Geostatistical Analysis Research Articles

Epidemiological etiology of Erysiphe sp. and putative viral and phytoplasma-like symptoms in Ayocote bean (Phaseolus coccineus)

Introduction/Objective. Ayocote bean (Phaseolus coccineus) has potential as a source of resistance in breeding programs because it exhibits greater tolerance to plant pathogens than P. vulgaris. However, its sanitary characterization is insipient; therefore, the purpose of this work was to carry out an etiological-epidemiological diagnosis, with emphasis on presumptive symptoms of viral and phytoplasmic organisms, and a typical fungal signs of powdery mildew. Materials and Methods. A plot (50 x 62 m) of flowering Ayocote bean was selected. It was divided into 80 (8 x 10) quadrats (6 x 6 m) and 720 subquadrats (2 x 2 m). From 25 plants with powdery-mildew-type leaf symptoms, mycelium was collected with adhesive tape for light microscopy observation and taxonomic identification. Length-width measurements were made on 60 conidia. Pure mycelium collected in situ and ex situ from 1-5 leaflets/plant was used for genomic analysis by PCR with universal primers ITS1 and ITS4. Samples were sequenced in Macrogen Inc. Korea. A total of 63 plants and 121 trifoliate leaves with viral and phytoplasmic symptoms were collected by direct sampling. In 88/121 samples, genomic analysis was performed by PCR with universal primers for Potyvirus (1), Begomovirus (2), and Phytoplasmas (1). Sequence editing and analysis were performed in SeqAssem and BLASTn/GenBank. Phylogenetic constructions were developed in Mega 11 with MUSCLE, Maximum Likelihood (ML), and HKY substitution model (1000-Bootstrap). Putative powdery mildew severity (%), flower damage (%), Macrodactylus sp. adult density, and plant vigor (%) were evaluated in 80 quadrats (3subquadrats/quadrat) with App-Monitor®v1.1 configured with a 5-class scale. In GoldenSurfer® v10, Kriging geostatistical analysis was performed to determine the spatial interrelationship between these variables. Results. Erysiphe vignae was identified as associated with powdery mildew of P. coccineus. The fungus, with hyaline, ovoid to ellipsoid conidia measuring 31.74 ± 0.3419 μm x 15.11 ± 0.1579 μm, without the presence of fibrosin bodies, had 100% genomic homology. This is the first report in Mexico. With average July-August temperature and relative humidity of 16.3 °C (±5.8) and 92.8 % (±10.7), respectively, powdery mildew leaf incidence and severity were 65.3 and 22.7 % (±16.9, range: 0 - 66.5 %), respectively. The most inductive focus (60-80 % severity) had an aggregate e 4-quadrat pattern (96 m2, lag = 4 and σ2-s = 450). Inoculum dispersal was significantly associated with dominant North-South winds and plant vigor (lag = 4 and σ2-s = 470). Flower damage was inconclusive in its spatial association with powdery mildew and Macrodactylus sp. suggesting uncorrelated events. No Potyvirus, Begomovirus, or Phytoplasmas were detected associated with yellowing, leaf distortion, mosaic, internode shortening, and other symptoms observed in situ. This confirms the relative tolerance/resistance reported for P. coccineus. Conclusion. E. vignae (Erysiphales: Erysiphaceae) associated with P. coccineus is reported for the first time in Mexico with moderate to intense epidemic level, which indicates its susceptible condition to this fungus. However, negative results for Potyvirus, Begomovirus, and Phytoplasmas, validate the apparent tolerance/resistance of P. coccineus to these organisms.

