scholarly journals Enhancing scatterplot matrices for data with ordering or spatial attributes

2006 ◽  
Author(s):  
Qingguang Cui ◽  
Matthew O. Ward ◽  
Elke A. Rundensteiner
Keyword(s):  
Spatial Attributes ◽  
Scatterplot Matrices

scholarly journals Applications of Clustering with Mixed Type Data in Life Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 47
Author(s):  
Shuang Yin ◽  
Guojun Gan ◽  
Emiliano A. Valdez ◽  
Jeyaraj Vadiveloo
Keyword(s):  
Life Insurance ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Financial Statements ◽  
Life Insurers ◽  
Insurance Portfolio ◽  
Policy Information ◽  
Spatial Attributes ◽  
Type Data ◽  
Significant Mortality

Death benefits are generally the largest cash flow items that affect the financial statements of life insurers; some may still not have a systematic process to track and monitor death claims. In this article, we explore data clustering to examine and understand how actual death claims differ from what is expected—an early stage of developing a monitoring system crucial for risk management. We extended the k-prototype clustering algorithm to draw inferences from a life insurance dataset using only the insured’s characteristics and policy information without regard to known mortality. This clustering has the feature of efficiently handling categorical, numerical, and spatial attributes. Using gap statistics, the optimal clusters obtained from the algorithm are then used to compare actual to expected death claims experience of the life insurance portfolio. Our empirical data contained observations of approximately 1.14 million policies with a total insured amount of over 650 billion dollars. For this portfolio, the algorithm produced three natural clusters, with each cluster having lower actual to expected death claims but with differing variability. The analytical results provide management a process to identify policyholders’ attributes that dominate significant mortality deviations, and thereby enhance decision making for taking necessary actions.

scholarly journals ANÁLISIS ESPACIO-TEMPORAL DE LA MALARIA EN MATINA, LIMÓN, COSTA RICA

2016 ◽  
Vol 1 (54) ◽  
pp. 67
Author(s):  
Silvia Argüello Vargas ◽  
Elba de la Cruz Malavassi ◽  
Marco V Herrero Acosta
Keyword(s):  
Costa Rica ◽  
Temporal Trend ◽  
Space Time ◽  
Time Analysis ◽  
Time Pattern ◽  
Future Studies ◽  
Household Level ◽  
Factores De Riesgo ◽  
Spatial Attributes ◽  
Los Grupos

<p>El objetivo de este estudio fue establecer el patrón espacio-temporal de la malaria en Matina y relacionarlo con factores ambientales. Se utilizaron tecnologías espaciales para capturar, almacenar, analizar y visualizar información relacionada con localidades y viviendas. Los atributos no espaciales fueron analizados usando pruebas paramétricas y no paramétricas. Los datos fueron obtenidos de las bases de datos de casos clínicos del Área Rectora del Ministerio de Salud en Matina. Se presentan los descriptores puntuales de las localidades positivas para los años 2005 y 2006 y en los grupos de viviendas positivo y negativo en la localidad piloto. Se propone una clasificación de áreas macroambientales en el cantón y se relaciona con la distribución de la Incidencia Parasitaria Anual (IPA). Se identificaron factores de riesgo a nivel de vivienda en la localidad piloto. Se describe la ocurrencia temporal de la actividad malárica en el cantón. El patrón espacio-temporal que se presenta en este informe puede servir de línea base para estudiar cambios que podrían ocurrir en el futuro.</p><p> </p><p>SPACE-TIME ANALYSIS OF MALARIA IN MATINA, LIMÓN, COSTA RICA</p><p><strong>ABSTRACT</strong><br /> The purpose of this study was to describe the space-time pattern of the disease, and relate it to environmental factors. Spatial technologies were used to collect, store, analyze and display information regarding locations and household locations. Non-spatial attributes were analyzed using parametric and non parametric tests. The information was obtained from databases of clinical cases form the Governing Area of the Health Ministry in Matina. Centrographic parameters were calculated for localities within Matina and for households within the pilot location. Parasitic Incidence (IPA) was associated with a proposed environmental classifiation for Matina. At the household level, risk factors were determined. The temporal pattern of the disease in Matina is described. A similar temporal trend is shown for households within the pilot location. This is the fist time that the information collected in the Matina Governing Area is used to describe the spatial patterns of malaria.<br /> This pattern will be useful as a comparative baseline for future studies.</p><p> </p><p><span><br /></span></p>

