Location Of Clusters Research Articles

A spatial autocorrelation analysis of road traffic crash by severity using Moran’s I spatial statistics: A comparative study of Addis Ababa and Berlin cities

Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries. The study used three-year crash data from each city. It employed the average nearest neighbor distance (ANND) method to determine the significance of spatial clustering of crash data by severity, Global Moran's I to examine the statistical significance of spatial autocorrelation, and Local Moran's I to identify significant cluster locations with High-High (HH) and Low-Low (LL) crash severity values. The ANND analysis reveals a significant clustering of crashes by severity in both cities, except in Berlin's fatal crashes. However, different Global Moran's I results were obtained for the two cities, with a strong and statistically significant value for Addis Ababa compared to Berlin. The Local Moran's I result indicates that the central business district and residential areas have LL values, while the city's outskirts exhibit HH values in Addis Ababa. With some persistent HH value locations, Berlin's HH and LL grid clusters are intermingled on the city's periphery. Socio-economic factors, road user behavior and roadway factors contribute to the difference in the result. Nevertheless, it is interesting to note the similarity of significant HH value locations on the outskirts of both cities. Finally, the results are consistent with previous studies and indicate the need for further investigation in other locations.

Spatial and Temporal Changes in Taenia solium Cysticercosis Seroprevalence among Pigs in the Southern Highlands of Tanzania.

Taenia solium remains among the major causes of epilepsy in sub-Saharan Africa. Understanding the geospatial distribution of the parasite is important to inform control strategies. This study assessed spatiotemporal changes in the T. solium infection among pigs from a rural area in southwestern Tanzania. Secondary data from a previous repeated cross-sectional study in the Mbeya and Mbozi districts were used, involving sixteen villages, eight from each of the two districts. Serum samples were collected from 482, 460, and 421 pigs in 221, 196, and 139 households, respectively, at seven-month intervals. Ag-ELISA was used to analyse the samples for circulating antigens. Geographic coordinates of the pig corrals adjacent to the households were also recorded using a hand-held GPS device. SatScan v9.7 software was used to perform purely spatial scan analysis, based on the Bernoulli distribution model. Spatial clustering was assumed based on the relative risk of T. solium seropositivity. Statistical significance (set at 0.05) of the cluster was determined by comparing the log-likelihood ratio with the null distribution derived by Monte Carlo simulations. The window with the highest log-likelihood ratio was regarded as a primary cluster. Results showed significant clustering of T. solium cases with the presence of a single primary cluster during each phase. The relative risk of T. solium infection among pigs within the primary cluster areas ranged from 3.0 to 6.7. The radii of the clusters expanded from 1.83 to 27.7 km before shrinking to 1.5 km, and cluster location drifted from north to central and then to the southern part of the study area. These findings suggest that with restricted resources, control measures in the area could be focused on pockets of high transmission. However, changes in the size and location of clusters suggest that to be effective, interventions should be instituted soon after clusters are identified.

The effect of public health interventions on COVID-19 incidence in Queensland, Australia: a spatial cluster analysis

Background Using SaTScan™ Geographical Information Systems (GIS), spatial cluster analysis was used to examine spatial trends and identify high-risk clusters of Coronavirus 2019 (COVID-19) incidence in response to changing levels of public health intervention phases including international and state border closures, statewide vaccination coverage, and masking requirements. Methods Changes in COVID-19 incidence were mapped at the statistical area 2 (SA2) level using a GIS and spatial cluster analysis was performed using SaTScan™ to identify most-likely clusters (MLCs) during intervention phases. Results Over the study period, significant high-risk clusters were identified in Brisbane city (relative risk = 30.83), the southeast region (RR = 1.71) and moving to Far North Queensland (FNQ) (RR = 2.64). For masking levels, cluster locations were similar, with MLC in phase 1 in the southeast region (RR = 2.56) spreading to FNQ in phase 2 (RR = 2.22) and phase 3 (RR = 2.64). All p values <.0001. Conclusions Movement restrictions in the form of state and international border closures were highly effective in delaying the introduction of COVID-19 into Queensland, with very low levels of transmission prior to border reopening while mandatory masking may have played a role in decreasing transmission through behavioural changes. Early clusters were in highly populated regions, as restrictions eased clusters were identified in regions more likely to be rural or remote, with higher numbers of Indigenous people, lower vaccination coverage or lower socioeconomic status.

