Spatial Distribution Of Points Research Articles

GreenRoute Ballari: A Hybrid Approach for Optimizing Waste Collection Routes Using K-Means Clustering and Genetic Algorithm

Abstract: The exponential growth of the population in Ballari has exacerbated the challenge of municipal waste management, necessitating the development of efficient waste collection strategies to address environmental and social concerns. In response to that our system introduces a hybrid approach that employs K-means clustering and Genetic Algorithm, for optimizing waste collection routes tailored to the unique waste generation profiles of the region. By utilizing Geographic Information System (GIS) data that is in the form of GeoJSON files containing the spatial distribution of waste collection points, we propose a methodology that combines K-means clustering and Genetic Algorithm to develop intelligent and fuel-efficient routes. Our system not only benefits municipal authorities in resource deployment and planning but also aids waste carrier vehicle drivers in prioritizing routes. Our findings contribute to the transformation of Ballari City towards eco-friendly and smart urban development

Spatial distribution of trace and rare earth elements of bottom sediments in Lake Ostrowite, Bory Tucholskie National Park, Poland

AbstractLake pollution has attracted concerns worldwide; especially the excessive accumulation of trace elements (TEs) and rare earth elements (REEs) in bottom sediments can pose a serious threat to ecosystem health. However, there is still a knowledge gap on the level of sediment pollution in lakes isolated from the direct impact of pollution sources, their spatial variability, and also on the factors influencing this state. The aim of this study is to investigate the level and spatial variability of TEs and REEs concentrations, as well as to determine their source and the factors determining their distribution in the bottom sediments of Lake Ostrowite. Lake Ostrowite is the largest and deepest water body located in the Bory Tuholskie National Park (in northern Poland), which completely isolates the lake from the direct impact of pollution sources. The study covered analyses of 32 surface samples of bottom sediments. The concentrations of 24 TEs and 14 REEs were determined using inductively coupled plasma mass spectrometry (ICP‐QQQ‐MS). The assessment of the enrichment of bottom sediments in TEs and REEs employed geochemical background values (GBV) that provided the basis for the calculation of relative concentrations and geochemical indices. The determination of their sources and supply routes was based on the cluster analysis and principal component analysis. The obtained results point to the enrichment of the bottom sediments with TEs and REEs. Relative concentration values of TEs and REEs in reference to geochemical background values were in ranges from 0.01 to 7.31, at an average of 0.99, and from 0.03 to 4.29, averaging 1.76, respectively. The enrichment factor values show moderately severe enrichment of sediments at the study sites. This was primarily determined by the concentrations of Ag (from the TEs group) and Lu (from the REEs group). The metal pollution index values showed an approximate spatial distribution of points in terms of the presence of TEs and REEs. The lowest concentrations of TEs and REEs occurred on the eastern shore of the western basin of Lake Ostrowite. TEs and REEs concentrations in sediments are positively correlated with the content of organic matter and depth and negatively correlated with distance of the sampling point from the river outflow from Lake Ostrowite. On the eastern shore of the western basin, TEs and REEs concentrations are additionally shaped by wind, predominantly from the western direction. With water wave action, organic matter is transported to the central part of the western basin, where it is accumulated. Since the lake is isolated from point and nonpoint pollution sources, relevant from a biogeochemical point of view are dry and wet depositions from the atmosphere as well as aquatic vegetation, shoreline vegetation, forest litter, soil, and groundwater.

Abstract 3648: The spatial organization of cells expressing MYC and BCL2 affects immune microenvironment composition and prognosis in DLBCL