Apartment Valuation Models for a Big City Using Selected Spatial Attributes

2019 ◽  
pp. 363-376 ◽  
Author(s):  
Michał Talaga ◽  
Mateusz Piwowarczyk ◽  
Marcin Kutrzyński ◽  
Tadeusz Lasota ◽  
Zbigniew Telec ◽  
...  
Keyword(s):  
Big City ◽  
Spatial Attributes

scholarly journals Study on the Behaviors of Inbound Tourists based on Geotagged Photo Metadata – A Case of the City Group in the Upper Reach of the Yellow River in China

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Ruru Shen ◽  
Haowen Yan ◽  
Qinke Sun ◽  
Xiaojun Li
Keyword(s):  
Network Analysis ◽  
Spatial Information ◽  
Yellow River ◽  
Tourism Industry ◽  
Qinghai Lake ◽  
The Yellow River ◽  
Single Node ◽  
Area Of Interest ◽  
Spatial Attributes ◽  
Geotagged Photos

<p><strong>Abstract.</strong> The spatial distribution of geotagged photos is a projection of the tourist's tourism activities in the geospatial space, which contains spatial attributes and interrelationships of tourists’ activities. Using the Flickr photo sharing website, the paper utilizes new data mining technologies to discover and capture the metadata of geotagged photos uploaded by visitors from January 2008 to October 2018 in the upper reach of the Yellow River in China. The spatial information processing and expression of the collected data are processed and the characteristics of the inbound tourists’ behavior are explored by the P-DBSCAN, the path tracking technology and the UCINET network analysis. The main results are as follows: (1) By using the P-DBSCAN cluster analysis, the area of interest (AOI) has a feature of high agglomeration and forms a “V” shaped in the Xining-Lanzhou-Yinchuan area. The concentration of AOIs is closely related to the urban functional area and has a clear Urban functional orientation. (2) Using tracking analysis, the paper reveals single node trajectory, intraregional path trajectory and interregional path trajectory. Among them, 68.42% visitors chose single node trajectory, 9.78% visitors chose intraregional path trajectory and 21.80% tourists chose interregional path trajectory. (3) Ten cross-regional tourism mainstream lines are picked by the UCINET network analysis mode. It has been found that the tourists tend to visit those famous scenic spots (points) such as the Qinghai Lake, the YaDan Geological Park, the ‘Danxia’ Landform, the Zhenbeibu China West Film Studio. It is apparent that the Gansu-Qinghai Great Circle Tour is a hot tourist route that tourists are keen to choose. The research results have certain reference significance for improving the transformation and upgrading of tourism industry in the upper reach of the Yellow River.</p>

scholarly journals On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements

2018 ◽  
Vol 8 (2) ◽  
pp. 20170039 ◽  
Author(s):  
Zhan Li ◽  
Michael Schaefer ◽  
Alan Strahler ◽  
Crystal Schaaf ◽  
David Jupp
Keyword(s):  
Laser Scanning ◽  
Spatial Information ◽  
Information Source ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Biochemical Properties ◽  
Dual Wavelength ◽  
Wavelength Scanning ◽  
Spatial Attributes

The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWEL's novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultaneous scanning at both 1064 nm and 1548 nm by DWEL instruments provides a new spectral dimension to TLS data that joins the 3D spatial dimension of lidar as an information source. Our point cloud classification algorithm explores the utilization of both spectral and spatial attributes of individual points from DWEL scans and highlights the strengths and weaknesses of each attribute domain. The spectral and spatial attributes for vegetation element classification each perform better in different parts of vegetation (canopy interior, fine branches, coarse trunks, etc.) and under different vegetation conditions (dead or live, leaf-on or leaf-off, water content, etc.). These environmental characteristics of vegetation, convolved with the lidar instrument specifications and lidar data quality, result in the actual capabilities of spectral and spatial attributes to classify vegetation elements in 3D space. The spectral and spatial information domains thus complement each other in the classification process. The joint use of both not only enhances the classification accuracy but also reduces its variance across the multiple vegetation types we have examined, highlighting the value of the DWEL as a new source of 3D spectral information. Wider deployment of the DWEL instruments is in practice currently held back by challenges in instrument development and the demands of data processing required by coaxial dual- or multi-wavelength scanning. But the simultaneous 3D acquisition of both spectral and spatial features, offered by new multispectral scanning instruments such as the DWEL, opens doors to study biophysical and biochemical properties of forested and agricultural ecosystems at more detailed scales.

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