Effect of multiple-compaction on laterite soil: geotechnical and geo-statistical analysis

Multiple-compaction demonstrates cyclic loading on laterite soils used in highway construction and its effects on engineering properties. The method determines the mechanical stability of derived soils from porphyritic granite, granite gneiss, and charnockite. Fifty-one soil samples were obtained from the horizons of the laterite soil profiles. Four sets of dynamic compactions were carried out on each sample. Index engineering properties such as specific gravity, Atterberg limits, and particle size distribution were investigated before and after multiple-compaction. The changes in engineering properties and moisture-density characteristics were investigated using the granulometric modulus and hardness index. Through multiple-compaction, larger grains in soils derived from granite gneiss and porphyritic granite disintegrated into smaller particles. The fine grains break down more easily than the large grains of quartz bound by clayey materials in charnockite-derived soils. Interestingly, the maximum dry density and optimum moisture content remain consistent in porphyritic and granite gneiss-derived soils after multiple compactions. Based on the densification and behaviour of the derived soils under multiple compaction in highway construction, porphyritic granite and granite gneiss-derived soils are more suitable as engineering materials than charnockite-derived soils.

Spatial Distribution of Arsenic in the Aksu River Basin, Xinjiang, China: The Cumulative Frequency Curve and Geostatistical Analysis

The quality of drinking water is crucial for human health and the sustainable development of societies. The Aksu River Basin, a typical inland river system, has areas where groundwater arsenic levels exceed safe drinking water standards (i.e., arsenic concentrations greater than 10 μg/L). Identifying the causes of high arsenic levels in the basin’s groundwater requires further study. Analyzing the hydrogeochemical composition of the Aksu River basin helps us to understand the spatial distribution of groundwater environments and locate areas with dangerously high arsenic levels. In this research, we collected 196 groundwater samples from along the river. Out of these, 38 samples had arsenic levels above 10 μg/L, which represents 19.4% of the total samples collected. By examining the slope changes in the cumulative frequency curves of major ion ratios and employing geostatistics (specifically, the Kriging interpolation), and taking into account the environmental characteristics of the entire basin, we divided the study area into five sub-regions (Zone I through Zone V). The geostatistical analysis showed a significant spatial variability in groundwater arsenic levels, with a clear spatial correlation. Our findings demonstrate that arsenic concentrations in the Aksu River basin’s groundwater vary widely, with Zones II and III—mainly located in the northeastern part of the basin and in Awat County—being hotspots for high-arsenic water. Factors such as a weak reducing environment, intense evaporation, strong cation exchange, and the low-permeability recharge of surface water contribute to the accumulation of arsenic in the basin’s groundwater. The results of this study are vital for assessing the risk of arsenic contamination in groundwater in similar basins and for identifying critical areas for further investigation and research.

A geostatistical analysis of multiscale metallicity variations in galaxies – III. Spatial resolution and data quality limits

ABSTRACT Geostatistical methods are powerful tools for understanding the spatial structure of the metallicity distribution of galaxies, and enable construction of accurate predictive models of the 2D metallicity distribution. However, so far these methods have only been applied to very high spatial resolution metallicity maps, leaving it uncertain if they will work on lower quality data. In this study, we apply geostatistical techniques to high-resolution spectroscopic maps of three local galaxies convolved to eight different spatial resolutions ranging from ∼40 pc to ∼1 kpc per pixel. We fit a geostatistical model to the data at all resolutions, and find that for metallicity maps where small-scale structure is visible by eye (with ≳10 resolution elements per Re), all parameters, including the metallicity correlation scale, can be recovered accurately. At all resolutions tested, we find that point metallicity predictions from such a geostatistical model outperform a circularly symmetric metallicity gradient model. We also explore dependence on the number of data points, and find that N ≳ 100 spatially resolved metallicity values are sufficient to train a geostatistical model that yields more accurate metallicity predictions than a radial gradient model. Finally, we investigate the potential detrimental effects of having spaxels smaller than an individual H ii region by repeating our analysis with metallicities integrated over H ii regions. We see that spaxel-based measurements have more noise, as expected, but the underlying spatial metallicity distribution can be recovered regardless of whether spaxels or integrated regions are used.

