Distribution Pattern Of Points Research Articles

Cluster Mapping of Waste Exposure Using DBSCAN Approach: Study of Spatial Patterns and Potential Distribution in Bantul Regency

High level of plastic waste production is a common issue in various places, including the Special Region of Yogyakarta province. It is proven that the Piyungan Integrated Waste Disposal Site (TPST) or Final Disposal Site (TPA) in Bantul Regency has been closed several times due to capacity exceeding the quota and some blockages from residents around the TPA. Issues related to microplastic contamination resulting from discarded plastic waste are fascinating to study, considering the long-term impact of microplastic contamination on human health. This research aims to map the distribution of locations with the potential for waste accumulation to reduce the negative consequences of microplastic contamination. The population used included TPS and markets in Bantul District, with the sample being the distribution of TPS and market points in Bantul District in 2023. The results of checking the point distribution pattern using the quadrant and nearest neighbor method showed that the distribution of waste accumulation points had a clustered design; thus, it could be continued with cluster analysis using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. Meanwhile, the number of clusters was determined iteratively from a combination of DBSCAN parameters (MinPts and Epsilon). The best method was evaluated based on the Silhouette Coefficient (SC) value, which in this case was 0.78 (MinPts 7 and Eps 1500) and included in the strong category. Subsequently, exploration was carried out by reducing the MinPts value and the lowest limit value of strong SC.

Supply and demand of higher vocational education in China: Comprehensive evaluation and geographical representation from the perspective of educational equality.

The imbalanced regional development of higher vocational education, particularly the disparity between the supply and demand of educational resources, has emerged as the primary factor impeding the provision of high-quality higher education in China during the establishment of a universal education system. Based on the 1,482 higher vocational education institutions recognized by the Ministry of Education of China in 2021 as the research objects, the development of higher vocational education in China was explored from the perspective of supply and demand using the entropy weight TOPSIS method and coupling coordination degree model. It was found that China's higher vocational institutions were mainly located in provincial capitals, representing a point distribution pattern. From a comprehensive evaluation of the supply level, areas such as the Beijing-Tianjin-Hebei region, Yangtze River Delta, and central Henan Province have become the catchment areas for the development of higher vocational education, laying the foundation for regional network cooperation. From the perspective of educational equality, the higher vocational education in China was found to be sufficient to match the supply and demand, and a balance between supply and demand was apparent in provincial capitals. The coupling degree between supply and demand exhibited an "olive-type" spatial structure pattern, indicating that the development of higher vocational education in most cities in China is still in the transformation stage. The results provide a scientific basis for optimizing resources in the provision of higher vocational education.

Identifying urban functional zones by capturing multi-spatial distribution patterns of points of interest

ABSTRACT Urban Functional Zone (UFZ) identification is vital for urban planning, renewal, and development. Point of Interest (POI), as one of the most popular data in UFZ studies, is transformed into a geo-corpus under specific sampling strategies, which can be used with Natural Language Processing (NLP) technology to extract geo-semantic features and identify UFZs. However, existing studies only capture a single spatial distribution pattern of POIs, while ignoring the other spatial distribution information. In this paper, we developed an integrated geo-corpus construction approach to capture multi-spatial distribution patterns of POIs that were represented by different modal POI embeddings. Subsequently, random forest model was leveraged to classify UFZs based on those embeddings. A set of combination experiments were designed for performance validation. The results show that our proposed method can effectively identify UFZs with an accuracy of 72.9%, with an improvement of 8.5% compared to the baseline methods. The outcome of this study will help urban planners to better understand UFZs through investigating the integrated spatial distribution patterns of POIs embedded in UFZs.

Analysis of Hotels Spatial Clustering in Bali: Density-Based Spatial Clustering of Application Noise (DBSCAN) Algorithm Approach

Bali is one of the hearts of tourism in Indonesia. The existence of the Covid-19 pandemic has made this tourist paradise also affected the wheels of the economy. Based on this, this study aims to determine the density clustering of one of the economic supporters in Bali, namely hospitality. The study began with the quadrant method and Ripley's K-Function to measure the distribution pattern of hospitality. From the results of the two methods, the distribution pattern of hotels in Bali is more towards clusters than random or regular distribution. If the point distribution pattern is more towards the cluster, it is continued with the Density-Based Spatial Clustering of Application Noise (DBSCAN) algorithm to form spatial clustering. In the DBSCAN algorithm, a combination of parameters, namely minimum points (MinPts) and epsilon (Eps), is carried out with evaluation using the silhouette average width value. From the results of the DBSCAN algorithm, the clustering results show that the distribution of hotels in Bali forms clusters and tends to approach the surrounding tourist attractions, such as near the beach, city market, and mountainous areas. It can help policymakers if they want to prioritize economic recovery after the Covid-19 pandemic.

