Mapping telecommunication networks is of great importance to both urban and rural regions, particularly as telecommunication networks become even more vital for our daily lives. Studies that forecast the placement of cellular network towers are one area of focus for researchers and planners. Terrain, vegetation, urban regions, and other potential interference are often assessed together and GIS tools such as ArcGIS have developed into an industry standard for forecasting cellular tower locations. Furthermore, data are not always static, as some interference may be temporary, long-term, or recurrent.
Forecasting the optimal height of a tower is another dimension that GIS tools (such as line of sight) incorporate. This requires a multi-criteria approach that incorporates elements of time variation of potential interference. Another related problem is the continuous space maximal coverage problem (CSMCP), which looks at continuous coverage planning in given regions, where some areas present greater challenges. Finding ideal locations is a complex problem of wide regions of analysis in that maintaining information at a small spatial scale is also necessary. Discrete coverage models, which parse the problem into discrete sets such as the terrain, are generally used to allow rapid computation of detailed area coverage and optimization of coverage in different regions. Additionally, as multiple companies are often involved in the construction of towers, data between different construction companies is often unknown. To make planning more efficient, WebGIS applications are developed to help share data that improves business operations and management.
A larger area of research, however, in recent years is on human and traffic mobility studies that utilize cellular network data from mobile phones. This has become more of an area of interest given its potential to understanding issues of traffic, where consumers are, pollution studies, areas of infrastructure demand, and general human behavior. In effect, the placement of telecommunication towers become more vital as increased demand for data are likely to happen now advertisers and others are aware of the potential of mobile data.
 For an example of how a multi-criteria, algorithmic approach is used, see: Kashyap, R., Bhuvan, M.S., Chamarti, S., Bhat, P., et al. (2014) Algorithmic Approach for Strategic Cell Tower Placement. In: [Online]. January 2014 IEEE. pp. 619–624.
 For more on the continuous space maximal coverage problem, see: Wei, R. (2016) Coverage Location Models: Alternatives, Approximation, and Uncertainty. International Regional Science Review. [Online] 39 (1), 48–76. Available from: doi:10.1177/0160017615571588.
 For an example of using WebGIS for planning and management of cellular towers, see: Jiang, H. (2014) The Sharing Telecommunication Information Resources Management System Based on B/S model and GIS. TELKOMNIKA Indonesian Journal of Electrical Engineering. [Online] 12 (3).
 For a recent article, see: Williams, N.E., Thomas, T.A., Dunbar, M., Eagle, N., et al. (2015) Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data Sergio Gómez (ed.). PLOS ONE. [Online] 10 (7), e0133630.