Mixed-use urban land parcels identification integrating geospatial data and machine learning

Qu, Shijin; Wang, Hui; Hu, Zongnan; Wang, Zhenzhen; Hu, Shougeng

doi:10.1080/10095020.2024.2374996

Geo-spatial Information Science

2024

DOI: 10.1080/10095020.2024.2374996

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Mixed-use urban land parcels identification integrating geospatial data and machine learning

Shijin Qu,

Hui Wang,

Zongnan Hu

et al.

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Cited by 2 publications

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Identifying vehicle types from trajectory data based on spatial-semantic information

Zhang,

Xie,

Shi

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Geo-spatial Information Science

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Identifying vehicle types from trajectory data based on spatial-semantic information

Zhang,

Xie,

Shi

et al. 2024

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The Impact of Built Environment on Mixed Land Use: Evidence from Xi’an

Li,

Chen,

Zhao

et al. 2024

Land

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Mixed land use is recognized as a crucial strategy for enhancing urban vitality and promoting urban renewal. While existing research has mainly focused on measuring mixed land use along single dimensions, there remains a notable gap in studies that explore multidimensional measurements and their impacts at the block scale. This study introduces a multidimensional mixed-degree index based on diverse data sources such as POI and land use status to comprehensively assess mixed land use levels at the block scale in Xi’an’s central urban area. Additionally, a multiple linear regression model is applied to analyze how the built environment influences mixed land use. Findings reveal that mixed land use at the block scale can be objectively evaluated across three dimensions: quantity, distance, and attribute. In Xi’an, mixed land use demonstrates a spatial distribution characterized by core agglomeration and concentric decline. The study highlights that block area and road network density significantly influence mixed land use, with block area negatively impacting it the most and road network density positively affecting it secondarily. These insights provide valuable guidance for optimizing land use practices and promoting high-quality urban development.

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Mixed-use urban land parcels identification integrating geospatial data and machine learning

Cited by 2 publications

References 58 publications

Identifying vehicle types from trajectory data based on spatial-semantic information

Identifying vehicle types from trajectory data based on spatial-semantic information

The Impact of Built Environment on Mixed Land Use: Evidence from Xi’an

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