Abstract Point process analyses are widely used in ecology and geography to understand the impact and relevance of spatial distribution of points in a complex system. To date, only few studies have explored point process analyses in the context of tumor heterogeneity. Malignancies often show variable patterns of cellular distribution, and the relationship of these topographic variables with underlying biological processes and clinical outcomes is not well understood. Here, we performed spatial point process analyses in diffuse large B-cell lymphoma (DLBCL), leveraging advances in multiplexed immunohistochemistry and cellular phenotyping, with downstream multi-omic and clinicopathological analyses. We have demonstrated that survival in DLBCL is strongly associated with the fraction of malignant cells showing co-expression of the oncogenes MYC and BCL2 in the absence of BCL6 (M+2+6-). These cells display non-random spatial organization within tumor infiltrates, deviating from a poisson distribution. We therefore modeled the spatial organization of M+2+6- cells within DLBCL, using x-y coordinate information from multiplexed fluorescent immunohistochemistry (mfIHC) in four independent cohorts (N=449 patients). We derived spatial point patterns, upon which Geyers point process models were applied. We observed that patients could be divided into two groups based on these: one group showed “clustered” spatial organization, while the other displayed a “dispersed” M+2+6- cell distribution. Cases with predominantly “dispersed” M+2+6- cells showed shorter overall survival in all analyzed cohorts (P < 0.05 in 4/4 cohorts). We performed multi-omic analyses to identify potential biological explanations for this association between topography and clinical outcome. Using digital spatial profiling of the transcriptome from CD20, CD3 and CD68 compartments in 47 DLBCL samples, we observed that lymphomas with an M+2+6- ‘dispersed’ phenotype had an immunologically cold microenvironment enriched in Tregs and exhausted CD4+ and CD8+ T cells. Through an integration with single-cell transcriptomic analyses (N=22 samples) we observed that malignant B-cells from cases with M+2+6- ‘dispersed’ phenotype expressed genes implicated in cell migration and adhesion, but also in potentially targetable immune checkpoints such as LAG3. In summary, by integrating multiplexed immunophenotyping and point process analysis with molecular profiling in the setting of DLBCL, we show that a dispersed pattern of distribution of the M+2+6- subclonal fraction uniquely confers poor prognosis, and associates with a potentially reversible immune cold microenvironment composition. This represents the first demonstration that the spatial distribution of malignant cell subpopulations in lymphoma can embody biological and clinical significance. Citation Format: Shruti Shridar, Michal Marek Hoppe, Patrick Jaynes, Gayatri Kumar, Siddham Jasoria, Ziwei Meng, Vaibhav Rajan, Kasthuri Kannan, Claudio Tripodo, Anand Devaprasath Jeyasekharan. The spatial organization of cells expressing MYC and BCL2 affects immune microenvironment composition and prognosis in DLBCL [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 3648.

Type A standard uncertainty evaluation in one measurement through uncertainty propagation from voxel values’ distribution for computed tomography metrology

According to the guide to the expression of uncertainty in measurement, ‘type A evaluation’ generally requires repeated measurements, which are time-consuming for CT scans. To solve this problem, we developed a method for estimating the standard deviation of measurement results in one measurement through uncertainty propagation, which can be regarded as repeatability standard deviation to evaluate the type A standard uncertainty. The method first fits the CT voxel value distribution, uses the ISO50 method to determine the spatial distribution of surface points from the voxel value distribution and edge shape interpolation, and then derives the measurement results by fitting geometric parameters with the least square algorithm. Finally, the standard deviation of the measurement results is evaluated according to the distribution of the surface point position through uncertainty propagation. We performed simulations and experiments using the hole-plate with 28 holes to compare the uncertainty evaluated by our method and the type A standard uncertainty evaluated on the basis of a series of observations obtained under repeatability conditions. Both simulation and experimental results show that these two uncertainties follow the same statistical variation pattern. The Pearson correlation coefficients of the two uncertainties in simulation and experiment are 0.79 and 0.33, respectively, indicating that the uncertainty evaluated by the proposed method can directly replace the type A uncertainty or provide a reference similar to type A uncertainty for the evaluation of the combined uncertainty.

Exploring the spatial distribution and properties of solar bright points in different solar areas: A statistical analysis of network and internetwork features

Solar bright points (BPs) are small-scale magnetic features observed on the solar surface. They play a significant role in the transfer of energy and mass between the solar photosphere and the corona. This article presents a comprehensive statistical analysis of BPs in different solar areas: quiet Sun (QS), active regions (AR), and coronal holes (CH). We separate the BPs into two categories: network and internetwork. Our study aims to understand the spatial distribution and properties of BPs in these areas, which can help improve our knowledge of the Sun’s magnetic activity and energy transfer processes. Our research highlights the close relationship between the spatial distribution and characteristics of BPs and the solar magnetic field. Using machine learning, we achieved efficient and accurate BP detection, enabling analysis of a larger dataset. The prevalence of network BPs in the QS and internetwork BPs in CH suggests differences in magnetic field concentration. In AR, BPs are evenly distributed between network and internetwork areas, reflecting the complex nature of the magnetic field.

