Improving and evaluating boundary algebra filling for identifying polygon intersections

<p><strong>Background.</strong> Forest fires play an important role in ecosystems; however, uncontrolled wildfires can damage ecosystems and emit CO<sub>2</sub> into the atmosphere. The state of Chiapas ranks among the top ten places in Mexico in terms of the frequency of forest fires and the area damaged by them. The occurrence of fire and damage is the result of the complex interaction of social, climatic, and biophysical factors. <strong>Objective.</strong> To analyze the spatio-temporal dynamics of forest fires in the state of Chiapas during the period 2010-2024, as well as the leading causes of these events and their relationship with the trends of climate change. <strong>Hypothesis: </strong>The recurrence of forest fires can be explained by patterns of climate change.<strong> </strong> <strong>Methodology.</strong> The work was carried out by analyzing historical records of wildfires, as well as the trends of climatic variables, especially temperature and precipitation. The fire-prone areas were identified using the intersection of polygons across a temporal series in a Geographic Information System. The relationship between the recurrence of wildfire and climatic variables was determined by correlation and regression analysis. <strong>Results.</strong> The recurrent areas correspond, in great part, to the Sierra Madre of Chiapas, which, due to its physical-geographical characteristics, makes it prone to fire development. Likewise, during 2021-2024, the greatest fire damage was observed, mainly in areas where broadleaf and conifer vegetation predominates. <strong>Conclusion.</strong> The increase in the recurrence of forest fires in Chiapas is not entirely attributed to current climate trends; rather, part of these events is also related to anthropogenic causes and inadequate fire management, primarily due to agricultural activities. It is observed that these events are more frequent during the dry season, mainly between April and May. <strong>Implication.</strong> This type of research enables us to identify the primary conditions that favour wildfires and their relationship with climate change. The results will help in developing forest fire prevention and mitigation strategies.</p>

The rapid accessibility of information is an important requirement of the hypermap systems or the geographical informations systems. But the massive volumes of data oblige us to store it in secondary memory which greatly slows down the access time. Thus the indexing technique, which determines the way of secondary memory access, is an essential point in hypermap systems. There are several researches on spatial indexing techniques, but due to the lack of comparative studies which must be based on the analysis of the nature of the spatial data, it is difficult to say that one is better than another.

The Liupan Mountains are located in the southern Ningxia Hui Autonomous Region of China, that forms an important divide between landforms and biogeographic regions. The populated part of the Liupan Mountain Region has suffered tremendous ecological damage over time due to population pressure, excessive demand and inappropriate use of agricultural land resources. To present the relationship between land use/cover change and spatio-temporal variation of soil erosion, data sets of land use between the late 1980s and 2000 were obtained from Landsat Thematic Mapper (TM) imagery, and spatial models were used to characterize landscape and soil erosion conditions. Also, soil erosion in response to land use and land cover change were quantified and analyzed using data from geographical information systems and remote sensing. Soil erosion by water was the dominant mode of soil loss, while soil erosion by wind was only present on a relatively small area. The degree of soil erosion was classified into five severity classes: slight, light, moderate, severe, and very severe. Soil erosion in the Liupan Mountain Region increased between the late 1980s and 2000, both in terms of acreage and severity. Moderate, severe, and very severe eroded areas accounted for 54.86% of the total land area. The lightly eroded area decreased, while the moderately eroded area increased by 368817 ha (22%) followed by severe erosion with 146552 ha (8.8%), and very severe erosion by 97067.6 ha (5.8%). Soil loss on sloping cropland increased with slope gradients. About 90% of the cropland was located on slopes less than 15°. Most of the increase in soil erosion on cropland was due to conversion of steep slopes to cropland and degradation of grassland and increased activities. Soil erosion was severe on grassland with a moderate or low grass cover and on dry land. Human activities, cultivation on steep slopes, and overgrazing of pastures were the main reasons for the increase in erosion severity.

