Regional Mapping Research Articles

Mapping party systems over time: a spatial distribution of ideological competition in Chile

ABSTRACT This article introduces a novel method to map party systems over time, addressing the challenge of assigning comprehensive spatial ideological scores to coalitions, parties, and independents. Combining public opinion polls, manifesto analysis, and expert surveys, this method evaluates fixed and variable standard deviation models to score distinct 4,157 electoral lists from 5,195 district-level elections across 27 elections in 12 Chilean electoral systems. It proposes a synthetic data approach to simulate party positions within political family constraints, producing a high-density region map and a political lineage chart that visualize ideological evolution over time. Robustness checks, including diagnostics with controlled random noise and variance adjustments, confirm the reliability of these results. Key findings reveal an earlier-than-expected trend of fragmentation, a leftward drift among left-leaning parties, and an asymmetrical party system where fragmentation affects the left while polarization intensifies on the right. This study underscores the importance of expanding within-system estimators to enhance reliability and concludes with research questions that the resulting dataset can address. These include constituency-level shifts in ideological positioning, fragmentation linked to specific institutional reforms, and the dynamics of localized polarization within multiparty systems.

Regional map of orientation of effective stresses for the area of the Republic of North Ossetia–Alania

Regional map of orientation of effective stresses for the area of the Republic of North Ossetia–Alania

Unraveling Various Sources of Particulates Matter in Air-Dust Samples Collected from North India Through Elemental Mapping and Concentration Correlation Using Micro-Particle-Induced X-Ray Emission Spectroscopy.

Particulate matter (PM) found in the air is one of the major sources of pollution and air-borne diseases. Therefore, it is imperative to examine the elemental concentration distribution of the PM to identify the pollutant sources. In this study, it has demonstrated the capabilities of micro-particle-induced X-ray emission (micro-PIXE) spectroscopy in quantitative analysis of air samples collected from the Old Delhi outdoor market and indoor locations in the Panjab University hostel in the winter months. A 2-million electronvolts energetic scanning proton micro-beam (diameter ≈1 µm2) is used in micro-PIXE experiments generating high-resolution elemental maps of different regions of interest (ROI). Micro-PIXE along with the GeoPIXE analysis provides a non-destructive, standard-less, and ng/mg level-sensitive tool for the investigation of elemental distributions and highlighting pixels, which correlates to specific concentration ratios between elements at ROIs, thereby enabling a comprehensive understanding of the source of each elemental particulate. Si, Ca, and K detected in indoor PM suggest the source to soil erosion and crop burning, while high S levels in outdoor PM are primarily associated with coal power plants. Additionally, Sc, Ti, Cr, Mn, and Zn are found in outdoor samples, while indoor locations also contained trace amounts of V, Co, and Cu.

Mapping Bedrock Outcrops in the Sierra Nevada Mountains (California, USA) Using Machine Learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

First-in-human experience performing high-resolution cortical mapping using a novel microelectrode array containing 1,024 electrodes.

Localization of function within the brain and central nervous system is an essential aspect of clinical neuroscience. Classical descriptions of functional neuroanatomy provide a foundation for understanding the functional significance of identifiable anatomic structures. However, individuals exhibit substantial variation, particularly in the presence of disorders that alter tissue structure or impact function. Furthermore, functional regions do not always correspond to identifiable structural features. Understanding function at the level of individual patients-and diagnosing and treating such patients-often requires techniques capable of correlating neural activity with cognition, behavior, and experience in anatomically precise ways. 

Approach: Recent advances in brain-computer interface technology have given rise to a new generation of electrophysiologic tools for scalable, nondestructive functional mapping with spatial precision in the range of tens to hundreds of micrometers, and temporal resolutions in the range of tens to hundreds of microseconds. Here we describe our initial intraoperative experience with novel, thin-film arrays containing 1024 surface microelectrodes for electrocorticographic mapping in a first-in-human study. 

Main results: Six patients undergoing standard electrophysiologic cortical mapping during resection of eloquent-region brain tumors consented to brief sessions of concurrent mapping (micro-electrocorticography) using the novel arrays. Three patients underwent motor mapping using somatosensory evoked potentials while under general anesthesia, and three underwent awake language mapping, using both standard paradigms and the novel microelectrode array. Somatosensory evoked potential phase reversal was identified in the region predicted by conventional mapping, but at higher resolution (0.4 mm) and as a contour rather than as a point. In Broca's area (confirmed by direct cortical stimulation), speech planning was apparent in the micro-electrocorticogram as high-amplitude beta-band activity immediately prior to the articulatory event.