A modified U-Net convolutional neural network for segmenting periprostatic adipose tissue based on contour feature learning

ObjectiveThis study trains a U-shaped fully convolutional neural network (U-Net) model based on peripheral contour measures to achieve rapid, accurate, automated identification and segmentation of periprostatic adipose tissue (PPAT). MethodsCurrently, no studies are using deep learning methods to discriminate and segment periprostatic adipose tissue. This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. This paper adopts a supervised learning method on the labeled dataset, combining the probability and spatial distribution of control points, and proposes a weighted loss function to optimize the neural network's convergence speed and detection performance. Based on high-precision detection of control points, this paper uses a convex curve fitting to obtain the final PPAT contour. The imaging segmentation results were compared with those of a fully convolutional network (FCN), U-Net, and semantic segmentation convolutional network (SegNet) on three evaluation metrics: Dice similarity coefficient (DSC), Hausdorff distance (HD), and intersection over union ratio (IoU). ResultsCropped images with a 270 × 270-pixel matrix had DSC, HD, and IoU values of 70.1%, 27 mm, and 56.1%, respectively; downscaled images with a 256 × 256-pixel matrix had 68.7%, 26.7 mm, and 54.1%. A U-Net network based on peripheral contour characteristics predicted the complete periprostatic adipose tissue contours on T2W images at different levels. FCN, U-Net, and SegNet could not completely predict them. ConclusionThis U-Net convolutional neural network based on peripheral contour features can identify and segment periprostatic adipose tissue quite well. Cropped images with a 270 × 270-pixel matrix are more appropriate for use with the U-Net convolutional neural network based on contour features; reducing the resolution of the original image will lower the accuracy of the U-Net convolutional neural network. FCN and SegNet are not appropriate for identifying PPAT on T2 sequence MR images. Our method can automatically segment PPAT rapidly and accurately, laying a foundation for PPAT image analysis.

Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region.

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east-west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.

Mobility and raw material procurement by Fishtail people in Uruguay: Evaluation of silcrete long distance transport between campsites and outcrops during the late Pleistocene (ca. 12,900–12,250 cal BP)

Lithic artifacts from late Pleistocene manufactured in silcrete are used to understand Fishtail behaviors linked to mobility, land use and raw material acquisition during early prehistory in Uruguay. The data analyzed here include three categories of silcrete evidence: (1) a database of Fishtail points, (2) regional outcrops of these lithic resource, (3) debitage and unifacial tools from campsites. We calculated the cumulative cost of movement between campsites and the nearest lithic resources in space, located within the silcrete area, the distances involved in such movements, and the possible connection routes between campsites and silcrete resources. We analyzed the raw materials, density and spatial distribution of Fishtail points, and generated a Predictive Model of Transit Pathways. The results obtained indicate that silcretes were transported over long distances in mobility networks by an extensive Paleoamerican social landscape from outcrops located in the western and southwestern to campsites in northwestern Uruguay at distances ranging between ∼ 179 and 482 km. This new data confirms long distance displacements, the high social value, and the preference of this lithic resource for the production of tool assemblages by the Fishtail peoples of Uruguay.