Data agreement analysis and correction of comparative geomagnetic vector observations

Geomagnetism, similar to other areas of geophysics, is an observation-based science. Data agreement between comparative geomagnetic vector observations is one of the most important evaluation criteria for high-quality geomagnetic data. The main influencing factors affecting the agreement between comparative observational data are the attitude angle, scale factor, long-term time drift, and temperature. In this paper, we propose a method based on a genetic algorithm and linear regression to correct for these effects and use the distribution pattern of points in Bland–Altman plots with a 95% confidence interval length to qualitatively and quantitatively evaluate the agreement between the comparative observational data. In Bland–Altman plots with better agreement, that is, with the corrected data, more than 95% of the points are distributed within the 95% confidence interval and there is no obvious pattern in the distribution of the points. Meanwhile, the length of 95% confidence interval decreased significantly after the correction. The method presented here has positive effects on the vector instrumentation detection and would enhance the robustness of geomagnetic observatory by bringing the data quality of the backup variometer data in line with the primary variometer.Graphical

Detection of Individual Trees in UAV LiDAR Point Clouds Using a Deep Learning Framework Based on Multichannel Representation

Individual tree detection is critical for forest investigation and monitoring. Several existing methods have difficulties to detect trees in complex forest environments due to insufficiently mining descriptive features. This study proposes a deep learning (DL) framework based on a designed multichannel information complementarity representation for detecting trees in complex forest using UAV laser scanning point clouds. The proposed method consists of two main stages: ground filtering and tree detection. In the first stage, a modified graph convolution network with a local topological information layer is designed to separate the ground points. Unlike most existing parametric methods, our ground filtering method avoids the optimal parameters selection to adapt to different kinds of environments. For tree detection, a top-down slice (TDS) module is first designed to mine the vertical structure information in a top-down way. Then, a special multichannel representation (MCR) is developed to preserve different distribution patterns of points from complementary perspectives. Finally, a multibranch network (MBNet) is proposed for individual tree detection by fusing multichannel features, which can provide discriminative information for MBNet to detect trees more accurately. MBNet was evaluated on seven forest areas [UAV light detection and ranging (LiDAR) data with the mean size of 14 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$000~\text {m}^{2}$ </tex-math></inline-formula> and point density of 250 points/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {m}^{2}$ </tex-math></inline-formula> ]. Experimental results showed that the proposed framework achieves excellent performance. Our method obtains promising performance with a mean recall of 89.23% and a mean F1-score of 87.04%.

Assessing inclination angles of tree branches from terrestrial laser scan data using a skeleton extraction method

Assessing the inclination angles (IAs) of tree branches is essential to evaluate their rainfall interception ability. However, depicting the IA distribution within a forest canopy is still a challenge. This study developed the WoodSKE method to extract skeletons from discrete point clouds of tree branches collected by terrestrial laser scanners (TLSs) for assessing their IAs. A TLS point cloud of tree branches was firstly contracted according to the pointwise local point distribution pattern to extract its coarse skeleton. Then, the coarse skeleton would be thinned and optimized by a noise filtering method. The IAs of tree branches were estimated based on their skeleton distribution. For each point cloud that has the reference skeleton, the average and root mean squared error (RMSE) of offset distance for its WoodSKE-extracted skeleton was less than 0.011 m and less than 0.019 m, respectively. Furthermore, the WoodSKE method was robust to process point clouds with noise. Comparing to the measured IAs at sample points selected from tested point clouds, the mean absolute error and RMSE of their skeleton-assessed IAs were 7˚ and 11.7˚, respectively. Over 86% of the number of sample points in each tested point cloud with IA assessment error lower than 15˚. Extracting skeletons from TLS point clouds of tree branches provides a base for assessing the distribution of IA-related structure features within a forest canopy.

Spatial-temporal variations of soil erosion in Southern Yunnan Mountainous Area using GIS and RUSLE: A case study in Yuanyang County, Yunnan Province, China.

It is of great significance to understand the spatial-temporal change of soil erosion in the Southern Yunnan Mountainous Area, especially for soil and water conservation planning and ecolo-gical protection in the world cultural heritage area. Based on the RUSLE model and GIS/RS space information technology, we examined the spatial-temporal variation of soil erosion and its relationship with environmental factors in Yuanyang County from 2005 to 2015. The results showed that, during the study period, the spatial differentiation of soil erosion in Yuanyang County was substantial. The soil erosion area was concentrated on the southeast and southwest region. Erosion widely distributed among both sides of Tengtiao River. Extreme and severe erosion presented a point distribution pattern. The average soil erosion modulus was 11.06 t·hm-2·a-1 from 2005 to 2015. The erosion level was basically slight and mild, accounting 80% of the total. Mild and moderate erosion contributed nearly 50% of total annual soil erosion, which was the key level of soil erosion control. During the study period, soil erosion had been slowed down with the improvement of forest cover. The terrain was complicated and changeable in Yuanyang County, and soil erosion distribution had a prominent correlation with altitude and slope. The formation of this distribution pattern was driven by numerous natural and human factors. The key area of soil and water protective governance were below 500 m, above 1500 m and slope located at 25°-45°.