A quantitative assessment of the diversity of the soil cover components of the Arctic and Subarctic as shown on the Soil Map of Russian Federation, scale 1: 2.5 M was performed. Soil units of this map were combined with the mapping units on the map of soil-ecological zoning of the same scale, namely, soil districts (on the plains and in the mountains). For each soil district (total 60), average, minimum and maximum sizes of soil polygons, richness indices (total number of polygons and number of taxonomic units) were calculated, as well as species richness (Margalef, Menhinick), domination indices (Berger-Parker, MkIntosh, Simpson), complexity of the soil cover (divisibility index, complexity coefficient), diversity indices (Shannon, normalized Shannon index, Gini-Simpson), lateral differentiation, and a total score was derived by accounting the values of all calculated indices. The following quantitative characteristics of the soil cover were used to calculate the indices: the number of soil polygons, soil taxonomic groups, the length of boundaries, the area of polygons (including the average and maximum values) and their ratio. The soil diversity increases from the Arctic soil zone to the Subarctic arctic-tundra subzone and is accompanied by an increase in the areas of soil polygons. Pechorsko-Karskiy, Mezensko-Cheshskiy and Nizhneeniseyskiy plain soil districts, Koryakskiy, Anadyirskiy and Pekulneysko-Iskatenskiy mountain soil districts are characterized by maximum soil diversity, while the South-Novaya Zemlya mountain and plain soil districts – by the lowest one.

Adaptive indexing allows for the progressive and simultaneous query-driven exploration and indexing of memoryresident data, starting as soon as they become available without upfront indexing. This technique has been so far applied to onedimensional and multidimensional data, as well as to objects with spatial extent arising in geographic information systems. However, existing spatial adaptive indexing methods cater to static data made available in an one-off manner. To date, no spatial adaptive indexing method can ingest data updates interleaved with data exploration. In this paper we introduce GLIDE, a novel method that intertwines the adaptive indexing and incremental updating of a spatial-object data set. GLIDE builds a hierarchical spatial index incrementally in response to queries and also ingests updates judiciously into it. We examine several design choices and settle for a variant that combines gradual self-driven top-down insertions with query-driven indexing operations. In an extensive experimental comparison, we show that GLIDE achieves a lower cumulative cost than upfront-indexing methods and adaptiveindexing baselines.

Virtual reality offers the promise of highly interactive, natural control of the visualization process, greatly enhancing the scientific value of the data produced by medical imaging systems. Due to the computational and real time update requirements of virtual reality interfaces, however, the complexity of polygonal surfaces which can be displayed is limited. In this paper, we present a novel method for the production of a polygonal surface containing a pre-specified number of polygons from volumetric data. To preserve surface detail, we extract a set of curvature weights from the volumetric data and use these weights as the input vectors to a 2-D Kohonen network. The adaptation of the network to the input vectors results in a display surface that preserves useful detail relative to the number of polygons used.

Time-dependent changes in extreme precipitation occurrence across the northeastern United States are evaluated in terms of areal extent. Using gridded precipitation data for the period from 1950 to 2018, polygons are defined that are based on isohyets corresponding to extreme daily precipitation accumulations. Across the region, areal precipitation is characterized on the basis of the annual and seasonal number of extreme precipitation polygons and the area of the polygons. Using the subset of grid points that correspond to station locations in the northeastern United States, gridded precipitation replicates the observed trends in extreme precipitation based on station observations. Although the number of extreme precipitation polygons does not change significantly through time, there is a marked increase in the area covered by the polygons. The median annual polygon area nearly doubles from 1950 to 2013. Consistent results occur for percentiles other than the median and a range of extreme precipitation amount thresholds, with the most pronounced changes observed in spring and summer. Like trends in station data, outside the northeastern United States trends in extreme precipitation polygon area are negative, particularly in the western United States, or they are not statistically significant. Collectively, the results suggest that the increases in heavy precipitation frequency and amount observed at stations in the northeastern United States are a manifestation of an expansion of the spatial area over which extreme precipitation occurs rather than a change in the number of unique extreme precipitation polygons.

ABSTRACTSince flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.

Interaction analysis for moving individuals in space and time can contribute to understanding urban dynamics and human social networks. Recent advancements in trajectory analytics have created methods to identify and extract spatiotemporal patterns of interaction using movement tracking data. However, existing definitions and classifications of interaction between moving individuals are isolated. This research is the first attempt toward developing a taxonomy of human interaction in space and time that are observable from movement data. In the proposed framework, interaction between moving individuals is divided into four categories based on the time when it occurs and the duration of the interaction, including concurrent encounter, concurrent interaction, delayed encounter, and delayed interaction. While the duration of interaction can play a significant role in classifying human interaction, techniques to quantify the duration of interaction are limited. This article incorporates an evaluation of the time‐geographic interaction analysis method to quantify the duration of interaction between moving individuals using human‐generated GPS tracking data in California. The evaluation is a comparison between the existing time‐geographic‐based approach that mainly relies on potential path area computation and polygon intersection and the proximity‐based approach (i.e., based on the intersection of spatial and temporal buffers of two individuals) that is commonly used in digital contact tracing technologies. The results show that the time‐geographic‐based approach can measure the duration of interaction more accurately, whereas the proximity‐based approach underestimates the duration of interactions.

