Spatial Index Research Articles

Spatial information extraction of fishing grounds for light purse seine vessels in the Northwest Pacific Ocean based on AIS data

Ecological fishery management requires high-precision fishery information to support resource management and marine spatial planning. In this paper, the Automatic Identification System (AIS) was adopted to extract the spatial information on the fishing grounds of light purse seine vessels in the Northwest Pacific Ocean. The spatial distributions of fishing grounds mapped by the data mining, kernel density analysis and hotspot analysis methods were compared. The spatial similarity index was applied to determine the spatial consistency between the computed spatial information and fisheries resource information. Finally, the spatial information derived by the best method was used to investigate the characteristics of fishing activity. The results showed that: the speed of light purse seine vessels related to operations was lower than 1.6 knots. The spatial information extracted by the three methods was consistent with the catch data distribution, and the spatial similarity between the fishing effort and catch data was the highest. The spatial variation in fishing activity was similar to that in the chub mackerel migration route. AIS data could be used to provide high-resolution fishery information. Light purse seine fishing vessels typically operate and travel along the exclusive economic zone boundary, and increased attention must be given to fishing vessel operation supervision. A comprehensive supervision system can be employed to monitor the operations of fishing vessels more effectively. The results of this study can provide technical support for the management of fishing activities and conservation of marine resources in this region using AIS data.

Evaluating the spatial representativeness of ground-based observations for satellite total ozone products

External verification based on ground-based data is commonly used to assess the accuracy of satellite products for total ozone. However, the spatial representativeness of ground-based observations at the pixel scale and the effects of the spatial mismatch between ground observations and satellite pixels are often overlooked. Therefore, this study focused on an Ozone Monitoring Instrument (OMI) and proposed a novel method to evaluate spatial representativeness using the total ozone product itself without relying on higher spatial resolution imagery from other sensors. A Pixel-level Spatial Mismatch Index (PSMI) was developed to quantify the peaks and stable intervals of the spatial response to the relative deviation of total ozone observations. The analysis revealed that increased uncertainty at the pixel edges can cause significant relative deviation, with the peak deviation occurring within a range of 4–7 km near the pixel boundary. The position of the high precision interval in PSMI was found to be <–22.38 km or >10 km, with mean values of 3.70 % and standard deviations of 3.45 % for the relative deviation. This interval is spatially distance-independent and can be observed on both sides of the pixel boundary. These findings highlight the importance of considering spatial patterns between satellite pixels and ground stations for external validation. Furthermore, avoiding a spatial response peak of the relative deviation at the pixel edge is crucial. Overall, this research provides new perspectives and insights for the verification of total ozone products. By considering these factors mentioned above, future verification of total ozone products can be improved, leading to enhanced their evaluation systems.

Analysis of morphological change of lentic water bodies by using spatial vulnerability index (SVI) in Tarai region of Rapti river plains, India

ABSTRACT Spatio-temporal analysis of waterbodies in terms of detection of morphological change could serve as a guiding tool to restore the lentic ecosystem. This research analysed the loss of lake extent within 50 years (1969–2019) through natural, anthropogenic reasons, as well as a lack of attention to protecting lakes in the Tarai region. In 1969, 75 lakes were found, but now only 46 lakes are alive. Among the 75 lakes, seventeen transformed into large ponds after reduction in extent, and twelve lakes either appeared as waterlogging zones during the rainy season or were permanently extinct and used as agricultural fields’ built-up activities. The spatial vulnerability index (SVI) measured the level of deterioration and urgency of lake restoration. 8 lakes were determined to be highly vulnerable (below 29.26%) and lost about 311.20 hectares of potential area of lake beds due to gradual increasing shallowness. Regression analysis measured a very weak correlation between the reducing percent of lake extent (during 50 years) and current lake extent (R2 = 0.1336), average depth (R2 = 0.0035), circumference (R2 = 0.0543), and volume (R2 = 0.0728) of 46 lakes. This study elucidated the restoration practices of the lakes to prevent their gradual extinction.

