Spatial-Textal Correlation Analysis Based on Crowdsource Geospatial Data

Crowdsource geospatial data is one kind of open geographic data collected by the public. A wealth of spatial information and knowledge are hidden in crowdsource geospatial data. Check-in data is one of the representative crowdsource geospatial data. Most existing work on spatial-textual objects, such as evaluating the similarity of two spatial-textual objects in spatial keyword query, considers that spatial similarity and text similarity are independent of each other. According to the first law of geography: Everything is connected; the closer two objects are, the stronger their connection is. We explore the correlation between spatial information and textual information in the real check-in data scrawled from the location based social networks. After data preprocess and geographic mapping, we computed the textual attribute values in each region. Then, we use the exploratory spatial analysis to analyze the global spatial autocorrelation and local spatial autocorrelation in different the spatial scales, that is different states in United States and the two cities such as New York and Los Angeles, respectively. The results show that different textual attributes in different regions have different global spatial autocorrelation; the results obtained from the local autocorrelation analysis show the phenomenon that the textual attributes get together. Both the above results provide the basis for research on the assumption that "the texts are similar in the near space". Furthermore, the conclusion can help departments or enterprises to make reasonable decisions.