Significance: These findings support the feasibility and potential clinical utility of incorporating micro-electrocorticography into the intraoperative workflow for systematic cortical mapping of functional brain regions.

Picosecond regional delay mapping of arrayed VCSELs with a honeycomb alignment

Picosecond regional delay mapping of arrayed VCSELs with a honeycomb alignment

Vegetable Fields Mapping in Northeast China Based on Phenological Features

Developing vegetable agriculture is crucial for ensuring a balanced dietary structure and promoting nutritional health. However, remote sensing extraction in open-field vegetable planting areas faces several challenges, such as the mixing of target crops with natural vegetation caused by differences in climate conditions and planting practices, which hinders the development of large-scale vegetable field mapping. This paper proposes a classification method based on vegetable phenological characteristics (VPC), which takes into account the spatiotemporal heterogeneity of vegetable cultivation in Northeast China. We used a two-step strategy. First, Sentinel-2 satellite images and land use data were utilized to identify the optimal time and key indicators for vegetable detection based on the phenological differences in crop growth. Second, spectral analysis was integrated with three machine learning classifiers, which leveraged phenological and spectral features extracted from satellite images to accurately identify vegetable-growing areas. This combined approach enabled the generation of a high-precision vegetable planting map. The research findings reveal a consistent year-by-year increase in the planting area of vegetables from 2019 to 2023. The overall accuracy (OA) of the results ranges from 0.81 to 0.93, with a Kappa coefficient of 0.83. Notably, this is the first 10 m resolution regional vegetable map in China, marking a significant advancement in economic vegetable crop mapping.

Lime and marble deposits mapping and estimation through deep neural layers-random forest merger and remote sensing imagery

Abstract Lime and marble are sedimentary rocks (also called carbonated rocks), mainly composed of calcite and dolomite as major ingredients. Calcite (CaCo3) and dolomite (MgCo3) offer excellent reflection in visible and short-wave infrared bands (0.4 to 2.5 µm), which distinguish them from other rock types and makes them a solid application area for multispectral sentinel-2 sensors. In this study, monumental deposits of carbonated rocks of the intended study region are mapped and estimated using a novel Deep Neural Layers-Random Forest merger model and Sentinel-2 imagery. We used ArcGIS 10.5 to prepare a high-quality classified map of the study regions, which can be used further to effectively and efficiently reach out and extract those deposits. The proposed model, trained on enough purified data containing 131,750 pixels of size 10 m, mapped the deposits with 0.95 accuracy (kappa coefficient 0.94), which is 70 % of the total mineral deposits of the study regions. The proposed model is the improved version of a prior experimented model consisting of convolution layers and dense layers only, whose accuracy was 0.93.

Distribution and Contribution of Ethnic Groups on Fertility in Indonesia: Population Census Long Form and National Socio-economy Survey

Most Indonesian ethnicities have different perspectives in preferring how many children in their family have and what kind of gender must be a priority in their family. This study aimed to analyze the distribution of the ethnic groups and the effect of ethnicity on fertility in Indonesia. This study used the 15 largest ethnic groups in Indonesia using samples of the 2020 Population Census Long Form microdata (N=275,773,774) and married women number 45,153,267. The results of the analysis of ethnic distribution mapping in several regions show that the diversity of the ethnic groups spread to all regions in Indonesia such as the ethnicity of Minangkabau, Bugis, Malay, Chinese, Batak, and Dayak. It was found that the Sasak ethnic group had the highest influence on the birth of many children, which was 8.89 times higher than other ethnic groups in married women even after controlling for other factors. In the simple model, it was also found that the Sasak ethnic group was also the most influential in determining the birth of many children. The Indonesian government implemented policies that may utilize a cultural approach to organize population diversity and distribution, and use performance indicators using the local socio-cultural norms approach.