Spatial Variability of Selected Soil Chemical Parameters in Low-yielding Paddy Production Block in Mahaweli System H, Sri Lanka

Although water availability for paddy production in Mahaweli System H is abundant, paddy yield is lower in Nochchiyagama (278 km2), chosen for soil assessment. Paddy yield data were used to identify the low-yielding division using the area-weighted average. Twenty-five random locations were generated, and soil samples were collected for pH, soil conductivity (EC), salinity, and total dissolved solids (TDS) analysis. ArcGIS software was used to create spatial distribution maps and related geostatistical analyses for each parameter. Vector files were created with their associated properties, and thematic maps were generated using spatial interpolation techniques, such as universal kriging (UK), ordinary kriging (OK), and inverse distance weighted (IDW) methods of interpolation techniques to identify the best interpolation method for soil chemical parameters mapping. The entire Nochchiyagama land was observed to have a slightly acidic pH (5.6-5.9) range that may have affected rice crop growth due to nutrient mobility and uptake issues. The spatial interpolation evaluation suggests that at least two-thirds of the area observed for lower TDS levels (591-654 mg/L) is potentially unsafe paddy production. Elevated levels of EC (3.1-7.24 dS/m) along with TDS may lead to physiological drought due to interferences in ion uptake. In overall, spatial interpolation evaluation indicators suggest that the UK method was observed with a lower mean relative error (MRE) than the other two interpolations. However, EC distribution showed low MRE in both IDW and OK interpolation techniques. The IDW method was observed to have a lower RMSE (Root mean square error). The UK spatial interpolation performed better for TDS and salinity predictions than other methods. This study found consistent regional differences in low paddy yields in Mahaweli system H using the UK method for analyzing soil chemical parameters. 
 Keywords: Electrical conductivity, Interpolation errors, Salinity, Spatial interpolation

Geostatistical modeling approach for studying total soil nitrogen and phosphorus under various land uses of North-Western Himalayas

The distribution of total soil nitrogen (TSN) and total soil phosphorus (TSP) plays a pivotal role in shaping soil quality, fertility, agricultural practices, and environmental balance, especially in ecologically sensitive regions like the North-Western Himalayas (NWH). The primary objectives of this study were to contribute to clarify the impact and the rationale of various land uses on the spatial variation of TSN and TSP in the corresponding soils. This study aimed to explore the relation of TSN and TSP distribution in NWH soils with various factors like landscape physiography and soil physical and chemical properties using random sampling and geostatistical analyses. Employing random sampling, 300 soil surface samples (at a depth of 0–20 cm) were collected across various 500 m × 500 m grids from agriculture, horticulture, forest and fallow lands in the NWH region. The spatial land heterogeneity of TSN and TSP were systematically analyzed using standard statistical and geostatistical approaches (Gaussian, spherical, exponential, and linear). Results revealed a decreasing order of TSN and TSP levels i.e., horticulture (0.410 and 0.723 mg/kg) > agriculture (0.314 and 0.597 mg/kg) > forest (0.236 and 0.572 mg/kg) > fallow (0.275 and 0.342 mg/kg). Stepwise multiple regression results demonstrated a correlation between TSN and soil organic carbon (SOC), while TSP was correlated with soil organic carbon (SOC) and fine-grained soil particles. Nugget % values indicated the following spatial variability for TSN: agricultural (1.4) > horticultural (3.2) > forest (3.9) > fallow land (4.8) > mixed land (5.8), whereas the spatial variability of TSP showed a similar trend for all land uses. The optimized conceptual framework and isotropy models varied for TSN and TSP on dependence on land use type. The results of this study revealed the spatial patterns and land use-related variations and improved the prediction of nutrient distribution, so contributing an optimized conceptual framework for future studies. Finally, this study provided crucial insights to enhance soil quality, fertility, agricultural sustainability, and environmental equilibrium in the ecologically fragile NWH region, contributing to solve a significant research gap in the global understanding of soil dynamics.

