{"title":"A global polycenter identification method with single‐source data: The integration of local multisource data recognition","authors":"Yichen Ruan, Xiaoyi Zhang, Qiuxiao Chen, Mingyu Zhang","doi":"10.1111/tgis.13211","DOIUrl":null,"url":null,"abstract":"With the widespread application of multisource data, the identification of urban polycenters faces the challenge of increasing data costs. This study developed a cost‐effective model for identifying urban polycenters by employing a combination of the Random Forest algorithm and Local Moran's <jats:italic>I</jats:italic> index. Using point‐of‐interest data from Amap, our model was benchmarked against a multisource data model to verify its effectiveness and accuracy. The results indicate that the single‐source model possesses an accuracy comparable to that of the multisource model in determining the centrality and spatial distribution of urban centers, thus offering a substantial capability to reduce reliance on multisource data. The random forest method exhibits a significant accuracy advantage over traditional ordinary least squares regression methods. However, it also exhibited susceptibility to overfitting and variations in data sampling. This suggests that while the model is highly effective for large‐scale urban studies, it requires careful handling of data inputs. This model can be applied to actual urban planning and research, providing a useful instrument for investigating urban polycentric structures at different spatial scales. This will increase the usefulness of the model in real‐world scenarios and lower the expenses related to analyzing urban data.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"16 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions in GIS","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1111/tgis.13211","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 0
Abstract
With the widespread application of multisource data, the identification of urban polycenters faces the challenge of increasing data costs. This study developed a cost‐effective model for identifying urban polycenters by employing a combination of the Random Forest algorithm and Local Moran's I index. Using point‐of‐interest data from Amap, our model was benchmarked against a multisource data model to verify its effectiveness and accuracy. The results indicate that the single‐source model possesses an accuracy comparable to that of the multisource model in determining the centrality and spatial distribution of urban centers, thus offering a substantial capability to reduce reliance on multisource data. The random forest method exhibits a significant accuracy advantage over traditional ordinary least squares regression methods. However, it also exhibited susceptibility to overfitting and variations in data sampling. This suggests that while the model is highly effective for large‐scale urban studies, it requires careful handling of data inputs. This model can be applied to actual urban planning and research, providing a useful instrument for investigating urban polycentric structures at different spatial scales. This will increase the usefulness of the model in real‐world scenarios and lower the expenses related to analyzing urban data.
随着多源数据的广泛应用,城市多中心的识别面临着数据成本增加的挑战。本研究采用随机森林算法和本地莫兰 I 指数相结合的方法,开发了一种经济高效的城市多中心识别模型。利用 Amap 的兴趣点数据,我们的模型与多源数据模型进行了基准测试,以验证其有效性和准确性。结果表明,单源模型在确定城市中心的中心性和空间分布方面具有与多源模型相当的准确性,从而大大减少了对多源数据的依赖。与传统的普通最小二乘回归方法相比,随机森林方法在准确性方面具有显著优势。不过,它也表现出易受过度拟合和数据采样变化的影响。这表明,虽然该模型在大规模城市研究中非常有效,但需要谨慎处理数据输入。该模型可应用于实际的城市规划和研究,为研究不同空间尺度的城市多中心结构提供有用的工具。这将提高模型在现实世界场景中的实用性,并降低与分析城市数据相关的费用。
期刊介绍:
Transactions in GIS is an international journal which provides a forum for high quality, original research articles, review articles, short notes and book reviews that focus on: - practical and theoretical issues influencing the development of GIS - the collection, analysis, modelling, interpretation and display of spatial data within GIS - the connections between GIS and related technologies - new GIS applications which help to solve problems affecting the natural or built environments, or business