Spatial-Temporal Analysis of Point Distribution Pattern of Schools Using Spatial Autocorrelation Indices in Bojnourd City

In recent years, attention has been given to the construction and development of new educational centers, but their spatial distribution across the cities has received less attention. In this study, the Average Nearest Neighbor (ANN) and the optimized hot spot analysis methods have been used to determine the general spatial distribution of the schools. Also, in order to investigate the spatial distribution of the schools based on the substructure variables, which include the school building area, the results of the general and local Moran and Getis Ord analyses have been investigated. A differential Moran index was also used to study the spatial-temporal variations of the schools’ distribution patterns based on the net per capita variable, which is the amount of school building area per student. The results of the Average Nearest Neighbor (ANN) analysis indicated that the general spatial patterns of the primary schools, the first high schools, and the secondary high schools in the years 2011, 2016, 2018, and 2021 are clustered. Applying the optimized hot spot analysis method also identified the southern areas and the suburbs as cold polygons with less-density. Also, the results of the differential Moran analysis showed the positive trend of the net per capita changes for the primary schools and first high schools. However, the result is different for the secondary high schools.

Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis.

We propose a new metric to characterize the complexity of weighted complex networks. Weighted complex networks represent a highly organized interactive process, for example, co-varying returns between stocks (financial networks) and coordination between brain regions (brain connectivity networks). Although network entropy methods have been developed for binary networks, the measurement of non-randomness and complexity for large weighted networks remains challenging. We develop a new analytical framework to measure the complexity of a weighted network via graph embedding and point pattern analysis techniques in order to address this unmet need. We first perform graph embedding to project all nodes of the weighted adjacency matrix to a low dimensional vector space. Next, we analyze the point distribution pattern in the projected space, and measure its deviation from the complete spatial randomness. We evaluate our method via extensive simulation studies and find that our method can sensitively detect the difference of complexity and is robust to noise. Last, we apply the approach to a functional magnetic resonance imaging study and compare the complexity metrics of functional brain connectivity networks from 124 patients with schizophrenia and 103 healthy controls. The results show that the brain circuitry is more organized in healthy controls than schizophrenic patients for male subjects while the difference is minimal in female subjects. These findings are well aligned with the established sex difference in schizophrenia.

SEMANTIC VALIDATION OF SOCIAL MEDIA GEOGRAPHIC INFORMATION: A CASE STUDY ON INSTAGRAM DATA FOR EXPO MILANO 2015

Abstract. Social media data, such as Instagram posts, can be associated with spatial positions. This information can be exploited to perform spatial analyses, such as identifying distribution patterns of points representing the positions of social media users during an emergency or while attending a specific event or exhibition. However, the geolocation provided by Social Media Geographic Information (SMGI) needs to be validated, in order for the spatial data to be used in a meaningful way in subsequent spatial analyses or mapping procedures. In this paper, a case study is presented based on Instagram data collected during the first two months of the Expo Milano 2015 exhibition, where the spatial data have been validated by exploiting the semantic component of the posts.

Seasonal change of leaf and woody area profiles in a midlatitude deciduous forest canopy from classified dual-wavelength terrestrial lidar point clouds

This study demonstrates the retrieval of separate vertical height profiles of leaf and woody areas in both leaf-off and leaf-on seasons at a largely broadleaf deciduous forest site in the Harvard Forest of central Massachusetts, USA, using point clouds acquired by a terrestrial laser scanner (TLS), the Dual-Wavelength Echidna® Lidar (DWEL). Drawing on dual-wavelength information from the DWEL, we classified points as leafy or woody hits using their near-infrared (1064 nm) and shortwave infrared (1548 nm) apparent reflectance coupled with the 3-D spatial distribution patterns of points. We developed a new indirect assessment approach that quantified the accuracies (user’s, producer’s and overall) and variance of accuracies of the 3-D point classifications. The overall classification accuracy estimated by this indirect approach was 0.60 ± 0.01 – 0.77 ± 0.01 for leaf-off points and 0.71 ± 0.02 – 0.78 ± 0.01 for leaf-on points. These estimated accuracies were then utilized to adjust the proportions of separate gap probabilities to reduce the biases in the separate leaf and woody area profiles due to classification errors. Separate retrievals of leaf and woody area profiles revealed the change in their spatial heterogeneity over the 1-ha plot with season. These retrievals also allowed height-explicit estimation of the woody-to-total ratio, which is an empirical parameter often used to remove woody contributions to leaf area index retrievals made by optical methods. The estimates suggested the woody-to-total ratios generally stayed stable along height in the middle and upper canopy for this site but varied in the lower canopy. More accurate estimates of leaf area and its vertical profile are important for better measurement and modeling of the radiation regime of forest canopies, and thus their photosynthetic capacity. By separating leafy and woody materials in three dimensions, dual-wavelength TLS offers the prospect of better understanding of forest cycling of matter and energy within local and global ecosystems.

