Point of Interest Matching between Different Geospatial Datasets

Abstract

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.

A System for Aligning Geographical Entities from Large Heterogeneous Sources

“…There are quite a few works on aligning geographical entities from heterogeneous sources. As described by Deng et al [6], the geographical entity alignment problem has been divided into three main areas. The first area focuses on the geometric or geospatial attributes of data [4].…”

“…More recently, ref. [6] proposed an approach that relies on the same attribute components, but additionally supported a measure for the address component. Overall, the label component made use of a normalized Levenshtein edit distance.…”

Different Sourcing Point of Interest Matching Method Considering Multiple Constraints

“…The first type is the complete semantic mapping of class nodes, and the semantic distance is 0. For the second type, the mapping relationships of the class nodes are determined through their parent nodes, and the semantic distance can be 1, 2 or 3 (Deng et al, 2019). For the third type, there are no complete mapping relationships, and the semantic distance is +∞.…”

“…Based on spatial distance attributes, Huang et al, (2018) applied a nonspatial attribute, i.e., name similarity, to enhance the fusion accuracy of POI data from different sources [15]. Li et al, (2016) and Deng et al, (2019) proposed POI matching methods that combined the similarities of multiple attributes and their corresponding appropriate weights and demonstrated that among the existing methods [18,19], the method integrating the spatial distance, name and class attained the best performance.…”

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

“…As a unified integrated organism, urban land should be designed based on different functions to meet the needs of working, recreation, commuting, and communication [3], mainly including residential areas, commercial areas, industrial areas, etc. These areas are designed by city planners and can be changed due to people's lifestyles [11]. Due to the different socioeconomic attributes of urban functional zones, their spatial distribution patterns exhibit differences.…”