Monitoring spatial tree diversity indices using relascope sampling: Estimators, interactions and limitations

With ongoing climate change the monitoring of tree diversity has become very important for avoiding or at least decelerating the loss of biodiversity and for maintaining forest ecosystem resilience. As part of such monitoring, spatial indices of tree diversity which are calculated for individual trees often serve as surrogates for more direct measures of biodiversity. Mainly for its efficiency and ease of application, relascope sampling is a widespread method applied in forest inventory of many countries and thus has often been suggested as a data source for the monitoring of tree diversity. Since the interaction between sampling design and spatial diversity indices is not always clear to data analysts, we reviewed existing estimators and experimentally examined a new one, conducted extensive sampling simulations using different indices and estimators and additionally analysed the data from a large-scale forest inventory in Austria. We found that both forest structure and index algorithm greatly influence the sampling error. The largest source of sampling error was the index variance and contrary to our expectation not so much the bias due to spatial effects. For diversity indices related to distances, it has turned out to be best to apply estimators that include spatial edge correction methods. For all other indices an estimator performed better that included information on both the sample trees and their nearest neighbours, as it much reduced overall index variance. However, if possible the plus-sampling edge correction method should be applied.

IB-CBB: an improved spatial index considering intersection based on clipped bounding boxes

ABSTRACT Efficiently querying multiple spatial datasets is a challenging task in geoscience. The majority of spatial processing techniques use minimum bounding box (MBB) to approximate neighbouring spatial objects and and place them adjacent in the spatial index. However, due to the existence of redundant space in MBB of these methods, this problem can significantly reduce the query efficiency. In this paper, we propose a novel two-stage adaptive method of clipping the bounding box in spatial query, called IB-CBB (Intersection Based Clipped Bounding Boxes). The first stage employs a clipped bounding box, which records the redundant spatial spaces within the bounding box of the spatial index by calculating the clip points. As a result, the computational complexity of indexed child nodes in the query process is reduced. The second stage optimizes the above query algorithm by judging the intersection of the query box and the MBB of index node, significantly reducing the query time. Experiments demonstrate that IB-CBB outperforms the baseline method in terms of reducing the computational time.

Blockchain-Based Method for Spatial Retrieval and Verification of Remote Sensing Images.

Remote sensing image is a vital basis for land management decisions. The protection of remote sensing images has seen the application of blockchain's notarization function by many scholars. Yet, research on efficient retrieval of such images on the blockchain remains sparse. Addressing this issue, this paper introduces a blockchain-based spatial index verification method using Hyperledger Fabric. It linearizes the spatial information of remote sensing images via Geohash and integrates it with LSM trees for effective retrieval and verification. The system also incorporates IPFS as an underlying storage unit for Hyperledger Fabric, ensuring the safe storage and transmission of images. The experiments indicate that this method significantly reduces the latency in data retrieval and verification without impacting the write performance of Hyperledger Fabric, enhancing throughput and providing a solid foundation for efficient blockchain-based verification of remote sensing images in land registry systems.

Abstract 3650: Deep learning-based image cytometry and co-localization index in tumor immune microenvironment

