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Spatial Data and Technology Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1049

Special Issue Editors


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Guest Editor
Department of Geography, Texas State University, San Marcos, TX 78666, USA
Interests: GIScience; urban and regional analysis; health and medical geography; crime geography

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Guest Editor
Department of Geography and Environmental Studies, Toronto Metropolitan University, Toronto, ON, Canada
Interests: GIS; spatial analysis

Special Issue Information

Dear Colleagues,

Spatial science and technology are pivotal for detailed analysis, enabling a comprehensive understanding of how geographic and environmental factors influence various outcomes. By utilizing Geographic Information Systems (GISs), remote sensing (RS), and other spatial data technologies, professionals can identify and visualize disparities across different regions, track trends, and assess environmental exposures such as pollution and access to amenities. This spatially driven approach supports targeted interventions, efficient resource allocation, and informed decision-making, ensuring equitable distribution and effective addressing of specific needs within communities. Moreover, spatial science and technology enhance community engagement by presenting complex data in an accessible visual format and empowering residents to participate in decision-making processes. Spatial data are becoming increasingly critical for understanding and addressing challenges across various sectors, and spatial technology provides effective approaches to promoting informed planning and equitable resource distribution.

This Special Issue aims to compile cutting-edge research and innovative applications of spatial data and technology in understanding, monitoring, and managing a wide array of issues. We invite high-quality, original research articles, reviews, case studies, and technical notes on topics including, but not limited to, the following:

  • Epidemiological Studies: employing spatial data and technology for tracking and analyzing patterns and trends.
  • Environmental Health: assessing the impact of urban environmental factors (e.g., air and water quality, noise pollution, etc.) on public health using spatial data and technology.
  • Healthcare Accessibility: spatial analysis of healthcare infrastructure, service accessibility, and optimization of healthcare delivery in urban or rural areas.
  • Urban Planning and Health: integrating spatial health data into urban planning to promote healthy living environments.
  • Chronic Disease Management: using spatial data and technology to monitor and manage chronic diseases such as diabetes, asthma, cardiovascular illnesses, and mental conditions.
  • Emergency Preparedness and Response: spatial modeling and analysis for preparedness and response in emergency and disaster scenarios.
  • Big Data and Machine Learning: application of big data analytics and machine learning techniques to spatial analysis.

Prof. Dr. Dongmei Chen
Prof. Dr. Yongmei Lu
Prof. Dr. Lu Wang
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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 analysis
  • geographic information systems
  • decision-making
  • urban planning
  • healthcare
  • epidemiology

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Published Papers (1 paper)

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Research

21 pages, 8004 KiB  
Article
Identifying the Spatial Range of the Pearl River Delta Urban Agglomeration from a Differentiated Perspective of Population Distribution and Population Mobility
by Yongwang Cao, Qingpu Li and Zaigao Yang
Appl. Sci. 2025, 15(2), 945; https://doi.org/10.3390/app15020945 - 18 Jan 2025
Viewed by 620
Abstract
Accurate identification of urban agglomeration spatial range is essential for scientific regional planning, optimal resource allocation, and sustainable development, forming the basis for regional development policy. To improve the accuracy of identifying urban agglomeration boundaries, this study fuses nighttime light data, which reflects [...] Read more.
Accurate identification of urban agglomeration spatial range is essential for scientific regional planning, optimal resource allocation, and sustainable development, forming the basis for regional development policy. To improve the accuracy of identifying urban agglomeration boundaries, this study fuses nighttime light data, which reflects urban economic levels, with LandScan data representing population distribution and heatmap data indicating population mobility. This fusion allows for identification from a differentiated perspective of population distribution and mobility. We propose a new method for identifying the dynamic boundaries of urban agglomerations through multi-source data fusion. This method not only provides technical support for scientific regional planning but also effectively guides the functional positioning of edge cities and the optimization of resource allocation. The results show that the spatial range identified by NTL_LS has an accuracy of 80.37% and a kappa coefficient of 0.5225, while NTL_HM achieves an accuracy of 89.17% with a kappa coefficient of 0.7342, indicating that the fusion of economic level with population mobility data more accurately reflects the spatial range of urban agglomerations in line with real development patterns. By adopting a differentiated perspective on population distribution and mobility, we propose a new approach to identifying urban agglomeration spatial range. The research results based on this method provide more comprehensive and dynamic decision-making support for optimizing transportation layouts, allocating public resources rationally, and defining the functional positioning of edge cities. Full article
(This article belongs to the Special Issue Spatial Data and Technology Applications)
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