Viewing preferences of TVRO users in Sabah: Identification of distribution patterns using spatial statistics

• Main Authors: Oliver Valentine Eboy,, Ramzah Dambul,
• Format: Article
• Language: English
• Published: Faculty of Social Sciences and Humanities, UKM,Bangi 2011
• Online Access: http://journalarticle.ukm.my/3153/1/5.geografia-2011-4-ramzah-ums-english-editedfinal.pdf; http://journalarticle.ukm.my/3153/; http://www.ukm.my/geografia/v1/index.php

The usage of Television Received Only (TVRO) medium was widespread in the Sabah state of Malaysia based on ground observations. The TVRO users were scattered all over Sabah. They were not only located in the interior regions, but also in the densely populated urban areas. The need to understand the reasons behind this widespread use of TVRO had garnered attention from the authorities, such as the Malaysian Communications and Multimedia Commission (MCMC). This paper focuses on the geographical aspect of the usage of TVRO users by presenting the measurement of its distribution and the identification of the spatial behavioural patterns. The method involved a combination of spatial and statistical analyses using the GIS software ArcGIS 9.3. The statistical mean centre and standard deviational ellipse were used to measure the spatial distribution and the local and global Moran’s I statistical techniques were employed to identify the spatial patterns. Using principal component analysis (PCA), the original 60 variables captured on the ground were simplified into six main components representing the viewers’ preferences. These significantly identified components were referred as TVRO-centric, Astro-centric, Free-To-Air (FTA)- centric, sexual, pride and conventional behaviours. The results showed that there were behavioural similarities in all of the six types of viewers in terms of spatial distribution and patterns. They seemed to be concentrated in the same area with different degrees of strength. The study also provided an opportunity to prove the capability of spatial statistics in producing a better description, prediction over space, and optimisation of the spatial data.