The Identification and Use Efficiency Evaluation of Urban Industrial Land Based on Multi-Source Data

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.

Can China’s Resource-Saving and Environmentally Friendly Society Really Improve the Efficiency of Industrial Land Use?

“…Chinese and foreign scholars have conducted wide-ranging research on the ILUE, including evaluation methods, regional differences in the ILUE, and factors affecting the ILUE [50][51][52][53][54][55][56][57][58][59][60]. Evaluation methods for the ILUE need to be quantitative.…”

“…They also measured the technical efficiency in calculating the ILUE without undesirable outputs. Regional differences in the ILUE have mainly been studied from the perspective of different countries, different provinces, and at different levels of urban economic development [55][56][57][58][59][60]. In recent years, many Chinese scholars have studied regional differences in the ILUE in China's eastern, central, and western regions, and prefecture-level cities [57][58][59].…”

“…Regional differences in the ILUE have mainly been studied from the perspective of different countries, different provinces, and at different levels of urban economic development [55][56][57][58][59][60]. In recent years, many Chinese scholars have studied regional differences in the ILUE in China's eastern, central, and western regions, and prefecture-level cities [57][58][59]. Factors that affect the ILUE from a micro-perspective include the type of industrial enterprise, intensity of industrial land use, scale of enterprises, geographical location, regional characteristics of mineral resources and infrastructure, and degree of openness [55][56][57][58][59][60].…”

“…In recent years, many Chinese scholars have studied regional differences in the ILUE in China's eastern, central, and western regions, and prefecture-level cities [57][58][59]. Factors that affect the ILUE from a micro-perspective include the type of industrial enterprise, intensity of industrial land use, scale of enterprises, geographical location, regional characteristics of mineral resources and infrastructure, and degree of openness [55][56][57][58][59][60]. Moreover, scholars have studied the impact on the ILUE at a macro level of land property rights, urban construction, social and economic factors, and the government's industrial land supply [55][56][57][58][59][60][61][62].…”

A Generalization of Ripley's K Function for the Detection of Spatial Clustering in Areal Data

“…However, the lack of cluster detection methods designed specifically for areal data have caused many researchers to use Ripley's K function on areal data, typically using the centroids and computing Ripley's K function using the resulting set of points. For example, a number of researchers have attempted to assess spatial patterns in land parcel data via Ripley's K function or Ripley's L function (a scaled version of Ripley's K function) (Lee and Lee, 2013;Siordia, 2013;Zipp et al, 2017;Qiao et al, 2019). Other researchers have taken public health data associated with a geographical region such as a city or health division and computed Ripley's K function using the centroids of these larger geographical regions (Wade, 2014;Karunaweera et al, 2020;Skog et al, 2014).…”