Background: East Manggarai is one of the District in East Nusa Tenggara Province with a high transmission area and a high number of Dengue Hemorrhagic Fever cases. Spatial analysis can provide ...spatial information related to the distribution pattern of DHF. Purpose: To spatially analyze the cases of DHF in East Manggarai District in 2021. Methods: This study used a descriptive survey method with a case study design sampling technique, Purposive sampling approach used a sample size of 103 cases. Data processing and spatial data analysis use Quantum Geographic Information Systems software with Nearest Neighbor Analysis and buffer analysis to describe the distribution of cases based on population density, House Index, Container Index, and Buffer Zone. Results: Spatial analysis using QGIS showed that the Nearest Neighbor Index was 0.323 < 1, and the CI value was 34.7, the HI value was 54 while the buffer zone showed the tendency for dengue transmission to occur in most cases within a radius of 100 meters. Conclusion: The pattern of DHF transmission is clustered, the sub-districts of Borong, Rana Mese, and Komba became the sources of DHF transmission where Container Index and House Index were classified as high categories, and population density was classified as high and low categories. Preventive action such as eliminating mosquito breeding areas were essential.
•Develop an XGBoost model to detect accidents with detection rate of 79 %, and AUC of 89 %.•The developed model is robust and interpretable thanks to SHAP.•Complex interrelated impacts of selected ...features are captured and analyzed.
Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)—a Machine Learning (ML) technique—to detect the occurrence of accidents using a set of real time data comprised of traffic, network, demographic, land use, and weather features. The data used from the Chicago metropolitan expressways was collected between December 2016 and December 2017, and it includes 244 traffic accidents and 6073 non-accident cases. In addition, SHAP (SHapley Additive exPlanation) is employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can detect accidents robustly with an accuracy, detection rate, and a false alarm rate of 99 %, 79 %, and 0.16 %, respectively. Several traffic related features, especially difference of speed between 5 min before and 5 min after an accident, are found to have relatively more impact on the occurrence of accidents. Furthermore, a feature dependency analysis is conducted for three pairs of features. First, average daily traffic and speed after accidents/non-accidents time at the upstream location are interpreted jointly. Then, distance to Central Business District and residential density are analyzed. Finally, speed after accidents/non-accidents time at upstream location and speed after accidents/non-accidents time at downstream location are evaluated with respect to the model’s output.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Assembling a collection of very prominent researchers in the field, the Handbook of Spatial Statistics presents a comprehensive treatment of both classical and state-of-the-art aspects of this ...maturing area. It takes a unified, integrated approach to the material, providing cross-references among chapters.The handbook begins with a historical intro
Unraveling the relative importance of ecological processes regulating microbial community structure is a central goal in microbial ecology. Here, we used high-throughput sequencing to examine the ...relative contribution of selective and neutral processes in the assembly of abundant and rare subcommunities from three subtropical bays of China. We found that abundant and rare bacterial taxa were distinctly different in diversity, despite the similar biogeographic patterns and strong distance-decay relationships, but the dispersal of rare bacterial taxa was more limited than that of abundant taxa. Furthermore, the environmental (selective processes) and spatial (neutral processes) factors seemed to govern the assembly and biogeography of abundant and rare bacterial subcommunities, although both factors explained only a small fraction of variation within the rare subcommunity. More importantly, variation partitioning (based on adjusted R
in redundancy analysis) showed that spatial factors exhibited a slightly greater influence on both abundant and rare subcommunities compared to environmental selection; however, the abundant subcommunity had a much stronger response to spatial factors (17.3% of pure variance was explained) than that shown by the rare bacteria (3.5%). These results demonstrate that environmental selection and neutral processes explained the similar biogeographic patterns of abundant and rare subcommunities, but a large proportion of unexplained variation in the rare taxa (91.1%) implies that more complex assembly mechanisms may exist to shape the rare bacterial assemblages in the three subtropical bays.
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Menoreh Mountain area is an area with very high geological uniqueness. One of the statuses that is contextually determined is a geopark because it provides a framework for conservation, education, ...and sustainable local economic activities as its pillars that are relevant to the agenda of economic development. It is necessary to characterize the existing geotourism potential in Menoreh Mountain area. This research attempts to identify and develop this potential using spatial and regional complex approaches. Technically, the method used is the Modified Geosites Assessment Model (M-GAM) combined with Hot-Spot Analysis to obtain spatial and non-spatial geotourism potential clustering. Clustering is done using K-Means Cluster Analysis. The results show that the existing geosites, spatially formed a cluster and can be used to determine the theme of the tourism activity to strengthen the geopark pillars. The development of geopark is hindered by several constraints including the division of administration and the disparity of each administrative region's capability. In general, a strategy to tighten the stakeholder and optimize the research is required to initially strengthen the geopark's pilars. It is also important to build a linkage of the geodiversity with the biodiversity and cultural diversity to enhance the significance value of Menoreh Mountain.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large ...diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.