Space on construction site is a limited resource. Building elements, construction activities, and material storage and access compete for limited spaces throughout the entire project life, leading to space conflicts that occur frequently on site. Space conflicts could substantially hinder the performance of interfering activities. Both academic and commercial computer applications are available to detect solid-solid conflicts (physical geometry crashes) among site objects, but spacesolid overlaps and space-space overlaps are not well supported, especially for objects with complex shapes. In addition, very few researches incorporate the evolution of the workspace. A challenge exists in how to efficiently store, represent, and update the geometry data. This paper presents a study on buffering workspaces of arbitrary shapes and detecting the space-solid and space-space overlaps in a 4D context with an implementation on the platform of Geographic Information Systems (GIS). The buffering approach is based on a Uniformly-Distributed Points (UDP) method that examines the distance between each point and a solid geometry with an arbitrary shape. The proposed method eliminates the needs to scan all the workspace geometries for analysis, especially facilitates the case that the workspace geometry is evolving along time. A prototype was implemented in a commercial GIS software package. Preliminary experiment results have revealed that the newly created buffering prototype can generate workspaces in any geometric shapes and detect the space-solid and space-space conflicts in an efficient manner.

With the development of the computer science and application of the Geographic Information System (GIS), the 2-D GIS can not meet people's needs in many areas. Therefore, the development of the 3-D GIS is an inevitable trend. Using the 2-D map of one university, the elevation of each building and other basic data, the 2-D GIS is built. After the processing of Google Sketch up, 3D Max, and ArcGIS, the 3-D is implemented. The system has the foundation of display, query analysis, target location on the electronic map and navigation or browsing in the 3-D space.

Automatic mapping and spatial analysis of high spatial resolution closed terrain depressions and mounds on martian landscapes

Optimal workpiece setups for 4-axis numerical control machining based on machinability

Abstract. Studies of glaciers generally require precise glacier outlines. Where these are not available, extensive manual digitization in a geographic information system (GIS) must be performed, as current algorithms struggle to delineate glacier areas with debris cover or other irregular spectral profiles. Although several approaches have improved upon spectral band ratio delineation of glacier areas, none have entered wide use due to complexity or computational intensity. In this study, we present and apply a glacier mapping algorithm in Central Asia which delineates both clean glacier ice and debris-covered glacier tongues. The algorithm is built around the unique velocity and topographic characteristics of glaciers and further leverages spectral and spatial relationship data. We found that the algorithm misclassifies between 2 and 10 % of glacier areas, as compared to a ~ 750 glacier control data set, and can reliably classify a given Landsat scene in 3–5 min. The algorithm does not completely solve the difficulties inherent in classifying glacier areas from remotely sensed imagery but does represent a significant improvement over purely spectral-based classification schemes, such as the band ratio of Landsat 7 bands three and five or the normalized difference snow index. The main caveats of the algorithm are (1) classification errors at an individual glacier level, (2) reliance on manual intervention to separate connected glacier areas, and (3) dependence on fidelity of the input Landsat data.

A digital version of the soil map of the Russian Federation, scale 1 : 2.5 M, is being prepared based on the analysis of the attributes of polygons with peat soils in the West-Siberian taiga and sub-taiga zones. The correction was perfomed in 795 polygons (with the total area of 179 483 km2) out of 1 711 polygons considered (with the total area of 262 204 km2). The currently formulated idea of the dominance of oligotrophic bogs in the West Siberian taiga region of mires served as the basis for suggestion to replace the mesotrophic peat soils by oligotrophic ones in 598 polygons of the total area of 87 250 km2. Similarly, the polygons of microcatenas comprising oligotrophic and mesotrophic peat soils (57 polygons, total area of 38 405 km2) were modified: only oligotrophic peat soils were considered to be the dominant ones there. At the same time, a number of polygons with prevailing oligotrophic soils, confined mainly to the sub-taiga zone were proposed to be replaced by polygons with mesotrophic peat soils. The thermokarst pools in ridge-hollow mire complexes that were shown on the soil map of Russia beyond the permafrost zone were eliminated from the map database; the mapping of destructive peat soils was rearranged in accordance with the new interpretation of this taxon in the Russian soil classification. This work should improve the quality of research in the field of assessing the resource potential of peat soils in West Siberia.