Feature Papers for Land Innovations—Data and Machine Learning II

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: 28 May 2024 | Viewed by 1371

Special Issue Editor


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Guest Editor
Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, 215 Glenbrook Rd., Unit 4148, Storrs, CT 06269, USA
Interests: geographical information science and systems; cyberinfrastructure; land use and land cover; spatial data analysis
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Special Issue Information

Dear Colleagues,

This Special Issue “Feature Papers for Land Innovations—Data and Machine Learning II” welcomes contributions relating to spatial data science for obtaining, processing, analyzing, harnessing, and visualizing social, economic, environmental, and other data related to land. Particularly, it welcomes geospatial artificial intelligence and machine learning techniques for dealing with spatial big data, including remotely sensed data and social media data. Manuscripts can be theoretical, applied, or review articles. Interdisciplinary manuscripts are particularly welcome.

Prof. Dr. Chuanrong Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spatial big data 
  • land use and land cover
  • geospatial artificial intelligence
  • machine learning
  • deep learning
  • big data processing
  • big data analysis
  • big data visualization

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Published Papers (1 paper)

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Research

21 pages, 326 KiB  
Article
Effects of Big Data on PM2.5: A Study Based on Double Machine Learning
by Xinyu Wei, Mingwang Cheng, Kaifeng Duan and Xiangxing Kong
Land 2024, 13(3), 327; https://doi.org/10.3390/land13030327 - 04 Mar 2024
Viewed by 820
Abstract
The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This [...] Read more.
The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This study employs a double machine learning model to explore the impact of big data development on the PM2.5 concentration in 277 prefecture-level cities across China. This analysis is grounded in the quasi-natural experiment named the National Big Data Comprehensive Pilot Zone. The findings reveal a significant inverse relationship between big data development and PM2.5 levels, with a correlation coefficient of −0.0149, a result consistently supported by various robustness checks. Further mechanism analyses elucidate that big data development markedly diminishes PM2.5 levels through the avenues of enhanced urban development and land use planning. The examination of heterogeneity underscores big data’s suppressive effect on PM2.5 levels across central, eastern, and western regions, as well as in both resource-dependent and non-resource-dependent cities, albeit with varying degrees of significance. This study offers policy recommendations for the formulation and execution of big data policies, emphasizing the importance of acknowledging local variances and the structural nuances of urban economies. Full article
(This article belongs to the Special Issue Feature Papers for Land Innovations—Data and Machine Learning II)
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