Abstract Background: In pathology, digitizing tissue slides has prompted a remarkable development in image analysis using deep learning. This technological advancement is anticipated to aid in pathological diagnosis and to enhance patient management. Deep learning-based image cytometry (DL-IC) enables accurate cell identification and counting and the acquisition of vast amounts of location information from tissue slides. DL-IC can capture information about the diverse and complex tumor immune microenvironment(TIME) and its constituent cells and help identify biomarkers to predict patient treatment efficacy and prognosis. This study will introduce a spatial interaction map and co-localization index (CLI) for the analysis of TIME using DL-IC. Materials and Methods: Cu-Cyto, a deep learning-based image analysis technology, was used in this study; bit-pattern kernel filtering technology, which can accurately count cells while avoiding the determination of multiple cell counts, was used by Cu-Cyto (Abe T, et al. Anticancer Res. 43:3755, 2023). First, the accuracy of cell counting using Cu-Cyto was evaluated. Second, tumor tissue slides with immunohistochemical (IHC) and hematoxylin-eosin (H&amp;E) staining were prepared from surgical specimens of patients with rectal cancer who had undergone neoadjuvant chemoradiotherapy (NACRT), and the relationship between the co-localization index (CLI) of cancer cells and CD8+T cells and prognosis was investigated. CLI was defined to predict cell- cell interactions on the basis of the relative distances between different cell types (Nagasaka T. PCT/JP 2021/021455). Results: The performances of three versions of Cu-Cyto were evaluated according to their learning stages. In the early stage of learning, the F1 score for immunostained CD8+ T cells (0.343) was higher than that for non-immunostained cells (adenocarcinoma cells [0.040] and lymphocytes [0.002]). In the latest stage of learning, the F1 scores for adenocarcinoma cells, lymphocytes, and CD8+ T cells were 0.589, 0.889, and 0.911, respectively. Next, we examined the correlation of CLI between cancer cells and CD8+ T cells with prognosis: patients with a higher CLI significantly prolonged five-year disease-free survival (P=0.038), while there was no substantial difference in five-year overall survival (P=0.57). Conclusion]: Cu-Cyto performed well in cell determination. In particular, IHC was able to increase the learning efficiencies in the early stages of learning. The CLI calculated using Cu-Cyto ts an objective, reproducible, and innovative quantitative approach for assessing cell-cell interactions, which has been shown to be associated with recurrence-free survival in patients with rectal cancer after NACRT. Its performance is expected to improve even further with continuous learning, and the DL-IC can contribute to the implementation of precision oncology. Citation Format: Tomoki Abe, Kimihiro Yamashita, Toru Nagasaka, Tomosuke Mukoyama, Souichirou Miyake, Yasuhiro Ueda, Masayuki Ando, Yuki Okazoe, Takao Tsuneki, Yukari Adachi, Ryunosuke Konaka, Ryuichiro Sawada, Hironobu Goto, Hiroshi Hasegawa, Shingo Kanaji, Takeru Matsuda, Takumi Fukumoto, Yoshihiro Kakeji. Deep learning-based image cytometry and co-localization index in tumor immune microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3650.

A short-cut methodology for the spatial assessment of the biochemical river quality

The deterioration of superficial water quality is a significant concern in water management. Currently, most European rivers do not achieve qualitative standards defined by Directive 2000/60/EC (Water Framework Directive, WFD), while the health status of many surface water bodies remains unknown. Within this context, we propose a new methodology to perform a semi-quantitative analysis of the pressure state of a river, starting from easily accessible data related to anthropic activities. The proposed approach aims to address the endemic scarcity of monitoring records. This study proposes a procedure to (i) evaluate the relative pressure of different human activities, (ii) identify allocation points of different pollutant sources along the river using a raster-based approach, and (iii) determine a spatial biochemical water quality index. The developed index expresses the overall biochemical state of surface water induced by pollutant sources that may simultaneously impact a single river segment. This includes establishments under the so-called Seveso Directive, activities subjected to the IPPC-IED discipline, wastewater treatment plants, and contaminated sites. The methodology has been tested over three rivers in Northern Italy, each exposed to different industrial and anthropogenic pressures: Reno, Enza, and Parma. A comparison with monitored data yielded convincing results, proving the consistency of the proposed index in reproducing the spatial variability of the river water quality. While additional investigations are necessary, the developed methodology can serve as a valuable tool to support decision-making processes and predictive studies in areas lacking or having limited water quality monitoring data.