Study on the Relationship between Hotspots and Air Pollution Standard Index Based on Carbon Monoxide Concentration in West Kalimantan

Hotspots, indicating active fires and potential sources of air pollution, are a major concern due to their impact on air quality in West Kalimantan. In this study, an analysis was carried out regarding the relationship of hotspots to the Air Pollution Standard Index (ISPU) based on the carbon monoxide (CO) parameter. This study aimed to determine the relationship between forest fires and air pollution levels. The data used in this study is in the form of monthly average CO concentration data and coordinates of hotspots in 2017-2021. To determine the effect of CO concentration, CO concentration was converted into ISPU and then mapped using the Inverse Distance Weighting (IDW) interpolation method. In addition, the overlay technique is also applied to the map and the correlation coefficient calculation between ISPU data and hotspot data. The study results show that the annual average number of hotspots ranges from 769 to 3,612, while the average ISPU ranges from 22.21 to 59.03. The highest average number of hotspots occurred in 2019, with 3,612 hotspots and the highest average ISPU value of 59.03, categorized as moderate. However, when examined monthly, the highest average number of hotspots is observed in August and September, with 8,505 and 8,321 hotspots, respectively, and average ISPU values of 55.36 and 88.32, categorized as moderate. Furthermore, the correlation coefficient between the average number of hotspots and the average ISPU per month is 0.91, and per year is 0.98, indicating a very high relationship. Spatially, clustered hotspot locations correspond to higher ISPU values. This implies that as the number of hotspots increases, the ISPU value based on CO concentration increases.

Measuring urban thermal environment from accessibility-based perspective: A case study in a populous city

Understanding the spatial interaction among residents, cooling service, and heat risk area in complex urban areas is conducive to developing targeted management. However, traditional urban thermal environment assessments typically relied on simple linear integration of associated indicators, often neglecting the spatial interaction effect. To explore the spatial interaction among the three elements, this study proposes an accessibility-based urban thermal environment assessment framework. Using Zhengzhou, a rapidly urbanizing city, as an example, remotely sensed images from three periods (2010, 2015 and 2020) were applied to extract urban green space (UGS) and hot island area (HIA). An improved two-step floating catchment area (2SFCA) method and bivariate local Moran’s I were employed to explore whether residents’ clustering locations are more likely to access cooling service or to be exposed to heat risk. The results demonstrate that the UGS in the city has been expanding, whereas the HIA shrank within the inner city in 2015 and then increased in 2020. Even though the urban thermal environment may have improved in the last decade, the spatial interaction among the residents, cooling service and heat risk area could be exacerbated. Spatial autocorrelation shows an increase in locations that are disadvantageous for resident congregation. Even when sufficient cooling services were provided, residents in these areas could still be exposed to high heat risk. The developed urban thermal environment framework provides a novel insight into the residents’ heat risk exposure and cooling service accessibility, and the findings could assist urban planners in targeting the improvement of extra heat exposure risk locations.

Global poverty estimation using private and public sector big data sources

Household surveys give a precise estimate of poverty; however, surveys are costly and are fielded infrequently. We demonstrate the importance of jointly using multiple public and private sector data sources to estimate levels and changes in wealth for a large set of countries. We train models using 63,854 survey cluster locations across 59 countries, relying on data from satellites, Facebook Marketing information, and OpenStreetMaps. The model generalizes previous approaches to a wide set of countries. On average, across countries, the model explains 55% (min = 14%; max = 85%) of the variation in levels of wealth at the survey cluster level and 59% (min = 0%; max = 93%) of the variation at the district level, and the model explains 4% (min = 0%; max = 17%) and 6% (min = 0%; max = 26%) of the variation of changes in wealth at the cluster and district levels. Models perform best in lower-income countries and in countries with higher variance in wealth. Features from nighttime lights, OpenStreetMaps, and land cover data are most important in explaining levels of wealth, and features from nighttime lights are most important in explaining changes in wealth.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Predicting Equatorial Ionospheric Convective Instability Using Machine Learning