Influence of plant diversity and spatial pattern of urban green spaces on socioeconomic activities: insights from Xiamen, China

ABSTRACT Urban green space is the fundamental component of urban ecosystems, but the processes and mechanisms underlying the influence of green space structure and quality on human social and economic activities are still unclear. Plant diversity in the green space may have an impact on socioeconomic activities, which can be characterized by the spatial distribution of points of interest (POIs). To test this hypothesis, 16 green spaces on Xiamen Island were taken as the research object to analyze the relationships among five plant diversity indices, three landscape indices, and seven POIs of green spaces. Principal component analysis and the partial least-squares regression model were used to study the distribution of the seven types of POIs and the influence of green space on POI distribution. The results showed that the closer to the green space, the greater the weight of the principal components of medical institutions. The effects of green space on the seven POIs were represented with spatial relationships within the buffers. With the change in buffer gradient, there are influence trends such as the ‘∪’ or ‘∩’ type on various POIs. POIs are more impacted by the internal plant diversity index than by the landscape index of the green space. There was a scale effect of the diversity and landscape indices on the number of POI changes. The effect of the relationship between plant diversity and the spatial pattern of urban green spaces on socioeconomic activities is not direct but rather indirect.

A novel method for inverting coseismic 3D surface deformation using InSAR considering the weight influence of the spatial distribution of GNSS points

The combination of InSAR and GNSS data can provide more accurate measurements of coseismic 3D surface deformations, which are crucial in determining earthquake source parameters. Currently, the methods for inverting coseismic 3D surface deformations by integrating GNSS and InSAR data using the strain model (SM) (e.g., Simultaneous and integrated strain tensor estimation from geodetic measurements to obtain 3D displacement maps (SISTEM), SM-VCE) simply select GNSS points based on a certain number or distance, without considering the weight influence of the spatial distribution of G NSS points on the InSAR and GNSS data fusion results. To address this issue, this paper proposes a new method that uses Voronoi diagrams analyze the contribution of GNSS points to the deformation at target points. In this paper, GNSS points with high contribution can be used to construct observation value equations and optimize the internal weight of GNSS data. The proposed approach characterizes the contribution of GNSS data to target points by analyzing the changes in the Delaunay triangulation area formed before and after the target points are added to the GNSS data. Simulation experiments reveal that the proposed method outperforms conventional methods in accuracy and completeness. The proposed method is then applied to map the coseismic 3D surface deformations of the 2020 Mw6.5 Monte Cristo Range earthquake and find that the maximum deformation in the east–west, north–south, and vertical directions is about 13 cm, 11 cm, and 24 cm, respectively.

Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation.

Cardiovascular diseases (CVDs) are the leading cause of death, affecting the cardiac dynamics over the cardiac cycle. Estimation of cardiac motion plays an essential role in many medical clinical tasks. This article proposes a probabilistic framework for image registration using compact support radial basis functions (CSRBFs) to estimate cardiac motion. A variational inference-based generative model with convolutional neural networks (CNNs) is proposed to learn the probabilistic coefficients of CSRBFs used in image deformation. We designed two networks to estimate the deformation coefficients of CSRBFs: the first one solves the spatial transformation using given control points, and the second one models the transformation using drifting control points. The given-point-based network estimates the probabilistic coefficients of control points. In contrast, the drifting-point-based model predicts the probabilistic coefficients and spatial distribution of control points simultaneously. To regularize these coefficients, we derive the bending energy (BE) in the variational bound by defining the covariance of coefficients. The proposed framework has been evaluated on the cardiac motion estimation and the calculation of the myocardial strain. In the experiments, 1409 slice pairs of end-diastolic (ED) and end-systolic (ES) phase in 4-D cardiac magnetic resonance (MR) images selected from three public datasets are employed to evaluate our networks. The experimental results show that our framework outperforms the state-of-the-art registration methods concerning the deformation smoothness and registration accuracy.

Force-Based Self-Organizing MANET/FANET with a UAV Swarm

This paper introduces a novel distributed algorithm designed to optimize the deployment of access points within Mobile Ad Hoc Networks (MANETs) for better service quality in infrastructure-less environments. The algorithm operates based on local, independent execution by each network node, thus ensuring a high degree of scalability and adaptability to changing network conditions. The primary focus is to match the spatial distribution of access points with the distribution of client devices while maintaining strong connectivity to the network root. Using autonomous decision-making and choreographed path-planning, this algorithm bridges the gap between demand-responsive network service provision and the maintenance of crucial network connectivity links. The assessment of the performance of this approach is motivated by using numerical results generated by simulations.