Nonreference object-based pansharpening quality assessment

Pansharpening involves the fusion of panchromatic (PAN) and multispectral (MS) images to obtain a high-resolution image with enhanced spectral and spatial information. Assessing the quality of the resulting fused image poses a challenge due to the absence of a high-resolution reference image. Numerous methods have been proposed to address this, from assessing quality at reduced resolution to full-resolution evaluations. Many existing approaches are pixel-based, where quality metrics are applied and averaged on individual pixels. In this article, we introduce a novel object-based method for assessing the quality of pansharpened images at full resolution. In object-based quality assessment methods, the reaction of different areas of the fused image to the fusion process is reflected. Our approach revolves around extracting objects from the given image and evaluating extracted objects. By doing so, the distinct responses of different objects within the fused image to the fusion process are captured. The proposed method leverages a unique object extraction technique known as segmentation by nearest neighbor (SNN) to extract objects of the MS image. This method extracts the objects based on the image’s characteristics without any requirement for parameter tuning. These extracted objects are then mapped onto both PAN and fused images. The proposed spectral index measures the spectral homogeneity of the fused image’s objects and the proposed spatial index measures the injected spatial content from the PAN image to the fused image’s objects. Experimental results underscore the robustness and reliability of the proposed method. Additionally, by visualizing distortion values on object-maps, we gain insights into fusion quality across diverse areas within the scene.

Spatial inequity index for essential facility access at building-level in highly dense urban areas

Equity and inclusion continue to pose significant challenges for many cities. Disparities in access to resources and facilities among the residents of these areas are often influenced by factors such as geographical location and income level, among others. Therefore, creating a quantitative basis to detect spatial and socio-economic inequities in residents' access to vital infrastructures is crucial for advancing smart cities. This paper introduces a novel measure of Spatial Inequity that is based on 1) computing building-level scores for each building in terms of access to facilities, and 2) comparing the distribution of the scores of these facilities using the Gini coefficient. The proposed building-level scores will enable the identification of GIS hotspots at a high resolution. Then, the Gini-based measure will facilitate a comparison of the distribution of the scores, and determine if they are evenly distributed and equitably accessible. The Gini coefficient was highest (0.89) for waste management and elderly care, and lowest (0.56) for hospitals, reflecting varying levels of inequality. This study applies this Spatial Ineqity methodological framework to Hong Kong, a representative of Highly Dense Cities. The framework benchmarks urban infrastructure, identifies hotspots, and selects interventions for equitable urban development.

Utilizing virtual reality before, versus during, the COVID-19 pandemic

As the COVID-19 pandemic abruptly pushed interior design (ID) instruction online, instructors were challenged to adapt, and students adapted a new method of virtual reality (VR). The VR method before COVID-19 was a Homido V2 VR headset with iPhone viewing 360-degree panorama jpeg, and during COVID-19 a liquid crystal display (LCD) computer monitor viewing 360-degree panorama jpeg. The purpose of this study was, if a statistically significant difference (SSD) in spatial presence was found between the two types of VR, then an argument could be supported to evaluate spatial presence, before VR is implemented into ID curriculum. This study was at one Midwestern United States university with a sample (N = 52) of ID undergraduate students. The results revealed an SSD in the spatial presence in the aforementioned VR types. This SSD was found in two of the three dependent variables: Spatial Presence: Possible Action (SPPA; U = 772, p < 0.001), example survey question feeling you could jump into the action, and Spatial Presence: Self Location (SPSL; U = 789, p < 0.001), example feeling you are in the middle of the action. The third dependent variable, Spatial Situation Model (SSM; U = 1320, p = 0.834) did not reveal an SSD, example imagining the arrangement of the spaces. To support results, the Virtual Reality Spatial Presence Index (VRSPI) applied scored neutral (neither strong, nor weak) for spatial presence in both. This study filled research gaps on VR spatial presence measurement, with implications supporting a measurable advantage in ID students using VR headsets and ID curriculum developers evaluating VR before implementation.

