potential for border “security” disruption to jaguar movement and futures in the borderlands.

The STAC is a recent geospatial standard that allows to describe and catalog geospatial assets. It is part of a broader innovation effort called Cloud-Native Geospatial, providing modern standards and tools to efficiently access raster and vector data in the cloud.

This article investigates the intersection of Artificial Intelligence (AI) with the space economy and its profound geopolitical implications. Artificial intelligence is increasingly powering space missions, satellite networks and resource utilization, thereby reshaping the global landscape of the space industry. AI-driven advances are fueling economic opportunities and competition among nations in space-related sectors and the resulting geopolitical effects. From satellite services to lunar and Martian exploration, AI is poised to become a driving force in shaping the balance of power in the space arena, making it a critical topic for policymakers, strategists and industry leaders.

Abstract Background The accessibility of pharmacies has been associated with overall health and wellbeing. Past studies have suggested that low income and racial minority communities are underserved by pharmacies. However, the literature is inconsistent in finding links between area-level income or racial and ethnic composition and access to pharmacies. Here we aim to assess area-level spatial access to pharmacies across New York State (NYS), hypothesizing that Census Tracts with higher poverty rates and higher percentages of Black and Hispanic residents would have lower spatial access. Methods The population weighted mean shortest road network distance (PWMSD) to a pharmacy in 2018 was calculated for each Census Tract in NYS. This statistic was calculated from the shortest road network distance to a pharmacy from the centroid of each Census block within a tract, with the mean across census blocks weighted by the population of the census block. Cross-sectional analyses were conducted to assess links between Tract-level socio demographic characteristics and Tract-level PWMSD to a pharmacy. Results Overall the mean PWMSD to a pharmacy across Census tracts in NYS was 2.07 Km (SD = 3.35, median 0.85 Km). Shorter PWMSD to a pharmacy were associated with higher Tract-level % poverty, % Black/African American (AA) residents, and % Hispanic/Latino residents and with lower Tract-level % of residents with a college degree. Compared to tracts in the lowest quartile of % Black/AA residents, tracts in the highest quartile had a 70.7% (95% CI 68.3–72.9%) shorter PWMSD to a pharmacy. Similarly, tracts in the highest quartile of % poverty had a 61.3% (95% CI 58.0-64.4%) shorter PWMSD to a pharmacy than tracts in the lowest quartile. Conclusion The analyses show that tracts in NYS with higher racial and ethnic minority populations and higher poverty rates have higher spatial access to pharmacies.

The World Health Organization identifies a strong surveillance system for malaria and its mosquito vector as an essential pillar of the malaria elimination agenda. Anopheles salivary antibodies are emerging biomarkers of exposure to mosquito bites that potentially overcome sensitivity and logistical constraints of traditional entomological surveys. Using samples collected by a village health volunteer network in 104 villages in Southeast Myanmar during routine surveillance, the present study employs a Bayesian geostatistical modeling framework, incorporating climatic and environmental variables together with Anopheles salivary antigen serology, to generate spatially continuous predictive maps of Anopheles biting exposure. Our maps quantify fine-scale spatial and temporal heterogeneity in Anopheles salivary antibody seroprevalence (ranging from 9 to 99%) that serves as a proxy of exposure to Anopheles bites and advances current static maps of only Anopheles occurrence. We also developed an innovative framework to perform surveillance of malaria transmission. By incorporating antibodies against the vector and the transmissible form of malaria (sporozoite) in a joint Bayesian geostatistical model, we predict several foci of ongoing transmission. In our study, we demonstrate that antibodies specific for Anopheles salivary and sporozoite antigens are a logistically feasible metric with which to quantify and characterize heterogeneity in exposure to vector bites and malaria transmission. These approaches could readily be scaled up into existing village health volunteer surveillance networks to identify foci of residual malaria transmission, which could be targeted with supplementary interventions to accelerate progress toward elimination. [ABSTRACT FROM AUTHOR]