Integration of InSAR coherence and SAR backscatter increases accuracy of LULC mapping in tropical high-mountain ecosystems

Páramos, neotropical high-mountain biomes of the northern Andes, are increasingly threatened by human land use and climate change. Accurate land use/land cover (LULC) mapping is critical for monitoring these pressures. However, persistent cloud cover poses substantial challenges for optical satellite imagery. This study leverages multitemporal Interferometry SAR (InSAR) coherence and dual polarimetric backscatter intensity to improve LULC mapping accuracy in páramos ecosystems. Using a 1-year Sentinel-1 time series from January to December 2019, 17 LULC classes were mapped across 17,000 hectares. Results demonstrate that integrating backscatter and coherence increased F1-Score and overall accuracy by 9% and 11%, respectively, compared to backscatter alone. This emphasizes the critical role of multitemporal SAR for LULC mapping in cloud-prone regions. Additionally, this study underscores the utility of cloud-based platforms for efficient SAR data processing, offering a scalable approach for mapping high-mountain ecosystems worldwide, especially in inaccessible and heavy precipitation regions.

Neuroimaging model of visceral manipulation in awake rat.

Reciprocal neuronal connections exist between the internal organs of the body and the nervous system. These projections to and from the viscera play an essential role in maintaining and finetuning organ responses in order to sustain homeostasis and allostasis. Functional maps of brain regions participating in this bidirectional communication have been previously studied in awake humans and anesthetized rodents. To further refine the mechanistic understanding of visceral influence on brain states, however, new paradigms that allow for more invasive, and ultimately more informative, measurements and perturbations must be explored. Further, such paradigms should prioritize human translatability. In the current paper, we address these issues by demonstrating the feasibility of non-anesthetized animal imaging during visceral manipulation. More specifically, we used a barostat interfaced with an implanted gastric balloon to cyclically induce distension of a non-anesthetized male rat's stomach during simultaneous BOLD fMRI. General linear modeling and spatial independent component analysis revealed several regions with BOLD activation temporally coincident with the gastric distension stimulus. The ON-OFF (20 mmHg - 0 mmHg) barostat-balloon pressure cycle resulted in widespread BOLD activation of the inferior colliculus, cerebellum, ventral midbrain, and a variety of hippocampal structures. These results suggest that neuroimaging models of gastric manipulation in the non-anesthetized rat are achievable and provide an avenue for more comprehensive studies involving the integration of other neuroscience techniques like electrophysiology.Significance Statement It is unclear to what extent measurements of brain activity are affected by interoceptive processes. To advance our understanding of ongoing visceral activity's influence on brain states, here we provide a proof of concept, anesthesia-free animal model of visceral manipulation during simultaneous BOLD fMRI. We successfully demonstrated BOLD activation during gastric distension of the unanesthetized rat in both classically reported (cerebellum, hippocampus) and novel (inferior colliculus) regions. This paradigm establishes an important foundation for further interrogation of viscera-brain interactions.

Ibn Khaldūn’s Muqaddima and the Maps of al-Idrīsī

It has long been noted that in compiling a geographical preface to his famous Muqaddima, Ibn Khaldūn relied on the maps contained in the twelfth-century geographer al-Idrīsī’s Nuzhat al-mushtāq fī ikhtirāq al-āfāq. Yet Ibn Khaldūn’s reading of nearly seventy regional maps of al-Idrīsī has not to date been the subject of a detailed examination. This article seeks, first, to establish patterns in Ibn Khaldūn’s map reading as recorded in the Muqaddima, noting the focus and direction of his reading as well as its omissions. In addition to his descriptions of the maps, it considers Ibn Khaldūn’s use of the text of the Nuzhat al-mushtāq and occasional examples of his updating and addition of information. Second, this analysis leads to a discussion of the significance—or lack thereof—of al-Idrīsī’s maps for the larger project of the Muqaddima: what role, in the end, did geography play within Ibn Khaldūn’s theory of history?

Improving Forest Canopy Height Mapping in Wuyishan National Park Through Calibration of ZiYuan-3 Stereo Imagery Using Limited Unmanned Aerial Vehicle LiDAR Data

Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there is a lack of accurate FCH distribution in large areas. To address this issue, this study selected Wuyishan National Park in China as a case study to explore the calibration method for mapping FCH in a complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery and limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson’s correlation analysis, Categorical Boosting (CatBoost) feature importance analysis, and causal effect analysis were used to examine major factors causing extraction errors of digital surface model (DSM) data from ZY3 stereo imagery. Different machine learning algorithms were compared and used to calibrate the DSM and FCH results. The results indicate that the DSM extraction accuracy based on ZY3 stereo imagery is primarily influenced by slope aspect, elevation, and vegetation characteristics. These influences were particularly notable in areas with a complex topography and dense vegetation coverage. A Bayesian-optimized CatBoost model with directly calibrating the original FCH (the difference between the DSM from ZY3 and high-precision digital elevation model (DEM) data) demonstrated the best prediction performance. This model produced the FCH map at a 4 m spatial resolution, the root mean square error (RMSE) was reduced from 6.47 m based on initial stereo imagery to 3.99 m after calibration, and the relative RMSE (rRMSE) was reduced from 36.52% to 22.53%. The study demonstrates the feasibility of using ZY3 imagery for regional forest canopy height mapping and confirms the superior performance of using the CatBoost algorithm in enhancing FCH calibration accuracy. These findings provide valuable insights into the multidimensional impacts of key environmental factors on FCH extraction, supporting precise forest monitoring and carbon stock assessment in complex terrains in subtropical regions.

Regional mapping of ICT specialization and adoption of industry 4.0 technologies in Greece

Regional mapping of ICT specialization and adoption of industry 4.0 technologies in Greece

Predicting and Mapping the Phosphorus Adsorption Maximum and Phosphorus Adsorption Affinity Constant at Regional Scale

ABSTRACTInsight into the variation of the soil phosphorus (P) adsorption maximum (Qmax) and the P adsorption affinity constant (KL) is crucial for accurately assessing the dynamics of P availability, P uptake and P leaching in agricultural systems at regional scale. Data on the variation in soil P adsorption characteristics, derived from traditional batch experiments, combined with data on soil properties affecting them, such as pH, clay and organic matter content, can be used to assess the influence of soil properties on P adsorption characteristics. However, current studies are limited to explaining the variation in Qmax using linear models, focusing on either noncalcareous or calcareous soils. This study aims to (1) identify the soil properties governing both Qmax and KL for a combination of noncalcareous and calcareous soils, including nonlinear and interaction effects; and (2) create spatial maps depicting the variations in both soil P adsorption characteristics at the regional scale (two typical Chinese counties). We leveraged 83 data points of both Qmax and KL from 16 publications with main soil properties affecting P adsorption, that is, pH and the content of soil organic matter (SOM), clay and oxalate extractable Fe and Al (FeOX and AlOX), to develop predictive models for soil P adsorption. General linear regression (GLM) and extreme gradient boosting (XGB) models were used to unravel the relationships between soil properties and P adsorption characteristics. The XGB model outperformed GLM model, explaining more than 80% of the variations in both Qmax and KL in noncalcareous and calcareous soils, while the GLM model explained 52% for Qmax and only 21% for KL. Key drivers influencing Qmax were found to be FeOX, AlOX and pH, while clay and pH played significant roles in explaining the variability in KL. When applying these models at the county level using county‐level inventory data, noncalcareous soils generally exhibited higher P sorption capacity and binding energy than calcareous soils. To enhance the accuracy of soil P sorption predictions and guide sustainable P fertiliser use, regional mapping of FeOX and AlOX content is essential.

Huajuapan Lithostratigraphic Map, Puebla and Oaxaca states, Mexico (E14-D14)

ABSTRACT This paper presents a 1:50,000 scale map and lithostratigraphic synthesis of the Huajuapan region, which has one of Mexico's most complete geological records. The region represents a critical area for understanding the marine flooding process after the breakup of Pangea. This process formed small extensional basins, such as the Ayuquila basin, and dismantled a primary fluvial system that drained the ancient central Pacific slope of Pangea. In the Huajuapan region, the Ayuquila basin developed in a tectonic SSE–NNW extension framework that prevailed from the Middle Jurassic to the Turonian. In its beginnings, this basin developed a thick siliciclastic fluvial deposit during the Middle Jurassic. Later, during the Late Jurassic, although with local unconformities, the deposition of marine successions began. Marine deposits continued throughout the Cretaceous. During the Cenozoic, continental conditions were re-established with clastic-evaporite successions and volcanic activity.

