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Applied Sciences
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Decision Making with Geospatial Information
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Decision Making with Geospatial Information

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

Deadline for manuscript submissions: closed (10 November 2023) | Viewed by 4858

Special Issue Editors

Dr. Ran Tao

E-Mail Website
Guest Editor
School of Geosciences, University of South Florida, Tampa, FL, USA
Interests: GIScience; spatial interactions; spatial statistics; movement and& mobility analysis; migration
Special Issues, Collections and Topics in MDPI journals
Dr. Zhaoya Gong

E-Mail Website
Guest Editor
School of Urban Planning and Design, Peking University, Beijing 100871, China
Interests: GeoComputation; spatial data science; urban analytics and simulation; urban and regional sciences
Special Issues, Collections and Topics in MDPI journals
Dr. Jinfeng Du

E-Mail Website
Guest Editor
School of Public Policy and Administration, Xi’an Jiaotong University, Xi'an 710049, China
Interests: land use policy; China's land reform; urban regeneration; urban and regional development
Prof. Dr. Joni A. Downs

E-Mail Website
Guest Editor
School of Geosciences, University of South Florida, Tampa, FL 33620, USA
Interests: GIScience; wildlife ecology health; transportation

Special Issue Information

Dear Colleagues,

The normal functioning of any society, government, civil community, or private corporation requires that decisions be made individually or collectively. Many decision situations can be described as the process of choosing courses of action based on multiple criteria in a fashion that is consistent with goals and constraints. Geospatial data, which comprise a wide collection of data sources such as remote sensing imagery, points of interest (POIs), GPS trajectories of human or other moving objects, geo-tagged social media data, land use and parcel data, and demographic data down to the census block level, have gained a critical role in facilitating decision making in various fields and applications, for example: urban planning and land management; disease control and mitigation; business location choice; environmental policy making; transportation planning and traffic monitoring; disaster preparedness, responses, and recovery. Thanks to the recent advancement in location-aware technologies such as GPS-equipped smartphones, unmanned aerial vehicles (UAVs), Internet of Things (IoT), airborne and terrestrial light detection and ranging (LiDAR), crowdsourcing, and social network platforms, geospatial data are becoming ubiquitous, easy to collect, rich in volume, and fine in spatial and temporal granularity. This Special Issue aims to harvest the latest innovative applied studies that emphasize the value of geospatial information in their decision-making process, and also the studies that adopt geographic visualization technologies such as digital and web maps to illustrate their made decisions.

Dr. Ran Tao
Dr. Zhaoya Gong
Dr. Jinfeng Du
Prof. Dr. Joni A. Downs
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

  • geospatial data
  • location
  • human mobility
  • GIS
  • big data
  • decision making
  • multicriteria
  • visualization
  • resilience
  • global change

Published Papers (4 papers)

