Search Results - Spatial analysis (Statistics)

Land Health Surveillance: Mapping Soil Carbon in Kenyan Rangelands by Vågen, Tor-Gunnar, Davery, F.A., Shepherd, Keith D.

“…Key elements of the science methodological framework are (1) probability-based sampling of well-defined populations of sample units; (2) standardized protocols for data collection to enable statistical analysis of patterns, trends, and associations; and (3) multilevel statistical modelling of land health attributes at different scales, including in relation to satellite imagery for spatial interpolation. …”
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Seroprevalence of Leptospira antibodies in dogs and cats from Santa Fe, a city in East-Central Argentina endemic for leptospirosis by Ricardo, Tamara, Bazán Domínguez, Ludmila R., Beltramini, Lucila, Prieto, Yanina, Montiel, Anahí, Margenet, Leticia, Schmeling, María Fernanda, Chiani, Yosena, Signorini Porchiett, Marcelo Lisandro, Previtali, M. Andrea

“…We used generalized linear mixed effects models (GLMM) to examine individual and census tract-level risk factors for seropositivity, and local Moran’s I statistic for spatial clusters. Results showed higher leptospiral antibody prevalence in dogs (18.2 %) than cats (3.6 %, p = 0.002). …”
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Skilda vägar : rumsliga statistiska analyser av kvinnors och mäns rörelsemönster by Sevelin, Göran

Modeling turbidity of aquaculture pond in the East Kolkata Wetlands using Sentinel-2 bands and ground data through multivariate regression on Google Earth Engine by Mullick, A., Ghosh, Surajit, Chowdhury, A., Bhattacharyya, S.

“…The MRM generates a turbidity image as a continuous raster layer showing predicted turbidity levels (NTU) across the area of interest, enabling spatial and temporal comparisons and helping to identify consistent turbidity patterns and potential zones of elevated turbidity. …”
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Bias correction of daily chirps-V2 rainfall estimates in Ghana by Johnson, R.

“…Moreover, the extreme rainfall analysis produced results consistent with gauge values measured at the same time duration. …”
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Underlying drivers of deforestation in the Peruvian amazon and the highland region of the Andes by Juarez, H., Pradel, W., Navarrete, C., Gutiérrez, D., Hualla, V., Vanegas, M., Sylvester, J., Castro, A.

“…The methodology integrates spatial analysis and advanced statistical modeling, leveraging a dataset encompassing demographic, climatic, economic, and ecological predictors. …”
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A participatory ecohealth study of smallholder pig system in upland and lowland of Lao PDR by Inthavong, Phouth, Durr, P.A., Khamlome, Boualam, Blaszak, Kate, Somoulay, V., Allen, J., Gilbert, Jeffrey

“…Further potential for multivariate statistical analysis exists. Outputs: Through the identification of the spatial patterning of seroprevalence and risk factors associated with exposure to these diseases, the IEC materials such as posters and brochures on human health and animal health risk reduction have been developed and produced as well as guide future research and policy.…”
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Precipitation characteristics of the South American monsoon system derived from multiple datasets by Carvalho, Leila M. V., Jones, C., Posadas, A., Quiróz, R., Bookhagen, B., Liebmann, B.

“…This study compares several statistical properties of daily gridded precipitation from different data (1998–2008): 1) Physical Sciences Division (PSD), Earth System Research Laboratory [1.0° and 2.5° latitude (lat)/longitude (lon)]; 2) Global Precipitation Climatology Project (GPCP; 1° lat/lon); 3) Climate Prediction Center (CPC) unified gauge (CPC-uni) (0.5° lat/lon); 4) NCEP Climate Forecast System Reanalysis (CFSR) (0.5° lat/lon); 5) NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis (0.5° lat/0.3° lon); and 6) Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 data (0.25° lat/lon). …”
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Influence of reed (Phragmites australis) belts in the Baltic Sea archipelago on pike (Esox lucius) and other coastal fish species by Niemi, Niklas

“…Unfortunately, too few pike were caught to allow statistical analysis, longer time series of pike abundance data are necessary. …”
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Changing composition of private investment in Indian agriculture and its relationship with public investment and input subsidies by Kumar, Anjani, Bathla, Seema, Verma, Smriti

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Evaluating the impact of climate variability and water hazards on vector-borne disease patterns to develop early warning signals by Jampani, Mahesh, Amarnath, Giriraj

“…These two case studies utilized earth observation and recorded case data to evaluate the intrinsic links between water, climate, disease prevalence, and health risks using statistical and spatial analysis and predictive modeling. …”
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Vulnerability to climate change of cocoa in West Africa: patterns, opportunities and limits to adaptation by Schroth, Götz, Läderach, Peter R.D., Martínez Valle, Armando Isaac, Bunn, Christian, Jassogne, Laurence T.P.

