Nowadays with the rapid development of cities, understanding the human mobility patterns of subscribers is crucial for urban planning and for network infrastructure deployment. Today mobile phones are electronic devices used for analyzing the mobility patterns of the subscribers in the network, because humans in their daily activities they carry mobile phones for communication purpose. For effective utilization of network infrastructure (NI) there is a need to study on mobility patterns of subscribers.

The aim of the thesis is to simulate the geospatial Telenor mobility data (i.e. three different subscriber categorized segments) and provide a visual support in google maps using google maps API, which helps in decision making to the telecommunication operators for effective utilization of network infrastructure (NI).

 In this thesis there are two major objectives. Firstly, categorize the given geospatial telenor mobility data using subscriber mobility algorithm. Secondly, providing a visual support for the obtained categorized geospatial telenor mobility data in google maps using a geovisualization simulation tool.

 The algorithm used to categorize the given geospatial telenor mobility data is subscriber mobility algorithm. Where this subscriber mobility algorithm categorizes the subscribers into three different segments (i.e. infrastructure stressing, medium, friendly). For validation and confirmation purpose of subscriber mobility algorithm a tetris optimization model is used. To give visual support for each categorized segments a simulation tool is developed and it displays the visualization results in google maps using Google Maps API.

The result of this thesis are presented to the above formulated objectives. By using subscriber mobility algorithm and tetris optimization model to a geospatial data set of 33,045 subscribers only 1400 subscribers are found as infrastructure stressing subscribers. To look informative, a small region (i.e. boras region) is taken to visualize the subscribers from each of the categorized segments (i.e. infrastructure stressing, medium, friendly).

 The conclusion of the thesis is that the functionality thus developed contributes to knowledge discovery from geospatial data and provides visual support for decision making to telecommunication operators.

Nowadays with the rapid development of cities, understanding the human mobility patterns of subscribers is crucial for urban planning and for network infrastructure deployment. Today mobile phones are electronic devices used for analyzing the mobility patterns of the subscribers in the network, because humans in their daily activities they carry mobile phones for communication purpose. For effective utilization of network infrastructure (NI) there is a need to study on mobility patterns of subscribers.

The aim of the thesis is to simulate the geospatial Telenor mobility data (i.e. three different subscriber categorized segments) and provide a visual support in google maps using google maps API, which helps in decision making to the telecommunication operators for effective utilization of network infrastructure (NI).

 In this thesis there are two major objectives. Firstly, categorize the given geospatial telenor mobility data using subscriber mobility algorithm. Secondly, providing a visual support for the obtained categorized geospatial telenor mobility data in google maps using a geovisualization simulation tool.

 The algorithm used to categorize the given geospatial telenor mobility data is subscriber mobility algorithm. Where this subscriber mobility algorithm categorizes the subscribers into three different segments (i.e. infrastructure stressing, medium, friendly). For validation and confirmation purpose of subscriber mobility algorithm a tetris optimization model is used. To give visual support for each categorized segments a simulation tool is developed and it displays the visualization results in google maps using Google Maps API.

The result of this thesis are presented to the above formulated objectives. By using subscriber mobility algorithm and tetris optimization model to a geospatial data set of 33,045 subscribers only 1400 subscribers are found as infrastructure stressing subscribers. To look informative, a small region (i.e. boras region) is taken to visualize the subscribers from each of the categorized segments (i.e. infrastructure stressing, medium, friendly).

 The conclusion of the thesis is that the functionality thus developed contributes to knowledge discovery from geospatial data and provides visual support for decision making to telecommunication operators.