街景精细尺度下城市经济发展预测及有效性评估

Prediction and effectiveness evaluation of urban economic development on fine scale of streetscape

随着城市化进程加快,传统大尺度遥感影像数据和经济统计方法无法从精细尺度刻画城市经济发展动态。街景影像可以反映城市建成环境物质空间特征,基于此可以在更精细尺度上对经济发展指标预测。本文提出一种改进的Deeplabv3_MEP街景影像语义分割模型,提取街景要素占比,采用图神经网络与卷积神经网络分别以街景要素指标和街景影像作为输入,预测经济指标;并利用XGBoost模型分析经济指标驱动因素,计算碳汇并构建Lasso回归模型评估行政区绿色经济发展有效性。结果发现:①在市级区划尺度上,济南市经济指标呈现向市中心高度集中、向外围逐渐降低趋势;②在区级区划尺度上,历下区经济发展水平最高,东部零星地区发展水平极高,其余地区较低;③在街道级区划尺度上,越靠近区或县中心街道,居民平均收入水平越高,越靠近市中心街道,居民平均收入水平更高;④驱动因素方面,围墙、天空、道路、建筑与汽车等要素对经济发展指标贡献较大,电线杆、摩托车等要素对经济发展贡献较小,公交车要素最低;⑤济南市存在绿色经济发展指数与经济发展水平不匹配现象。本文的研究方法不仅为精细尺度城市经济发展预测提供了研究框架的借鉴,也为理解城市绿色经济发展效能提供了新视角,有助于城市规划和可持续发展政策的制定。

With the acceleration of urbanization,traditional large-scale remote sensing image and economic statistical methods cannot accurately depict the dynamics of urban economic development from a fine scale.Streetscape images can reflect the material spatial features of the urban built environment.And based on this,economic development indicator(EDI)can be predicted at a finer scale.The study proposes an improved Deeplabv3_MEP semantic segmentation model for streetscape images to extract the percentage of streetscape elements.Then,graph neural network(GCN)and convolutional neural network(CNN)are used separately with streetscape factor index and streetscape images as inputs to predict EDI.And the XGBoost model is used to analyze the driving factors of EDI.The carbon sinks are calculated and a Lasso regression model is constructed to evaluate the effectiveness of green economic development in administrative regions.The results show that:(1)At the city-level division scale,the economic indicator of Jinan city shows a trend of high concentration towards the city center and a gradual decrease towards the outskirts.(2)At the district-level division scale,Lixia district has the highest level of economic development.And the development level in sporadic areas in the east is very high,while that in other areas is lower.(3)At the streetlevel division scale,the closer the street is to the district or county center,the higher the average income level of residents,and the closer the street is to the city center,the higher the average income level of residents.(4)Driving factors such as wall,sky,road,and car,contribute more to EDI,while factors such as pole and motorcycle contribute less,with bus being the lowest.(5)There is a phenomenon of mismatch between the green economic development index and the level of economic development.