Special Issue "Spatial Analysis and Geographic Information Systems"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Prof. Dr. Alexandre B. Gonçalves
Website
Guest Editor
Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Interests: geographical information systems; spatial analysis; optimization; geographic modeling; spatial data algorithms; computational geometry; spatial data infrastructures; applications of spatial data
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Special Issue Information

Dear Colleagues,

The spatial analysis of georeferenced data encompasses a series of procedures to extract valuable information based on its location, providing the ground for understanding complex spatial interactions. As such, exploring and mapping these relations using Geographic Information Systems (GIS) is critical to support informed decisions in almost every human activity. Due to their capacity to integrate datasets of various types, to the potential to develop analytical processes over such data, to produce quantitative analyses, and to communicate effectively with stakeholders, GIS are also essential tools in the multidisciplinary aspects that are involved in sustainability-related projects.

This Special Issue will include a selection of contributions on the theory and practice of analysing spatial data and the use of GIS in all aspects within sustainability studies. We encourage researchers to submit contributions through articles, reviews, case studies and position papers focusing the role and contribution of spatial analysis and geospatial techniques in the wide scope of sustainability. This includes, in a non-exclusive list of potential topics, contributions focusing on:

  • planar, 3D and spatiotemporal simulation or modelling of data in resources, energy and land use studies
  • methodological aspects of geospatial data analysis impacting sustainability studies
  • data handling techniques for the spatialization of sustainability-related indicators
  • case studies of GIS-based resources and environmental evaluation
  • impacts of spatial data models, quality, transformation and processing in sustainability assessment
  • applications for spatial data mining, geovisualization or spatial decision-support systems in sustainability-related case studies

Prof. Dr. Alexandre B. Gonçalves
Guest Editor

Manuscript Submission Information

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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. Sustainability 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 1800 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
  • Geomatics
  • Spatial data mining
  • Geovisualization
  • Spatial decision-support systems
  • Sustainability

Published Papers (8 papers)

