Twitter text-based geotagging often uses geospatial words to determine locations. While much work has been done in word geospatiality analysis, there has been little work on temporal variations in the geospatial spread of word usage. In this paper, we investigate geospatial words relative to their temporal locality patterns by fitting periodical models over time. The model jointly captures inherent geospatial locality and periodical factor for a word. The resultant factorisation enables better understanding of word temporal trends and improves geotagging accuracy by only using inherent geospatial local words.