New Combining Rules for Spatial Clustering Methods Using Sigma-Count for Spatial Epidemiology

2020 ◽  
Author(s):  
Laisa Ribeiro de Sa ◽  
Liliane dos Santos Machado ◽  
Jordana de Almeida Nogueira ◽  
Ronei Marcos de Moraes
Keyword(s):  
Spatial Epidemiology ◽  
Spatial Clustering ◽  
Clustering Methods

Statistical Methods in Spatial Epidemiology

2018 ◽  
Author(s):  
Samson Y. Gebreab
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Data ◽  
Spatial Epidemiology ◽  
Spatial Clustering ◽  
Spatial Models ◽  
Scan Statistics ◽  
Spatial Dependency ◽  
Neighborhood Characteristics ◽  
Spatial Effects ◽  
Clustering Methods

Most studies evaluating relationships between neighborhood characteristics and health neglect to examine and account for the spatial dependency across neighborhoods, that is, how neighboring areas are related to each other, although the possible presence of spatial effects (e.g., spatial dependency, spatial heterogeneity) can potentially influence the results in substantial ways. This chapter first discusses the concept of spatial autocorrelation and then provides an overview of different spatial clustering methods, including Moran’s I and spatial scan statistics as well as different models to map spatial data, for example, spatial Bayesian mapping. Next, this chapter discusses various spatial regression methods used in spatial epidemiology for accounting spatial dependency and/or spatial heterogeneity in modeling the relationships between neighborhood characteristics and health outcomes, including spatial econometric models, Bayesian spatial models, and multilevel spatial models.

scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

scholarly journals Evidence for rapid avoidance of rockfish habitat under reduced quota and comprehensive at-sea monitoring in the British Columbia Pacific halibut fishery

2020 ◽  
Vol 77 (8) ◽  
pp. 1409-1420
Author(s):  
Robyn E. Forrest ◽  
Ian J. Stewart ◽  
Cole C. Monnahan ◽  
Katherine H. Bannar-Martin ◽  
Lisa C. Lacko
Keyword(s):  
British Columbia ◽  
Spatial Clustering ◽  
Clustering Methods ◽  
Quota System ◽  
Encounter Rates ◽  
System A ◽  
Pacific Halibut ◽  
Hippoglossus Stenolepis ◽  
Footprint Area ◽  
Management Changes

The British Columbia longline fishery for Pacific halibut (Hippoglossus stenolepis) has experienced important recent management changes, including the introduction of comprehensive electronic catch monitoring on all vessels; an integrated transferable quota system; a reduction in Pacific halibut quotas; and, beginning in 2016, sharp decreases in quota for yelloweye rockfish (Sebastes ruberrimus, an incidentally caught species). We describe this fishery before integration, after integration, and after the yelloweye rockfish quota reduction using spatial clustering methods to define discrete fishing opportunities. We calculate the relative utilization of these fishing opportunities and their overlap with areas with high encounter rates of yelloweye rockfish during each of the three periods. The spatial footprint (area fished) increased before integration, then decreased after integration. Each period showed shifts in utilization among four large fishing areas. Immediately after the reductions in yelloweye rockfish quota, fishing opportunities with high encounter rates of yelloweye rockfish had significantly lower utilization than areas with low encounter rates, implying rapid avoidance behaviour.

Abstract MP41: Comparisons of Built Environment Characteristics Inside and Outside of Spatial Clusters of Physical Activity and Obesity in Older U.S. Women

Circulation ◽  
2013 ◽  
Vol 127 (suppl_12) ◽  
Author(s):  
Kosuke Tamura ◽  
Robin C Puett ◽  
Jaime E Hart ◽  
Heather A Starnes ◽  
Francine Laden ◽  
...  
Keyword(s):  
Physical Activity ◽  
Built Environment ◽  
Spatial Clustering ◽  
Health Study ◽  
Clustering Methods ◽  
Spatial Clusters ◽  
Spatial Scan Statistic ◽  
Complex Picture ◽  
Intersection Density ◽  
Study Participants