Simulating Urban Expansion from the Perspective of Spatial Anisotropy and Expansion Neighborhood

Exploring spatial anisotropy features and capturing spatial interactions during urban change simulation is of great significance to enhance the effectiveness of dynamic urban modeling and improve simulation accuracy. Addressing the inadequacies of current cellular automaton-based urban expansion models in exploring spatial anisotropy features, overlooking spatial interaction forces, and the ineffective expansion of cells due to traditional neighborhood computation methods, this study builds upon the machine learning-based urban expansion model. It introduces a spatial anisotropy index into the comprehensive probability module and incorporates a gravity-guided expansion neighborhood operator into the iterative module. Consequently, the RF-CNN-SAI-CA model is developed. Focusing on the 21 districts of the main urban area in Chongqing, the study conducts comparative analysis and ablation experiments using different models to simulate the land use changes between 2010 and 2020. Different model comparison results show that the recommended model in this study has a Kappa value of 0.8561 and an FOM value of 0.4596. Compared with the RF-CA model and the FA-MLP-CA model, the Kappa values are higher by 0.0407 and 0.1577, respectively, while the FOM values are improved by 0.0529 and 0.0654, respectively. Ablation experiment results indicate that removing gravity, SAI, and expansion neighborhood operators leads to a decrease in both Kappa and FOM values. These findings demonstrate that the RF-CNN-SAI-CA model, based on the expanded neighborhood iteration algorithm, effectively integrates spatial anisotropy features, captures spatial interaction forces, and resolves neighborhood cell failure issues, thereby significantly improving simulation effectiveness.

Chronological age-based vs. Bio-banded friendly football tournament: Effects on physical performance and spatial exploration behaviour in 7v7 match play

ABSTRACT Biological age-based competitions have been suggested to mitigate maturation bias among youth football players. This study analysed the physical demands and spatial exploration behaviour when playing football matches against biological or chronological age-based opposition. One-hundred and sixteen U-13 and U-14 male regional-level football players were recruited from five Portuguese football academies. Each player completed eight games, four per condition (chronological and biological-based), on the same day. Games were played in 7 vs. 7 formats, with 20-min duration, and conformed to standard officiating and rule procedures. The following variables were considered from GPS-data in each game: distance covered, high-intensity accelerations and decelerations, body impacts, and average and peak speeds. Furthermore, average, and peak heart rates (HRpeak) and spatial exploration index were collected. Players covered significantly less distance (ES = 1.11), performed fewer decelerations (ES = 0.82), achieved lower average speeds (ES = 1.15), had fewer body impacts (ES = 0.94), and recorded lower HRpeak (ES = 0.56) in the biological age-based competition compared to the chronological-age-based. No significant differences were found between the two formats in spatial exploration. Both game formats offer distinct physical demands at the external and internal levels. Finally, practitioners can manipulate the type of opposition to get distinct physical demands to mitigate the effect of maturation bias during development.

Study on Spatio-Temporal Indexing Model of Geohazard Monitoring Data Based on Data Stream Clustering Algorithm

Yunnan Province, residing in the eastern segment of the Qinghai–Tibet Plateau and the western part of the Yunnan–Guizhou Plateau, faces significant challenges due to its intricate geological structures and frequent geohazards. These pose monumental risks to community safety and infrastructure. Unfortunately, conventional spatial indexing methods struggle with the enormous influx of geohazard data, exhibiting inadequacies in efficient spatio-temporal querying and failing to meet the swift response imperatives for real-time geohazard monitoring and early warning mechanisms. In response to these challenges, this study proffers a cutting-edge spatio-temporal indexing model, the BCHR-index, undergirded by data stream clustering algorithms. The operational schema of the BCHR-index model is bifurcated into two stages: real-time and offline. The real-time phase proficiently uses micro-clusters shaped by the CluStream algorithm in unison with a B+ tree to construct indices in memory, thereby satisfying the exigent response necessities for geohazard data streams. Conversely, the offline stage employs the CluStream algorithm and the Hilbert curve to manage heterogeneously distributed spatial objects. Paired with a B+ tree, this framework promotes efficient spatio-temporal querying of geohazard data. The empirical results indicate that the indexing model implemented in this study affords millisecond-level responses when faced with query requests from real-time geohazard data streams. Moreover, in aspects of spatial query efficiency and data-insertion performance, it demonstrates superior results compared to the R-tree and Hilbert-R tree models.