Groundwater is an essential natural resource that sustains numerous ecological systems and human lifestyles. The Jhargram district is facing persistent groundwater development issues, requiring comprehensive monitoring and planning, as most farmers heavily rely on groundwater for crop production. The groundwater potential zone of the Jhargram district was classified into five classes, viz., very high (5.82%), high (50.81%), moderate (30.33%), low (13.01%), and very low (0.03%), zones, respectively. The results were assessed for validity using ROC curves, which demonstrated an accuracy rate of 80.4%. The calculation of the AUC was performed in order to assess the overall accuracy of the predicted GWPZ. The GWPZ map is crucial for implementing artificial recharge structures like percolation ponds, bunds and trenching in semi-arid regions. It aids in developing sustainable groundwater management policies, mitigating drought, climate change, and water scarcity, and aiding farmers, regional planners, policymakers, climate change experts, and local governments. [ABSTRACT FROM AUTHOR]

FedLens is the federated learning model based on viewing world tourist spots virtually in a privacy-preserving manner. We define Virtual Tourism as enjoying the natural beauty, other related activities online, using AR/VR/MR technology-based 'virtual eye', to interact actively with nature and people at tourist spots. Federated learning-based mobile crowdsensing is an emerging collaborative distributed learning paradigm for privacy-preserving, energy-efficient, and scalable networks. Edge intelligent mobile crowdsensing uses geotagged tourist attractions. The purpose of this study is to explore the geo-statistics of tourist areas. The proposed 'FedLens' brings tourists closer to the interests using augmented reality through the virtual guide. ArcGIS software maps a tourist area. 5G mobile crowdsensing helps to explore unknown tourist spots in real time. 'FedLens' provides a privacy-preserving incentive mechanism to encourage reliable contributors to get better Quality of Information. The average global data aggregation time is approximately 12%. The contributor's collection time is 88% of the total processing time. The contributors use multifaceted intelligent federated computing to provide detailed geospatial information and promote sustainable ecotourism. Augmented reality-based virtual tourism ecosystem development is the ultimate goal of this work to attract more virtual tourists for a sustainable environment. Future physical tour-planning recommendation systems are incorporated in the proposed model. [ABSTRACT FROM AUTHOR]

The STAC is a recent geospatial standard that allows to describe and catalog geospatial assets. It is part of a broader innovation effort called Cloud-Native Geospatial, providing modern standards and tools to efficiently access raster and vector data in the cloud. [ABSTRACT FROM AUTHOR]

Background: The accessibility of pharmacies has been associated with overall health and wellbeing. Past studies have suggested that low income and racial minority communities are underserved by pharmacies. However, the literature is inconsistent in finding links between area-level income or racial and ethnic composition and access to pharmacies. Here we aim to assess area-level spatial access to pharmacies across New York State (NYS), hypothesizing that Census Tracts with higher poverty rates and higher percentages of Black and Hispanic residents would have lower spatial access. Methods: The population weighted mean shortest road network distance (PWMSD) to a pharmacy in 2018 was calculated for each Census Tract in NYS. This statistic was calculated from the shortest road network distance to a pharmacy from the centroid of each Census block within a tract, with the mean across census blocks weighted by the population of the census block. Cross-sectional analyses were conducted to assess links between Tract-level socio demographic characteristics and Tract-level PWMSD to a pharmacy. Results: Overall the mean PWMSD to a pharmacy across Census tracts in NYS was 2.07 Km (SD = 3.35, median 0.85 Km). Shorter PWMSD to a pharmacy were associated with higher Tract-level % poverty, % Black/African American (AA) residents, and % Hispanic/Latino residents and with lower Tract-level % of residents with a college degree. Compared to tracts in the lowest quartile of % Black/AA residents, tracts in the highest quartile had a 70.7% (95% CI 68.3–72.9%) shorter PWMSD to a pharmacy. Similarly, tracts in the highest quartile of % poverty had a 61.3% (95% CI 58.0-64.4%) shorter PWMSD to a pharmacy than tracts in the lowest quartile. Conclusion: The analyses show that tracts in NYS with higher racial and ethnic minority populations and higher poverty rates have higher spatial access to pharmacies. [ABSTRACT FROM AUTHOR]

Research interest in Augmented Reality (AR) has been steadily increasing in the past decades. However, while we see a wide range of publications implementing AR nowadays, it is often the case that the software is not included in the publication, leading to future research having to re-introduce similar custom AR solutions. We identify one such case, that of geolocated AR, which poses a deceptively complex issue, namely that of correctly aligning a virtual view with geo-referenced material around the user. We present here UnityGeoAR, a package for the widely used game engine Unity3D, that handles geolocation, reprojection, and AR view alignment through a minimal Application Programming Interface (API), in an effort to standardize geo-AR development and reduce development and implementation time for such a widely used aspect of AR.

