It is meaningful to analyze urban spatial structure by identifying urban subcenters, and many methods of doing so have been proposed in the published literature. Although these methods are widely applied, they exhibit obvious shortcomings that limit their further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we propose the density contour tree (DCT) method for detecting urban polycentric structures and their spatial distributions. Conceptually, this method is based on an analogy between urban spatial structure and terrain. The point-of-interest (POI) density is visualized as a continuous mathematical surface representing the urban terrain. Peaks represent the regions of the most frequent human activity, valleys represent regions with small population densities in the city, and slopes represent spatial changes in urban land-use intensity. Using this method, we have detected the urban “polycentric” structure of Beijing and determined the corresponding spatial relationships. In addition, several important properties of the urban centers have been identified. For example, Beijing has a typical urban polycentric structure with an urban center area accounting for 5.9% of the total urban area, and most of the urban centers in Beijing serve comprehensive functions. In general, the method and the results can serve as references for the later research on analyzing urban structure.

Chinese address element segmentation is a basic and key step in geocoding technology, and the segmentation results directly affect the accuracy and certainty of geocoding. However, due to the lack of obvious word boundaries in Chinese text, the grammatical and semantic features of Chinese text are complicated. Coupled with the diversity and complexity in Chinese address expressions, the segmentation of Chinese address elements is a substantial challenge. Therefore, this paper proposes a method of Chinese address element segmentation based on a bidirectional gated recurrent unit (Bi-GRU) neural network. This method uses the Bi-GRU neural network to generate tag features based on Chinese word segmentation and then uses the Viterbi algorithm to perform tag inference to achieve the segmentation of Chinese address elements. The neural network model is trained and verified based on the point of interest (POI) address data and partial directory data from the Baidu map of Beijing. The results show that the method is superior to previous neural network models in terms of segmentation performance and efficiency.

Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.

Point of interest (POI) matching finds POI pairs that refer to the same real-world entity, which is the core issue in geospatial data integration. To address the low accuracy of geospatial entity matching using a single feature attribute, this study proposes a method that combines the D–S (Dempster–Shafer) evidence theory and a multiattribute matching strategy. During POI data preprocessing, this method calculates the spatial similarity, name similarity, address similarity, and category similarity between pairs from different geospatial datasets, using the multiattribute matching strategy. The similarity calculation results of these four types of feature attributes were used as independent evidence to construct the basic probability distribution. A multiattribute model was separately constructed using the improved combination rule of the D–S evidence theory, and a series of decision thresholds were set to give the final entity matching results. We tested our method with a dataset containing Baidu POIs and Gaode POIs from Beijing. The results showed the following—(1) the multiattribute matching model based on improved DS evidence theory had good performance in terms of precision, recall, and F1 for entity-matching from different datasets; (2) among all models, the model combining the spatial, name, and category (SNC) attributes obtained the best performance in the POI entity matching process; and (3) the method could effectively address the low precision of entity matching using a single feature attribute.

Understanding the balance between the supply and demand of leisure services (LSs) in urban areas can benefit urban spatial planning and improve the quality of life of residents. In cities in developing countries, the pursuit of rapid economic growth has ignored residents’ demand for LSs, thereby leading to a high demand for and short supply of these services. However, due to the lack of relevant research data, few studies have focused on the spatial mismatch in the supply and demand of LSs in urban areas. As typical representatives of multisource geographic data, social sensing data are readily available at various temporal and spatial scales, thus making social sensing data ideal for quantitative urban research. The objectives of this study are to use openly accessible datasets to explore the spatial pattern of the supply and demand of LSs in urban areas and then to depict the relationship between the supply and demand by using correlation analysis. Therefore, taking Beijing, China, as an example, the LS supply index (SI) and societal needs index (SNI) are proposed based on open data to reflect the supply and demand of LSs. The results show that the spatial distribution of the LS supply and demand in Beijing varies with a concentric pattern from the urban center to suburban areas. There is a strong correlation between the supply and demand of commercial and multifunctional services in Chaoyang, Fengtai, Haidian and Shijingshan, but there is no obvious correlation between the supply and demand of ecological and cultural services in Beijing. Especially in Dongcheng and Xicheng, there is no obvious correlation between the supply and demand of all services. The proposed approach provides an effective urban LS supply and demand evaluation method. In addition, the research results can provide a reference for the construction of “happy cities” in China.