Spatiotemporal analysis of urban development patterns and COVID-19 in Mashhad, Iran

This study evaluates the impact of city spatial development patterns on the spread of airborne pandemics, focusing on Mashhad, Iran. Using ArcGIS spatial statistics, 91 spatial indices were examined between March 5, 2020, and October 2, 2021, during five distinct waves of the virus, revealing that private vehicle ownership and limited use of public transportation did not significantly reduce COVID-19 infection rates in Mashhad. Instead, factors such as proximity to transportation hubs, healthcare facility polarization, and retail shop distribution had a significant impact. Surprisingly, demographic vulnerability and conventional population-related factors had relatively minor effects. The study also highlights the influence of building concentration with high floor area ratios (FAR) and the impact of local authorities' optimism in reducing social distancing measures, especially in travel and traffic control, on the increased spread of the virus during recent waves.

Ground reaction forces better than center of pressure differentiate postural control between young female volleyball players and untrained peers

A comprehensive explanation of the relationship between postural control and athletic performance requires compare body balance in athletes with their never training counterparts. To fill this gap in relation to volleyball, the aim of this study was to compare the balance of intermediate adolescent female players (VOL, n = 61) with inactive peers (CON, n = 57). The participants were investigated in normal quiet stance during 20 s trials on a Kistler force plate. The traditional spatial (amplitude and mean speed) and temporal (frequency and entropy) indices were computed for ground reaction forces (GRF) and center-of-pressure (COP) time-series. The spatial parameters of the both time-series did not discriminate the two groups. However, the temporal GRF parameters revealed much lower values in VOL than in CON (p < .0001). This leads to three important conclusions regarding posturography applications. First, GRF and COP provide different information regarding postural control. Second, measures based on GRF are more sensitive to changes in balance related to volleyball training and perhaps to similar training and sports activity regimens. And third, the indicators calculated based on these two time series can complement each other and thus enrich the insight into the relationship between balance and sports performance level.

Geographic Information System-Based Comprehensive Shared Micromobility Station Siting Optimization for Small Urban Areas

Shared micromobility travel modes such as dockless e-scooters and bikeshare programs have become increasingly popular in the U.S. in the last decade because of their potential to improve multi-modal accessibility within communities. Smaller urbanized areas with lower population densities and fewer resources for system planning, operation, and maintenance present unique challenges with siting optimal station/service area locations. This research develops a comprehensive geographic information system (GIS)-based methodology for optimizing micromobility stations/service area locations using available and rasterized geospatial data to capture bikeshare demand indicators. Inputs are prioritized by overall importance according to the results of a survey of transportation professionals, with weights calculated by an analytic hierarchy process. These different factors are combined to create a new spatial index value to identify hotspots for candidate station/service area locations, which can be further analyzed to choose optimal locations based on the budgeted quantity of station/service area locations and ideal spacing between stations/service areas. The case study of the methodology is presented on a bikeshare station siting study in Iowa City, Iowa, U.S., using data from the Metropolitan Planning Organization of Johnson County. This research seeks to improve shared micromobility station/service area planning to better orient service to a variety of transportation goals including regular/commuting use, recreational use, equity, first/last mile connection to transit, and operational partnership opportunities. Multimodal travel times and job accessibility in the study area are evaluated both before and after the introduction of bikeshare, and both greatly improve with the introduction of optimal stations. Public agencies could expect to benefit from this comprehensive methodology because it uses easily obtainable data sources and provides the flexibility to weight the importance of factors differently to fit their communities’ specific transportation goals.

