Spatial Cluster Analysis as a Sampling Approach in Public Health Research

Abstract

Introduction: Sampling is the process of selecting unit from population of interest. Spatial cluster analysis
is also a sampling strategy in large scale data on population/public health research; K Mean centroid is
an exploratory tool to find the natural spatial clusters at focused level for both categorical and continues
variables. Hence this study attempt.
Objective: The objective of the study was to develop a methodology for defining natural neighborhoods.
Materials and Method: The exploratory study was carried out during Nov 2016 to Dec 2017, using Primary
Census Abstract of Kancheepuram district, Tamil Nadu issued from census 2011. Village data was extracted
and the variables were made as domains by factor reduction and its scores were calculated by factor analysis.
The villages were grouped with similar characteristics as clusters by K mean, Hierarchical and K Mean
Centroid. The SPSS 16v, QGIS, GeoDa software were used.
Results: Out of 1020 villages 917 had selected after data mining and connectivity map was made. The census
variables reduced as factors like Area, population, spatial distance, health facilities and recreation facilities
by factor analysis. These factors scores were taken for the analysis after calculated weighted matrix. Villages
were segregated as 5 clusters in every mapping, K Mean Centroid produced both clustering and significant
map.
Conclusion: K Mean Centroid will give better understand about heterogeneity of large scale data. It helps us
to select appropriate geographical locations to be sampled with existing data for further research.