Optimal selection of GCPs from Global Land Survey 2005 for precision geometric correction of Landsat-8 imagery

To conduct precision geometric correction of Landsat-8 data, all ground control points from the Global Land Survey (GLS) 2005 are typically selected, thereby making the process time consuming and labor intensive. This paper developed an optimal selection algorithm for choosing representative points. The optimal technique consists of three steps, including 1) evaluating the spatial distribution patterns of points from GLS2005, 2) extracting ideal points positions from each scene based on the spatial distribution patterns obtained in the first step, and 3) selecting real representatives GCPs from the original large number of GCPs based on the positions of ideal points. One hundred individual Landsat-8 images were chosen for precision geometric correction to assess the robustness and efficiency of the method. Experimental result demonstrated that the approach could only consume 1/10 processing time or less when compared to that using the full set of original GCPs while still achieving comparable geometric accuracy. The developed technique will make an important contribution to improving the efficiency of precision geometric product generation systems for Landsat-8 images.

Analysis of a Distribution of Point Events Using the Network‐Based Quadrat Method

This study proposes a new quadrat method that can be applied to the study of point distributions in a network space. While the conventional planar quadrat method remains one of the most fundamental spatial analytical methods on a two‐dimensional plane, its quadrats are usually identified by regular, square grids. However, assuming that they are observed along a network, points in a single quadrat are not necessarily close to each other in terms of their network distance. Using planar quadrats in such cases may distort the representation of the distribution pattern of points on a network. The network‐based units used in this article, on the other hand, consist of subsets of the actual network, providing more accurate aggregation of the data points along the network. The performance of the network‐based quadrat method is compared with that of the conventional quadrat method through a case study on a point distribution on a network. The χ2 statistic and Moran's I statistic of the two quadrat types indicate that (1) the conventional planar quadrat method tends to overestimate the overall degree of dispersion and (2) the network‐based quadrat method derives a more accurate estimate on the local similarity. The article also performs sensitivity analysis on network and planar quadrats across different scales and with different spatial arrangements, in which the abovementioned statistical tendencies are also confirmed.

A Generalized Model for Predicting Load-Extension Properties of Woven Fabrics

A method to predict the load-extension behavior of woven fabrics is proposed. Kawabata et al.'s finite deformation theory, which predicts load-extension properties of plain weave and 2/2 twill fabrics in terms of fabric construction parameters and yarn tensile properties, is the basis for the study. The model is generalized to predict the load-elongation behavior of any weave by introducing a parameter that characterizes the interlacing point distribution pattern of the weave. Using the biaxial and uniaxial strain modes, the load-deformation curves over an entire range of strains can be generated. The model applies to woven structures of different degrees of openness, of which the fabric jamming condition is an extreme case.

A Graphic Approach to Analyzing Codon Usage in 1562 Escherichia coli Protein Coding Sequences

The occurrence frequencies of the four bases (adenine, cytosine, guanine and thymine) at each of the three codon positions for 1562 Escherichia coli protein coding sequences have been calculated. The 1562 × 4 × 3 = 18,744 data thus obtained have been analyzed by a graphic method in which the four base occurrence frequencies at each codon position for each coding sequence are represented by a point in a three-dimensional space. Thus, the 18,744 data, which would otherwise occupy several printed pages, can be intuitively displayed by a graph. The point distribution pattern for each of the three codon positions has been analyzed. The results of our analysis indicate that the patterns for the first two codon positions reflect the origin for producing native folding structures of proteins. We thus come to the conclusion that the distribution patterns for the first two codon positions should be basically species-independent, as confirmed by studies for a number of other species. However, the distribution pattern for the third codon position is species-dependent. Based on the point distribution of the third codon position, six collective parameters have been defined to describe the overall feature of the pattern concerned. These collective parameters can be generally used to classify different species, and hence would be a useful vehicle for studies in taxonomy. In addition to E. coli, the collective parameters for a number of other species have been calculated and analyzed.