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The La Prele site (ca. 12,940 cal BP) is a deeply buried, single-component mammoth kill and campsite in Wyoming (USA). The site was discovered eroding from a creek bank 3 m deep within a 7-m tall terrace scarp, and prior investigations have primarily focused on excavations accessible from the creek bank, using heavy machinery to remove sterile overburden to access the deeply buried deposits. This approach has allowed excavations to occur safely outside of deep pits, but it has limited our ability to assess the total size and density of the site. To determine total site extent, we conducted systematic bucket auger testing of the La Prele site terrace, attempting 189 augers between 1.6 m and 6.2 m deep across the landform. We use a simulation and other mathematical procedures to infer artifact density from auger artifact counts and interpolate artifact densities across the site using GIS. We determine that La Prele is around 4500 m2 in area and likely contains a buried bison bonebed and two additional artifact concentrations comparable to or exceeding the size and density of previously excavated areas. We use these insights to infer Early Paleoindian group size, concluding that around 30 people occupied La Prele. [ABSTRACT FROM AUTHOR]

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]

Sustainable Development Goal‐7 (SDG‐7) of the United Nations promotes the use of renewable and affordable energy. Solar energy holds significant promise as an effective and affordable renewable source in tropical countries. GIS‐based Multi‐Criteria Decision Analysis (MCDA) approach has been applied to evaluate land suitability for installing ground‐mounted and grid‐connected solar photovoltaic power plants in Purulia district of West Bengal (India) to satisfy the growing demand of energy in an eco‐friendly manner. The study reveals that variation in Global Horizontal Irradiance (GHI) is the most relevant parameter for installing solar PV power plants in the district among the 13 physical parameters, followed by the aspect, slope, and proximity to the grid. The site‐suitable map created using the weights of these factors has revealed that about 33.4% of the district's total land area is ideal for installing solar PV power plants, with most of these areas being in the western and central regions. [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]

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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]

With the increasing prevalence and social impacts of Alzheimer's disease (AD), innovative approaches to our understanding of its etiology, progression, and potential interventions are critical. Geospatial applications are a promising tool in addressing the complex spatial and temporal dynamics of AD. A substantial knowledge gap remains, however, regarding the comprehensive utilization and impact of geospatial applications in AD research. We comprehensively consider the current status of geospatial-information application in AD based on 159 research articles on geospatial applications published between 1991 and 2022 from the Web of Science and PubMed databases. Four main research themes are identified: geographic environmental factors, geographic disparities, activity behaviors, and care services. Geospatial information has been extensively applied using techniques such as spatial clustering and hotspot analysis. We investigate the influence of location and spatial factors on AD risk, the activity behavior of patients with AD using location data and spatial analysis, and medical-resource proximity via geospatial modeling and predictive analysis. Additionally, we identify three knowledge gaps: geographic environment measurement, spatiotemporal contrastive analysis of disease prevalence, and multidisciplinary integration. The review underscores the value of geospatial information in AD-related research and public health, offering researchers insights into prospective directions and collaborative opportunities with geographers. [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.

Background: Place is a critical determinant of health. Recent novel analyses have explored health outcome estimation for small geographies, such as census tracts, as well as health outcome aggregation to geopolitical geographies with accountable political representatives, such as congressional districts. In one such application, combining these approaches, researchers aggregated census tract estimates of life expectancy at the congressional district level to derive local estimates, but such an approach has not been validated. Methods: Here, we compared two sources and approaches to calculating life expectancy data for Pennsylvania congressional districts. We used 2010–2015 census tract life expectancy estimates from the US Small-area Life Expectancy Estimates Project and dasymetric methods to compute population-weighted life expectancy aggregated to the congressional district level. Using georeferenced Vital Statistics data, we aggregated age-specific census tract death and population counts to congressional districts and used abridged life tables to estimate life expectancy. To validate the dasymetric aggregated estimates we compared absolute differences, assessed the correlation, and created Bland–Altman plots to visualize the agreement between the two measures. Results: We found strong agreement between congressional district estimates of life expectancy at birth derived using the dasymetric Life Expectancy Estimates Project model–based approach and the Vital Statistics direct estimates approach, though life expectancy at older ages (75 years and older) showed weak correlations. Conclusions: This validation contributes to our understanding of geospatial aggregation methods for novel geographies including congressional districts. Health outcome data aggregated to the congressional district geography can support congressional policymaking aimed at improving population health outcomes. Video Abstract EDE.0000000000001797video1.mov Kaltura [ABSTRACT FROM AUTHOR]

