Special Issue "Spatio-Temporal Models and Geo-Technologies"

Special Issue Editors

Dr. Géraldine Del Mondo
E-Mail Website
Guest Editor
Laboratory of Computer Science, Information Processing and Systems (LITIS), 76800 Saint-Étienne-du-Rouvray, France
Interests: spatial and temporal information modelling; qualitative modelling and reasonning; multi-scale modelling; graph theory; spatial dynamics; geographical information science; risk management; historical data
Dr. Peng Peng
E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: geographical information science; spatial and temporal information modelling; complex network analysis; maritime transportation; trajectory data minning
Special Issues and Collections in MDPI journals
Prof. Dr. Feng Lu
E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101 Beijing, China
Interests: geographical information science; spatio-temporal databases; Geo-spatial data mining; machine learning; complex network analysis; natural language processing; computational transportation science
Prof. Dr. Jérôme Gensel
E-Mail Website
Guest Editor
Grenoble Informatcs Laboratory (LIG), Université Grenobles Alpes, 38400 Grenoble, France
Interests: space and time representation and reasoning; semantic web; crowdsourcing, geographic information systems; geomatics, knowledge representation; constraint programming

Special Issue Information

Dear Colleagues,

Over the past few years, several theoretical spatiotemporal models have been successively proposed for a better representation of geographical phenomena. From early GIS modelling approaches, a series of extensions have been suggested to integrate time within object-based, field-based and dual representations of geographical data, while a series of formal qualitative approaches have also contributed to more fundamental frameworks that support advanced spatial reasoning capabilities. Meanwhile, many successful environmental and urban GIS research studies have demonstrated that the temporal dimension can be implicitly integrated by different representation and analytical frameworks. This Special Issue is calling for innovative works that integrate the spatial and temporal dimensions within theoretical, formal and practical GIS solutions as well as urban and environmental applications that demonstrate a sound integration of the spatial and temporal dimensions. The topics of interest include, but are not limited to:

- Formal spatiotemporal reasoning;

- Spatiotemporal ontologies and standards;

- Spatiotemporal modelling approaches;

- Graph-based representations to space and time phenomena;

- Qualitative spatial and temporal reasoning;

- Multiscale modelling;

- Combination of qualitative and quantitative approaches;

- Spatiotemporal query languages within GIS;

- Spatiotemporal GIS interfaces;

- Temporal indoor GIS;

- Location-based time GIS;

- Real-time GIS;

- Temporal web and wireless GIS;

- Spatiotemporal technologies;

- Spatiotemporal urban and environmental GIS;

- Spatiotemporal semantic web;

- Spatiotemporal data mining.

 

Dr. Géraldine Del Mondo
Dr. Peng Peng
Prof. Dr. Feng Lu
Prof. Dr. Jérôme Gensel
Guest Editors

Manuscript Submission Information

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Keywords

  • Spatiotemporal GIS
  • Spatiotemporal Models
  • Spatiotemporal Technologies
  • Real-Time GIS
  • Location-Based GIS

Published Papers (9 papers)