Abstract Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully‐connected neural networks.

Abstract Both developed and developing countries carry a large burden of pediatric intussusception. Sentinel site surveillance-based studies have highlighted the difference in the regional incidence of intussusception. The objectives of this manuscript were to geospatially map the locations of hospital-confirmed childhood intussusception cases reported from sentinel hospitals, identify clustering and dispersion, and reveal the potential causes of the underlying pattern. Geospatial analysis revealed positive clustering patterns, i.e., a Moran’s I of 0.071 at a statistically significant (p value

Introduction: Pennsylvania opened its first medical marijuana (MMJ) dispensary in 2018. Qualifying conditions include six conditions determined to have no or insufficient evidence to support or refute MMJ effectiveness. We conducted a study to describe MMJ dispensary access in Pennsylvania and to determine whether dispensary proximity was associated with MMJ certifications and community demographics. Methods: Using data from the Pennsylvania Department of Health, we geocoded MMJ dispensary locations and linked them to US Census Bureau data. We created dispensary access measures from the population-weighted centroid of Zip Code Tabulation Areas (ZCTAs): distance to nearest dispensary and density of dispensaries within a 15-min drive. We evaluated associations between dispensary access and the proportion of adults who received MMJ certification and the proportion of certifications for low evidence conditions (amyotrophic lateral sclerosis, epilepsy, glaucoma, Huntington’s disease, opioid use disorder, and Parkinson’s disease) using negative binomial modeling, adjusting for community features. To evaluate associations racial and ethnic composition of communities and distance to nearest dispensary, we used logistic regression to estimate the odds ratios (OR) and 95% confidence intervals (CI), adjusting for median income. Results: Distance and density of MMJ dispensaries were associated with the proportion of the ZCTA population certified and the proportion of certifications for insufficient evidence conditions. Compared to ZCTAs with no dispensary within 15 min, the proportion of adults certified increased by up to 31% and the proportion of certifications for insufficient evidence decreased by up to 22% for ZCTAs with two dispensaries. From 2018 to 2021, the odds of being within five miles of a dispensary was up to 20 times higher in ZCTAs with the highest proportions of individuals who were not White (2019: OR: 20.14, CI: 10.7–37.8) and more than double in ZCTAs with the highest proportion of Hispanic individuals (2018: OR: 2.81, CI: 1.51–5.24), compared to ZCTAs with the lowest proportions. Conclusions: Greater dispensary access was associated with the proportions of certified residents and certifications for low evidence conditions. Whether these patterns are due to differences in accessibility or demand is unknown. Associations between community demographics and dispensary proximity may indicate MMJ access differences.

In recent times, the widespread adoption of GPS-enabled devices has resulted in an exponential surge in the amount of spatio-temporal data streams. These data streams, often referred to as trajectory data streams, represent a sequence of spatial locations of mobile objects. As these streams continue to grow, so does the demand for its efficient processing and analysis. Various government agencies and businesses now require real-time or low-latency solutions for continuous query processing and interactive analysis of trajectory streams, driven by applications like real-time route guidance during disasters, tracking critically ill patients to prevent mishaps, ongoing road traffic management to avert accidents and congestion, and more. Trajectory streams, due to their volume and velocity, necessitate scalable Stream Processing Engines (SPEs) for effective handling. However, currently available state-of-the-art scalable spatial SPEs do not adequately support low-latency and continuous processing of trajectories. Conversely, while scalable SPEs do exist, they lack the necessary data structures, operators, and indices essential for processing trajectory streams. To bridge this gap, our work introduces TStream, a distributed, low-latency, and continuous trajectory stream processing system. TStream addresses this challenge by offering support for spatial data types, spatial indices, and spatial continuous queries such as range, nearest neighbor searches (kNN), and joins over spatial trajectory streams. To substantiate the efficacy of TStream, we present an extensive experimental study conducted on two real-world trajectory datasets.

