Using Spatial Semantics and Interactions to Identify Urban Functional Regions

The spatial structures of cities have changed dramatically with rapid socio-economic development in ways that are not well understood. To support urban structural analysis and rational planning, we propose a framework to identify urban functional regions and quantitatively explore the intensity of the interactions between them, thus increasing the understanding of urban structures. A method for the identification of functional regions via spatial semantics is proposed, which involves two steps: (1) the study area is classified into three types of functional regions using taxi origin/destination (O/D) flows; and (2) the spatial semantics for the three types of functional regions are demonstrated based on point-of-interest (POI) categories. To validate the existence of urban functional regions, we explored the intensity of interactions quantitatively between them. A case study using POI data and taxi trajectory data from Beijing validates the proposed framework. The results show that the proposed framework can be used to identify urban functional regions and promotes an enhanced understanding of urban structures

Rapid estimation of an earthquake impact area using a spatial logistic growth model based on social media data

Rapid estimates of impact areas following large earthquakes constitute the cornerstone of emergency response scenarios. However, collecting information through traditional practices usually requires a large amount of manpower and material resources, slowing the response time. Social media has emerged as a source of real-time ‘citizen-sensor data’ for disasters and can thus contribute to the rapid acquisition of disaster information. This paper proposes an approach to quickly estimate the impact area following a large earthquake via social media. Specifically, a spatial logistic growth model (SLGM) is proposed to describe the spatial growth of citizen-sensor data influenced by the earthquake impact strength after an earthquake; a framework is then developed to estimate the earthquake impact area by combining social media data and other auxiliary data based on the SLGM. The reliability of our approach is demonstrated in two earthquake cases by comparing the detected areas with official intensity maps, and the time sensitivity of the social media data in the SLGM is discussed. The results illustrate that our approach can effectively estimate the earthquake impact area. We verify the external validity of our model across other earthquake events and provide further insights into extracting more valuable earthquake information using social media

Qualitative spatial reasoning on topological relations by combining the semantic web and constraint satisfaction

Qualitative spatial reasoning on topological relations can extract hidden spatial knowledge from qualitatively described topological information, which is of significant importance for decision-making and query optimization in spatial analysis. Qualitative reasoning on spatial topological information based on semantic knowledge and reasoning rules is an efficient means of reducing both the known relations and the corresponding rules, which can result in enhanced reasoning performance. This paper proposes a qualitative reasoning method for spatial topological relations based on the semantic description of reasoning rules and constraint set. Combined with knowledge from the Semantic Web, the proposed method can easily extract potential spatial results consistent with both unique and non-unique rules. The Constraint-Satisfaction-based approach, describing constraint set with semantic expressions, is then used together with an improved path consistency algorithm to verify the consistency of the unique-rules-based and non-unique-rules-based reasoning results. The verification can eliminate certain reasoning results to ensure the reliability of the final results. Thus, the task of qualitative spatial reasoning on topological relations is completed