Urban Spatial Feature Recognition And Medical Facility Layout Optimization Based On Multi-source Data

As we enter the "14th Five-Year Plan" period,optimizing the layout of public service facilities to help address the issue of unbalanced and inadequate urban development in China has become a hot topic in urban planning research.In recent years,the widespread use of intelligent mobile devices has enabled the storage of massive amounts of human activity data,resulting in structurally diverse multi-source big data.By using machine learning and spatial statistical analysis to extract information from multi-source big data,optimizing the design of public service facility layout becomes an effective means.This thesis combines multiple data sources,including urban remote sensing image data,Point of Interest(POI)data,and demographic data,to analyze and study urban functional areas,facility service accessibility,and facility location recommendations from the perspective of urban spatial feature recognition,achieving more precise recognition and comprehensive optimization of the spatial layout of urban public service facilities.The main work includes the following three aspects:Firstly,based on the Mask R-CNN algorithm and sample density method,taking Lanzhou’s main urban area as an example,this article mines the natural features of remote sensing image data and the humanistic features of POI data to identify building outlines and relevant functional information,dividing the urban functional areas and identifying their functional types.Compared with the identification of functional areas using a single perspective and data,this method achieves more accurate and objective results,better fitting the urban reality.Secondly,using an improved two-step Floating Catchment method,this article measures the spatial accessibility of medical service facilities in Lanzhou’s main urban area.By using data from the seventh national census,POI data,and statistical yearbooks,three levels of medical service facilities are defined,and service radii are constructed to calculate service distances from the supply and demand perspectives.This method aims to more accurately reflect the current spatial accessibility of medical facilities in the research area,and it better fits the service mode of medical facilities.The results show that the overall spatial accessibility of medical facilities in the research area is good,but there are still some areas where the distribution of facilities is uneven.Finally,by using the classification function of the random forest algorithm,this thesis analyzes the distribution of medical service facilities in Lanzhou’s main urban area and makes location recommendations.By introducing machine learning algorithms and extracting the spatial distribution characteristics of medical facilities from POI data,this method predicts the recommendation degree of medical facility distribution in different regions,reducing the subjectivity of the location recommendation model.The results show that the regions with high location recommendation degrees are mostly located in the suburbs of the city,which is consistent with the current situation.After considering the comprehensive spatial distribution characteristics of medical facilities in Lanzhou’s main urban area,an optimized layout plan for medical facilities in Lanzhou’s main urban area is proposed.