This dataset consists of 190,832 manually-digitized cropland field boundaries, with associated attributes, within Brazil, Ukraine, United States of America, Canada, and Russia. Specifically, 22 regions of various sizes (74km2 - 38,000km2) spanning 5 countries were digitized over a range of predominant crop types over different time periods. These field boundaries were drawn over 20 m Sentinel-2 imagery. This field boundary dataset is a byproduct of a larger effort to map cropland burned area (Global Cropland Area Burned: GloCAB product [1]), however, it has several benefits beyond its original intent, including as a training dataset for machine-learning field size analyses, or a dataset to derive cropland field characteristics across different predominant crop types and geographies.

This study investigated land use and land cover changes in the Gauteng Province of South Africa with emphasis on wetland ecosystems. Using Landsat images and various image manipulation software such as ArcGIS and ENVI, this study conducted a predictive analysis of the potential state of wetland ecosystems in the Gauteng Province of South Africa by year 2040, using illegal dumping trends in the last 20 years. Based on this methodological framework, it is found that continued illegal dumping trends would lead to an almost total disappearance of wetlands in the study area by 2040. It is argued that despite the pro-environmental initiatives introduced in the early 2000s to conserve wetlands in South Africa, wetland deterioration has increased rapidly due to the associated methodological flaws, which further supports the findings of this study. Ultimately, it is recommended that a collaborative approach to wetland conservation, and a robust methodological shift are required to mitigate the threats faced by wetlands in the Gauteng Province of South Africa, and the model can be deployed across developing countries.

Due to the outbreak of Coronavirus, humans all over the world are facing several health problems. The present study has explored the spatio-temporal pattern of Coronavirus spread in India including spatial clustering, identification of hotspot, spatial heterogeneity, and homogeneity, spatial trend, and direction of COVID-19 cases using spatial statistical analysis during the period of 30 January to 20 June 2020. Besides, the polynomial regression model has been used for predictions of COVID-19 affected population and related deaths. The study found positive spatial heterogeneity in COVID-19 cases in India. The study has also identified 17 epicentres across the country with high incidence rates. The directional distribution of ellipse polygon shows that the spread of COVID-19 now trending towards the east but the concentration of cases is mainly in the western part of the country. The country\'s trend of COVID-19 follows a fourth-order polynomial growth and is characterized by an increasing trend. The prediction results show that as on 14 October India will reach 14,660,400 COVID-19 cases and the death toll will cross 152,945. Therefore, a \"space-specific\" policy strategy would be a more suitable strategy for reducing the spatial spread of the virus in India. Moreover, the study has broadly found out seven sectors, where the Government of India lacks in terms of confronting the ongoing pandemic. The study has also recommended some appropriate policies which would be immensely useful for the administration to initiate strategic planning.
Debido al brote del Coronavirus, los humanos de todo el mundo se enfrentan a varios problemas de salud. En el presente estudio se han explorado las pautas espacio‐temporales de la propagación del Coronavirus en la India, entre ellas la agrupación espacial, la identificación de focos, la heterogeneidad espacial y la homogeneidad, la tendencia espacial y la dirección de los casos de COVID‐19, usando un análisis estadístico espacial para el período entre el 30 de enero y el 20 de junio de 2020. Además, se ha utilizado el modelo de regresión polinómica para las predicciones de la población afectada por COVID‐19 y las muertes relacionadas. El estudio encontró una heterogeneidad espacial positiva en los casos de COVID‐19 en la India. También identificó 17 epicentros en todo el país con altas tasas de incidencia. La distribución direccional del polígono de la elipse muestra que la propagación de COVID‐19 ahora tiende hacia el este, pero la concentración de casos se encuentra principalmente en la parte occidental del país. La tendencia del país para COVID‐19 sigue un crecimiento polinómico de cuarto orden y se caracteriza por una tendencia al alza. Los resultados de la predicción muestran que, a 14 de octubre, la India alcanzará 14.660.400 casos de COVID‐19 y el número de muertes sobrepasará las 152.945. Por consiguiente, una estrategia política “espacialmente específica” sería una estrategia más adecuada para reducir la propagación espacial del virus en la India. Además, el estudio ha descubierto en general siete sectores en los que el Gobierno de la India carece de medios para hacer frente a la pandemia. En el estudio también se recomiendan algunas políticas apropiadas que serían inmensamente útiles para que la administración inicie una planificación estratégica.
新型コロナウイルスの発生により、世界中の人々は健康問題に直面している。本稿では、2020年1月30日~6月20日の期間で、新型コロナウイルス感染症 (COVID‐19)症例の空間クラスタリングや、ホットスポット、空間的な異質性及び同質性、空間的トレンド、方向性を、空間的統計分析を用いて特定し、インドにおけるコロナウイルスの拡大の時空間パターンを探索する。本研究から、インドのCOVID‐19症例には間的異質性が認められ、全国で、発生率の高い17のエピセンターも確認された。楕円多角形の方向分布は、現在COVID‐19は東に向かって拡大しているが、症例は主に国の西部に集中していることを示している。インドにおけるCOVID‐19のトレンドは4次多項式的に上昇する増加傾向が特徴である。インドのCOVID‐19症例は、10月14日の時点で14,660,400例に達し、COVID‐19による死亡者数は152,945人を超えると予想された。そのため、インドにおける新型コロナウイルスの空間的拡大を抑制するための戦略としては、地域に特異的な政策をとることがより適切であると考えられる。さらに、現在も続くパンデミックに対処する上でインド政府に不足している7つの分野が明らかになった。また、インド政府が戦略的計画を開始する際に非常に有用となると考えられる、適切な政策を推奨する。.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