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Research

Article
What Is the Shape of Geographical Time-Space? A Three-Dimensional Model Made of Curves and Cones
ISPRS Int. J. Geo-Inf. 2021, 10(5), 340; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050340 - 17 May 2021
Viewed by 328
Abstract
Geographical time-spaces exhibit a series of properties, including space inversion, that turns any representation effort into a complex task. In order to improve the legibility of the representation and leveraging the advances of three-dimensional computer graphics, the aim of the study is to [...] Read more.
Geographical time-spaces exhibit a series of properties, including space inversion, that turns any representation effort into a complex task. In order to improve the legibility of the representation and leveraging the advances of three-dimensional computer graphics, the aim of the study is to propose a new method extending time-space relief cartography introduced by Mathis and L’Hostis. The novelty of the model resides in the use of cones to describing the terrestrial surface instead of graph faces, and in the use of curves instead of broken segments for edges. We implement the model on the Chinese space. The Chinese geographical time-space of reference year 2006 is produced by the combination and the confrontation of the fast air transport system and of the 7.5-times slower road transport system. Slower, short range flights are represented as curved lines above the earth surface with longer length than the geodesic, in order to account for a slower speed. The very steep slope of cones expresses the relative difficulty of crossing terrestrial time-space, as well as the comparably extreme efficiency of long-range flights for moving between cities. Finally, the whole image proposes a coherent representation of the geographical time-space where fast city-to-city transport is combined with slow terrestrial systems that allow one to reach any location. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Mining Topological Dependencies of Recurrent Congestion in Road Networks
ISPRS Int. J. Geo-Inf. 2021, 10(4), 248; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040248 - 08 Apr 2021
Viewed by 384
Abstract
The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence [...] Read more.
The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Multitemporal Analysis of Land Use and Land Cover within an Oil Block in the Ecuadorian Amazon
ISPRS Int. J. Geo-Inf. 2021, 10(3), 191; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030191 - 23 Mar 2021
Cited by 1 | Viewed by 687
Abstract
The Ecuadorian Amazon is considered a biodiverse region, and at the same time contains the largest number of oil blocks and oilfields in the country. Oil exploitation requires the implementation of oil facilities and related infrastructure, such as roads, water, and energy supply, [...] Read more.
The Ecuadorian Amazon is considered a biodiverse region, and at the same time contains the largest number of oil blocks and oilfields in the country. Oil exploitation requires the implementation of oil facilities and related infrastructure, such as roads, water, and energy supply, for operation. These large engineering works can alter the dynamics of the Amazonian natural ecosystems. This paper analyzes the land use and land cover (LULC) change and relates spatial patterns within an oil block located in the province of Orellana, Ecuador. The study was processed in two phases, the first corresponding to the collection and classification of LULC classes within the oil block. The second phase concerned the calculation of landscape metrics, with the purpose of quantitatively characterizing each class. This analysis was carried out for the pre-concession, post-concession scenarios of the oil block and the current scenario of the region. The results revealed that the low predominance of forest cover within the study region is not directly associated with the beginning of the Block 47 concession. On the other hand, a significant reduction of the Coca River was evidenced for the 2018 scenario. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Pyramidal Framework: Guidance for the Next Generation of GIS Spatial-Temporal Models
ISPRS Int. J. Geo-Inf. 2021, 10(3), 188; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030188 - 22 Mar 2021
Viewed by 821
Abstract
Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a [...] Read more.
Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
The Role of Spatio-Temporal Information to Govern the COVID-19 Pandemic: A European Perspective
ISPRS Int. J. Geo-Inf. 2021, 10(3), 166; https://doi.org/10.3390/ijgi10030166 - 14 Mar 2021
Viewed by 538
Abstract
The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of [...] Read more.
The Covid-19 pandemic put a heavy burden on member states in the European Union. To govern the pandemic, having access to reliable geo-information is key for monitoring the spatial distribution of the outbreak over time. This study aims to analyze the role of spatio-temporal information in governing the pandemic in the European Union and its member states. The European Nomenclature of Territorial Units for Statistics (NUTS) system and selected national dashboards from member states were assessed to analyze which spatio-temporal information was used, how the information was visualized and whether this changed over the course of the pandemic. Initially, member states focused on their own jurisdiction by creating national dashboards to monitor the pandemic. Information between member states was not aligned. Producing reliable data and timeliness reporting was problematic, just like selecting indictors to monitor the spatial distribution and intensity of the outbreak. Over the course of the pandemic, with more knowledge about the virus and its characteristics, interventions of member states to govern the outbreak were better aligned at the European level. However, further integration and alignment of public health data, statistical data and spatio-temporal data could provide even better information for governments and actors involved in managing the outbreak, both at national and supra-national level. The Infrastructure for Spatial Information in Europe (INSPIRE) initiative and the NUTS system provide a framework to guide future integration and extension of existing systems. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Urban Growth, Real Estate Development and Indigenous Property: Simulating the Expansion Process in the City of Temuco, Chile
ISPRS Int. J. Geo-Inf. 2021, 10(2), 101; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020101 - 22 Feb 2021
Viewed by 708
Abstract
Urbanization is spreading across the world and beyond metropolitan areas. Medium-sized cities have also undergone processes of accelerated urban expansion, especially in Latin America, thanks to scant regulation or a complete lack thereof. Thus, understanding urban growth in the past and simulating it [...] Read more.