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]

High-quality geospatial data are required to examine how the places in which we reside, work and play determine health outcomes; however, seldom is the quality of nationwide geospatial data reported. We examined the quality of geospatial data of public drinking water distribution zones (WDZ) across all territorial authorities in Aotearoa New Zealand to investigate structural inequities in data quality. In our national dataset of WDZ, we identified several differences in the quality of geospatial information that are associated with the population, area-level deprivation, ethnicity and most of all urban/rural classification. Our research highlights structural data inequity, which may undermine efforts to reduce health inequity. [ABSTRACT FROM AUTHOR]

In the Fall of 2022, the Kellogg Community College wanted to submit an application to gain recognition as a "Tree Campus" through the Arbor Day Foundation. The college was forming a committee to get this done and part of the requirements was Service Learning and an inventory of the trees on campus. In the Spring and Fall of 2023 forty students used ArcGIS Survey 123 to conduct the survey research on over 600 trees on campus. This will result in a final web based public map to be linked to the colleges web page to be completed by Spring 2025. [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]

The research was conducted to provide information about the physical characteristics of Erelu reservoir in Oyo West Local Government Area, Oyo, in Oyo State. Data acquisition featured sounding by means of an echo sounder and for depth and position determination by means of a GPS while the tidal data used were based on existing data. Initial processing performed on the observed bathymetric data included; sorting with PowerNav and HYPACK 2018. Further processing was carried out using ArcGIS 10.7 and SUFFER 2016 software. The modified normalized difference water index (MNDWI) was used to extract the shoreline of the study area, while the digital shoreline analysis system (DSAS) was used for shoreline analysis. The results of the analysis showed that the maximum and minimum depths observed were 6.03m and 0.56m respectively 2023 an indication that the water is shallow. The surface area and volume values obtained were 120.94ha and 2975635.28943.13m³. Based on a linear and areal analysis, the bathymetric survey revealed that the maximum length and area reached in 2022 were 7855.171m and 84.506ha respectively, while the minimum length and area in 2017 were 7460.733m and 78.303ha respectively. Based on the results from DSAS, it was deduced that the accretion rate was high, while the erosion rate was minimal. Finally, the processed depths were analysed and presented in the form of charts, which may be used in the near future for planning and decision-making for proper management of the reservoir. [ABSTRACT FROM AUTHOR]

The urban radio‐frequency (RF) noise generated by our cities continues to change with time. Although models exist to describe the RF noise as functions of frequency and urban land use types, very few models describe the spatial character or structure of the noise on the scales of city blocks (50–150 m). The goal of this work is to investigate the connection between urban morphology and the higher‐order spatial statistics of the noise field. To achieve this goal, a large measurement campaign was conducted in Boston, Massachusetts. Many spatial measurements allowed for calculation of spatial correlation functions of noise power in three different neighborhoods, which were used to quantify the spatial structure of the fields. A statistical point source model is then developed, with adjustable parameters relating to urban morphology. Good agreement between the model and the experimental correlation functions suggests the 25 MHz urban noise field is well described by a random network of fixed point sources, radiating with a 1/r power law behavior. Plain Language Summary: Our modern cities are filled with electronic devices. Each device can emit radiation and contribute to what is called the urban radio‐frequency noise field. The noise field is the combined effect from all these devices. If the noise field is strong enough it can negatively impact wireless communication, and the use of other electric devices. It is important to better understand the nature of the noise field in order to mitigate and plan for its negative effects. This paper describes in detail how the noise field is distributed in space, or its spatial structure. A theoretical model or tool is developed to help predict how the noise field looks spatially. Key Points: Radio‐frequency noise spatial correlation functions were calculated for three different neighborhoods in Boston, MassachusettsGood theoretical agreement suggests the noise field is well described by a random network of point sources radiating with a 1/r behavior [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]