Abstract
Urban industrial land (UIL), which is classified for industrial purposes, is an indispensable component of urban land. Obtaining up-to-date and timely UIL details from the industrial development perspective has practical significance for UIL planning. Therefore, we propose a practical method for integrating UIL identification and use efficiency evaluation at the parcel scale based on multi-source data. The Open Street Map (OSM) data were utilized to generate parcels, which served as basic analytical units. Point of Interest (POI) data combined with a Continuous Bag-of-Words (CBoW)-based Word2Vec model was utilized to acquire UIL information. The entropy-weight Technique for Order Preference by Similarity to Ideal Solution method, combined with economic and environmental UIL indicators obtained from remote sensing images, ground observation data, and statistical data, was used to evaluate UIL use efficiency, and the spatial distribution and utilization degree of UIL within Beijing’s fifth ring road was analyzed. The region within Beijing’s fifth ring road was classified into commercial land, industrial land, and other types, with an overall accuracy of 92.24%. With this method, we found that the distribution of UIL presented a ring structure developing outwards along the ring roads and contained concentrated commercial areas. UIL utilization exhibited a south–north differentiation, and industrial land had lower use efficiency. Our work fully utilized the available fine-scale multi-source data.
Highlights
China has been experiencing urban expansion at an unprecedented scale and rate, which can be attributed to rapid urbanization [1]
Considering the abovementioned issues, our study focuses on two objectives: first, we utilized timely and updatable Point of Interest (POI) data and a Word2Vec model to extract urban industrial land (UIL) types; we evaluated UIL use efficiency using the entropy-weight TOPSIS method by generating evaluation indicators from remote sensing data, ground observation data, and statistical data
UIL types in this study were classified according to the Chinese urban land use classification criterUiaIL(GtByp5e0s1i3n7–th2i0s11st)u, days swheorwe nclainssiTfiaebdleac1c. orCdoinngsidtoertihnegCthienresaeruerabafnewlanwdaruesheoculasessifwiciatthioinn Bceritjienrgia’s(fiGfBth5r0in1g37r–o2a0d1,1w),eacslasshsoifiwend winarTeahbolues1in. gCionntosiidnedriunsgtritahlelraenda.reUaILfceowntwaianreedhotwusoeps rwimitahriyn cBaeteijginogri’essf,icftohmrminegrcrioaaldla,nwdeacnldasisnidfiuesdtrwiaalrleahnodu, asnindgcionmtominerdcuiasltrlaianldlacnodn.siUstIeLdcoofnctoaminmedertcwiaol sperrivmicaersy, accactoemgomrioeds,atcioomn,menertecriatal inlamndenat,nadndinbduussintreiassl. land, and commercial land consisted of commercial services, accommodation, entertainment, and business
Summary
China has been experiencing urban expansion at an unprecedented scale and rate, which can be attributed to rapid urbanization [1] Several problems such as urban population expansion, unreasonable economic and industrial structures, and the inefficient utilization of land resources are becoming more prominent. Researchers have developed specific indicators to measure land use efficiency in China [26], which covers economic (e.g., the total assets investment) and environmental aspects (e.g., air quality and green rate) [23,27]. Considering the abovementioned issues, our study focuses on two objectives: first, we utilized timely and updatable POI data and a Word2Vec model to extract UIL types; we evaluated UIL use efficiency using the entropy-weight TOPSIS method by generating evaluation indicators from remote sensing data, ground observation data, and statistical data.
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