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Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. [ABSTRACT FROM AUTHOR]

The quality of street space has attracted attention. It is important to understand the needs of different population groups for street space quality, especially the rapidly growing elderly group. Improving the quality of street space is conducive to promoting the physical leisure activities of the elderly to benefit to their health. Therefore, it is important to evaluate street space quality for the elderly. The existing studies, on the one hand, are limited by the sample size of traditional survey data, which is hard to apply on a large scale; on the other hand, there is a lack of consideration for factors that reveal the quality of street space from the perspective of the elderly. This paper takes Guangzhou as an example to evaluate the quality of street space. First, the sample street images were scored by the elderly on a small scale; then the regression analysis was used to extract the street elements that the elderly care about. Last, the street elements were put into the random forest model to assess street space quality io a large scale. It was found that the green view rate and sidewalks are positively correlated with satisfaction, and the positive effect increases in that order. Roads, buildings, sky, vehicles, walls, ceilings, glass windows, runways, railings, and rocks are negatively correlated with satisfaction, and the negative effect increases in that order. The mean satisfaction score of the quality of street space for the elderly's recreational physical activities in three central districts of Guangzhou (Yuexiu, Liwan, and Haizhu) is 2.6, among which Xingang street gets the highest quality score (2.92), and Hailong street has the lowest quality score (2.32). These findings are useful for providing suggestions to governors and city designers for street space optimization. [ABSTRACT FROM AUTHOR]