During the past couple of years, the research group "Diverse Urban Spaces" has performed a thorough survey of how the Danish city of Aalborg is being used by a certain population group. This group consists of young people at age 17-23 years whose main occupation is high school or equivalent level of education. The core of the survey revolved around each participant (henceforth referred to as respondent) carrying a pocket-sized GPS receiver for a period of seven days. The GPS receiver would record the position of the respondent approximately every 8 th second. With a sample of 169 hand-picked respondents selected from a total group of 212 respondents, Diverse Urban Spaces gathered a vast amount of geodata which could be used to analyse popular city spaces, plazas and buildings as well as pointing out which roads and streets the group would use most frequently. However, the nature of everyday life involves a relatively high amount of immobility compared to movement. Attempting to analyse how this setup of respondents use the city becomes problematic when the gathered geodata consists of both the 1-2 hours each respondent spend on travelling through the city, and the remaining 22 hours of the day which the respondent would spend stationary at certain locations important to the respondent such as school, home address, work address etc. It would be much more convenient if the geodata could be divided into two datasets consisting of movement and stays respectively, so stay-related analyses such as pinpointing popular city squares wouldn't be affected by noise from movement data. This paper aims to describe the technique developed by Diverse Urban Spaces to counteract the above mentioned inconvenience by splitting the dataset into movement and stays. The paper will explain how the technique was commenced, how it works and its level of quality. ; During the past couple of years, the research group "Diverse Urban Spaces" has performed a thorough survey of how the Danish city of Aalborg is being used by a certain population group. This group consists of young people at age 17-23 years whose main occupation is high school or equivalent level of education. The core of the survey revolved around each participant (henceforth referred to as respondent) carrying a pocket-sized GPS receiver for a period of seven days. The GPS receiver would record the position of the respondent approximately every 8 th second. With a sample of 169 hand-picked respondents selected from a total group of 212 respondents, Diverse Urban Spaces gathered a vast amount of geodata which could be used to analyse popular city spaces, plazas and buildings as well as pointing out which roads and streets the group would use most frequently. However, the nature of everyday life involves a relatively high amount of immobility compared to movement. Attempting to analyse how this setup of respondents use the city becomes problematic when the gathered geodata consists of both the 1-2 hours each respondent spend on travelling through the city, and the remaining 22 hours of the day which the respondent would spend stationary at certain locations important to the respondent such as school, home address, work address etc. It would be much more convenient if the geodata could be divided into two datasets consisting of movement and stays respectively, so stay-related analyses such as pinpointing popular city squares wouldn't be affected by noise from movement data. This paper aims to describe the technique developed by Diverse Urban Spaces to counteract the above mentioned inconvenience by splitting the dataset into movement and stays. The paper will explain how the technique was commenced, how it works and its level of quality.