With the coming era of big data and the rapid development and widespread applications of Geographical Information Systems (GISs), geocoding technology is playing an increasingly important role in bridging the gap between non-spatial data resources and spatial data in various fields. However, Chinese geocoding faces great challenges because of the complexity of the address string format in Chinese, which contains no delimiters between Chinese words, and the poor address management resulting from the existence of multiple address authorities spread among different governmental agencies. This paper presents a geocoding service based on an optimized Chinese address matching method, including address modeling, address standardization and address matching. The address model focuses on the spatial semantics of each address element, and the address standardization process is based on an address tree model. A geocoding service application is implemented in practice using a large quantity of data from Shenzhen Municipality. More than 1,460,000 data records were used to test the geocoding service, and good matching rates were achieved with good adaptability and intelligence.

https://www.mdpi.com/2220-9964/5/5/65/pdf

Using an Optimized Chinese Address Matching Method to Develop a Geocoding Service: A Case Study of Shenzhen, China

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.

https://www.mdpi.com/2220-9964/9/11/635/pdf

Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation

Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a la...

A deep learning architecture for semantic address matching

Address standardization using the natural language process for improving geocoding results

Big enterprise registration data imputation: Supporting spatiotemporal analysis of industries in China

Address is a spatial location encoding method of individual geographical area.In China,address planning is relatively backward due to the rapid development of the city,resulting in the presence of large number of non-standard address.The space constrain relationship of standard address model is analyzed in this paper and a new method of standard address extraction based on the tree model is proposed,which regards topological relationship as consistent criteria of space constraints.With this method,standard address can be extracted and errors can be excluded from non-standard address.Results indicate that higher math rate can be obtained with this method.

Du Qingyun

Papers

A New Method of Chinese Address Extraction Based on Address Tree Model