Studying and mapping population mobility pattern in the city dynamically plays a vital role and has practical applications to urban planning and to plan better transport system. For long times the city authorities used traditional census and survey data to plan the city and allocate basic public facilities. Census and survey data are potentially weak to delineate the dynamic nature of the city as the data are static and generated within long time interval and decades. Former local researches in population distribution across the city were based on traditional data sources. Therefore to overcome the pitfalls of traditional data such as census and survey data we used the seven consecutive days Call detail record (CDR) data to analyze and map population mobility pattern. The massive CDR data that accounts 6,702,111 records was collected from Ethio telecom main data center. The data contained 564,721 and 543,691 distinct subscribers and devices. We used Jupyter notebook tool of (anaconda 3) environment of Python3.11 version. Arc map 3.10 had been used to develop a model. In order to identify the geographical location of cellular network tower and to reveal the call activity intensity of a certain area in a timely and spatial manner we used voronoi location algorithm. We also used K-mean clustering algorithm comparatively to justify the result found using voronoi algorithm. We carried out two major experiments in our work .In the first experiment we have used voronoi location algorithm technique. In the second experiment, we conducted K-mean clustering technique on the data set. Finally, we got satisfactory result as the two experiments yield the same result. In both experiments we have systematically identified cellular network towers in the study area that hosted high call activity intensity and low call activity intensity on both weekdays and weekends. It in turn enabled us to revealed the ambient (daytime) population concentration of areas of the city across time and space. Keywords: Population mobility, Pattern, Call detail records, K-mean clustering