Abstract Background Due to low numbers of active infections and persons presenting to health facilities for malaria treatment, case-based surveillance is inefficient for understanding the remaining disease burden in low malaria transmission settings. Serological data through the detection of IgG antibodies from previous malaria parasite exposure can fill this gap by providing a nuanced picture of where sustained transmission remains. Study enrollment at sites of gathering provides a potential approach to spatially estimate malaria exposure and could preclude the need for more intensive community-based sampling. Methods This study compared spatial estimates of malaria exposure from cross-sectional school- and community-based sampling in Haiti. A total of 52,405 blood samples were collected from 2012 to 2017. Multiplex bead assays (MBAs) tested IgG against P. falciparum liver stage antigen-1 (LSA-1), apical membrane antigen 1 (AMA1), and merozoite surface protein 1 (MSP1). Predictive geospatial models of seropositivity adjusted for environmental covariates, and results were compared using correlations by coordinate points and communes across Haiti. Results Consistent directional associations were observed between seroprevalence and environmental covariates for elevation (negative), air temperature (negative), and travel time to urban centers (positive). Spearman’s rank correlation for predicted seroprevalence at coordinate points was lowest for LSA-1 (ρ = 0.10, 95% CI: 0.09–0.11), but improved for AMA1 (ρ = 0.36, 95% CI: 0.35–0.37) and MSP1 (ρ = 0.48, 95% CI: 0.47–0.49). Conclusions In settings approaching P. falciparum elimination, case-based prevalence data does not provide a resolution of ongoing malaria transmission in the population. Immunogenic antigen targets (e.g., AMA1, MSP1) that give higher population rates of seropositivity provide moderate correlation to gold standard community sampling designs and are a feasible approach to discern foci of residual P. falciparum transmission in an area.

Abstract PyGPlates is an open‐source Python library to visualize and edit plate tectonic reconstructions created using GPlates. The Python API affords a greater level of flexibility than GPlates to interrogate plate reconstructions and integrate with other Python workflows. GPlately was created to accelerate spatio‐temporal data analysis leveraging pyGPlates and PlateTectonicTools within a simplified Python interface. This object‐oriented package enables the reconstruction of data through deep geologic time (points, lines, polygons and rasters), the interrogation of plate kinematic information (plate velocities, rates of subduction and seafloor spreading), the rapid comparison between multiple plate motion models, and the plotting of reconstructed output data on maps. All tools are designed to be parallel‐safe to accelerate spatio‐temporal analysis over multiple CPU processors.

Abstract Objective Cancer is the third leading cause of death in the world with increasing trends in Iran. The study of epidemiology, trend, and geospatial distribution of pediatric cancers provides important information for screening as well as early detection of cancer and policy making. We aimed to assess the spatio-temporal disparity of childhood and adolescence cancer risk among provinces of Iran. Methods In this retrospective study, we estimated geospatial relative risk (RR) of childhood cancer in provinces of Iran using data from 29198 cases. We used BYM and its extended spatiotemporal model in Bayesian setting. This hierarchical model takes spatial and temporal effects into account in the incidence rate estimation simultaneously. Results The relative risk of cancer was > 1 for 45% of the provinces, where 27% of provinces had significantly ascending trend. North Khorasan, Yazd and Qazvin provinces had the highest risk rates while Sistan-Baluchistan province showed the lowest risk of cancer. However, the differential trends was highest in Sistan-Baluchistan, Bushehr, Hormozgan, and Kohgilouyeh-Boyerahmad. Both the point estimate and the trend of risk was high in Tehran. Conclusion The geographic pattern and trend of cancer in children seems to be different from that in adults that urges further studies. This could lead to increased health system capacity and facilitate the access to effective detection, research, care and treatment of childhood cancer.