Applying Geostatistics to Understand Seismic Activity Patterns in the Northern Red Sea Boundary Zone

A comprehensive geostatistical analysis was conducted on a dataset comprising 24,321 seismic events in the Red Sea region, spanning from 1997 to 2020. This analysis involved the creation of a new seismic activity database, incorporating data from both Egyptian and Saudi Seismic Networks. This enriched database provided a robust foundation for a detailed examination of the seismic patterns and activities in the region. Utilizing geographic information systems and various spatial analytic methods, it identifies seismic patterns and tectonic influences. The findings reveal significant seismic clustering along the Central Red Sea axis, indicative of active rifting between the Nubian and Arabian plates. The study demonstrates spatial autocorrelation in seismic activities, with high-high clusters marking zones of elevated seismicity. Kernel Density Estimator analyses highlight concentrated seismic activity in the Gulfs of Aqaba and Suez. Higher magnitude events are shown to localize in areas of greater tectonic stress, aligning with known geological features. This research provides critical insights into the seismic dynamics of the Red Sea, showcasing the effectiveness of geostatistical techniques in analyzing seismic data in tectonically active regions.

GIS Interpolation and Mapping of Soil Physicochemical Properties in Deep Medium Black Soils of Established Citrus Orchards

Soil properties are an important factor for orchard establishment, precise nutrient management and sustainable production of fruit crops. Therefore, it is important to assess the spatial distribution of fundamental soil properties in well-established orchards. Hence an attempt has been made to assess the extent of soil properties and its spatial distribution in citrus orchards in medium black soils of Madhya Pradesh. The present study was conducted for the assessment of the spatial distribution of physicochemical properties viz. pH, electrical conductivity (EC) and soil organic carbon (SOC) of citrus orchards in medium-deep black soils of India. Results revealed that soil pH ranged from 6.83-8.84 (mean 7.80), soil EC varied from 0.07-0.34dS m-1 (mean 0.18 dS m-1) and soil organic carbon ranged from 0.13-0.89% (mean 0.47%) in 0-20 cm of surface soil layer. Geostatistical analysis showed that the slope of the prediction function of best-fit model (exponential) for soil pH, EC and SOC was 0.31, 0.22 and 0.77, respectively. The corresponding values of root mean square error (RMSE) were 0.35, 0.03, and 0.14. Interpolation of soil properties indicated that 89.2 % area had soil pH between 7.20 to 8.00, 83.4 % area had soil EC between 0.10 to 0.20 dS m-1, while>90 % area had SOC content ranged from 0.25 to 0.75%. Geo-statistical analysis revealed that spatial dependency was moderate for pH and strong spatial dependency was estimated for EC and SOC content. Based on RMSE and slope of prediction function, an exponential model was best-fit model in ordinary kriging for interpolation of measured soil properties.

Impact of jittering on raster- and distance-based geostatistical analyses of DHS data

Fine-scale covariate rasters are routinely used in geostatistical models for mapping demographic and health indicators based on household surveys from the Demographic and Health Surveys (DHS) program. However, the geostatistical analyses ignore the fact that GPS coordinates in DHS surveys are jittered for privacy purposes. We demonstrate the need to account for this jittering, and we propose a computationally efficient approach that can be routinely applied. We use the new method to analyse the prevalence of completion of secondary education for 20-49 year old women in Nigeria in 2018 based on the 2018 DHS survey. The analysis demonstrates substantial changes in the estimates of spatial range and fixed effects compared to when we ignore jittering. Through a simulation study that mimics the dataset, we demonstrate that accounting for jittering reduces attenuation in the estimated coefficients for covariates and improves predictions. The results also show that the common approach of averaging covariate values in windows around the observed locations does not lead to the same improvements as accounting for jittering.

Geo-Statistical Analysis of Soil Pollutants of Tannery Industrial Area, Kanpur

Geo-Statistical Analysis of Soil Pollutants of Tannery Industrial Area, Kanpur

Three-dimensional modeling and geostatistical structural analysis for ore deposit prospecting trend and wisdom mining: a case study in the Jiaoxibei gold field, China

