Estimating Nighttime and Daytime PopulationsUsing Space Syntax : A Case Study of the Greater Copenhagen Area

• Main Author: Zandi, Maryam
• Format: Others
• Language: English
• Published: Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad 2013
• Subjects: Space Syntax
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-16851

Space syntax uncovers hidden perspectives on geographic spaces and facilitates the study of the structure and form of spaces. Correlations of the human movement and space configurations are interesting observations revealed by space syntax. Much research demonstrates that urban configurations can affect the distribution of human flows in space and even form of land use patterns. The high correlation between the human movement and space structures can lead to the hypothesis that through this high correlation, it is possible to obtain information about a particular type of human activity and the number of people in a region. The present research investigates this possibility and tries to generate models for predicting the number of people who live in a region and the number of people who work in that region. The project takes a street network and calculates the space syntax’s measures and length of the streets. Based on regional boundaries in which the measures are located, sum, average, maximum and minimum of all measures are computed and assigned to the related regions. Next, correlations between them and nighttime (the number of people who live in the region) and daytime (the number of people who work in the region) populations are calculated. The significance test is run to check if the calculated correlations are real. From the significant correlations, the measures with high correlations are selected for the regression analyses and different regression models are generated. Finally the project selects the model which has 79% correspondence with the population counts as the result. The main application of this method is in Location-Based Services (LBS) which collect users’ trajectories via mobile positioning and communication technologies. However, hidden information in trajectories can be abused and can threaten the privacy and security of the users. Indeed this research is a preface for a new approach for trajectory anonymization. The method - based on the street network properties - counts the number of people that live in a region and work in another, to construct regions for the user such that the count is above a threshold then it cloaks the user’s trajectory within the constructed regions.