Redai dili (Aug 2024)
A Comparative Study on the Type Recognition and Spatial Organization Characteristics of Sea-Related Enterprises Based on Machine Learning in Cities
Abstract
Maritime Power has gradually increased as a national strategy. In this process, gross marine products continue to grow, and the marine industry has become the most fundamental and critical object. The spatial layout and industrial organization of maritime enterprises are fundamental related tasks. Domestic research can be divided into two main categories, based on the data used. One is to use economic and social statistical data, which have a large spatial scope but large granularity and cannot reflect the details of the industrial layout. The other is to use point-of-interest data, which are often not fully mined owing to the heavy workload of data processing. Therefore, there is little relevant content on departmental and urban comparisons in the existing research. Four representative cities-Dalian, Qingdao, Ningbo, and Xiamen-were selected as the research areas. According to the Industrial Classification for Ocean Industries and Their Related Activities, the research objects were identified as the marine core layer, marine support layer, and marine peripheral layer industries and further refined into eight subcategories. This study is based on the information of maritime enterprises registered for business registration, and uses Python to crawl geographic coordinates to improve the spatial information of enterprises. An innovative task is to identify the industry categories of enterprises. This task was performed using fastText, Convolutional Neural Networks, and Recurrent Neural Network. Thus, a spatial enterprise information database, including multiple marine industry departments, was established. Kernel density analysis, standard deviational ellipse analysis, buffer analysis, and other methods were used. Finally, by comparing the visualization results of the marine industrial spatial layout in the four cities, we delved into the marine industrial spatial differentiation patterns. In the experiment, machine learning models, such as artificial neural networks, exhibited high accuracy and recall when completing human recognition tasks, and these methods were effective. Empirical research on the spatial layout and industrial organization of maritime enterprises revealed the following: 1)From the perspective of spatial pattern, the overall pattern is a balanced pattern of "large dispersion and small agglomeration." By comparing the distribution and organization of different types of marine industries, we found that there is industry agglomeration in the location selection of enterprises, resulting in industry agglomeration characteristics. The land sea relationship is reflected in the high-density single peak or "coastal zone-city center" Multimodal distribution pattern. 2) From the perspective of spatial organization mode, industrial clusters have multilevel hierarchical characteristics corresponding to population size and administrative levels. In addition to single core structures, multi core structures generally exhibit a "primary-secondary dual core" or "primary core-multiple radial" pattern, with spatial connections between core intervals forming a multi node axis or network structure. 3) From the perspective of spatial matching relationships, the elliptical area is positively related to the urban area, the direction of the long axis is close to the coastal direction, and the industrial distribution has a clear matching relationship with the urban center, ports, and other transportation hubs, bay terrain, coastline, and other spatial elements.
Keywords