AbstractThe numerical forecast methods used to predict ionospheric convective plasma instabilities associated with Equatorial Spread‐F (ESF) have limited accuracy and are often computationally expensive. We test whether it is possible to bypass first‐principle numeric simulations and forecast irregularities using machine learning models. The data are obtained from the incoherent scatter radar at the Jicamarca Radio Observatory located in Lima, Peru. Our models map vertical plasma drifts, time, and solar activity to the occurrence and location of clusters of echoes telltale of ionospheric irregularities. Our results show that these models are capable of identifying the predictive power of the tested inputs, obtaining accuracies around 75%.

The intelligent optimization of perforation cluster locations incorporating the fiber optics monitoring results

During horizontal well multi-stage fracturing (HWMF), superfractures are often identified. To promote the uniform propagation of multiple fractures, it is necessary to finely optimize the perforation cluster locations based on the geological and engineering parameters. This work proposes an efficient method to design the perforation cluster locations in consideration of the geoengineering sweet spots with similar mechanical properties. Well log data and the precise fiber optics (FO) monitoring results are combined to find the main influencing factors. The principal component is conducted by introducing correlation analysis and Random Forest. Moreover, the K-means++ clustering method is used to evaluate reservoir quality. The fracturing sweet index (FSI) is proposed to measure the fracturing performance of each category quantitatively. The proposed workflow is effectively validated by two production scenarios. Moreover, the workflow can automatically evaluate reservoir quality based on intelligent clustering methods. Compared with the original design, the updated design lowers the gap among multiple fractures within one stage and increases the well production by 20%–50%. This work is beneficial for the on-site treatment of its feasibility and generalizability.

Study on Assessment Method of Failure Pressure for Pipelines with Colony Corrosion Defects Based on Failure Location

Evaluating the burst pressure of corrosion cluster defects necessitates considering the interaction of contiguous defects. The importance of this interaction cannot be emphasized enough, as it plays a crucial role in determining the failure pressure of such pipelines. Current methods for assessing the failure pressure of corrosion cluster defects have drawbacks, including complex evaluation procedures and limited applicability. In this research, the failure mode and location of corrosion clusters with two or more defects are studied with a burst experiment on a full-scaled pipeline. Based on the failure position of the corrosion cluster, a “center failure location” method is proposed to estimate the burst pressure of colony corrosion defects. The method takes the defect in the failure position as the center; the influence of contiguous defects that interact with the central defect on its failure pressure is considered, and subsequently, the burst pressure of colony corrosion centered on the failure location is evaluated. In contrast with the current assessment methods, while the proposed approach does not reduce prediction errors, it requires fewer evaluation conditions and is operationally simpler and more versatile.

Comparative Hotspot Analysis of Urban Living Environments and Transit-Oriented Development (TOD) Strategies: A Case Study of Beijing and Xi’an

The quality of urban living environments has become a focal point for local governments and citizens. By conducting a thorough analysis of the human settlement environment, the study can not only gain an intuitive insight into the quality of life of residents but also propose forward-thinking and sustainable suggestions for areas of improvement. This study optimizes and analyzes open platform data closely related to residents and assesses the suitability of different areas for living from diverse perspectives and methodologies. This study has chosen Beijing and Xi’an as the primary case studies. The local living environment is categorized into residential, living, recreational environment, transportation convenience, and safety. Our evaluation combines subjective and objective analysis methods and considers hotspot and cold spot analyses. This study employs the Analytic Hierarchy Process (AHP) as a subjective analysis method and the entropy method for objective analysis. By integrating both methods, it assesses the living environment conditions of Beijing and Xi’an. Furthermore, using GIS software, hotspot analysis is conducted for both cities, identifying areas of high and low quality. Detailed analysis is subsequently carried out for the low-quality clusters. Ultimately, this study, grounded in the theory of Transit-Oriented Development (TOD), presents recommendations for sustainable development aimed at representative rural towns and streets. City centers in Beijing and Xi’an have high-quality environments, while the outskirts show declining quality. Xi’an has uneven resource distribution, while Beijing is more balanced, with hotspot analyses indicating specific high- and low-quality cluster locations in both cities. These disparities and characteristics of the low-quality clusters offer insights for future urban development.