The orientation of deep-seated orebodies was observed shifted from the northwest to north at the Xincheng deposit in the Jiaoxibei gold field during the mining operations. To make informed decisions, understanding the geological factors influencing this directional change is crucial for mining engineers. Prior research suggested that the ore-controlling fault morphology might correlate with the orebody positions and the thickness of alteration zones. Hence, the morphology of fault structures emerges as a key point in this work. First, we constructed a three-dimensional (3D) fault surface model. A large fault framework was interpreted using gravity–magnetic–electrical geophysical datasets, and then, the local details of the fault surfaces were characterized using an interpolation method based on the drillhole fault gouge data. Subsequently, we statistically assessed the spatial association between fault morphology and orebody positioning using the standardized C-values method and demarcated the ore-controlling structures on fault surfaces using quantitative indicators. The indicators of fault dips, fault azimuths, and the second-order trend residuals of fault surfaces represented the fault morphology. The drillhole grade data (Σsampling grade * sampling length) were used to describe the scale of mineralization zones, and the cumulative thickness of the pyrite–sericite altered rocks indicated the intensity of alteration zones. Finally, a geostatistical structural analysis was used to depict the mineralization orientation at different locations. Through these methods, we aim to propose a potential pattern of how ore-controlling fault morphology spatially influences the positions and orientations of orebodies. This comprehensive approach offers valuable insights into exploring deep-seated mineralization at the Xincheng deposit, aiding in a better understanding of the deposit’s geological dynamics for future mining endeavors.

Heavy metals contamination and ecological risks in agricultural soils of Uşak, western Türkiye: a geostatistical and multivariate analysis.

This research aimed to determine and evaluate the concentrations ofAs, Cu, Hg, Ni, and Pb, and the physicochemical properties of 48 agricultural soil samples, to identify potential ecological risks and their sources associated with heavy metals contamination in Usak, western Türkiye. Various methods were used to assess ecological risks, including geoaccumulation index (Igeo), enrichment factor (EF), degree of contamination (Cdeg), potential ecological risk (RI), and pollution load index (PLI). The heavy metals concentrations ranged from 4 to 61mg/kg for As, 8-48mg/kg for Cu, 0.01-0.06mg/kg for Hg, 30-813mg/kg for Ni, and 4-30mg/kg for Pb. The mean As and Ni concentrations were much greater than Earth's crustal average, the world's mean values, and mean values from many other emerging countries. Igeo and EF values for As, Ni, and Pb indicate various degrees of contamination. Cdeg values show that 96% of the study area is affected to some degree by contamination. For RI values, 38% indicate ecological risks ranging from moderate to considerable degrees. PLI values show that 75% of the agricultural soils are moderately polluted. Spatial distribution maps of Cdeg, RI, and PLI show that the northeastern and southwestern parts of the study area have been polluted to different levels by As, Ni, and Pb. Industrial activities and excessive use of fertilizers, pesticides, fungicides, and herbicides were identified as major sources of heavy metals contamination in the agricultural soils of Uşak.

Identification Of Locally Transmitted COVID-19 Spatial Clusters And Hotspots

Background: The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world. We conducted this study to identify locally transmitted COVID-19 spatial clusters and hotspots in this phrase of the fourth wave in Vietnam. Data used and Methods: A total of 9,192 locally transmitted cases confirmed in this phrase in the fourth wave were used in study. Global and local Moran’s I and Getis-Ord’s G_i^* statistics were employed to identify spatial autocorrelation and hotspots of COVID-19 cases. Results: It was found that global Moran’s I statistic indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran’s I statistic successfully identified three high-high spatial clusters of COVID-19 cases in Bac Giang (5,083 cases), Bac Ninh (1,407 cases), and Hanoi (464 cases). In addition, hotspots of COVID-19 cases were mainly detected in Bac Giang (5,083 cases), Bac Ninh (1,470 cases), Hanoi (464 cases), Hai Duong (51 cases), and Thai Nguyen (7 cases). Conclusion: The results of this work offer new perspectives on the geostatistical analysis of COVID-19 clusters and hotspots, which could help policy planners anticipate the dynamics of spatiotemporal transmission and develop critical control measures for SARS-CoV-2 in Vietnam. Future pandemics and epidemics can be avoided and controlled with the help of geospatial analysis techniques.

