Abstract: Objective In the big data era,massive volumes of individual-level movements,extracted from variousgeospatial data,including mobile phone data,public transportation card records,social media check-indata,taxi trajectories,and bank card records,are available for scholars in different fields to study hu-man mobility patterns.These studies enrich spatio-temporal analysis methods in GIS and provide anew perspective to human-environment interactions.Observed human mobility patterns and modelscan be applied to many applications such as transportation and public health.This paper presents a ge-neric workflow for big-data-driven human mobility analyses and summaries major movement meas-ures.By comparing a number of models used to interpret and reproduce the observed pattern,this pa-per emphasizes the geographical impact on human mobility patterns.