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

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 June 2021).

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, Collections and Topics 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, Beijing 100101, 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

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 papers will be 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. ISPRS International Journal of Geo-Information 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 1400 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

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

Published Papers (17 papers)

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Research

Article
Rice Yield Simulation and Planting Suitability Environment Pattern Recognition at a Fine Scale
ISPRS Int. J. Geo-Inf. 2021, 10(9), 612; https://doi.org/10.3390/ijgi10090612 - 15 Sep 2021
Viewed by 528
Abstract
Analyzing rice yields and multidimensional environmental factors at a fine scale facilitates the discovery of the planting environment patterns that guide the spatial layout of rice production. This study uses Pucheng County, Fujian Province, a demonstration county of China Good Grains and Oils, [...] Read more.
Analyzing rice yields and multidimensional environmental factors at a fine scale facilitates the discovery of the planting environment patterns that guide the spatial layout of rice production. This study uses Pucheng County, Fujian Province, a demonstration county of China Good Grains and Oils, as the research area. Using actual rice yield sample data and environment data, a yield simulation model based on random forest regression is constructed to realize a fine-scale simulation of rice yield and its spatial distribution pattern in Pucheng County. On this basis, we construct a method system to identify spatial combination patterns between rice yields and fine-scale multidimensional environmental planting suitability using rice yield data and environmental planting suitability evaluation data. We categorize the areas into four combination model areas to analyze the spatial correlation model of planting suitability, multidimensional environment, and yield: higher-yield and higher-suitability cluster–comprehensive environmental-advantage areas, high-yield and high-suitability cluster–soil condition-limited areas, moderate-yield and moderate-suitability cluster–irrigation and drainage condition-limited areas, and low-yield and low-suitability cluster–site condition-limited areas. The following results are found. (1) The rice yield simulation model, which is based on random forest regression, considers the various complex relationships between yield and natural as well as human factors to realize the refined simulation of rice yields at a county scale. (2) The county rice yield has a strong positive spatial correlation, and the spatial clustering characteristics are obvious; these relationships can provide a basis for effectively implementing intensive rice planting in Pucheng County. (3) We construct a spatial combination pattern recognition method based on rice yield and environmental planting suitability. We can use this method to effectively identify the spatial relationship between yield and planting suitability as well as the shortcomings and advantages of different regions in terms of the climate, soil, irrigation, site, mechanical farming, and similar factors. On this basis, we can provide regional rice planting guidance for Pucheng County. In addition, this method system also provides a new perspective and method for research into spatial combination models and related spatial issues. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
A Unifying Framework for Analysis of Spatial-Temporal Event Sequence Similarity and Its Applications
ISPRS Int. J. Geo-Inf. 2021, 10(9), 594; https://doi.org/10.3390/ijgi10090594 - 09 Sep 2021
Viewed by 355
Abstract
Measures of similarity or differences between data objects are applied frequently in geography, biology, computer science, linguistics, logic, business analytics, and statistics, among other fields. This work focuses on event sequence similarity among event sequences extracted from time series observed at spatially deployed [...] Read more.
Measures of similarity or differences between data objects are applied frequently in geography, biology, computer science, linguistics, logic, business analytics, and statistics, among other fields. This work focuses on event sequence similarity among event sequences extracted from time series observed at spatially deployed monitoring locations with the aim of enhancing the understanding of process similarity over time and geospatial locations. We present a framework for a novel matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This unified representation of spatiotemporal event sequences (STES) supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the Jaccard index with temporal order constraints and accommodates different event data types. The approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. As a case study, we demonstrate the use of these similarity measures in a spatiotemporal analysis of event sequences extracted from space time series of a water quality monitoring system. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Multi-Level and Multiple Aspect Semantic Trajectory Model: Application to the Tourism Domain
ISPRS Int. J. Geo-Inf. 2021, 10(9), 592; https://doi.org/10.3390/ijgi10090592 - 08 Sep 2021
Viewed by 458
Abstract
Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment data integrating spatial, temporal and thematic dimensions [...] Read more.
Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment data integrating spatial, temporal and thematic dimensions and (iii) the association of such data with positions or with trajectory segments. Each of these features is necessary for the processing and analysis of tourist mobility data, which we will detail. For validation purposes, we experiment our model on two outdoor mobility track scenarios computed in a processing chain. We also show that our model is generic and extensible thanks to two other scenarios on different datasets. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
An Efficient 2.5D Shadow Detection Algorithm for Urban Planning and Design Using a Tensor Based Approach
ISPRS Int. J. Geo-Inf. 2021, 10(9), 583; https://doi.org/10.3390/ijgi10090583 - 28 Aug 2021
Viewed by 505
Abstract
Urbanization is leading us to a more chaotic state where healthy living becomes a prime concern. The high-rise buildings influence the urban setting with a high shadow rate on surroundings that can have no positive impact on the general neighborhood. Nevertheless, shadows are [...] Read more.
Urbanization is leading us to a more chaotic state where healthy living becomes a prime concern. The high-rise buildings influence the urban setting with a high shadow rate on surroundings that can have no positive impact on the general neighborhood. Nevertheless, shadows are the main factor of defeatist virtual settings, they are expensive to render in real-time. This paper investigates how the amount of sunlight varies by season and how seasons can indicate the time of year to understand how shadows vary in length at different times of the day and how they change over the seasons. We propose a novel efficient (fast and scalable) algorithm to calculate a 2.5D cast-shadow map from a given LiDAR-derived Digital Surface Model (DSM). We present a proof-of-concept demonstration to examine the technical practicability of the introduced algorithm. Tensor-based techniques such as singular value decomposition, tensor unfolding are examined and deployed to represent the multidimensional data. The proposed method exploits horizon mapping ideas and extends the method to a modern graphics algorithm (Bresenham’s line drawing algorithm) to account for the DSM’s underlying surface geometry. A proof-of-concept is developed utilizing Python’s TensorFlow library, exploring data flow graphs and the tensor data structure. The heavy computer graphics algorithm used in this paper is parallelized using PySpark. Results explicate significant enhancements in overall performance while preserving accuracy at negligible variations. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Influential Factor Detection for Tourism on the Qinghai-Tibet Plateau Based on Social Media Data
ISPRS Int. J. Geo-Inf. 2021, 10(9), 579; https://doi.org/10.3390/ijgi10090579 - 27 Aug 2021
Viewed by 474
Abstract
Tourism is playing an important role in the economic development of the Qinghai-Tibet Plateau (QTP). To better develop tourism in this region, the spatial heterogeneity of influencing factors on tourism needs to be studied. Using the spatial distribution of tourism potential from social [...] Read more.
Tourism is playing an important role in the economic development of the Qinghai-Tibet Plateau (QTP). To better develop tourism in this region, the spatial heterogeneity of influencing factors on tourism needs to be studied. Using the spatial distribution of tourism potential from social media data, this paper analyzes the influencing factors of tourism on the QTP from the perspective of spatial heterogeneity. We extract microblogs related to travel topics connected to the QTP in 2017 from Sina Weibo to capture tourism potential. Then, factors considered from six aspects (tourism resources, amenities, transportation, geography, population, and the economy) are selected, and a geographic detector (Geodetector) is employed to detect the explanatory power of these factors for tourism potential. The results indicate different influential tourism factors in Qinghai and Tibet. In Qinghai, the main factors are hotels, tourist attractions, and road network density, and the explanatory power of the factors mainly comes from eastern and western Qinghai. In Tibet, the main factors are road network density, regional GDP (Gross Domestic Product), and urban land. It is suggested that tourism in the central region of Qinghai can be improved by enhancing the publicity and utilization of tourism resources, and Tibet should enhance tourism resource utilization and improve tourism amenities and infrastructure. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation
ISPRS Int. J. Geo-Inf. 2021, 10(8), 541; https://doi.org/10.3390/ijgi10080541 - 12 Aug 2021
Viewed by 666
Abstract
This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. While the integration of time within GIS has long been a domain of major interest, alternative modelling and data manipulation [...] Read more.
This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. While the integration of time within GIS has long been a domain of major interest, alternative modelling and data manipulation approaches derived from graph and knowledge-based principles provide many opportunities for many application domains. We first survey graph principles and how they have been applied to GIS and a few representative domains to date. A comprehensive analysis of the principles behind K-graphs, respective data representation and manipulation capabilities is discussed. The perspectives offered by a close integration of ST-graphs and K-graphs are explored. The whole approach is illustrated and discussed in the context of maritime transportation. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
