Equity of the utilization of medical facilities is extremely dependant on the spatial distribution of healthcare amenities. In this study, an attempt is made to study the geographic distribution of the various categories of healthcare facilities in the city of Thiruvananthapuram, South India using the centrographic analysis that includes the Mean Center, Standard Deviational Ellipse and Average Nearest Neighbour and it was found that there is an uneven distribution. Network-based point pattern analysis is also performed and cross k-function is used to determine the distribution of medical shops relative to the healthcare services. A weighted node approach is used to calculate the indices of centrality by weighing all nodes based on their degree using Geographical Information System (GIS). Multi centrality assessment model consisting of betweenness, closeness and straightness centrality is used to compute the weighted road centrality on a local and global scale. Kernel Density Estimation technique is applied to modify the centrality values and the vector points to a basic raster framework. Correlation analysis was performed to find the role of the network centrality on the location of healthcare facilities in the study area. Correlation values are greatest among category 2 hospitals; straightness centrality and betweenness centrality favours the location of category 1 hospitals and homeopathy hospitals respectively. The results confirm that network topology influences the location of medical facilities in the study area.
There are qualitative differences in the modal hypothesis-sampling systems of preoperational, concrete operational, and formal operational children and young adults. These are congenial to predictions derived from Piaget. Preoperational kindergarten children show mostly stereotypes, while their concrete operational peers exhibit use of dimension and hypothesis-checking in more than half their problems. After training formal operational eighth graders and college students show perfect processing in most problems; those who score as concrete operational appear unable to acquire this strategy. Cognitive level is not a good predictor of performance among middle-aged (40 to 50 yr.) and older adults (60 to 80 yr.).
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