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Research

16 pages, 8688 KiB  
Article
A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
by Feifei Zhang, Yong Wang, Yawen Du and Yijia Zhu
Appl. Sci. 2023, 13(23), 12658; https://doi.org/10.3390/app132312658 - 24 Nov 2023
Viewed by 919
Abstract
Remote sensing image semantic segmentation plays a crucial role in various fields, such as environmental monitoring, urban planning, and agricultural land classification. However, most current research primarily focuses on utilizing the spatial and spectral information of single-temporal remote sensing images, neglecting the valuable [...] Read more.
Remote sensing image semantic segmentation plays a crucial role in various fields, such as environmental monitoring, urban planning, and agricultural land classification. However, most current research primarily focuses on utilizing the spatial and spectral information of single-temporal remote sensing images, neglecting the valuable temporal information present in historical image sequences. In fact, historical images often contain valuable phenological variations in land features, which exhibit diverse patterns and can significantly benefit from semantic segmentation tasks. This paper introduces a semantic segmentation framework for satellite image time series (SITS) based on dilated convolution and a Transformer encoder. The framework includes spatial encoding and temporal encoding. Spatial encoding, utilizing dilated convolutions exclusively, mitigates the loss of spatial accuracy and the need for up-sampling, while allowing for the extraction of rich multi-scale features through a combination of different dilation rates and dense connections. Temporal encoding leverages a Transformer encoder to extract temporal features for each pixel in the image. To better capture the annual periodic patterns of phenological phenomena in land features, position encoding is calculated based on the image’s acquisition date within the year. To assess the performance of this framework, comparative and ablation experiments were conducted using the PASTIS dataset. The experiments indicate that this framework achieves highly competitive performance with relatively low optimization parameters, resulting in an improvement of 8 percentage points in the mean Intersection over Union (mIoU). Full article
(This article belongs to the Special Issue Decision Making with Geospatial Information)
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20 pages, 6721 KiB  
Article
GIS-Based Multi-Objective Routing Approach for Street-Based Sporting-Event Routing
by Young-Joon Yoon, Seo-Yeon Kim, Yun-Ku Lee, Namhyuk Ham, Ju-Hyung Kim and Jae-Jun Kim
Appl. Sci. 2023, 13(14), 8453; https://doi.org/10.3390/app13148453 - 21 Jul 2023
Viewed by 1177
Abstract
This study proposes a decision-making framework that integrates a routing model based on the geographic information system (GIS) and a genetic algorithm into a building-information modeling (BIM) environment to overcoming the limitations of the planning process of traditional street-based sporting events. There is [...] Read more.
This study proposes a decision-making framework that integrates a routing model based on the geographic information system (GIS) and a genetic algorithm into a building-information modeling (BIM) environment to overcoming the limitations of the planning process of traditional street-based sporting events. There is a lack of research on improving the manually conducted decision-making processes for street-based sporting events. Moreover, previous routing studies were limited to GIS environments, and proposals for decision-making models integrated with BIM environments are lacking. In this study, the applicability of the framework was verified by presenting the variables of the existing GIS-based routing model as environmental variables to consider the impact of street-based sports events on a city. The evaluation model for the route selection was parameterized independently, such that its priority could be changed according to the user’s needs. Moreover, we integrated the data into BIM to create and analyze models that assess urban effects. This method is a decision-making system for policymakers and event planners to promptly conduct initial venue surveys through the technological integration of GIS–routing–BIM. Additionally, the GIS stipulated in this study can be applied to other cities. The Gwanghwamun area of Seoul, South Korea, was selected as the case study. Full article
(This article belongs to the Special Issue Decision Making with Geospatial Information)
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19 pages, 7935 KiB  
Article
Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
by Zhenhui Sun, Peihang Li, Dongchuan Wang, Qingyan Meng, Yunxiao Sun and Weifeng Zhai
Appl. Sci. 2023, 13(10), 6300; https://doi.org/10.3390/app13106300 - 22 May 2023
Cited by 1 | Viewed by 1123
Abstract
The identification of urban functional zones (UFZs) is crucial for urban planning and optimizing industrial layout. Fusing remote sensing images and social perception data is an effective way to identify UFZs. Previous studies on UFZs recognition often ignored band information outside the red–green–blue [...] Read more.
The identification of urban functional zones (UFZs) is crucial for urban planning and optimizing industrial layout. Fusing remote sensing images and social perception data is an effective way to identify UFZs. Previous studies on UFZs recognition often ignored band information outside the red–green–blue (RGB), especially three-dimensional (3D) urban morphology information. In addition, the probabilistic methods ignore the potential semantic information of Point of Interest (POI) data. Therefore, we propose an “Image + Text” multimodal data fusion framework for UFZs recognition. To effectively utilize the information of Gaofen-7(GF-7) stereo images, we designed a semi-transfer UFZs recognition model. The transferred model uses the pre-trained model to extract the deep features from RGB images, and a small self-built convolutional network is designed to extract the features from RGB bands, near-infrared (NIR) band, and normalized digital surface model (nDSM) generated by GF-7. Latent Dirichlet allocation (LDA) is employed to extract POI semantic features. The fusion features of the deep features of the GF-7 image and the semantic features of POI are fed into a classifier to identify UFZs. The experimental results show that: (1) The highest overall accuracy of 88.17% and the highest kappa coefficient of 83.91% are obtained in the Beijing Fourth Ring District. (2) nDSM and NIR data improve the overall accuracy of UFZs identification. (3) POI data significantly enhance the recognition accuracy of UFZs, except for shantytowns. This UFZs identification is simple and easy to implement, which can provide a reference for related research. However, considering the availability of POI data distribution, other data with socioeconomic attributes should be considered, and other multimodal fusion strategies are worth exploring in the future. Full article
(This article belongs to the Special Issue Decision Making with Geospatial Information)
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16 pages, 3223 KiB  
Article
Day and Night: Locating the General Practitioner’s Panel after Hours
by John Charles Campbell, Majid Taghavi and Peter T. VanBerkel
Appl. Sci. 2023, 13(10), 6273; https://doi.org/10.3390/app13106273 - 20 May 2023
Viewed by 781
Abstract
Location science is used to determine the optimal geographical placement of primary care resources with operations research models. In determining the optimal placement, we account for the objectives of both patients and physicians. These objectives and the methods used to address them differ [...] Read more.
Location science is used to determine the optimal geographical placement of primary care resources with operations research models. In determining the optimal placement, we account for the objectives of both patients and physicians. These objectives and the methods used to address them differ between daytime and after-hours settings. These time settings are treated separately since primary care services are typically limited during after-hours operations. Three solution approaches are considered to address both time settings: independent, sequential, and simultaneous. The independent approach is based on the p-Median problem, and the other two approaches use modified forms of the p-Median. Three case studies are examined by applying these models to census data from Nova Scotia. Solving the daytime and after-hours problem simultaneously consistently yields the best results while considering facility-sharing constraints. Full article
(This article belongs to the Special Issue Decision Making with Geospatial Information)
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