Distance deterrence comparison in urban commute among different socioeconomic groups: A normalized linear piece-wise gravity model

Understanding commuting behavior among socioeconomic groups is essential in promoting equity across the housing and transportation system. This field is well-studied, but existing research failed to reach a complete agreement on the relationship between socioeconomic groups and commuting distance. Our study establishes a framework to model distance deterrence impacts on various socio-economic groups of commuters by adapting a commonly used gravity model approach. We apply this framework to explore commuting distance on income groups in 12 major U.S cities, delineate how the deterrence impact increases with distance, and demonstrate a significant discrepancy in it between the low- and high-income groups. Results indicate that high-income commuters are less sensitive to increasing distance. And this inequity is more severe in cities that have higher population density and more clustered job locations. Our findings provide insights for equity analysis in public transit infrastructures and land use.

Mapping high-risk clusters and identifying place-based risk factors of mental health burden in pregnancy

PurposeDespite affecting up to 20% of women and being the leading cause of preventable deaths during the perinatal and postpartum period, maternal mental health conditions are chronically understudied. This study is the first to identify spatial patterns in perinatal mental health conditions, and relate these patterns to place-based social and environmental factors that drive cluster development. MethodsWe performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters. ResultsThe most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters. ConclusionsResults provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.

U2 and U4 snDNA Comparative Chromosomal Mapping in the Neotropical Fish Genera Apareiodon and Parodon (Characiformes: Parodontidae).

Small nuclear DNA (snDNA) are valuable cytogenetic markers for comparative studies in chromosome evolution because different distribution patterns were found among species. Parodontidae, a Neotropical fish family, is known to have female heterogametic sex chromosome systems in some species. The U2 and U4 snDNA sites have been found to be involved in Z and W chromosome differentiation in Apareiodon sp., Apareiodon affinis, and Parodon hilarii. However, few studies have evaluated snDNA sites as propulsors of chromosome diversification among closely related fish species. In this study, we investigated the distribution of U2 and U4 snDNA clusters in the chromosomes of 10 populations/species belonging to Apareiodon and Parodon, aiming to identify chromosomal homeologies or diversification. In situ localization data revealed a submetacentric pair carrying the U2 snDNA site among the populations/species analyzed. Furthermore, all studied species demonstrated homeology in the location of U4 snDNA cluster in the proximal region of metacentric pair 1, besides an additional signal showing up with a divergence in Apareiodon. Comparative chromosomal mapping of U4 snDNA also helped to reinforce the proposal of the ZZ/ZW1W2 sex chromosome system origin in an A. affinis population. According to cytogenetic data, the study corroborates the diversification in Parodontidae paired species with uncertain taxonomy.

Analisa Perbandingan Complate Linkage AHC dan K-Medoids Dalam Pengelompokkan Data Kemiskinan di Indonesia

