Modeling of hotspot detection using cluster outlier analysis and Getis-Ord Gi* statistic of educational development in upper-primary level, India

Abstract

In recent years educational variability has threatened the sustainability of elementary level in Indian educational system. Systematic methodology to assess the educational development of the elementary sector to educational variability is currently not available. Towards this end, the present paper pays attention with the assessment of spatial structure of Indian educational development to elementary levels in 35 states including union territories (UTs) of all over country. For this purpose, a set of indicators was selected for each of the four components of educational development index (EDI). The data were arranged in the form of diagonal matrix and normalized them using functional relationship. Upon receiving normalized values, here researchers consider a method of unequal weights followed. The choice of the weights in this manner would ensure that large variation in any one of the indicators would not unduly dominate the contribution of the rest of the indicators and distort inter states as well as union territories (UTs) comparisons. It is well known that, in statistical comparisons, it is more efficient to compare two or more means after equalizing their variances. The principal objectives of this article to identification of Hotspot and cold-spot and delineate the spatial cluster of upper primary education level in India based on Getis-Ord Gi* statistic using fixed distance band in ArcGIS software.

Multiple-scale spatial analysis of paediatric, pedestrian road traffic injuries in a major city in North-Eastern Iran 2015–2019

“…The Getis-Ord Gi* statistc shows where features with either high or low values cluster spatially and identifies each feature within the context of neighbouring features [33]. A feature is a geographical unit, for example, a neighbourhood.…”

An Analysis of Online Reputation Indicators by Means of Geostatistical Techniques—The Case of Rural Accommodation in Extremadura, Spain

“…Despite the fact that the results obtained by means of the application of geostatistical techniques are interesting, the use of these tools is not without its problems. These do not lie in the techniques, which are very solid and have been adopted by numerous researchers analysing varied themes [74][75][76][77], but in their configuration. The main problems include in particular the need to choose a conceptualization of spatial relationships by means of various alternatives: inverse distance, inverse distance squared, fixed distance band, or zone of indifference.…”

Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

“…The idea of risk mapping is to assign a probability value to the occurrence of defined condition indicating some form of unusual aggregation in spatial information (Jeefoo et al 2011;Osei and Duker 2008). The spatial clustering of high values is known as hotspot, whereas the cluster with low values is known as coldspot (Jana and Sar 2016). With advancements in GIS, the hotspot (or coldspot) detection method is widely used in disease surveillance system (Ahmad et al 2015;Bhunia et al 2013;Jana and Sar 2016;Jeefoo et al 2011;Ng et al 2018;Osei and Duker 2008;Panahi et al 2020;Willerson et al 1981).…”

“…The spatial clustering of high values is known as hotspot, whereas the cluster with low values is known as coldspot (Jana and Sar 2016 ). With advancements in GIS, the hotspot (or coldspot) detection method is widely used in disease surveillance system (Ahmad et al 2015 ; Bhunia et al 2013 ; Jana and Sar 2016 ; Jeefoo et al 2011 ; Ng et al 2018 ; Osei and Duker 2008 ; Panahi et al 2020 ; Willerson et al 1981 ). In the current pandemic situation, various studies were carried out on detecting hotspot as well as coldspot in different countries to fight against it, for example, Al-Jumaili and Hamed ( 2020 ), Arab-Mazar et al 2020 , Bouffanais and Lim ( 2020 ), Eckerle and Meyer ( 2020 ), Mo et al ( 2020 ).…”