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Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

This paper introduces a novel clustering approach that enhances the traditional Density‐Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm by integrating a grid search method and Nash Equilibrium principles and addresses the limitations of DBSCAN parameterization, particularly its inefficiency with big data. The use of Nash equilibrium allows the identification of clusters with different densities and the determination of DBSCAN parameters and the selection of cells from the network, and significantly improves the efficiency and accuracy of the clustering process. The proposed method divides data into grid cells, applies DBSCAN to each cell, and then merges smaller clusters, capitalizing on dynamic parameter calculation and reduced computational complexity. The performance of the proposed method was assessed over 3 big‐size and 11 middle‐size datasets. The achieved results implied the superiority of the proposed method to DBSCAN, ST‐DBSCAN, P‐DBSCAN, GCBD, and CAGS methods in terms of clustering accuracy (purity) and processing time. It proposes an enhanced method combining the density‐based spatial clustering of applications with noise (DBSCAN) with a dynamic approach and Nash equilibrium to improve efficiency and accuracy.

Semantic segmentation of large unstructured 3D point clouds is important problem for 3D object recognition which in turn is essential to solving more complex tasks such as scene understanding. The problem is highly challenging owing to large scale of data, varying point density and localization errors of 3D points. Nevertheless, with recent successes of deep neural network architectures to solve complex 2D perceptual problems, several researchers have shown interest to translate the developed 2D networks to 3D point cloud segmentation by a prior voxelization step for an explicit neighborhood representation. However, such a 3D grid representation loses the fine details and inherent structure due to quantization artifacts. For this purpose, this paper proposes an approach to performing semantic segmentation of 3D point clouds by exploiting the idea of super-point based graph construction. The proposed architecture is composed of two cascaded modules including a light-weight representation learning module which uses unsupervised geometric grouping to partition the large-scale unstructured 3D point cloud and a deep context aware sequential network based on long short memory units and graph convolutions with embedding residual learning for semantic segmentation. The proposed model is evaluated on two standard benchmark datasets and achieves competitive performance with the existing state-of-the-art datasets. The code and the obtained results have been made public at https://github.com/saba155/DCARN .

Background & objectives: Malaria infects around 216 million people annually with estimated 445,000 deaths globally. Anopheles culicifacies is the vector of malaria in Sri Lanka, a complex of five morphologically identical sibling species of which precise identification using DNA-based methods is still under experimentation. This study was carried out in Sri Lanka to observe the utility of BCE-PCR assay based on mitochondrial Cytochrome Oxidase II (COII) developed in India, in sibling species B and E identification in Sri Lanka, to characterize nucleotide and corresponding amino acid sequences of COII region in major vector sibling species E in Sri Lanka and to analyze the spatial distribution pattern of sibling species E in Sri Lanka using microsatellite markers. Methods: BCE-PCR was carried out for the samples to identify their sibling status. Sequencing of COII region was then carried out to investigate the genetic diversity of Sri Lankan sibling species E, sequences were aligned and compared; microsatellite genotyping was carried out and the spatial clustering pattern was analyzed. Results: Identification of sibling species B and E using BCE-PCR was confusing due to the heterogeneity in the COII region of sibling species in Sri Lanka. Non-synonymous substitutions were detected in COII gene amongst sibling species E. Spatial distributed two clusters were detected in the studied population. Interpretation & conclusion: Existence of genetic variants among sibling species is suggested in Sri Lanka. Further, the pattern of sibling species identification in BCE-PCR was reflected in the spatial clustering of sibling E in Sri Lanka.

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR’s ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).

Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.

Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden clustering is one of the algorithms of choice in the single-cell community. In the field of spatial omics, Leiden is often categorized as a “non-spatial” clustering method. However, we show that by integrating spatial information at various steps Leiden clustering is rendered into a computationally highly performant, spatially aware clustering method that compares well with state-of-the art spatial clustering algorithms.

This study develops a hedonic pricing model to improve the estimation of locational effects by leveraging clustering algorithms. Specifically, we examine and compare the predictive accuracy of four models: a baseline specification without locational effects, a standard model with traditional administrative boundaries, one using constrained K means clustering, and another employing a constrained spectral clustering. Focusing on Berlin’s housing market (2021–2022), our results show that spectral clustering improves predictive accuracy across standard metrics (root mean square error (RMSE), R squared (R2), mean absolute error (MAE)). To the best of our knowledge, this is the first study to apply spectral clustering to estimate locational effects within a hedonic pricing framework. Beyond the methodological contribution, we also address Berlin’s regulatory and institutional housing context and highlight how improved modelling of locational effects can enhance fairness in property taxation by providing more accurate assessments of locational value.

Geography of innovation, creative clustering, urban buzz and innovation districts are place-based concepts that have emerged as a result of the US economy’s transformation to knowledge-intensive economies. The notable built environment characteristics of these concepts are spatial clustering, walkability and proximity to urban amenities, diversity, regional connectivity and agglomeration. While several of these characteristics have been associated with urban sprawl in previous studies, there is a lack of direct evidence on how urban sprawl affects innovation productivity. This national study seeks to examine the relationship between urban sprawl, place-based characteristics and innovation productivity. We used Multilevel Modelling to account for built environment characteristics at both neighbourhood and regional levels. We found that innovative firms tend to locate more in census tracts that are less compact but offer spatial proximity to firms in related business sectors. This is likely due to the higher land and property value in compact areas, which could make it unaffordable for small businesses. We also found that the regional compactness positively and significantly affects the number of innovative firms. This is likely due to the role of compact regions in supporting public transit investments, enhancing social capital and reducing poverty and racial segregation. 随着美国经济向知识密集型经济转型，出现了创新地理、创意产业集群、都市奔忙、创新区等地方型的概念。这些概念具有空间聚类、可步行性、接近城市便利设施、多元化、区域连通性和聚集性等显著的建筑环境特点。尽管之前的一些研究表明这些特点与城市扩张存在关联，目前仍然没有直接的证据能够证明城市扩张对创新生产力产生了怎样的影响。 这项全国性研究的目的是探究城市扩张、地方型特征与创新生产力之间的关系。我们使用多层模型，从街区和区域层面来解释建筑环境特征。我们发现，创新型企业倾向于选址在人口不算密集但与相关业务领域企业距离较近的地段。这很可能是因为人口密集地区的土地和房产价格较高，小企业无法负担。我们还发现，区域密集度对创新型企业的数量具有显著的正面影响。这很可能是因为密集区域在支持公共交通投资、提升社会资本、减少贫困和种族隔离方面所发挥的作用。