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 Spatial analysis for political scientists

2021 ◽  
pp. 1-17
Author(s):  
Jessica Di Salvatore ◽  
Andrea Ruggeri
Keyword(s):  
Comparative Politics ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Statistical Tests ◽  
Spatial Regression ◽  
Spatial Models ◽  
Building Blocks ◽  
Spatial Relationships ◽  
Electoral Studies ◽  
Data Points

Abstract How does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

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 Spatial heterogeneity and spatial bias analyses in hedonic price models: some practical considerations

2015 ◽  
Vol 28 (28) ◽  
pp. 113-128 ◽  
Author(s):  
Haniza Khalid
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Dependence ◽  
Agricultural Land ◽  
Spatial Models ◽  
Future Research ◽  
Dependence Structure ◽  
Spatial Effects ◽  
Hedonic Price ◽  
Spatial Bias ◽  
Spatial Error

Abstract A great number of contemporary studies are incorporating explicit consideration of spatial effects in the estimation of hedonic price functions. At the most basic level, interactive spatial regime models are employed to detect the presence of spatial heterogeneity in datasets. A full-scale spatial analysis would include determination and adjustments for spatial lag and spatial error dependences. However, there is still plenty of room for future research to help unravel the numerous modelling and practical issues associated with a comprehensive spatial examination, such as the specification of the spatial dependence structure or functional ‘neighbourhoods’. Another important issue relates to the use of spatial multipliers to filter spatial bias particularly in models which use log-transformed variables. Estimation of a hedonic price function using Malaysian dataset of agricultural land sale values indicates spatial disaggregation and spatial dependence. However, diagnostic tests and actual estimation of spatial models do not always provide unambiguous conclusions while predicted errors do not vary all that much from those generated by simpler models. Despite the conceptual appeal of spatial analyses, the inefficiency attributable to spatial biases may not be large enough to cause critical errors in policy decisions.

scholarly journals Spatial machine learning: new opportunities for regional science

2021 ◽  
Author(s):  
Katarzyna Kopczewska
Keyword(s):  
Machine Learning ◽  
Spatial Econometrics ◽  
Spatial Data ◽  
Quantitative Methods ◽  
Cross Validation ◽  
Spatial Clustering ◽  
Regional Science ◽  
Fine Tuning ◽  
Clustering Methods ◽  
Spatial Data Integration

AbstractThis paper is a methodological guide to using machine learning in the spatial context. It provides an overview of the existing spatial toolbox proposed in the literature: unsupervised learning, which deals with clustering of spatial data, and supervised learning, which displaces classical spatial econometrics. It shows the potential of using this developing methodology, as well as its pitfalls. It catalogues and comments on the usage of spatial clustering methods (for locations and values, both separately and jointly) for mapping, bootstrapping, cross-validation, GWR modelling and density indicators. It provides details of spatial machine learning models, which are combined with spatial data integration, modelling, model fine-tuning and predictions to deal with spatial autocorrelation and big data. The paper delineates “already available” and “forthcoming” methods and gives inspiration for transplanting modern quantitative methods from other thematic areas to research in regional science.

scholarly journals Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Arvind Sharma ◽  
R. K. Gupta ◽  
Akhilesh Tiwari
Keyword(s):  
Data Mining ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Objects

There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System), GPS (Global Positioning System), weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise). The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

A method for efficient clustering of spatial data in network space

2021 ◽  
pp. 1-18
Author(s):  
Trang T.D. Nguyen ◽  
Loan T.T. Nguyen ◽  
Anh Nguyen ◽  
Unil Yun ◽  
Bay Vo
Keyword(s):  
Spatial Data ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Spatial Data Analysis ◽  
Clustering Methods ◽  
Dbscan Algorithm ◽  
Density Based Clustering ◽  
Network Space

Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time.

scholarly journals An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

2020 ◽  
Vol 12 (1) ◽  
pp. 580-597
Author(s):  
Mohamad Hamzeh ◽  
Farid Karimipour
Keyword(s):  
Spatial Data ◽  
Spatial Models ◽  
Model Performance ◽  
Essential Elements ◽  
Petroleum System ◽  
Petroleum Exploration ◽  
System Level ◽  
Data Sets ◽  
Petroleum Potential ◽  
Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

scholarly journals Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1056
Author(s):  
Yu Chen ◽  
Mengke Zhu ◽  
Qian Zhou ◽  
Yurong Qiao
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Central Area ◽  
Urban System ◽  
Entropy Method ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Urban Resilience

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

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