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Research

Open AccessArticle
Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing
Sustainability 2020, 12(22), 9662; https://doi.org/10.3390/su12229662 - 19 Nov 2020
Abstract
Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable [...] Read more.
Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management. Full article
(This article belongs to the Special Issue Spatial Analysis and Geographic Information Systems)
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Open AccessArticle
Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest
Sustainability 2020, 12(18), 7787; https://doi.org/10.3390/su12187787 - 21 Sep 2020
Abstract
The main purpose of this study was to compare the prediction accuracies of various seismic vulnerability assessment and mapping methods. We applied the frequency ratio (FR), decision tree (DT), and random forest (RF) methods to seismic data for Gyeongju, South Korea. A magnitude [...] Read more.
The main purpose of this study was to compare the prediction accuracies of various seismic vulnerability assessment and mapping methods. We applied the frequency ratio (FR), decision tree (DT), and random forest (RF) methods to seismic data for Gyeongju, South Korea. A magnitude 5.8 earthquake occurred in Gyeongju on 12 September 2016. Buildings damaged during the earthquake were used as dependent variables, and 18 sub-indicators related to seismic vulnerability were used as independent variables. Seismic data were used to construct a model for each method, and the models’ results and prediction accuracies were validated using receiver operating characteristic (ROC) curves. The success rates of the FR, DT, and RF models were 0.661, 0.899, and 1.000, and their prediction rates were 0.655, 0.851, and 0.949, respectively. The importance of each indicator was determined, and the peak ground acceleration (PGA) and distance to epicenter were found to have the greatest impact on seismic vulnerability in the DT and RF models. The constructed models were applied to all buildings in Gyeongju to derive prediction values, which were then normalized to between 0 and 1, and then divided into five classes at equal intervals to create seismic vulnerability maps. An analysis of the class distribution of building damage in each of the 23 administrative districts showed that district 15 (Wolseong) was the most vulnerable area and districts 2 (Gangdong), 18 (Yangbuk), and 23 (Yangnam) were the safest areas. Full article
(This article belongs to the Special Issue Spatial Analysis and Geographic Information Systems)
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Open AccessArticle
Real-Time Pedestrian Flow Analysis Using Networked Sensors for a Smart Subway System
Sustainability 2019, 11(23), 6560; https://doi.org/10.3390/su11236560 - 20 Nov 2019
Abstract
The application of smart city technologies requires new data analysis methods to interpret the voluminous data collected. In this study, we first analyzed the transfer behavior of subway pedestrians using the fingerprinting technique using data collected by more than 100 MAC (Media Access [...] Read more.
The application of smart city technologies requires new data analysis methods to interpret the voluminous data collected. In this study, we first analyzed the transfer behavior of subway pedestrians using the fingerprinting technique using data collected by more than 100 MAC (Media Access Control) ID sensors installed in a congested subway station serving two subway lines. We then developed a model that employs an AI (Artificial Intelligence)-based methodology, the cumulative visibility of moving objects (CVMO), to present the data in such a manner that it could be used to address pedestrian flow issues in this real-world implementation of smart city technology. The MAC ID location data collected during a three-month monitoring period were mapped using the fingerprinting wireless location sensing method to display the congestion situation in real time. Furthermore we developed a model that can inform immediate response to identified conditions. In addition, we formulated several schemes for disbursing congestion and improving pedestrian flow using behavioral economics, and then confirmed their effectiveness in a follow-up monitoring period. The proposed pedestrian flow analysis method cannot only solve pedestrian congestion, but can also help to prevent accidents and maintain public order. Full article
(This article belongs to the Special Issue Spatial Analysis and Geographic Information Systems)
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Open AccessArticle
Location-Based Tracking Data and Customer Movement Pattern Analysis for Sustainable Fashion Business
Sustainability 2019, 11(22), 6209; https://doi.org/10.3390/su11226209 - 06 Nov 2019
Cited by 1
Abstract
Retailers need accurate movement pattern analysis of human-tracking data to maximize the space performance of their stores and to improve the sustainability of their business. However, researchers struggle to precisely measure customers’ movement patterns and their relationships with sales. In this research, we [...] Read more.
Retailers need accurate movement pattern analysis of human-tracking data to maximize the space performance of their stores and to improve the sustainability of their business. However, researchers struggle to precisely measure customers’ movement patterns and their relationships with sales. In this research, we adopt indoor positioning technology, including wireless sensor devices and fingerprinting techniques, to track customers’ movement patterns in a fashion retail store over four months. Specifically, we conducted three field experiments in three different timeframes. In each experiment, we rearranged one element of the visual merchandising display (VMD) to track and compare customer movement patterns before and after the rearrangement. For the analysis, we connected customers’ discrete location data to identify meaningful patterns in customers’ movements. We also used customers’ location and time information to match identified movement pattern data with sales data. After classifying individuals’ movements by time and sequences, we found that stay time in a particular zone had a greater impact on sales than the total stay time in the store. These results challenge previous findings in the literature that suggest that the longer customers stayed in a store, the more they purchase. Further, the results confirmed that effective store rearrangement could change not only customer movement patterns but also overall sales of store zones. This research can be a foundation for various practical applications of tracking data technologies. Full article
(This article belongs to the Special Issue Spatial Analysis and Geographic Information Systems)
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Open AccessArticle
Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China
Sustainability 2019, 11(21), 6152; https://doi.org/10.3390/su11216152 - 04 Nov 2019
Cited by 1
Abstract
Automatic vehicle identification (AVI) systems collect 24 h vehicle travel data for the efficient management of traffic flows. The automatic vehicle identification data collected by an overhead traffic monitoring system provides a means for understanding urban traffic flows and human mobility. This article [...] Read more.