Built Equality and Sustainable Urban Cultural Space: A Case Study of Quanzhou, China

Geographic information and the mapping analysis of scientific knowledge have proven to be effective for the management and monitoring of built heritage. To understand built heritage and the urban cultural space, this study incorporates the following integrated data: public surveys, researchers’ knowledge graphs, and urban map information. Firstly, the urban map of Quanzhou is used to analyze the spatial distribution of cultural heritage points. Secondly, Citespace literature co-citation analysis technology is used to establish a preliminary evaluation index of urban culture governance. Thirdly, a questionnaire survey was conducted on the creative economy and urban culture space, and a theoretical model of the urban culture ecosystem was established using Amos software, which includes six dimensions of “cultural inclusiveness, cultural sustainability, cultural integrity, cultural creativity, and cultural vitality”. Finally, a comparative analysis is conducted on the number of visitors to different types of cultural public spaces in Quanzhou City. The results show that exemplary local knowledge and deep historical memory are core elements for building an ideal cultural ecosystem and are necessary to promote cities toward a sustainable future.

Application of IoT technology based on neural networks in basketball training motion capture and injury prevention

Basketball players need to frequently engage in various physical movements during the game, which puts a certain burden on their bodies and can easily lead to various sports injuries. Therefore, it is crucial to prevent sports injuries in basketball teaching. This paper also studies basketball motion track capture. Basketball motion capture preserves the motion posture information of the target person in three-dimensional space. Because the motion capture system based on machine vision often encounters problems such as occlusion or self occlusion in the application scene, human motion capture is still a challenging problem in the current research field. This article designs a multi perspective human motion trajectory capture algorithm framework, which uses a two-dimensional human motion pose estimation algorithm based on deep learning to estimate the position distribution of human joint points on the two-dimensional image from each perspective. By combining the knowledge of camera poses from multiple perspectives, the three-dimensional spatial distribution of joint points is transformed, and the final evaluation result of the target human 3D pose is obtained. This article applies the research results of neural networks and IoT devices to basketball motion capture methods, further developing basketball motion capture systems.

In-orbit cross-calibration of millimeter conically scanning spaceborne radars

Abstract. The planned and potential introduction in global satellite observing systems of conically scanning Ka- and W-band atmospheric radars (e.g., the radars in the Tomorrow.IO constellation, https://www.tomorrow.io/space/, last access: 1 June 2022, and the Wivern (WInd VElocity Radar Nephoscope) radar, https://www.wivern.polito.it, last access: 1 July 2022) calls for the development of methodologies for calibrating and cross-calibrating these systems. Traditional calibration techniques pointing at the sea surface at about 11∘ incidence angle are in fact unfeasible for such fast rotating systems. This study proposes a cross-calibration method for conically scanning spaceborne radars based on ice cloud reflectivity probability distribution functions (PDFs) provided by reference radars like the Global Precipitation Measurement (GPM) Ka-band radar or the W-band radars planned for the ESA-JAXA EarthCARE or for the NASA Atmosphere Observing System missions. In order to establish the accuracy of the methodology, radar antenna boresight positions are propagated based on four configurations of expected satellite orbits so that the ground-track intersections can be calculated for different intersection criteria, defined by cross-over instrument footprints within a certain time and a given distance. The climatology of the calibrating clouds, derived from the W-band CloudSat and Ka-band GPM reflectivity records, can be used to compute the number and the spatial distribution of calibration points. Finally, the mean number of days required to achieve a given calibration accuracy is computed based on the number of calibration points needed to distinguish a biased reflectivity PDF from the sampling-induced noisiness of the reflectivity PDF itself. Findings demonstrate that it will be possible to cross-calibrate, within 1 dB, a Ka-band (W-band) conically scanning radar like that envisaged for the Tomorrow.io constellation (Wivern mission) every few days (a week). Such uncertainties are generally meeting the mission requirements and the standards currently achieved with absolute calibration accuracies.

