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This research proposes a method for capturing “relatedness between geographical entities” based on the co-occurrences of their names on web pages. The basic assumption is that a higher count of co-occurrences of two geographical places implies a stronger relatedness between them. The spatial structure of China at the provincial level is explored from the co-occurrences of two provincial units in one document, extracted by a web information retrieval engine. Analysis on the co-occurrences and topological distances between all pairs of provinces indicates that: (1) spatially close provinces generally have similar co-occurrence patterns; (2) the frequency of co-occurrences exhibits a power law distance decay effect with the exponent of 0.2; and (3) the co-occurrence matrix can be used to capture the similarity/linkage between neighboring provinces and fed into a regionalization method to examine the spatial organization of China. The proposed method provides a promising approach to extracting valuable geographical information from massive web pages.

This study adopts a near real-time space-time cube approach to portray a dynamic urban air pollution scenario across space and time. Originating from time geography, space-time cubes provide an approach to integrate spatial and temporal air pollution information into a 3D space. The base of the cube represents the variation of air pollution in a 2D geographical space while the height represents time. This way, the changes of pollution over time can be described by the different component layers of the cube from the base up. The diurnal ambient ozone (O3) pollution in Houston, Texas is modeled in this study using the space-time air pollution cube. Two methods, land use regression (LUR) modeling and spatial interpolation, were applied to build the hourly component layers for the air pollution cube. It was found that the LUR modeling performed better than the spatial interpolation in predicting air pollution level. With the availability of real-time air pollution data, this approach can be extended to produce real-time air pollution cube is for more accurate air pollution measurement across space and time, which can provide important support to studies in epidemiology, health geography, and environmental regulation.

Multi-scale effects of spatial autocorrelation may be present in datasets. Given the importance of detecting local non-stationarity in many theoretical as well as applied studies, it is necessary to “remove” the impact of large-scale autocorrelation before common techniques for local pattern analysis are applied. It is proposed in this paper to employ the regionalized range to define spatially varying sub-regions within which the impact of large-scale autocorrelation is minimized and the local patterns can be investigated. A case study is conducted on crime data to detect crime hot spots and cold spots in San Antonio, Texas. The results confirm the necessity of treating the non-stationarity of large-scale spatial autocorrelation prior to any action aiming at detecting local autocorrelation.