What drives densification and sprawl in cities? A spatially explicit assessment for Vienna, between 1984 and 2018

The spatial arrangement of settlements constitutes a long-lasting legacy and shapes the prospects for transformations toward sustainability. Thus, understanding the drivers of changes in settlement patterns is essential. In this article, we present a spatially explicit, geostatistical analysis of settlement dynamics, and a qualitative investigation of its regulative, demographic, and economic drivers, using the example of Vienna, Austria between 1984 and 2018. Combining spatially explicit metrics of urban sprawl and cluster analysis, we analyzed high-resolution maps of buildings, population, and jobs to identify distinct settlement trajectories. Societal drivers of more or less sprawled settlement dynamics are analyzed with desk research and expert interviews. We distinguish five types of settlement dynamics: persistently dense areas with increasing use intensity, re-densification of dense areas, persistently sprawled areas, redensification of sprawled areas, and persistently isolated buildings. Urban renewal schemes have fostered the re-densification of dense areas in response to population growth and urban economic restructuring. The combination of urban renewal schemes and green space policies has successfully limited urban expansion. Challenges arise from the demand for single-family housing and corresponding zoning regulations. These factors solidify existing sprawled settlements, posing obstacles to the efficient re-densification of such areas crucial for sustainable urban development.

Spatiotemporal variability in human thermal comfort perception in open-air spaces: application to the state of Minas Gerais, Brazil.

The objective of this study was to propose bioclimatic zoning to classify human thermal comfort and discomfort in the state of Minas Gerais, Brazil; both historical and future scenarios are considered. Thus, historical series (1961 to 2017) of the effective temperature index as a function of the wind (ETW) were obtained as a function of the monthly average values of the minimum, mean, and maximum dry-bulb air temperatures (tdb,min, tdb,mean, and tdb,max, respectively), in addition to the mean relative humidity ([Formula: see text], %) and mean wind speed ([Formula: see text], m s -1). The data were obtained from 34 weather stations and subjected to trend analysis by using the nonparametric Mann-Kendall test, thus enabling the simulation of future scenarios (for 2028 and 2038). Then, to define the thermal ranges of the bioclimatic zoning, maps of ETWmin, ETWmean, and ETWmax were created from geostatistical analysis. Overall, the results show warming trends for the upcoming years in Minas Gerais municipalities. All climatic seasons showed an increase in the frequency of new classifications in the upper adjacent classes, which indicates climate warming. Therefore, when considering future scenarios for the autumn and winter seasons, attention should be given to changes in predicted thermal sensation, especially in the Central Minas Gerais, Belo Horizonte Metropolitan, South/Southwest Minas, Campo das Vertentes, and Zona da Mata.

Análisis espacial de las precipitaciones en el Estado de Río Grande do Norte, Brasil

The main goal of this research was to perform a spatial analysis of precipitation in the state of Rio Grande do Norte (RN) through geostatistical techniques, comparing the historical average data with rainy and dry standard years. Three indicators called Moran's Global Index, Moran Map, and Moran's Scattering Diagram were used for geostatistical analysis. The results showed that precipitation has a more evident pattern in the Eastern Potiguar mesoregion since both in the year considered rainy and, in a year considered dry, most of this mesoregion presented a (HH) pattern, that is, the municipalities form a cluster that represents spatial autocorrelation associated with higher values of precipitation in the state. The regions of Agreste and Central Potiguar were the driest.

PENGARUH VARIABILITAS TOPOGRAFI DAN SIFAT FISIK TANAH TERHADAP PRODUKSI KENTANG DI KECAMATAN BUMIAJI, KOTA BATU

Soil erosion causes a loss of nutrients in the soil and is evident in the agricultural areas of Batu City, primarily due to the steep or very steep topography. So it will affect the level of production. Determination of the effectiveness of observation was based on four classes of slope: 0-8%, 8-15%, 15-30%, and >30% at various altitudes: <1,100 m, 1,100-1,300 m, 1,300-1,500 m, 1,500-1,700 m, and >1,700 m above sea level 40 observation points. Geostatistical analysis with ArcMap software was conducted to determine the pattern of semivariograms. The results of this study indicated that the slope, altitude, and organic matter affect the yield of potatoes. The resulting formula is y = 11.845 - 0.258x1 + 0.007x2 + 2.454x3, where y is the potato productivity, x1 is slope, x2 is altitude, and x3 is the organic C, with r value is 0.87 and R2 value = 0.74. The highest potential production of potatoes in Bumiaji reached 33.06 t ha-1, and the lowest was only 7.12 t ha-1. Available land planted with potatoes can produce 35,891 t of potato, with average productivity reaching 24.6 t ha-1.