The poverty rate in Indonesia has increased from 9.54 percent in March 2022 to 9.57 percent in September 2022 due to inflation and low wages and people's incomes. To overcome this problem, steps such as providing social assistance, creating decent jobs, and increasing wage standards are needed to increase people's purchasing power and reduce poverty in the future. The government needs to pay special attention to provinces with high poverty rates through special programs and efforts to increase income and the economy in these areas. Data Mining is a solution in solving this problem by utilizing the clustering method which is known as the clustering method. The clustering method used in this study is the AHC method and the K-Medoids method. In order to determine the provinces with the highest number of poor people, the AHC and K-Medoids clustering methods will be applied separately so that the final results of each will be analyzed. The results of the analysis show the formation of three clusters with different cluster locations. The application of the AHC method resulted in cluster 2 with the largest number of provinces, namely 22 provinces, followed by cluster 0 with 9 provinces, and cluster 1 with only 3 provinces. While the application of the K-Medoids method resulted in cluster 1 with the largest number of provinces, namely 22 provinces, followed by cluster 0 with 9 provinces, and cluster 2 with only 3 provinces. Although the location of the clusters is different between the two methods, the number of provinces in the cluster is the same so that a cluster with a total of 3 provinces is declared the province with the largest number of poor people.

Parkinson's Disease Tremor Differentially Responds to Levodopa and Subthalamic Stimulation.

Tremor in Parkinson's disease (PD) has an inconsistent response to levodopa and subthalamic deep brain stimulation (STN-DBS). To identify predictive factors of PD tremor responsiveness to levodopa and STN-DBS. PD patients with upper limb tremor who underwent STN-DBS were included. The levodopa responsiveness of tremor (overall, postural, and rest sub-components), was assessed using the relevant Unified Parkinson's Disease Rating Scale-III items performed during the preoperative assessment. Post-surgical outcomes were similarly assessed ON and OFF stimulation. A score for the rest/postural tremor ratio was used to determine the influence of rest and postural tremor severity on STN-DBS outcome. Factors predictive of tremor responsiveness were determined using multiple linear regression modeling. Volume of tissue activated measurement coupled to voxel-based analysis was performed to identify anatomical clusters associated with motor symptoms improvement. One hundred and sixty five patients were included in this study. Male gender was negatively correlated with tremor responsiveness to levodopa, whereas the ratio of rest/postural tremor was positively correlated with both levodopa responsiveness and STN-DBS tremor outcome. Clusters corresponding to improvement of tremor were in the subthalamic nucleus, the zona incerta and the thalamus, whereas clusters corresponding to improvement for akinesia and rigidity were located within the subthalamic nucleus. More severe postural tremor and less severe rest tremor were associated with both poorer levodopa and STN-DBS response. The different locations of clusters associated with best correction of tremor and other parkinsonian features suggest that STN-DBS effect on PD symptoms is underpinned by the modulation of different networks.

Designing a hybrid intelligent transportation system for optimization of goods distribution network routing problem

Given that finding the right and appropriate route in the daytime and busy city with the occurred traffic limitations is a major problem that not only causes inefficient performance in distribution networks but also causes irreparable environmental damage to society. This study focuses on improving the routing of the goods distribution network using the intelligent transportation system. In this regard, first, the problem is modeled, and then an intelligent transportation system is combined with some meta-heuristic algorithms to solve it. In the proposed algorithm, we first use the clustering algorithm to cluster location of customers and then create sub-clusters based on the time window. The proposed routes are created by using the genetic and particle swarm optimization meta-heuristic algorithms as the static part of the approach, and if the traffic conditions change, the Vehicular Ad - hoc Network (Vanet), which is one of the sub-systems of the intelligent transportation system as the dynamic part of the approach checks the new traffic conditions and sends the new information to the proposed algorithms to recheck the route. The Aarhus-Denmark data set is selected due to having urban traffic information, meteorology, and urban areas. This is related to the City Pulse project. According to the obtained results, in terms of reducing the cost of transmission, including the cost of service delay and total cost of moving, the proposed method reached better solutions comparing to the meta-heuristic algorithms of literature.

Descending interneurons of the stick insect connecting brain neuropiles with the prothoracic ganglion.