A Set of Integral Grid-Coding Algebraic Operations Based on GeoSOT-3D
ISPRS Int. J. Geo-Inf. 2021, 10(7), 489; https://doi.org/10.3390/ijgi10070489 - 19 Jul 2021
Viewed by 578
Abstract
As the amount of collected spatial information (2D/3D) increases, the real-time processing of these massive data is among the urgent issues that need to be dealt with. Discretizing the physical earth into a digital gridded earth and assigning an integral computable code to [...] Read more.
As the amount of collected spatial information (2D/3D) increases, the real-time processing of these massive data is among the urgent issues that need to be dealt with. Discretizing the physical earth into a digital gridded earth and assigning an integral computable code to each grid has become an effective way to accelerate real-time processing. Researchers have proposed optimization algorithms for spatial calculations in specific scenarios. However, a complete set of algorithms for real-time processing using grid coding is still lacking. To address this issue, a carefully designed, integral grid-coding algebraic operation framework for GeoSOT-3D (a multilayer latitude and longitude grid model) is proposed. By converting traditional floating-point calculations based on latitude and longitude into binary operations, the complexity of the algorithm is greatly reduced. We then present the detailed algorithms that were designed, including basic operations, vector operations, code conversion operations, spatial operations, metric operations, topological relation operations, and set operations. To verify the feasibility and efficiency of the above algorithms, we developed an experimental platform using C++ language (including major algorithms, and more algorithms may be expanded in the future). Then, we generated random data and conducted experiments. The experimental results show that the computing framework is feasible and can significantly improve the efficiency of spatial processing. The algebraic operation framework is expected to support large geospatial data retrieval and analysis, and experience a revival, on top of parallel and distributed computing, in an era of large geospatial data. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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Article
Spatial and Temporal Characteristics of Urban Tourism Travel by Taxi—A Case Study of Shenzhen
ISPRS Int. J. Geo-Inf. 2021, 10(7), 445; https://doi.org/10.3390/ijgi10070445 - 30 Jun 2021
Viewed by 634
Abstract
Tourism networks are an important research part of tourism geography. Despite the significance of transportation in shaping tourism networks, current studies have mainly focused on the “daily behavior” of urban travel at the expense of tourism travel, which has been regarded as an [...] Read more.
Tourism networks are an important research part of tourism geography. Despite the significance of transportation in shaping tourism networks, current studies have mainly focused on the “daily behavior” of urban travel at the expense of tourism travel, which has been regarded as an “exceptional behavior”. To fill this gap, this study proposes a framework for exploring the spatial and temporal characteristics of urban tourism travel by taxi. We chose Shenzhen, a densely populated mega-city in China with abundant tourism resources, as a case study. First, we extracted tourist trips from taxi trajectories and used kernel density estimation to analyze the spatial aggregation characteristics of tourist trip origins. Second, we investigated the spatial dependence of tourist trips using local spatial autocorrelation analysis (Getis-Ord Gi*). Third, we explored the correlations between the tourist trip origins and urban geographic contextual factors (e.g., catering services and transportation facilities) using a geographically weighted regression model. The results show the following: (1) the trends between the coverage of tourist travel networks and the volume of tourist trips are similar; (2) the spatial interaction intensity of urban tourism has grouping and hierarchical characteristics; and (3) the spatial distribution of tourist trips by taxi is uneven and influenced by the distribution of urban morphology, tourism resources, and the preferences of taxi pick-up passengers. Our proposed framework and revealed spatial and temporal patterns have implications for urban tourism traffic planning, tourism product development, and tourist flow control in tourist attractions. Full article
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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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://doi.org/10.3390/ijgi10050340 - 17 May 2021
Viewed by 598
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://doi.org/10.3390/ijgi10040248 - 08 Apr 2021
Cited by 1 | Viewed by 701
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://doi.org/10.3390/ijgi10030191 - 23 Mar 2021
Cited by 4 | Viewed by 1275
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://doi.org/10.3390/ijgi10030188 - 22 Mar 2021
Viewed by 1039
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
Cited by 2 | Viewed by 760
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://doi.org/10.3390/ijgi10020101 - 22 Feb 2021
Viewed by 948
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://doi.org/10.3390/ijgi9120757 - 18 Dec 2020
Viewed by 779
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://doi.org/10.3390/ijgi9120756 - 18 Dec 2020
Cited by 2 | Viewed by 688
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://doi.org/10.3390/ijgi9070441 - 16 Jul 2020
Cited by 1 | Viewed by 891
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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