BACKGROUND: Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities.
OBJECTIVE: In this study, we developed a geoprocessing toolbox including Python script tools for the ArcGIS Pro environment to measure the spatial accessibility of health services using both classic and enhanced versions of the 2-step floating catchment area method.
METHODS: Each of our tools incorporated both distance buffers and travel time catchments to calculate accessibility scores based on users\' choices. Additionally, we developed a separate tool to create travel time catchments that is compatible with both locally available network data sets and ArcGIS Online data sources. We conducted a case study focusing on the accessibility of hemodialysis services in the state of Tennessee using the 4 versions of the accessibility tools. Notably, the calculation of the target population considered age as a significant nonspatial factor influencing hemodialysis service accessibility. Weighted populations were calculated using end-stage renal disease incidence rates in different age groups.
RESULTS: The implemented tools are made accessible through ArcGIS Online for free use by the research community. The case study revealed disparities in the accessibility of hemodialysis services, with urban areas demonstrating higher scores compared to rural and suburban regions.
CONCLUSIONS: These geoprocessing tools can serve as valuable decision-support resources for health care providers, organizations, and policy makers to improve equitable access to health care services. This comprehensive approach to measuring spatial accessibility can empower health care stakeholders to address health care distribution challenges effectively.

Among all other valuable natural resources, groundwater is crucial for global economic growth and food security. This study aimed to delineate groundwater potential zones (GWPZ) in the Gidabo watershed of the Main Ethiopian Rift. The demand for groundwater supplies for various applications has risen recently in the watershed due to rapid population upsurge. An integrated Geographical Information System, Remote Sensing, and Analytical Hierarchy Process (AHP) has been utilized. Eight groundwater regulating factors, including rainfall, elevation, drainage density, soil types, lineament density, slope, lithology, and land use/land cover, have been taken in the analysis. To assign suitable weights to each factor, AHP was employed, as each element contributes differently to groundwater occurrence. The weighted overlay analysis (WOA) technique was then used in the ArcGIS environment to integrate all thematic layers and generate a GWPZ map. The delineated GWPZ in the watershed was classified into five categories. The poor GWPZ covered 18.7 %, the low GWPZ covered 33.8 %, the moderate GWPZ covered 23.4 %, the high GWPZ covered 18.1 %, and the very high GWPZ covered 5.8 % of the area. Well and spring data were used to validate the model, and the ROC (Receiver Operating Characteristic) curve method was applied. The results showed good accuracy of 76.8 %. The result of this research can be valuable for planning and managing groundwater resources in the Gidabo watershed.

The study focuses on the chemistry of groundwater and if it is suitable for drinking and for use in agriculture using water quality indices, GIS mapping, and multivariate analyses in Sharsa Upazila, Jashore district, Bangladesh. In this study, the concentration of NH4+, K+, Ca2+, EC, Turbidity overstep BDWS drinking standards in 69 %, 14 %, 100 %, 40 % (WHO), 73 % of samples respectively. The value of Water Quality Indices (WQI) results inferred that the maximum specimen was held good quality for drinking uses, and the values distributed central eastern part to the south-eastern part were good quality water in the selected studied area. The study area\'s PH, EC, SAR, Na (%), TH, and NO3- values were mapped using GIS tools to show their spatial distribution. The cluster and correlation matrix analyses are used to validate for Principle Component Analysis (PCA). The five PCA results exhibited that the presence of EC, turbidity, K+, SO42- and NO3- was significant and was caused by both geogenic (rock weathering and cation exchange) and anthropogenic (agrochemicals, animal feedback) factor. According to the hydro-geochemical data, the maximum number of samples is of the Ca-Mg-HCO3-Cl type and is dominated by rocks. The irrigation water indices like MH, KR, SAR, and %Na indicate show high-quality groundwater for irrigation purposes. Most of the samples were satisfactory and compiled with WHO and Bangladeshi criteria for standard drinking water guideline values.