Not All Neighbors Matter: Point Distribution-Aware Pruning for 3D Point Cloud

Applying deep neural networks to 3D point cloud processing has demonstrated a rapid pace of advancement in those domains where 3D geometry information can greatly boost task performance, such as AR/VR, robotics, and autonomous driving. However, as the size of both the neural network model and 3D point cloud continues to scale, reducing the entailed computation and memory access overhead is a primary challenge to meet strict latency and energy constraints of practical applications. This paper proposes a new weight pruning technique for 3D point cloud based on spatial point distribution. We identify that particular groups of neighborhood voxels in 3D point cloud contribute more frequently to actual output features than others. Based on this observation, we propose to selectively prune less contributing groups of neighborhood voxels first to reduce the computation overhead while minimizing the impact on model accuracy. We apply our proposal to three representative sparse 3D convolution libraries. Our proposal reduces the inference latency by 1.60× on average and energy consumption by 1.74× on NVIDIA GV100 GPU with no loss in accuracy metric

Comparing Two Strategies for Locating Hydrogen Refueling Stations under High Demand Uncertainty

This research aims to model and compare two strategies for locating new hydrogen refueling stations (HRS) in a context of high uncertainty on H<sub>2</sub> demand and on the spatial distribution of demand points. The first strategy S1 represented by an agent-based model integrating a particle swarm optimization metaheuristic consists of finding the best HRS locations by adapting to the real evolution of the demand. A second strategy S2 consists in solving a classical capacitated <em>p</em>-median problem based on H<sub>2</sub> consumption forecasts over a given deterministic horizon in order to define in advance <em>p</em> optimal future HRS locations. Assuming that the same distributor gradually implements future HRSs in a given area between 2023 and 2030, both models minimize the sum of travel distances between each demand point and its assigned SRH. The results show that during the growth phase of the fuel cell electric vehicle (FCEV) market, with two different compound annual growth rates (medium and strong), the conservative S1 strategy performs better than S2 as these rates increase. However, while S2 remains suboptimal throughout the sales growth period, it becomes more effective once demand stabilizes. Another finding is that different uniform distributions of H<sub>2</sub> demand points in the same space have only a small long-term influence on the performance of these two models. This research advises investors to study the influence of different location strategies and models on the performance of a final HRS network in a given region. Models can be easily configured and adapted to a particular spatial distribution of demand points in a specific environment, more flexible H<sub>2</sub> production capabilities, or different behaviors of FCEV drivers that could be geo-located.

Spatiotemporal trip profiles in public transportation reveal city modular structure

Understanding urban mobility patterns within public transportation (PT) systems is key for cities to improve services and promote sustainable mobility. Exploring daily PT riders’ traffic flows using anonymized big data is a first step to analyze when and to where people travel. Nevertheless, assessing the degree to which PT usage patterns correlate with the spatial distribution of points of interest (POI) and subsequently affect the city structure is less attempted. Classic approaches use questionnaires and survey data that are costly and often yielding limited statistical significance. The purpose of this research is to understand associations between travel patterns of urban commuters and the functional organization of a city. To this end, we propose a network-constrained temporal distance measure for modeling PT rider travel patterns from smart card data; and further introduce a fully autonomous approach to describe the span of services available at catchment areas around metro stations. The end result is a detailed analytical prescription of spatiotemporal commuting patterns in the city of Lisbon as well as an analytical contextual information that enables us to understand the functional modular structure of urban facilities. Using the city of Lisbon as the guiding case study, the gathered results confirm the hypothesis that PT rider’s daily flows along the PT network of metro stations reveal a city modular structure.

The use of GIS technologies for the analysis of geodetic provision of the territory of the Dnistrovsky district of the Chernivtsi region

