Peking University

Cognition, analysis and computation of huge volume of spatial-temporal data

The advent of Big Data Era and the emergence of various and massive spatial-temporal data bring the new challenges and chances to the research of geographic information science. Considering that humans are the center of geography study, we have addressed the cognitive representation, behavior analysis, and high-performance computation of spatial-temporal data from the views of both human cognition process and spatial behaviors. For the cognitive representation, we proposed the formal theoretical models and methods, which is consistent with the human cognition process, to reduce the gap between the geometric representations and geographic semantics. Special attention were focused on fuzziness in the representation of spatial features and spatial relations. For the human behavior analysis, we quantitatively measured the correlations between human mobility patterns and geographic environment, and explored several essential issues in this field, including distance-decay effects, spatial heterogeneity, spatial autocorrelation and scale effects. For the high-performance geocomputation, we addressed the strategies for distributed storage and parallel processing of huge amount of spatial-temporal data, and provided the platforms for the cognition and analysis of spatial-temporal big data. Up to now, we have published 85 papers indexed by SCI/SSCI, and among which 12 were published on the leading GIS journal (i.e., IJGIS). These papers received more than 450 citations in ISI database, and two achievements were cited in the classical textbook in GIScience (Geographical Information Science and Systems). We also published two books, and won one second prize for scientific and technological progress in Surveying and Mapping.