Land subsidence (LS) is among the most critical environmental problems, affecting both agricultural sustainability and urban infrastructure. Existing methods often use either simple regression models ...or complex hydraulic models to explain and predict LS. There are few studies that identify the risk factors and predict the risk of LS using machine learning models. This study compares four tree-based machine learning models for land subsidence hazard modelling at a study area in Hamadan plain (Iran). The study also analyzes the importance of six risk factors including topography (elevation, slope), geomorphology (distance from stream, drainage density), hydrology (groundwater drawdown) and lithology on LS. Thematic layers of each variable related to the LS phenomenon are prepared and utilized as the inputs to the four tree-based machine learning models, including the Rule-Based Decision Tree (RBDT), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and the Random Forest (RF) algorithms to produce a consolidated LS hazard map. The accuracy of the generated maps is then evaluated using the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS). The RF approach had the lowest predictive error for mapping the LS hazard (i.e., AUC 96.7% for training, AUC 93.8% for validation, TSS 0.912 for training, TSS 0.904 for validation) followed by BRT. Groundwater drawdown was seen to be the most influential factor that contributed to land subsidence in the present study area, followed by lithology and distance from the stream network.
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•Four tree-based models were applied for land subsidence modelling.•RF is the best model to spatially predict land subsidence hazard.•Groundwater drawdown was the most influential factor.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
El aseguramiento de vivienda como medio que permite la protección de una persona, es considerado una de las necesidades de primer orden en tanto su ausencia amenaza la supervivencia humana. Su ...demanda constante hace que mecanismos de formalización como las normas y las políticas la contemplen como un bien de relevancia social que debe ser asegurado y reconocido por el Estado y acogido por sus destinatarios. En ese sentido se propone comprender la apropiación socioespacial de los beneficiarios de VIP de Las Flores y La Aurora, Nuevo Occidente, en Medellín, Colombia. La metodología empleada fue cualitativa y sobre su base se aplicó el método fenomenológico en la modalidad de estudio de caso interpretativo. Se usó la entrevista semiestructurada, la cartografía social y la observación participante en la recolección de información. Los resultados evidencian las tensiones entre la (VIP) que entrega el Estado y las formas que los residentes la habitan y la significan. Los obstáculos en su apropiación responden principalmente a que la infraestructura no satisface las expectativas de las personas en términos de calidad y tamaño, por lo que en la discusión se conceptualiza la habitabilidad interna y la habitabilidad externa, la primera asociada a la apropiación de la vivienda y la segunda ligada a la satisfacción residencial y la calidad de vida.
Considering the geographical location and climatic conditions of Iran, solar energy can provide a considerable portion of the energy demand for the country. This study develops a two-step framework. ...In the first step, the map of unsuitable regions is extracted based on the defined constraints. In the next step, in order to identify the suitability of different regions, 11 defined criteria, including solar radiation, average annual temperatures, distance from power transmission lines, distance from major roads, distance from residential area, elevation, slope, land use, average annual cloudy days, average annual humidity and average annual dusty days, are identified. The relative weights of defined criteria and sub-criteria are also determined applying fuzzy analytical hierarchy process (FAHP) technique. Next, by overlaying these criteria layers, the final map of prioritization of different regions of Iran for exploiting solar photovoltaic (PV) plants is developed. Based on Iran’s political divisions, investigation and analysis of the results have been presented for a total of 1057 districts of the country, where each district stands in one of the five defined classes of excellent, good, fair, low, and poor level. The obtained data indicate that 14.7% (237,920 km2), 17.2% (278,270 km2), 19.2% (311,767 km2), 11.3% (183,057 km2), 1.8% (30,549 km2) and 35.8% (580,264 km2) of Iran’s area are positioned as excellent, good, fair, low, poor and unsuitable areas, respectively. Moreover, Kerman, Yazd, Fars, Sisitan and Baluchestan, Southern Khorasan and Isfahan are included in the regions as the most excellent suitable provinces for exploiting solar PV plants.
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