This study aimed to locate areas along the Debretabor-Alember route segment in northern Ethiopia that are susceptible to landslides. Geospatial tools, specifically frequency ratios (FR) and information values (IV), were used to develop landslide susceptibility maps (LSMs). A comprehensive on-site investigation and analysis of Google Earth imagery were conducted, resulting in the detection and analysis of 89 landslides, including current and historical events. The dataset used for validation comprised 78% of the previously documented landslides, whereas the remaining 22% was used for training. Several factors were considered in this study to determine landslide susceptibility, including "slope, aspect, curvature, elevation, lithology, distance from streams, land use and cover, precipitation, normalized difference vegetation index (NDVI)", and the FR and IV models. Based on the results obtained using the FR approach, specific areas exhibited different levels of susceptibility, ranging from very low to moderately high, medium, high, and very high. These areas covered a total of 18.4 km2 (19.9%), 18.9 km2 (20.5%), 19.7 km2 (20.3%), 17.7 km2 (20%), and 17.7 km2 (19%), respectively. The LSMs generated by the IV model indicated multiple susceptibility classes in the study area, varying from very low to very high. These maps revealed that 18.4 km2 (19.8%), 18.8 km2 (20%), 18.9 km2 (19.5%), 18.8 km2 (20.5%), and 18.3 km2 (19.8%) of the area fell into these susceptibility classes. The landslide density indicator method was employed to validate the LSMs. The FR and IV models demonstrated that a significant proportion of confirmed past and current landslide records (72.16% and 73.86%, respectively) occurred in regions with a high or very high susceptibility to landslides. Overall, the IV model, which utilized latent variable structural modeling (LSM) in the independent variable model, outperformed the fixed effects regression model (FR).

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning and development. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by rapid population growth. Over the past decade, multiple projects have been undertaken to produce regional and national land cover maps. However, using different classification systems and legends has made updating and sharing land cover information challenging. This has resulted in the inefficient use of human and financial resources. The development of the Land Cover Meta Language (LCML) based on International Organization for Standardization (ISO) standards offers an opportunity to create a standardized classification system. This system would enable easier integration of regional and national data, efficient management of information, and better resource utilization in West Africa. This article emphasizes the process and the need for multistakeholder collaboration in developing a standardized land cover classification system for West Africa, which is currently nonexistent. It presents the survey data collected to evaluate historical, current, and future land cover mapping projects in the region and provides relevant use cases as examples for operationalizing a standardized land cover classification legend for West Africa.

Suicide is a major public health problem affecting US Veterans and the US in general. Many variables (e.g., demographic, clinical, biological, geographic) have been associated with risk for suicide and suicidal behavior, including altitude; however, the exact nature of the relationship between altitude and suicide remains unclear in part due to the fact that previous studies have used either geospatial data or individual-level data, but not both. Prior research has also failed to consider the full range of suicidal thoughts and behaviors, ranging from suicidal ideation to suicide deaths. Accordingly, the objective of the present research was to use both geospatial data (county and zip codes) and individual-level data to comprehensively assess the association between altitude and suicide mortality, suicide attempts, and suicidal ideation among US Veterans between 2000 and 2018. Taken together, our results demonstrate that there is a strong correlation between altitude and suicide rates at all the levels investigated and using different statistical analyses and even after controlling for significant covariates such as percent of age >50yr, percent male, percent white, percent non-Hispanic, median household income, and population density. We show that there is a positive correlation between altitude and suicide attempts especially when controlling by the covariates and a weak correlation between altitude and suicide ideation and the combination of suicide, suicide attempts and suicide ideation.

This article investigates the intersection of Artificial Intelligence (AI) with the space economy and its profound geopolitical implications. Artificial intelligence is increasingly powering space missions, satellite networks and resource utilization, thereby reshaping the global landscape of the space industry. AI-driven advances are fueling economic opportunities and competition among nations in space-related sectors and the resulting geopolitical effects. From satellite services to lunar and Martian exploration, AI is poised to become a driving force in shaping the balance of power in the space arena, making it a critical topic for policymakers, strategists and industry leaders. [ABSTRACT FROM AUTHOR]

