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We are now in an information age. The ubiquitous smartphones and location-aware technologies generate vast amounts of geotagged big data on peoples' spatial activities and movement trajectories. With these geotagged big data, researchers can include population movement into socio-spatial segregation assessment in finer spatial and temporal scales. However, further study is still needed to apply the patterns and structural characteristics of population mobility to research on socio-spatial segregation assessment. Therefore, this research aims to understand and quantify the socio-spatial segregation from activity spaces and human mobility perspective using Volunteered Geographic Information (VGI). I designed a comprehensive analytical framework to evaluate and analyze socio-spatial segregation using human mobility information obtained from VGI. It includes collecting VGI data, classifying VGI users, extracting mobility information from VGI, constructing and analyzing interaction networks, evaluating dynamic socio-spatial segregation, representativeness analysis of VGI, and results’ visualization and mapping. This dissertation focuses on analyzing mobility patterns for different VGI user groups, evaluating dynamic socio-spatial segregation, and representativeness analysis of VGI.