Increasing mobile internet subscriptions is not enough to address accessibility to information and communications technology since the presence of infrastructures necessary to ensure reliable connectivity to the mobile network needs to be kept in place. As such SDG Indicator 9.c.1 seeks to measure the proportion of population that is covered by a mobile network disaggregated by the type of technology. Globally, data is being collected by the International Telecommunication Union (ITU) from a questionnaire filled-out by ICT ministries or regulatory authorities. Philippine data compiled by the ITU is composed equally by ITU estimates and inputs from national agencies signifying potential inadequate capacity to generate the indicator in a timely and consistent manner. This prompts the exploratory study to try using non-traditional data source to calculate SDG Indicator 9.c.1. The proposed methodology utilizes crowdsourced geospatial data such as administrative boundary files from GADM and cell tower data from OpenCelliD and granular population estimates from the WorldPop. These data are analyze using QGIS, an open-sourced GIS software. Estimation of SDG Indicator 9.c.1 for all types of technology from 2G up to 5G technology was done using the crowdsourced data wherein the data from the UN SDG Global Database for the Philippines is only available only up to 4G technology. However, the results of the estimation using crowdsourced data was greatly lower than the figures from the UN SDG Global Database at the national level (at least 2G: 50.78% vs 99.00%). On another note, estimation of SDG Indicator 9.c.1 can be expanded to a more geographically disaggregated estimates (e.g., regional and provincial) using the crowdsourced data. In conclusion, the use of non-traditional data sources to estimate SDG Indicator 9.c.1 is capable as an alternative to generate the indicator whenever official data sources are not readily available such as industry data from the private sector wherein government has limited to no access to. Certain caution, however, should be exercised in using non-traditional sources such as crowdsourced data since the shared and collected data may be incomplete and inaccurate. Thus, review of the data as to completeness and accuracy of the data may be done together with the assistance of the private sector through discussions and agreements. On another note, more geographic disaggregated SDG Indicator 9.c.1 can be available through crowdsourced data.

Keywords:telecommunications, coverage, connectivity