Stick insects respond to visual or tactile stimuli with whole-body turning or directed reach-to-grasp movements. Such sensory-induced turning and reaching behaviour requires interneurons to convey information from sensory neuropils of the head ganglia to motor neuropils of the thoracic ganglia. To date, descending interneurons are largely unknown in stick insects. In particular, it is unclear whether the special role of the front legs in sensory-induced turning and reaching has a neuroanatomical correlate in terms of descending interneuron numbers. Here, we describe the population of descending interneurons with somata in the brain or gnathal ganglion in the stick insect Carausius morosus, providing a first map of soma cluster counts and locations. By comparison of interneuron populations with projections to the pro- and mesothoracic ganglia, we then estimate the fraction of descending interneurons that terminate in the prothoracic ganglion. With regard to short-latency, touch-mediated reach-to-grasp movements, we also locate likely sites of synaptic interactions between antennal proprioceptive afferents to the deutocerebrum and gnathal ganglion with descending or ascending interneuron fibres. To this end, we combine fluorescent dye stainings of thoracic connectives with stainings of antennal hair field sensilla. Backfills of neck connectives revealed up to 410 descending interneuron somata (brain: 205 in 19 clusters; gnathal ganglion: 205). In comparison, backfills of the prothorax-mesothorax connectives stained only up to 173 somata (brain: 83 in 16 clusters; gnathal ganglion: 90), suggesting that up to 60% of all descending interneurons may terminate in the prothoracic ganglion (estimated upper bound). Double stainings of connectives and antennal hair field sensilla revealed that ascending or descending fibres arborise in close proximity of afferent terminals in the deutocerebrum and in the middle part of the gnathal ganglia. We conclude that two cephalothoracic pathways may convey cues about antennal movement and pointing direction to thoracic motor centres via two synapses only.

Spatial Precision Tailoring the Catalytic Activity of Graphene Monolayers for Designing Janus Swimmers.

Graphene monolayers have interesting applications in many fields due to their intrinsic physicochemical properties, especially when they can be postmodified with high precision. Herein, we describe the highly site-selective functionalization of freestanding graphene monolayers with platinum (Pt) clusters by bipolar electrochemistry. The deposition of such metal spots leads to catalytically active hybrid two-dimensional (2D) nanomaterials. Their catalytic functionality is illustrated by the spatially controlled decomposition of hydrogen peroxide, inducing motion at the water/air interface due to oxygen bubble evolution. A series of such 2D Janus structures with Pt deposition at predefined positions (corners and edges) is studied with respect to the generation of autonomous motion. The type and speed of motion can be fine-tuned by controlling the deposition time and location of the Pt clusters. These proof-of-principle experiments indicate that this type of hybrid 2D object opens up interesting perspectives in terms of applications, such as environmental detection or remediation.

The location and development of Replicon Cluster Domains in early replicating DNA.

Background: It has been known for many years that in metazoan cells, replication origins are organised into clusters where origins within each cluster fire near-synchronously. Despite clusters being a fundamental organising principle of metazoan DNA replication, the genomic location of origin clusters has not been documented. Methods: We synchronised human U2OS by thymidine block and release followed by L-mimosine block and release to create a population of cells progressing into S phase with a high degree of synchrony. At different times after release into S phase, cells were pulsed with EdU; the EdU-labelled DNA was then pulled down, sequenced and mapped onto the human genome. Results: The early replicating DNA showed features at a range of scales. Wavelet analysis showed that the major feature of the early replicating DNA was at a size of 500 kb, consistent with clusters of replication origins. Over the first two hours of S phase, these Replicon Cluster Domains broadened in width, consistent with their being enlarged by the progression of replication forks at their outer boundaries. The total replication signal associated with each Replicon Cluster Domain varied considerably, and this variation was reproducible and conserved over time. We provide evidence that this variability in replication signal was at least in part caused by Replicon Cluster Domains being activated at different times in different cells in the population. We also provide evidence that adjacent clusters had a statistical preference for being activated in sequence across a group, consistent with the 'domino' model of replication focus activation order observed by microscopy. Conclusions: We show that early replicating DNA is organised into Replicon Cluster Domains that behave as expected of replicon clusters observed by DNA fibre analysis. The coordinated activation of different Replicon Cluster Domains can generate the replication timing programme by which the genome is duplicated.