The existing geodetic provision of most of the territory of our country requires an increase in the number of points of the state geodetic network. In today's conditions, the analysis of the geodetic provision of the territory is extremely important and relevant. The latter is even more strengthened due to the implemented decentralization reform, which changed the administrative-territorial structure of the country. That is why it is necessary to carry out analysis, calculations, determine the features of spatial placement and distribution of both individual points of the DGM and geodetic support in general. The purpose of the study is the analysis of geodetic support using QGIS GIS of the territory of the Dnistrovsky district of the Chernivtsi region in conditions of decentralization. Methodology: GIS analysis methods were used to solve the problems that were formed in the work. Thanks to the software product QGIS, thematic layers were vectorized and based on them, maps were created, which allow to evaluate the existing geodetic support in the section of the newly created administrative and territorial units of the study area. Cartographic, statistical-mathematical, descriptive methods made it possible to determine the number and area of areas of territories that are characterized by inconsistency of geodetic support with regard to the requirements for the placement of DGM points for creating maps of the appropriate scale. Today, the geoportal of the State Government of Ukraine, the official website of decentralization is temporarily closed. Therefore, the use of GIS technologies in combination with topographic maps allows a slightly different approach to the analysis and assessment of the geodetic provision of the territory. The existence of separate, separate databases both at the state and regional levels makes it even more impossible to conduct an objective assessment. In view of the above, there is a need to analyze the topographic and geodetic support of the territory of Ukraine using GIS technologies. Research result: The number of geodetic points is 70 units, which is 26,4 % of the total number of points in the region (1st class – 7 units; 2nd class – 13 units; 3rd class – 29 units; digit geodetic network – 21 units). Vectorization of the objects characterizing the areas of inconsistency in the average density of DGM points was carried out. The total area is 600,6 km2 (28,3 % of the total area of the district). Thanks to QGIS, map sheets with a scale of 1 : 200 000 of the geodetic support of the territory as of the 60-s of the 20-th century were linked. The number of DGM points is 73 units. Moreover, 57 points have the same spatial arrangement in comparison with modern points. The correspondence of the geodetic support was considered and it was established that the area of the territory with indicators of non-compliance is 603 km2, which corresponds to 28.4% of the total area of the district. An assessment of the geodetic support of the territory was carried out, taking into account modern and lost points of land surveying. The area of non-conformity areas decreases from 600–603 km2 to 419,8 km2, which, as a percentage of the total area of the Dnistrovsky district, is a decrease from almost a third of the territory to its fifth part – 19,8 %. Scientific novelty: separate thematic vectorized polygonal layers of the territories of the settlements of the districts and modern territorial communities of the Dnistrovsky district of the Chernivtsi region were created, which made it possible, thanks to GIS technologies, to quantitatively determine the area of the territories, with the inconsistency of the geodetic support regarding the requirements for the placement of DGM points when creating cartographic materials of the appropriate scale. In this way, the inconsistency of the geodetic support of the territory of the Dnistrovskyi district with regard to the requirements of regulatory documents was established. Vectorization of areas of discrepancy in the average density of DGM points made it possible to quantitatively approach the assessment of geodetic support in terms of territorial communities. Solving the specified problem is possible thanks to the design and creation of additional points of DGM. The use of geodetic points that existed in the past years and their location can improve the geodetic support of the research area. Keywords: geodetic support, GIS, decentralization.

Revealing the Impact of COVID-19 on Urban Residential Travel Structure Based on Floating Car Trajectory Data: A Case Study of Nantong, China

The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics of urban mobility affected by the pandemic but has rarely examined the impact of COVID-19 on the travel conditions and psychological needs of residents. To quantitatively assess travel characteristics during COVID-19, this paper proposed a method by which to determine the purpose of residential travel by combining urban functional areas (UFAs) based on machine learning. Then, the residential travel structure, which includes origin–destination (OD) points, residential travel flow, and the proportion of flows for different purposes, was established. Based on taxi trajectory data obtained during the epidemic in Nantong, China, the case study explores changes in travel flow characteristics under the framework of the residential travel structure. Through comparison of the number and spatial distribution of OD points in the residential travel structure, it is found that residential travel hotspots decreased significantly. The ratios of commuting and medical travel increased from 43.8% to 45.7% and 7.1% to 8.1%, respectively. Conversely, the ratios of other travel types all decreased sharply. Moreover, under Maslow’s hierarchy of needs model, further insights into the impacts of COVID-19 on changes in residential psychological needs are discussed in this paper. This work can provide a reference for decision makers to cope with the change in urban traffic during a public health emergency, which is beneficial to the sustainable healthy development of cities.