Multi-criteria Estimation Method for Scale of Volunteered Vector Maps

Objectives The data scale is one of the key factors to be considered when integrating multi-source spatial data. Especially, when volunteered vector map data is involved in the multi-source spatial data integration, due to the lack of data scale information, it may lead to data inconsistency issues. This seriously affects the quality and applicability of the fused map data. How to automatically calculate the scale of the volunteer vector data is the key to improving the quality and application value of the fusion of volunteer vector data.

Methods An automatic scale estimation method for vector maps based on the elimination and choice translating reality with triangle interaction (Electre-tri) multi-criteria decision model is proposed. This method mainly consists of three parts. First, by analyzing the scale evaluation parameters of geographic spatial data, we select eight typical spatial scale evaluation parameters, which include the shortest straight line segment length, the minimum bending area, the minimum size, the median side length, the vertex density, the number of spatial targets, the type of map elements, and the data capture source. And we also establish the mapping relationship between these evaluation parameters and the scale of vector maps. Then, we develop an Electre-tri multi-criteria decision-making model by combing the characteristics of the volunteer vector maps. Based on the decision model, we achieve the correlation calculation between the vector map data to be evaluated and the reference sample map data by dynamically setting three types of thresholds that are preference threshold, indifference threshold, and rejection threshold. Finally, by leveraging the correlation with the reference sample map data, the scale of the volunteer vector map can be estimated according to different strategy criteria methods.

Results To verify the effectiveness of the proposed method, we select ten types of boundary scales of OpenStreetMap data in Beijing city, China as an example. The results indicate that the proposed method has strong practicability in estimating the scale of volunteer vector maps, with high operability of model parameters.

Conclusions The proposed method provides an operable solution for automated scale estimation in volunteered vector maps, effectively supporting multi-source spatial data fusion by reducing scale-induced inconsistencies.