Larger animal models with a well-developed Haversian system, as observed in humans, are ideal to analyze cortical bone remodeling in pharmacological studies of anti-osteoporosis drugs, although they have some limitations in controlling individual variability in size, weight, age, and number. This study aimed to morphometrically analyze cortical bone remodeling focusing on Haversian canals in dogs using four regimens of TPTD with daily and weekly administrations at lower and higher weekly doses (4.9 μg/kg/week and 19.8 μg/kg/week, respectively) for 9 months. A micro-computed tomography-based analysis showed no significant differences among regimen groups. By establishing artificial intelligence (AI)-driven morphometric analyses and geographical information system (GIS)-based spatial mapping of Haversian canals that does not require confocal microscopy but is possible with more commonly used wide field microscopes, we successfully observed significant morphometric distinctions among regimens applied even in dogs. Our analytical results suggested that the daily higher regimen specifically increased the number of eroded pores creating spaces between existing canals, thus stimulating cortical bone remodeling.

Geographical context is an important consideration for health system design to promote equality in access to care for patients with childhood heart disease (CHD), particularly those living in regional, rural, and remote areas. To help inform future policy and practice recommendations, this study aimed to (i) describe the geographic distribution of high-risk CHD patients accessing an Australian state-wide specialist service and (ii) estimate travel time for accessing healthcare via general practitioners (primary), nearest paediatric centre (secondary) and specialist paediatric cardiac centre (tertiary).
Participants included a cohort of children (0-18 year) who accessed state-wide specialist CHD services over a 3-year period (2019-2021) in Queensland, Australia. Locations for patient residence, general practitioner, closest paediatric centre and tertiary cardiac centre were mapped using geographical information system (GIS) software (ArcGIS Online). Travel distance and times were estimated using a Google Maps Application Programming Interface (API).
1019 patients (median age 3.8 years) had cardiac intervention and were included in the sample. Of this cohort, 30.2% lived outside the heavily urbanised South East Queensland (SEQ) area where the tertiary centre is located. These patients travel substantially further and longer to access tertiary level care (but not secondary or primary level care) compared to those in SEQ. Median distance for patients residing outside SEQ to access tertiary care was 953 km with a travel time of 10 h 43 min. This compares to 5.5 km to the general practitioner and 20.6 km to a paediatric service (8.9 and 54 min respectively).
This geographical mapping of CHD services has demonstrated a key challenge inherent in providing specialist cardiac care to children in a large state-based healthcare system. A significant proportion of high-risk patients live large distances from tertiary level care. The greater accessibility of primary care services highlights the importance of supporting primary care physicians outside metropolitan areas to acquire or build the ability and capacity to care for children with CHD. Strengthening local primary and secondary services not only has the potential to improve the outcomes of high-risk patients, but also to reduce costs and burden associated with potentially avoidable travel from regional, rural, or remote areas to access specialist CHD services.

The site selection of biomass energy facilities has always been a key part of energy spatial planning. The site suitability evaluation criteria of the existing studies are not comprehensive. On the other hand, most of the existing studies are to determine the only site, while less research on the multiple-facility planning. The aim of this paper is to identify the most effective number and location for biomass energy facilities. To achieve this objective, the Geographic Information System (GIS) is utilized to perform the following tasks: Generate a site suitability map for potential biomass energy facilities and identify suitable site candidates. The standardization of site suitability evaluation indicators is based on fuzzy logic, and indicator weights are determined based on the Analytic Hierarchy Process (AHP) evaluation of experts\' opinions. 2. Develop planning schemes for biomass energy facilities for various number of proposed facilities, and subsequently determine the optimal scheme using multi-objective fuzzy comprehensive evaluation. The weight of each indicator is again determined using the AHP method. Following the analysis, it was found that in the case study of Fuxin City, the plans of 1 and 40 biomass energy facilities can achieve the lowest cost and the highest energy self-sufficiency level. However, both options have potential drawbacks that must be considered. The plan of 30 energy facilities has the highest comprehensive benefits, corresponding to the 30,919.75 yuan of transport cost (3748 yuan lower than the average transport cost) and 75.49% of energy self-sufficiency (67.21% of the average value). This work maximizes the comprehensive positive impacts in economic, environmental and social aspects.