Spatial intratumor heterogeneity of programmed death-ligand 1 expression predicts poor prognosis in resected non-small cell lung cancer.

We quantified the pathological spatial intratumor heterogeneity of programmed death-ligand 1 (PD-L1) expression and investigated its relevance to patient outcomes in surgically resected non-small cell lung carcinoma (NSCLC). This study enrolled 239 consecutive surgically resected NSCLC specimens of pathological stage IIA-IIIB. To characterize the spatial intratumor heterogeneity of PD-L1 expression in NSCLC tissues, we developed a mathematical model based on texture image analysis and determined the spatial heterogeneity index of PD-L1 for each tumor. The correlation between the spatial heterogeneity index of PD-L1 values and clinicopathological characteristics, including prognosis, was analyzed. Furthermore, an independent cohort of 70 cases was analyzed for model validation. Clinicopathological analysis showed correlations between high spatial heterogeneity index of PD-L1 values and histological subtype (squamous cell carcinoma; P < .001) and vascular invasion (P = .004). Survival analysis revealed that patients with high spatial heterogeneity index of PD-L1 values presented a significantly worse recurrence-free rate than those with low spatial heterogeneity index of PD-L1 values (5-year recurrence-free survival [RFS] = 26.3% vs 47.1%, P < .005). The impact of spatial heterogeneity index of PD-L1 on cancer survival rates was verified through validation in an independent cohort. Additionally, high spatial heterogeneity index of PD-L1 values were associated with tumor recurrence in squamous cell carcinoma (5-year RFS = 29.2% vs 52.8%, P < .05) and adenocarcinoma (5-year RFS = 19.6% vs 43.0%, P < .01). Moreover, we demonstrated that a high spatial heterogeneity index of PD-L1 value was an independent risk factor for tumor recurrence. We presented an image analysis model to quantify the spatial intratumor heterogeneity of protein expression in tumor tissues. This model demonstrated that the spatial intratumor heterogeneity of PD-L1 expression in surgically resected NSCLC predicts poor patient outcomes.

EXPERIMENTING TIN DATA STRUCTURE FOR REPRESENTING PLANE SURFACE AND SUBSURFACE SPATIAL OBJECTS - PRELIMINARY WORK

Abstract. Understanding and analyzing complex spatial objects involves the integration of surface, terrain, and subsurface data into 3D spatial models. However, describing and efficiently analyzing the connections between these data model is particularly challenging. This study aims to generate TIN data structure that enable the 3D representation of surface terrain subsurface integration. Furthermore, to ensure accurate representation within a unified 3D model, spatial relationships between the TIN data model could be obtained. Consequently, three – dimensional Triangular Irregular Networks (3D TIN) mesh of spatial objects representations and indexing structures were created. The integration of spatial objects, encompassing surface, terrain, and subsurface elements, is realized through a unified model. This synergy is achieved by combining the distinct 2D and 3D topology structures of each component. The resulting comprehensive model captures the intricate relationships and interactions within the spatial environment. This study provides additional insight into the representation and analysis of surface, terrain, and subsurface management in 3D spatial models. Hence, better decision making, resource management, underground utility installations, and evaluation of spatial objects will be made possible in our future work.

How Much Do Urban Terminal Delivery Paths Depend on Urban Roads – A Research Based on Bipartite Graph Network

The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of a bipartite graph network. A spatial dependency matrix of delivery paths–urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. In addition, three spatial dependencies indexes, including degree centrality, betweenness centrality and closeness centrality were introduced to analyse the coupling features of these two objects. The results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. Firstly, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Secondly, betweenness centrality presents the road utilisation balance of terminal delivery paths. Thirdly, closeness centrality explains how easy it is for delivery paths to connect with others.