Context and background Multidimensional Poverty (MP) considers poverty in multiple dimensions of deprivations such as health, education, energy, the standard of living and access to basic services. MP remains a major challenge in Africa, with a large proportion of the population living in MP. According to United Nations Development Programme (UNDP), Africa has shown the highest Multidimensional Poverty Index (MPI) having over 40% of its population living in MP. Goal and Objectives: This paper is a review, aimed at assessing the potential of the integration of satellite and Artificial Intelligence (AI) in mapping MP, with a specific focus on Africa. Methodology: Based on the reviews of past studies, the combination of satellite data such as nighttime light, daytime satellite imagery and high-resolution settlement data in combination with techniques such as field surveys, statistical correlation models (transfer learning) and AI (deep learning) has been applied in mapping MP. Results: The findings from studies show that the combination of satellite data and AI has the capability of providing more accurate and granular MP maps, compared to the traditional approach. Again, this paper explains the concept of MP with a specific focus on Africa and presents a map depicting the current MPI in African countries. Finally, pitfalls especially in the accuracy, granularity and frequency of MP data were identified. Consequently, the satellite and AI approaches are recommended for more accurate, frequent, cost-effective and granular data, required in mapping poverty and design of interventions that effectively address the needs of the vulnerable populations in Africa. [ABSTRACT FROM AUTHOR]

Introduction: Pennsylvania opened its first medical marijuana (MMJ) dispensary in 2018. Qualifying conditions include six conditions determined to have no or insufficient evidence to support or refute MMJ effectiveness. We conducted a study to describe MMJ dispensary access in Pennsylvania and to determine whether dispensary proximity was associated with MMJ certifications and community demographics. Methods: Using data from the Pennsylvania Department of Health, we geocoded MMJ dispensary locations and linked them to US Census Bureau data. We created dispensary access measures from the population-weighted centroid of Zip Code Tabulation Areas (ZCTAs): distance to nearest dispensary and density of dispensaries within a 15-min drive. We evaluated associations between dispensary access and the proportion of adults who received MMJ certification and the proportion of certifications for low evidence conditions (amyotrophic lateral sclerosis, epilepsy, glaucoma, Huntington's disease, opioid use disorder, and Parkinson's disease) using negative binomial modeling, adjusting for community features. To evaluate associations racial and ethnic composition of communities and distance to nearest dispensary, we used logistic regression to estimate the odds ratios (OR) and 95% confidence intervals (CI), adjusting for median income. Results: Distance and density of MMJ dispensaries were associated with the proportion of the ZCTA population certified and the proportion of certifications for insufficient evidence conditions. Compared to ZCTAs with no dispensary within 15 min, the proportion of adults certified increased by up to 31% and the proportion of certifications for insufficient evidence decreased by up to 22% for ZCTAs with two dispensaries. From 2018 to 2021, the odds of being within five miles of a dispensary was up to 20 times higher in ZCTAs with the highest proportions of individuals who were not White (2019: OR: 20.14, CI: 10.7–37.8) and more than double in ZCTAs with the highest proportion of Hispanic individuals (2018: OR: 2.81, CI: 1.51–5.24), compared to ZCTAs with the lowest proportions. Conclusions: Greater dispensary access was associated with the proportions of certified residents and certifications for low evidence conditions. Whether these patterns are due to differences in accessibility or demand is unknown. Associations between community demographics and dispensary proximity may indicate MMJ access differences. [ABSTRACT FROM AUTHOR]

Spatial and temporal estimates of burned areas are often used to model greenhouse gas and air pollutant emissions from fire events that occur in a region of interest and over specified time frames. However, fire behaviour, fuel consumption, fire severity, and ecological effects vary over both time and space when a fire grows across varying fuels and topography under different environmental conditions. We developed a method for estimating the progression of individual wildfires (i.e., day-of-burn) employing ordinary kriging of a combination of different satellite-based active fire detection data sources. We compared kriging results obtained using active fire detection products from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and combined MODIS and VIIRS data to study how inferences about a wildfire's evolution vary among data sources. A quasi-validation procedure using combined MODIS and VIIRS active fire detection products that we applied to an independent data set of 37 wildfires that occurred in the boreal forest region of the province of Ontario, Canada, resulted in nearly half of each fire's burned area being accurately estimated to within one day of when it actually burned. Our results demonstrate the strengths and limitations of this geospatial interpolation approach to mapping the progression of individual wildfires in the boreal forest region of Canada. Our study findings highlight the need for future validations to account for the presence of spatial autocorrelation, a pervasive issue in ecology that is often neglected in day-of-burn analyses. [ABSTRACT FROM AUTHOR]