“…We use a combination of a statistical model of climatic suitability (Maxent) and the analysis of individual, potentially limiting climate variables. …”
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Temporal evaluation of retention patch development : retention patches in Swedish forestry by Wieser, Noel, Schönning, Per

“…Locations, forest cover types, original retention patch size, distance to clear-cut edge and clear-cut ID were the basis for a statistical analysis, assessing if the factors influenced retention patch areal decrease. …”

Jämställdhet i parker by Hasselberg, Susanne, Thor, Sara

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Site-specific calculation of corn bioethanol carbon footprint with Life Cycle Assessment by Ponieman, Karen Debora, Bongiovanni, Rodolfo, Battaglia, Martín L., Hilbert, Jorge Antonio, Cipriotti, Pablo A., Espósito, Gabriel

“…As opposed to a single CF value per field, assessing the CF at a site-specific scale allows us to explore the within-field variability caused by different input rates, its interaction with environmental factors and crop yields. Spatial and temporal statistical analysis is needed to understand the relation between nitrogen fertilisation and corn bioethanol CF. …”
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AI-imputed and crowdsourced price data show strong agreement with traditional price surveys in data-scarce environments by Adewopo, J., Andrée, B.P.J., Peter, H., Solano-Hermosilla, G., Micale, F.

“…However, in many food-insecure countries, price data is often delayed, lacks spatial detail, or is unavailable during crises when markets may become inaccessible, and rising prices can rapidly exacerbate hunger. …”
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Estimation of tsetse challenge and its relationship with trypanosomosis incidence in cattle kept under pastoral production systems in Kenya by Bett, Bernard K., Irungu, P., Nyamwaro, Sospeter S., Murilla, G., Kitala, P., Gathuma, J., Randolph, Thomas F., McDermott, John J.

“…These findings show that when the spatial unit of analysis in observational studies or on-farm trials is small, for instance a village, it may not be possible to demonstrate a statistically significant association between tsetse challenge and trypanosomosis incidence in livestock so as to effectively control for tsetse challenge.…”
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Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests by Besnard, H., Carvalhais, N., Arain, M.A., Black, A., Brede, B., Buchmann, Nina, Chen, J., Clevers, J.G.P.W., Dutrieux, L.P., Gans, F., Herold, M., Jung, M., Kosugi, Y., Knohl, A., Law, B.E., Paul-Limoges, E., Lohila, A., Merbold, Lutz, Roupsard, O., Valentini, R., Wolf, S., Zhang, X., Reichstein, Markus

“…The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. …”
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Performance evaluations of CMIP6 model simulations and future projections of rainfall and temperature in the Bale Eco-Region, Southern Ethiopia by Gashaw, Temesgen, Abeyou, Abeyou, Teferi Taye, Meron, Belay Lakew, Haileyesus, Seid, Abdulkarim, Haileslassie, Amare

“…Performance evaluations were performed using the Comprehensive Rating Index (CRI), which is based on four statistical metrics. The best performing CMIP6 model(s) were bias-corrected by Distribution Mapping (DM) for future climate analysis at different agro-ecological zones (AEZs) and at the eco-region level. …”
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A novel approach to analyze uncertainties and complexities while mapping groundwater abstractions in large irrigation schemes by Ali Nawaz, Rana, Awan, Usman Khalid, Anjum, L., Liaqat, Umar Waqas

“…Intra-grid annual comparison of in-situ measurements showed that tubewells were being governed by different rules and thus yielded different abstraction within a grid ranging from 854 mm (±105) at head, 742 mm (±220) at middle and 649 mm (±244) at tail grids. Statistical analysis showed that annual GW abstraction by in-situ measurements at head 814 mm (±52), middle 769 mm (±44) and tail 688 mm (±56) end reaches varied significantly at a confidence interval of 95%. …”
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