Urbanization is spreading across the world and beyond metropolitan areas. Medium-sized cities have also undergone processes of accelerated urban expansion, especially in Latin America, thanks to scant regulation or a complete lack thereof. Thus, understanding urban growth in the past and simulating it in the future has become a tool to raise its visibility and challenge territorial planners. In this work, we use Markov chains, cellular automata, multi-criteria multi-objective evaluation, and the determination of land use/land cover (LULC) to model the urban growth of the city of Temuco, Chile, a paradigmatic case because it has experienced powerful growth, where real estate development pressures coexist with a high natural value and the presence of indigenous communities. The urban scenario is determined for the years 2033 and 2049 based on the spatial patterns between 1985 and 2017, where the model shows the trend of expansion toward the northeast and significant development in the western sector of the city, making them two potential centers of expansion and conflict in the future given the heavy pressure on lands that are indigenous property and have a high natural value, aspects that need to be incorporated into future territorial planning instruments. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Understanding Spatiotemporal Variations of Ridership by Multiple Taxi Services
ISPRS Int. J. Geo-Inf. 2020, 9(12), 757; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120757 - 18 Dec 2020
Viewed by 616
Abstract
Recent years have seen the big growth of app-based taxi services by not only competing for rides with street-hailing taxi services but also generating new taxi rides. Moreover, the innovation in dynamic pricing also makes it competitive in both passenger and driver sides. [...] Read more.
Recent years have seen the big growth of app-based taxi services by not only competing for rides with street-hailing taxi services but also generating new taxi rides. Moreover, the innovation in dynamic pricing also makes it competitive in both passenger and driver sides. However, current literature still lacks better understandings of induced changes in spatiotemporal variations in multiple taxi ridership after app-based taxi service launch. This study develops two study cases in New York City to explore impacts of presence of app-based taxi services on daily total and street-hailing taxi rides and impacts of dynamic pricing on hourly app-based taxi rides. Considering the panel data and treatment effect measurement in this problem, we introduce a mixed modeling structure with both geographically weighted panel regression and difference-in-difference estimator. This mixed modeling structure outperforms traditional fixed effects model in our study cases. Empirical analyses identified the significant spatiotemporal variations in impacts of presence of app-based taxi services; for instance, impacts daily total taxi rides in 2014 and 2016 and impacts on street-hailing taxi rides from 2012 to 2016. Moreover, we capture the spatial variations in impacts of dynamic pricing on hourly app-based taxi rides, as well as significant impacts of time of day, day of week, and vehicle supply. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Identifying Port Calls of Ships by Uncertain Reasoning with Trajectory Data
ISPRS Int. J. Geo-Inf. 2020, 9(12), 756; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120756 - 18 Dec 2020
Cited by 2 | Viewed by 535
Abstract
Considering that ports are key nodes of the maritime transport network, it is of great importance to identify ships’ arrivals and departures. Compared with partial proprietary data from a port authority or shipping company, approaches based on compulsory Automatic Identification System (AIS) data [...] Read more.
Considering that ports are key nodes of the maritime transport network, it is of great importance to identify ships’ arrivals and departures. Compared with partial proprietary data from a port authority or shipping company, approaches based on compulsory Automatic Identification System (AIS) data reported by ships can produce transparent datasets covering wider areas, which is necessary for researchers and policy makers. Detecting port calls based on trajectory data is a difficult problem due to the huge uncertainty inherent in information such as ships’ ambiguous statuses and ports’ irregular boundaries. However, we noticed that little attention has been paid to this fundamental problem of shipping network analysis, and considerable noise may have been introduced in previous work on maritime network assessment based on AIS data, which usually modeled each port as a circle with a fixed radius such as 1 or 2 km. In this paper, we propose a method for identifying port calls by uncertain reasoning with trajectory data, which represents each port with an arbitrary shape as a set of geographical grid cells belonging to berths inside this port. Based on this high-spatial-resolution representation, port calls were identified when a ship was in any of these cells. Our method was implemented with around 14 billion AIS messages worldwide over 8 months, and examples of the results are provided. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Mining Evolution Patterns from Complex Trajectory Structures—A Case Study of Mesoscale Eddies in the South China Sea
ISPRS Int. J. Geo-Inf. 2020, 9(7), 441; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070441 - 16 Jul 2020
Cited by 1 | Viewed by 731
Abstract
Real-word phenomena, such as ocean eddies and clouds, tend to split and merge while they are moving around within a space. Their trajectories usually bear one or more branches and are accordingly defined as complex trajectories in this study. The trajectories may show [...] Read more.
Real-word phenomena, such as ocean eddies and clouds, tend to split and merge while they are moving around within a space. Their trajectories usually bear one or more branches and are accordingly defined as complex trajectories in this study. The trajectories may show significant spatiotemporal variations in terms of their structures and some of them may be more prominent than the others. The identification of prominent structures in the complex trajectories of such real-world phenomena could better reveal their evolution processes and even shed new light on the driving factors behind them. Methods have been proposed for the extraction of periodic patterns from simple trajectories (i.e., those with linear structure and without any branches) with a focus on mining the related temporal, spatial or semantic information. Unfortunately, it is not appropriate to directly use such methods to examine complex trajectories. This study proposes a novel method to study the periodic patterns of complex trajectories by considering the inherent spatial, temporal and topological information. First, we use a sequence of symbols to represent the various structures of a complex trajectory over its lifespan. We then, on the basis of the PrefixSpan algorithm, propose a periodic pattern mining of structural evolution (PPSE) algorithm and use it to identify the largest and most frequent patterns (LFPs) from the symbol sequence. We also identify potential periodic behaviors. The PPSE method is then used to examine the complex trajectories of the mesoscale eddy in the South China Sea (SCS) from 1993 to 2016. The complex trajectories of ocean eddies in the southeast of Vietnam show are different from other regions in the SCS in terms of their structural evolution processes, as indicated by the LFPs with the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. The LFP in the southeast of Vietnam has the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. Across the SCS, we found seven migration channels. The LFPs of the eddies that migrate through these channels have a temporal cycle of 17–24 years. These channels are also the regions where eddies frequently emerge, as revealed by flow field data. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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