Introduction: Spatial clustering methods have been applied to cancer for over a decade. These methods have been used in studies on physical activity (PA) and obesity. One recent study examined differences in built environment attributes inside and outside PA clusters. We tested two hypotheses: 1) PA and obesity would spatially cluster in older women; and 2) built environment attributes typically related to higher walkability would be found in high PA clusters, while attributes related to lower walkability would appear in high obesity clusters. Methods: We used data from 22,589 Nurses’ Health Study participants (mean age = 69.9 ± 6.8y) in California, Massachusetts, and Pennsylvania. Two outcomes were examined: meeting PA guidelines via self-reported walking (≥ 500 MET-min/week) and obesity (BMI ≥ 30.0). Objective built environment variables were created: population and intersection density, diversity of facilities, and facility density. We used a spatial scan statistic to detect clusters (i.e., areas with high or low rates) of the two outcomes. Built environment attributes were compared inside and outside clusters. Results: Six spatial clusters of PA were found in California and Massachusetts. Two obesity clusters were found in Pennsylvania. Overall there were significant differences (p<0.05) in population and intersection density, and diversity and density of facilities inside and outside clusters. In some cases, built environment attributes related to higher walkability appeared in high PA clusters, while in other PA clusters we did not find this pattern. Differences in built environment attributes inside and outside obesity clusters showed inconsistent patterns. Conclusion: Although PA and obesity clusters emerged, the comparison of built environment attributes inside and outside clusters revealed a complex picture not fully consistent with existing literature. Further examination of PA and obesity clusters in older adults should include other built environment factors that may be related to these outcomes.

Spatial distribution of Leishmania seropositive dogs in the Angelim neighborhood, Teresina, Piauí, Brazil: appraisal of three spatial clustering methods

GeoJournal ◽  
2020 ◽  
Author(s):  
Lília Aparecida Marques da Silva ◽  
José Ueleres Braga ◽  
João Pereira da Silva ◽  
Maria do Socorro Pires e Cruz ◽  
André Luiz Sá de Oliveira ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Clustering ◽  
Clustering Methods

scholarly journals Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion

2020 ◽  
Vol 12 (16) ◽  
pp. 2513 ◽  
Author(s):  
Qiwei Ma ◽  
Zhaoya Gong ◽  
Jing Kang ◽  
Ran Tao ◽  
Anrong Dang
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Spatial Clustering ◽  
Landscape Fragmentation ◽  
Analytical Framework ◽  
Urban Land Use ◽  
Urban Systems ◽  
Landsat 8 ◽  
Clustering Methods

Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.

scholarly journals Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

2020 ◽  
Vol 5 (10) ◽  
pp. e002919 ◽  
Author(s):  
Julius Nyerere Odhiambo ◽  
Chester Kalinda ◽  
Peter M Macharia ◽  
Robert W Snow ◽  
Benn Sartorius
Keyword(s):  
Spatial Clustering ◽  
Malaria Incidence ◽  
Data Extraction ◽  
Malaria Risk ◽  
Risk Mapping ◽  
Data Availability ◽  
Sub Saharan Africa ◽  
Clustering Methods ◽  
Manual Search ◽  
Geostatistical Models

BackgroundApproaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).MethodsA systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.ResultsOne hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.ConclusionsOur review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

Based on GIS Spatial Clustering Algorithm Research

2014 ◽  
Vol 971-973 ◽  
pp. 1565-1568
Author(s):  
Zhi Yong Wang
Keyword(s):  
Objective Function ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Direct Access ◽  
Cluster Center ◽  
Clustering Methods ◽  
Concept Clustering ◽  
Spatial Data Management ◽  
Calculated Distance

Facing the particularity of the current limitations and spatial clustering clustering methods, the objective function from concept clustering starting to GIS spatial data management and spatial analysis for technical support, explores the space between the sample direct access to the distance calculated distance and indirect reach up costs. K samples randomly selected as the cluster center, with space for the sample to reach the center of each cluster sample is divided according to the distance, the sum of the spatial clustering center of the sample to reach its cost objective function for clustering, introduction of genetic algorithm, a spatial clustering algorithm based on GIS. Finally, the algorithm is tested by examples.

scholarly journals Density-based classification with the DENCLUE algorithm

2021 ◽  
Vol 24 (1) ◽  
pp. 269
Author(s):  
Mouhcine El Hassani ◽  
Noureddine Falih ◽  
Belaid Bouikhalene
Keyword(s):  
Spatial Clustering ◽  
Data Partitioning ◽  
Clustering Methods ◽  
Data Set ◽  
Density Based Clustering ◽  
Marketing Data ◽  
Numerous Data ◽  
Bank Marketing ◽  
Probabilistic Nature

<p><span>Classification of information is a vague and difficult to explore area of research, hence the emergence of grouping techniques, often referred to Clustering. It is necessary to differentiate between an unsupervised and a supervised classification. Clustering methods are numerous. Data partitioning and hierarchization push to use them in parametric form or not. Also, their use is influenced by algorithms of a probabilistic nature during the partitioning of data. The choice of a method depends on the result of the Clustering that we want to have. This work focuses on classification using the density-based spatial clustering of applications with noise (DBSCAN) and DENsity-based CLUstEring (DENCLUE) algorithm through an application made in csharp. Through the use of three databases which are the IRIS database, breast cancer wisconsin (diagnostic) data set and bank marketing data set, we show experimentally that the choice of the initial data parameters is important to accelerate the processing and can minimize the number of iterations to reduce the execution time of the application.</span></p>

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