Literature analysis of international experiences in studying the theoretical and methodological framework of GIS-based demographic mapping processes

This research conducts a comprehensive analysis of GIS-based demographic mapping, synthesizing international literature to unravel evolving theoretical frameworks, spatial analysis techniques, and the integration of emerging technologies. The study reveals a convergence of Spatial Demography, Agent-Based Modeling, and Geodemographics, providing nuanced insights into population dynamics. Spatial clustering, gravity modeling, geostatistical analysis, and cellular automata modeling represent advancements in spatial analytics, enriching our understanding of migration patterns and population distribution. The integration of emerging technologies—LiDAR, Artificial Intelligence, and Blockchain—marks a transformative shift, enhancing accuracy in population density estimation and introducing novel dimensions of predictive modeling and data security. Ethical considerations, including anonymization techniques and algorithmic transparency, contribute to responsible GIS-based demographic mapping practices. Addressing challenges such as data quality issues, limited accessibility, and ethical considerations, the research proposes practical solutions, from citizen science integration to standardized GIS protocols. Future directions advocate for the adoption of 5G technology, spatial big data analytics, community-engaged mapping, and investigating the intersection of climate change and demography. The synthesis of these findings positions this research as a vital resource, guiding researchers, practitioners, and policymakers in navigating the dynamic landscape of GIS-based demographic analysis.

Spatiotemporal distribution of Schizaphis graminum (Rondani) and its natural enemy Coccinella septempunctata (Linnaeus) in graniferous sorghum crops.

At the global level, Sorghum bicolor (L.), is one of the most important crops, which ranks fifth among all cereals. It is cultivated in Africa, Asia, Oceania and the Americas, where it serves as a source of food and feed for humans and animals, respectively. However, production is constrained by several factors including pests and diseases. Among the pests that are known to attack Sorghum, Schizaphis graminum (Rondani), commonly called the green cereal aphid, is the most destructive pest of sorghum. S. graminum damage to sorghum is worsen by water deficit which favors its occurrence. Limited information on the spatial distribution of the pest and its natural enemies impedes the development of ecologically friendly management strategies. Therefore, the objective of this research was to determine the spatiotemporal distribution of S. graminum and its natural enemy Coccinella septempunctata (L.) during the vegetative and reproductive stage of the crop using geostatistical analysis. The spatiotemporal distribution of S. graminum was influenced by Sorghum development stage, surrounding landscape, and presence of its main predator C. septempunctata. Moreover, the abundance of C. septempunctata was influenced by the density of S. graminum. The findings from this study are required for developing sustainable pest management strategies against S. graminum.

UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization.

In various scientific and industrial domains, analyzing multivariate spatial data, i.e., vectors associated with spatial locations, is common practice. To analyze those datasets, analysts may turn to methods such as Spatial Blind Source Separation (SBSS). Designed explicitly for spatial data analysis, SBSS finds latent components in the dataset and is superior to popular non-spatial methods, like PCA. However, when analysts try different tuning parameter settings, the amount of latent components complicates analytical tasks. Based on our years-long collaboration with SBSS researchers, we propose a visualization approach to tackle this challenge. The main component is UnDRground Tubes (UT), a general-purpose idiom combining ideas from set visualization and multidimensional projections. We describe the UT visualization pipeline and integrate UT into an interactive multiple-view system. We demonstrate its effectiveness through interviews with SBSS experts, a qualitative evaluation with visualization experts, and computational experiments. SBSS experts were excited about our approach. They saw many benefits for their work and potential applications for geostatistical data analysis more generally. UT was also well received by visualization experts. Our benchmarks show that UT projections and its heuristics are appropriate.