Regional mapping of climate variables in Galicia from point samples

Galicia's (NW Spain) diverse climate, driven by various factors, exhibits significant spatial and temporal variability, directly affecting the region's economy and society. Taking this into account, it is of great importance to have climate observation networks that cover most of the territory, and in the case of not having them, to have tools that allow the estimation of climate parameters. The main tools used for the estimation of parameters in unmeasured locations are climate database and the interpolation of data from climate stations, with kriging being one of the most widely used interpolation methods. The evaluation of climatic database is very important to establish how well they estimate the parameters and to ensure that they result in a homogeneous data series. For this purpose, in the present study a comparison was made of point data from the MeteoGalicia weather station network with data extracted from the SIMPA, AEMET and ERA5 databases and with data resulting from interpolation using regression kriging methods. The climatic parameters for which data were used for the study are: mean temperature, precipitation and reference potential evapotranspiration (Eto). To extract the data from SIMPA, ERA5 and AEMET and to interpolate point data from MeteoGalicia, we worked with the statistical programming environment R. After extracting the data or interpolating them, they were compared with the data measured in the MeteoGalicia network of climatological stations using the Nash-Sutcliffe index. The results of this study seek to conclude which method is more effective for estimating each of the parameters studied. In general, the predictions based on interpolation (AEMET and regression kriging) presented the best goodness of fit, while SIMPA also presented good results for some parameters. In general, the parameters with the best estimation results were mean temperature, followed by precipitation and finally potential evapotranspiration.

Assessment of Parental Polymorphism between Swarna / Oryza rufipogon IC309814 Derived Mapping Population

Detection of molecular markers with parental polymorphism is a pre-requisite for QTL mapping and further identification of genomic regions. PCR based InDel markers tend to have a greater degree to show polymorphism among the parental genotypes for any specific cross. Polymorphic studies were carried out between the two parents Swarna (cultivated rice) and Oryza rufipogon IC309814 (Wild rice) using 192 randomly selected InDel markers and 40 gene specific markers for Iron and Zinc covering the entire 12 chromosomes. Results revealed that InDel markers were polymorphic between both the parents and highest percentage of polymorphism observed on chromosome 10 (75%) followed by chromosome 5 (60%) and chromosome 9 (55.6%) and lowest polymorphism percentage was shown in chromosome 3 (8.3%) with an overall 22.4 % polymorphism percentage among all the markers used in this study. Detected polymorphic (SSR) markers were for genotyping the mapping population derived from the cross between Swarna cultivar and Oryza rufipogon to map Quantitative Trait Loci (QTLs) associated with yield and grain quality traits.

Machine-learning approach for mapping stable orbits around planets

Numerical N-body simulations are typically employed to map stability regions around exoplanets. This provides insights into the potential presence of satellites and ring systems. We used machine-learning (ML) techniques to generate predictive maps of stable regions surrounding a hypothetical planet. This approach can also be applied to planet-satellite systems, planetary ring systems, and other similar systems. From a set of $10^5$ numerical simulations, each incorporating nine orbital features for the planet and test particle, we created a comprehensive dataset of three-body problem outcomes (star-planet-test particle). Simulations were classified as stable or unstable based on the stability criterion that a particle must remain stable over a time span of 104 orbital periods of the planet. Various ML algorithms were compared and fine-tuned through hyperparameter optimization to identify the most effective predictive model. All tree-based algorithms demonstrated a comparable accuracy performance. The optimal model employs the extreme gradient boosting algorithm and achieved an accuracy of 98.48<!PCT!> , with 94<!PCT!> recall and precision for stable particles and 99<!PCT!> for unstable particles. ML algorithms significantly reduce the computational time in three-body simulations. They are approximately $10^ $ times faster than traditional numerical simulations. Based on the saved training models, predictions of entire stability maps are made in less than a second, while an equivalent numerical simulation can take up to a few days. Our ML model results will be accessible through a forthcoming public web interface, which will facilitate a broader scientific application.

Taxonomic synopsis of the genus Rubus (Rosaceae) in Armenia

The article presents the results of a taxonomic study of the genus Rubus (Rosaceae) in the flora of Armenia comprising 14 species from 3 subgenera. The following taxa are identified: R. saxatilis (subgenus Cylactis), R. idaeus (subgenus Idaeobatus), R. sanctus, R. armeniacus, R. candicans, R. ibericus, R. cartalinicus, R. peruncinatus, R. takhtadjanii, R. canescens, R. piceetorum, R. zangezurus, R. caucasicus, R. caesius (subgenus Rubus). The occurrence of R. ibericus is confirmed in the flora of Armenia. A checklist and a key for identification of the species, data on their geographic and altitudinal distribution, habitats, terms of flowering and fruiting, synonyms, type citations are given. The article is illustrated by the map of the floristic regions of Armenia and original photographs.