Mapping Time-Invariant Geographic Variables

Research output: Non-textual formPerformance


In mapping dynamic geographic processes, the focus has been on the variables that change over time. Though largely neglected, revealing the relatively stable geographic context is equally important. In fact, in pattern recognition, identifying the time-invariant features is an essential task. Doing so in space-time mapping requires first a quantitative representation of geographic structure that influences the distribution of a specific geographic variable. Those aspects of the geographic structure that remain relatively unchanged through a specific period of time would be considered as time-variant geographic variables. We propose to use selective eigenvectors of the spatial weights matrix to characterize the geographic structure for a given landscape. And by applying linear mixed regression to space-time data, we can identify the common eigenvectors that are associated with the distribution of the dependent variable of interest over the entire studied period. In this paper, we will explain the methodology and demonstrate the resulting maps with real world data sets.
Original languageEnglish
StatePublished - Jul 7 2019
EventInternational Workshop on Geocomputation for Social Sciences and Intelligent Geospatial Information Service - Wuhan, China
Duration: Jul 7 2019Jul 7 2019


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