Automatic vehicle identification (AVI) systems collect 24 h vehicle travel data for the efficient management of traffic flows. The automatic vehicle identification data collected by an overhead traffic monitoring system provides a means for understanding urban traffic flows and human mobility. This article explores the weekly travel patterns of private vehicles based on AVI data in Wuhan, a megacity in Central China. We extracted origin–destination information and applied the K-Means clustering algorithm to classify spatial traffic hot spots by camera locations. Subsequently, the Latent Dirichlet Allocation algorithm was used to mine the temporal travel patterns of individual vehicles. The cluster results are summarized in nine travel probability matrixes. The effectiveness of this approach is illustrated by a case study using a large set of AVI data collected from 19 to 24 November 2018, in Wuhan, China. The results revealed six variations of the travel demand on weekdays and weekends—the commuting behaviors of private drivers triggered a tidal change in traffic flows. This study also exposed nine weekly travel patterns for private cars, reflecting temporal similarities of human mobility patterns. We identified four types of commuters. These results can help city managers understand daily changes in urban travel demands. Full article
(This article belongs to the Special Issue Spatial Analysis and Geographic Information Systems)
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Open AccessArticle
Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China
Sustainability 2019, 11(16), 4356; https://doi.org/10.3390/su11164356 - 12 Aug 2019
Cited by 4
Abstract
This study investigates the association between urban vitality and street centrality in Wuhan, China. Urban vitality was measured with social network review data. Street centrality was evaluated in terms of closeness, straightness, and betweenness in walking and driving mode. We constructed a square [...] Read more.
This study investigates the association between urban vitality and street centrality in Wuhan, China. Urban vitality was measured with social network review data. Street centrality was evaluated in terms of closeness, straightness, and betweenness in walking and driving mode. We constructed a square mesh to convert datasets of street centrality (segments) and urban vitality (points) into one unit for analysis. Geospatial visualization, a chi-square test, and correlation analysis were first employed to obtain an initial understanding of the spatial coupling relationship between urban vitality and street centrality. Then spatial regression models were applied to evaluate the significances and directions of the influences of street centrality on urban vitality. A geographical detector technique was utilized to further evaluate the magnitudes of these influences. The results suggest that street centrality plays an important role in shaping the spatial organization of urban vitality, and various street centralities presented marked differences in their association with urban vitality. More specifically, when considering street centrality in walking mode, betweenness affected urban vitality the most, followed by closeness and straightness. When considering street centrality in driving mode, straightness had the greatest influence on urban vitality, followed by closeness and betweenness. Full article
(This article belongs to the Special Issue Spatial Analysis and Geographic Information Systems)
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Open AccessArticle
Exploring the Spatial Pattern and Influencing Factors of Land Carrying Capacity in Wuhan
Sustainability 2019, 11(10), 2786; https://doi.org/10.3390/su11102786 - 15 May 2019
Cited by 4
Abstract
Land carrying capacity is an important factor for urban sustainable development. It provides essential insights into land resource allocation and management. In this article, we propose a framework to evaluate land carrying capacity with multiple data sources from the first geographical census and [...] Read more.
Land carrying capacity is an important factor for urban sustainable development. It provides essential insights into land resource allocation and management. In this article, we propose a framework to evaluate land carrying capacity with multiple data sources from the first geographical census and socioeconomic statistics. In particular, an index, Land Resource Pressure (LRP), is proposed to evaluate the land carrying capacity, and a case study was carried out in Wuhan. The LRP of Wuhan was calculated on 250 m * 250 m grids, and showed a circularly declining pattern from central to outer areas. We collected its influencing factors in terms of nature resources, economy, transportation and urban construction, and then analyzed its causes via geographically weighted (GW) models. Firstly, pair-wise correlations between LRP and each influencing factor were explored via the GW correlation coefficients. These local estimates provide an important precursor for the following quantitative analysis via the GW regression (GWR) technique. The GWR coefficient estimates interpret the influences on LRP in a localized view. Results show that per capita gross domestic product (PerGDP) showed a higher absolute estimate among all factors, which proves that PerGDP has a relieving effect on LRP, especially in the southwestern areas. Overall, this study provides a technical framework to evaluate land carrying capacity with multi-source data sets and explore its localized influences via GW models, which could provide practical guidance for similar studies in other cities. Full article
(This article belongs to the Special Issue Spatial Analysis and Geographic Information Systems)
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Open AccessArticle
Assessing Land Use Changes in Polish Territories: Patterns, Directions and Socioeconomic Impacts on Territorial Management
Sustainability 2019, 11(5), 1354; https://doi.org/10.3390/su11051354 - 05 Mar 2019
Cited by 5
Abstract
Bearing in mind the relationships between the territorial management and the achievement of sustainable development, studies regarding the land use changes are seen as pivotal. What is more, they can also enable us to understand the dynamics and variables for proper territorial management. [...] Read more.
Bearing in mind the relationships between the territorial management and the achievement of sustainable development, studies regarding the land use changes are seen as pivotal. What is more, they can also enable us to understand the dynamics and variables for proper territorial management. Therefore, a retrospective study has been carried out regarding the land use changes in the Polish territory in the years: 1990, 2000, and 2012, by means of GIS (Geographic Information Systems) tools such as the CLC (Corine Land Cover). Moreover, and considering the complex dynamics, patterns, and particularities that territories presents, a deep analysis of those land uses is critical for the identification of barriers and opportunities for long-term sustained development. The study enabled us to identify the land use changes in the last decades in the Polish territory—allowing us to establish a relationship and identification of the associated barriers and opportunities within the socioeconomic sphere. Full article
(This article belongs to the Special Issue Spatial Analysis and Geographic Information Systems)
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